GC[Formula: see text]NMF: A Novel Matrix Factorization Framework for Gene-Phenotype Association Prediction.

PubMed

Zhang, Yaogong; Liu, Jiahui; Liu, Xiaohu; Hong, Yuxiang; Fan, Xin; Huang, Yalou; Wang, Yuan; Xie, Maoqiang

2018-04-24

Gene-phenotype association prediction can be applied to reveal the inherited basis of human diseases and facilitate drug development. Gene-phenotype associations are related to complex biological processes and influenced by various factors, such as relationship between phenotypes and that among genes. While due to sparseness of curated gene-phenotype associations and lack of integrated analysis of the joint effect of multiple factors, existing applications are limited to prediction accuracy and potential gene-phenotype association detection. In this paper, we propose a novel method by exploiting weighted graph constraint learned from hierarchical structures of phenotype data and group prior information among genes by inheriting advantages of Non-negative Matrix Factorization (NMF), called Weighted Graph Constraint and Group Centric Non-negative Matrix Factorization (GC[Formula: see text]NMF). Specifically, first we introduce the depth of parent-child relationships between two adjacent phenotypes in hierarchical phenotypic data as weighted graph constraint for a better phenotype understanding. Second, we utilize intra-group correlation among genes in a gene group as group constraint for gene understanding. Such information provides us with the intuition that genes in a group probably result in similar phenotypes. The model not only allows us to achieve a high-grade prediction performance, but also helps us to learn interpretable representation of genes and phenotypes simultaneously to facilitate future biological analysis. Experimental results on biological gene-phenotype association datasets of mouse and human demonstrate that GC[Formula: see text]NMF can obtain superior prediction accuracy and good understandability for biological explanation over other state-of-the-arts methods.

Arabidopsis Seed Content QTL Mapping Using High-Throughput Phenotyping: The Assets of Near Infrared Spectroscopy

PubMed Central

Jasinski, Sophie; Lécureuil, Alain; Durandet, Monique; Bernard-Moulin, Patrick; Guerche, Philippe

2016-01-01

Seed storage compounds are of crucial importance for human diet, feed and industrial uses. In oleo-proteaginous species like rapeseed, seed oil and protein are the qualitative determinants that conferred economic value to the harvested seed. To date, although the biosynthesis pathways of oil and storage protein are rather well-known, the factors that determine how these types of reserves are partitioned in seeds have to be identified. With the aim of implementing a quantitative genetics approach, requiring phenotyping of 100s of plants, our first objective was to establish near-infrared reflectance spectroscopic (NIRS) predictive equations in order to estimate oil, protein, carbon, and nitrogen content in Arabidopsis seed with high-throughput level. Our results demonstrated that NIRS is a powerful non-destructive, high-throughput method to assess the content of these four major components studied in Arabidopsis seed. With this tool in hand, we analyzed Arabidopsis natural variation for these four components and illustrated that they all displayed a wide range of variation. Finally, NIRS was used in order to map QTL for these four traits using seeds from the Arabidopsis thaliana Ct-1 × Col-0 recombinant inbred line population. Some QTL co-localized with QTL previously identified, but others mapped to chromosomal regions never identified so far for such traits. This paper illustrates the usefulness of NIRS predictive equations to perform accurate high-throughput phenotyping of Arabidopsis seed content, opening new perspectives in gene identification following QTL mapping and genome wide association studies. PMID:27891138

Predicting Phenotypes from Genetic Crosses: A Mathematical Concept to Help Struggling Biology Students

ERIC Educational Resources Information Center

Baurhoo, Neerusha; Darwish, Shireef

2012-01-01

Predicting phenotypic outcomes from genetic crosses is often very difficult for biology students, especially those with learning disabilities. With our mathematical concept, struggling students in inclusive biology classrooms are now better equipped to solve genetic problems and predict phenotypes, because of improved understanding of dominance…

Autism beyond diagnostic categories: characterization of autistic phenotypes in schizophrenia.

PubMed

Kästner, Anne; Begemann, Martin; Michel, Tanja Maria; Everts, Sarah; Stepniak, Beata; Bach, Christiane; Poustka, Luise; Becker, Joachim; Banaschewski, Tobias; Dose, Matthias; Ehrenreich, Hannelore

2015-05-13

Behavioral phenotypical continua from health to disease suggest common underlying mechanisms with quantitative rather than qualitative differences. Until recently, autism spectrum disorders and schizophrenia were considered distinct nosologic entities. However, emerging evidence contributes to the blurring of symptomatic and genetic boundaries between these conditions. The present study aimed at quantifying behavioral phenotypes shared by autism spectrum disorders and schizophrenia to prepare the ground for biological pathway analyses. Specific items of the Positive and Negative Syndrome Scale were employed and summed up to form a dimensional autism severity score (PAUSS). The score was created in a schizophrenia sample (N = 1156) and validated in adult high-functioning autism spectrum disorder (ASD) patients (N = 165). To this end, the Autism Diagnostic Observation Schedule (ADOS), the Autism (AQ) and Empathy Quotient (EQ) self-rating questionnaires were applied back to back with the newly developed PAUSS. PAUSS differentiated between ASD, schizophrenia and a disease-control sample and substantially correlated with the Autism Diagnostic Observation Schedule. Patients with ADOS scores ≥12 obtained highest, those with scores <7 lowest PAUSS values. AQ and EQ were not found to vary dependent on ADOS diagnosis. ROC curves for ADOS and PAUSS resulted in AuC values of 0.9 and 0.8, whereas AQ and EQ performed at chance level in the prediction of ASD. This work underscores the convergence of schizophrenia negative symptoms and autistic phenotypes. PAUSS evolved as a measure capturing the continuous nature of autistic behaviors. The definition of extreme-groups based on the dimensional PAUSS may permit future investigations of genetic constellations modulating autistic phenotypes.

SNP-associations and phenotype predictions from hundreds of microbial genomes without genome alignments.

PubMed

Hall, Barry G

2014-01-01

SNP-association studies are a starting point for identifying genes that may be responsible for specific phenotypes, such as disease traits. The vast bulk of tools for SNP-association studies are directed toward SNPs in the human genome, and I am unaware of any tools designed specifically for such studies in bacterial or viral genomes. The PPFS (Predict Phenotypes From SNPs) package described here is an add-on to kSNP , a program that can identify SNPs in a data set of hundreds of microbial genomes. PPFS identifies those SNPs that are non-randomly associated with a phenotype based on the χ² probability, then uses those diagnostic SNPs for two distinct, but related, purposes: (1) to predict the phenotypes of strains whose phenotypes are unknown, and (2) to identify those diagnostic SNPs that are most likely to be causally related to the phenotype. In the example illustrated here, from a set of 68 E. coli genomes, for 67 of which the pathogenicity phenotype was known, there were 418,500 SNPs. Using the phenotypes of 36 of those strains, PPFS identified 207 diagnostic SNPs. The diagnostic SNPs predicted the phenotypes of all of the genomes with 97% accuracy. It then identified 97 SNPs whose probability of being causally related to the pathogenic phenotype was >0.999. In a second example, from a set of 116 E. coli genome sequences, using the phenotypes of 65 strains PPFS identified 101 SNPs that predicted the source host (human or non-human) with 90% accuracy.

Multiscale Systems Analysis of Root Growth and Development: Modeling Beyond the Network and Cellular Scales

PubMed Central

Band, Leah R.; Fozard, John A.; Godin, Christophe; Jensen, Oliver E.; Pridmore, Tony; Bennett, Malcolm J.; King, John R.

2012-01-01

Over recent decades, we have gained detailed knowledge of many processes involved in root growth and development. However, with this knowledge come increasing complexity and an increasing need for mechanistic modeling to understand how those individual processes interact. One major challenge is in relating genotypes to phenotypes, requiring us to move beyond the network and cellular scales, to use multiscale modeling to predict emergent dynamics at the tissue and organ levels. In this review, we highlight recent developments in multiscale modeling, illustrating how these are generating new mechanistic insights into the regulation of root growth and development. We consider how these models are motivating new biological data analysis and explore directions for future research. This modeling progress will be crucial as we move from a qualitative to an increasingly quantitative understanding of root biology, generating predictive tools that accelerate the development of improved crop varieties. PMID:23110897

Accuracy of different bioinformatics methods in detecting antibiotic resistance and virulence factors from Staphylococcus aureus whole genome sequences.

PubMed

Mason, Amy; Foster, Dona; Bradley, Phelim; Golubchik, Tanya; Doumith, Michel; Gordon, N Claire; Pichon, Bruno; Iqbal, Zamin; Staves, Peter; Crook, Derrick; Walker, A Sarah; Kearns, Angela; Peto, Tim

2018-06-20

Background : In principle, whole genome sequencing (WGS) can predict phenotypic resistance directly from genotype, replacing laboratory-based tests. However, the contribution of different bioinformatics methods to genotype-phenotype discrepancies has not been systematically explored to date. Methods : We compared three WGS-based bioinformatics methods (Genefinder (read-based), Mykrobe (de Bruijn graph-based) and Typewriter (BLAST-based)) for predicting presence/absence of 83 different resistance determinants and virulence genes, and overall antimicrobial susceptibility, in 1379 Staphylococcus aureus isolates previously characterised by standard laboratory methods (disc diffusion, broth and/or agar dilution and PCR). Results : 99.5% (113830/114457) of individual resistance-determinant/virulence gene predictions were identical between all three methods, with only 627 (0.5%) discordant predictions, demonstrating high overall agreement (Fliess-Kappa=0.98, p<0.0001). Discrepancies when identified were in only one of the three methods for all genes except the cassette recombinase, ccrC(b ). Genotypic antimicrobial susceptibility prediction matched laboratory phenotype in 98.3% (14224/14464) cases (2720 (18.8%) resistant, 11504 (79.5%) susceptible). There was greater disagreement between the laboratory phenotypes and the combined genotypic predictions (97 (0.7%) phenotypically-susceptible but all bioinformatic methods reported resistance; 89 (0.6%) phenotypically-resistant, but all bioinformatics methods reported susceptible) than within the three bioinformatics methods (54 (0.4%) cases, 16 phenotypically-resistant, 38 phenotypically-susceptible). However, in 36/54 (67%), the consensus genotype matched the laboratory phenotype. Conclusions : In this study, the choice between these three specific bioinformatic methods to identify resistance-determinants or other genes in S. aureus did not prove critical, with all demonstrating high concordance with each other and phenotypic/molecular methods. However, each has some limitations and therefore consensus methods provide some assurance. Copyright © 2018 American Society for Microbiology.

Identification of clinical phenotypes in knee osteoarthritis: a systematic review of the literature.

PubMed

Dell'Isola, A; Allan, R; Smith, S L; Marreiros, S S P; Steultjens, M

2016-10-12

Knee Osteoarthritis (KOA) is a heterogeneous pathology characterized by a complex and multifactorial nature. It has been hypothesised that these differences are due to the existence of underlying phenotypes representing different mechanisms of the disease. The aim of this study is to identify the current evidence for the existence of groups of variables which point towards the existence of distinct clinical phenotypes in the KOA population. A systematic literature search in PubMed was conducted. Only original articles were selected if they aimed to identify phenotypes of patients aged 18 years or older with KOA. The methodological quality of the studies was independently assessed by two reviewers and qualitative synthesis of the evidence was performed. Strong evidence for existence of specific phenotypes was considered present if the phenotype was supported by at least two high-quality studies. A total of 24 studies were included. Through qualitative synthesis of evidence, six main sets of variables proposing the existence of six phenotypes were identified: 1) chronic pain in which central mechanisms (e.g. central sensitisation) are prominent; 2) inflammatory (high levels of inflammatory biomarkers); 3) metabolic syndrome (high prevalence of obesity, diabetes and other metabolic disturbances); 4) Bone and cartilage metabolism (alteration in local tissue metabolism); 5) mechanical overload characterised primarily by varus malalignment and medial compartment disease; and 6) minimal joint disease characterised as minor clinical symptoms with slow progression over time. This study identified six distinct groups of variables which should be explored in attempts to better define clinical phenotypes in the KOA population.

Strategy Revealing Phenotypic Differences among Synthetic Oscillator Designs

PubMed Central

2015-01-01

Considerable progress has been made in identifying and characterizing the component parts of genetic oscillators, which play central roles in all organisms. Nonlinear interaction among components is sufficiently complex that mathematical models are required to elucidate their elusive integrated behavior. Although natural and synthetic oscillators exhibit common architectures, there are numerous differences that are poorly understood. Utilizing synthetic biology to uncover basic principles of simpler circuits is a way to advance understanding of natural circadian clocks and rhythms. Following this strategy, we address the following questions: What are the implications of different architectures and molecular modes of transcriptional control for the phenotypic repertoire of genetic oscillators? Are there designs that are more realizable or robust? We compare synthetic oscillators involving one of three architectures and various combinations of the two modes of transcriptional control using a methodology that provides three innovations: a rigorous definition of phenotype, a procedure for deconstructing complex systems into qualitatively distinct phenotypes, and a graphical representation for illuminating the relationship between genotype, environment, and the qualitatively distinct phenotypes of a system. These methods provide a global perspective on the behavioral repertoire, facilitate comparisons of alternatives, and assist the rational design of synthetic gene circuitry. In particular, the results of their application here reveal distinctive phenotypes for several designs that have been studied experimentally as well as a best design among the alternatives that has yet to be constructed and tested. PMID:25019938

Using Answer Set Programming to Integrate RNA Expression with Signalling Pathway Information to Infer How Mutations Affect Ageing

PubMed Central

Papatheodorou, Irene; Ziehm, Matthias; Wieser, Daniela; Alic, Nazif; Partridge, Linda; Thornton, Janet M.

2012-01-01

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects. PMID:23251396

Using answer set programming to integrate RNA expression with signalling pathway information to infer how mutations affect ageing.

PubMed

Papatheodorou, Irene; Ziehm, Matthias; Wieser, Daniela; Alic, Nazif; Partridge, Linda; Thornton, Janet M

2012-01-01

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects.

BioMog: A Computational Framework for the De Novo Generation or Modification of Essential Biomass Components

PubMed Central

Tervo, Christopher J.; Reed, Jennifer L.

2013-01-01

The success of genome-scale metabolic modeling is contingent on a model's ability to accurately predict growth and metabolic behaviors. To date, little focus has been directed towards developing systematic methods of proposing, modifying and interrogating an organism's biomass requirements that are used in constraint-based models. To address this gap, the biomass modification and generation (BioMog) framework was created and used to generate lists of biomass components de novo, as well as to modify predefined biomass component lists, for models of Escherichia coli (iJO1366) and of Shewanella oneidensis (iSO783) from high-throughput growth phenotype and fitness datasets. BioMog's de novo biomass component lists included, either implicitly or explicitly, up to seventy percent of the components included in the predefined biomass equations, and the resulting de novo biomass equations outperformed the predefined biomass equations at qualitatively predicting mutant growth phenotypes by up to five percent. Additionally, the BioMog procedure can quantify how many experiments support or refute a particular metabolite's essentiality to a cell, and it facilitates the determination of inconsistent experiments and inaccurate reaction and/or gene to reaction associations. To further interrogate metabolite essentiality, the BioMog framework includes an experiment generation algorithm that allows for the design of experiments to test whether a metabolite is essential. Using BioMog, we correct experimental results relating to the essentiality of thyA gene in E. coli, as well as perform knockout experiments supporting the essentiality of protoheme. With these capabilities, BioMog can be a valuable resource for analyzing growth phenotyping data and component of a model developer's toolbox. PMID:24339916

Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems

PubMed Central

Zhao, Jiangsan; Rewald, Boris; Leitner, Daniel; Nagel, Kerstin A.; Nakhforoosh, Alireza

2017-01-01

Abstract Root phenotyping provides trait information for plant breeding. A shortcoming of high-throughput root phenotyping is the limitation to seedling plants and failure to make inferences on mature root systems. We suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem. Sixteen pea genotypes were phenotyped in (i) seedling (Petri dishes) and (ii) mature (sand-filled columns) root phenotyping platforms. The RSA model RootBox was parameterized with seedling traits to simulate the fully developed root systems. Measured and modelled root length, first-order lateral number, and root distribution were compared to determine key traits for model-based prediction. No direct relationship in root traits (tap, lateral length, interbranch distance) was evident between phenotyping systems. RootBox significantly improved the inference over phenotyping platforms. Seedling plant tap and lateral root elongation rates and interbranch distance were sufficient model parameters to predict genotype ranking in total root length with an RSpearman of 0.83. Parameterization including uneven lateral spacing via a scaling function substantially improved the prediction of architectures underlying the differently sized root systems. We conclude that RSA models can solve the inference problem of seedling root phenotyping. RSA models should be included in the phenotyping pipeline to provide reliable information on mature root systems to breeding research. PMID:28168270

Assessment of Point-of-Care Diagnostics for G6PD Deficiency in Malaria Endemic Rural Eastern Indonesia

PubMed Central

Satyagraha, Ari W.; Sadhewa, Arkasha; Elvira, Rosalie; Elyazar, Iqbal; Feriandika, Denny; Antonjaya, Ungke; Oyong, Damian; Subekti, Decy; Rozi, Ismail E.; Domingo, Gonzalo J.; Harahap, Alida R.; Baird, J. Kevin

2016-01-01

Background Patients infected by Plasmodium vivax or Plasmodium ovale suffer repeated clinical attacks without primaquine therapy against latent stages in liver. Primaquine causes seriously threatening acute hemolytic anemia in patients having inherited glucose-6-phosphate dehydrogenase (G6PD) deficiency. Access to safe primaquine therapy hinges upon the ability to confirm G6PD normal status. CareStart G6PD, a qualitative G6PD rapid diagnostic test (G6PD RDT) intended for use at point-of-care in impoverished rural settings where most malaria patients live, was evaluated. Methodology/Principal Findings This device and the standard qualitative fluorescent spot test (FST) were each compared against the quantitative spectrophotometric assay for G6PD activity as the diagnostic gold standard. The assessment occurred at meso-endemic Panenggo Ede in western Sumba Island in eastern Indonesia, where 610 residents provided venous blood. The G6PD RDT and FST qualitative assessments were performed in the field, whereas the quantitative assay was performed in a research laboratory at Jakarta. The median G6PD activity ≥5 U/gHb was 9.7 U/gHb and was considered 100% of normal activity. The prevalence of G6PD deficiency by quantitative assessment (<5 U/gHb) was 7.2%. Applying 30% of normal G6PD activity as the cut-off for qualitative testing, the sensitivity, specificity, positive predictive value, and negative predictive value for G6PD RDT versus FST among males were as follows: 100%, 98.7%, 89%, and 100% versus 91.7%, 92%, 55%, and 99%; P = 0.49, 0.001, 0.004, and 0.24, respectively. These values among females were: 83%, 92.7%, 17%, and 99.7% versus 100%, 92%, 18%, and 100%; P = 1.0, 0.89, 1.0 and 1.0, respectively. Conclusions/Significance The overall performance of G6PD RDT, especially 100% negative predictive value, demonstrates suitable safety for G6PD screening prior to administering hemolytic drugs like primaquine and many others. Relatively poor diagnostic performance among females due to mosaic G6PD phenotype is an inherent limitation of any current practical screening methodology. PMID:26894297

Assessment of Point-of-Care Diagnostics for G6PD Deficiency in Malaria Endemic Rural Eastern Indonesia.

PubMed

Satyagraha, Ari W; Sadhewa, Arkasha; Elvira, Rosalie; Elyazar, Iqbal; Feriandika, Denny; Antonjaya, Ungke; Oyong, Damian; Subekti, Decy; Rozi, Ismail E; Domingo, Gonzalo J; Harahap, Alida R; Baird, J Kevin

2016-02-01

Patients infected by Plasmodium vivax or Plasmodium ovale suffer repeated clinical attacks without primaquine therapy against latent stages in liver. Primaquine causes seriously threatening acute hemolytic anemia in patients having inherited glucose-6-phosphate dehydrogenase (G6PD) deficiency. Access to safe primaquine therapy hinges upon the ability to confirm G6PD normal status. CareStart G6PD, a qualitative G6PD rapid diagnostic test (G6PD RDT) intended for use at point-of-care in impoverished rural settings where most malaria patients live, was evaluated. This device and the standard qualitative fluorescent spot test (FST) were each compared against the quantitative spectrophotometric assay for G6PD activity as the diagnostic gold standard. The assessment occurred at meso-endemic Panenggo Ede in western Sumba Island in eastern Indonesia, where 610 residents provided venous blood. The G6PD RDT and FST qualitative assessments were performed in the field, whereas the quantitative assay was performed in a research laboratory at Jakarta. The median G6PD activity ≥ 5 U/gHb was 9.7 U/gHb and was considered 100% of normal activity. The prevalence of G6PD deficiency by quantitative assessment (<5 U/gHb) was 7.2%. Applying 30% of normal G6PD activity as the cut-off for qualitative testing, the sensitivity, specificity, positive predictive value, and negative predictive value for G6PD RDT versus FST among males were as follows: 100%, 98.7%, 89%, and 100% versus 91.7%, 92%, 55%, and 99%; P = 0.49, 0.001, 0.004, and 0.24, respectively. These values among females were: 83%, 92.7%, 17%, and 99.7% versus 100%, 92%, 18%, and 100%; P = 1.0, 0.89, 1.0 and 1.0, respectively. The overall performance of G6PD RDT, especially 100% negative predictive value, demonstrates suitable safety for G6PD screening prior to administering hemolytic drugs like primaquine and many others. Relatively poor diagnostic performance among females due to mosaic G6PD phenotype is an inherent limitation of any current practical screening methodology.

Phenome-driven disease genetics prediction toward drug discovery.

PubMed

Chen, Yang; Li, Li; Zhang, Guo-Qiang; Xu, Rong

2015-06-15

Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data. To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e(-4)) and 81.3% (P < e(-12)) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn's disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn's disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn's disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. nlp. edu/public/data/DMN © The Author 2015. Published by Oxford University Press.

Childhood asthma-predictive phenotype.

PubMed

Guilbert, Theresa W; Mauger, David T; Lemanske, Robert F

2014-01-01

Wheezing is a fairly common symptom in early childhood, but only some of these toddlers will experience continued wheezing symptoms in later childhood. The definition of the asthma-predictive phenotype is in children with frequent, recurrent wheezing in early life who have risk factors associated with the continuation of asthma symptoms in later life. Several asthma-predictive phenotypes were developed retrospectively based on large, longitudinal cohort studies; however, it can be difficult to differentiate these phenotypes clinically as the expression of symptoms, and risk factors can change with time. Genetic, environmental, developmental, and host factors and their interactions may contribute to the development, severity, and persistence of the asthma phenotype over time. Key characteristics that distinguish the childhood asthma-predictive phenotype include the following: male sex; a history of wheezing, with lower respiratory tract infections; history of parental asthma; history of atopic dermatitis; eosinophilia; early sensitization to food or aeroallergens; or lower lung function in early life. Copyright © 2014 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells

PubMed Central

Bargaje, Rhishikesh; Trachana, Kalliopi; Shelton, Martin N.; McGinnis, Christopher S.; Zhou, Joseph X.; Chadick, Cora; Cook, Savannah; Cavanaugh, Christopher; Huang, Sui; Hood, Leroy

2017-01-01

Steering the differentiation of induced pluripotent stem cells (iPSCs) toward specific cell types is crucial for patient-specific disease modeling and drug testing. This effort requires the capacity to predict and control when and how multipotent progenitor cells commit to the desired cell fate. Cell fate commitment represents a critical state transition or “tipping point” at which complex systems undergo a sudden qualitative shift. To characterize such transitions during iPSC to cardiomyocyte differentiation, we analyzed the gene expression patterns of 96 developmental genes at single-cell resolution. We identified a bifurcation event early in the trajectory when a primitive streak-like cell population segregated into the mesodermal and endodermal lineages. Before this branching point, we could detect the signature of an imminent critical transition: increase in cell heterogeneity and coordination of gene expression. Correlation analysis of gene expression profiles at the tipping point indicates transcription factors that drive the state transition toward each alternative cell fate and their relationships with specific phenotypic readouts. The latter helps us to facilitate small molecule screening for differentiation efficiency. To this end, we set up an analysis of cell population structure at the tipping point after systematic variation of the protocol to bias the differentiation toward mesodermal or endodermal cell lineage. We were able to predict the proportion of cardiomyocytes many days before cells manifest the differentiated phenotype. The analysis of cell populations undergoing a critical state transition thus affords a tool to forecast cell fate outcomes and can be used to optimize differentiation protocols to obtain desired cell populations. PMID:28167799

Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells.

PubMed

Bargaje, Rhishikesh; Trachana, Kalliopi; Shelton, Martin N; McGinnis, Christopher S; Zhou, Joseph X; Chadick, Cora; Cook, Savannah; Cavanaugh, Christopher; Huang, Sui; Hood, Leroy

2017-02-28

Steering the differentiation of induced pluripotent stem cells (iPSCs) toward specific cell types is crucial for patient-specific disease modeling and drug testing. This effort requires the capacity to predict and control when and how multipotent progenitor cells commit to the desired cell fate. Cell fate commitment represents a critical state transition or "tipping point" at which complex systems undergo a sudden qualitative shift. To characterize such transitions during iPSC to cardiomyocyte differentiation, we analyzed the gene expression patterns of 96 developmental genes at single-cell resolution. We identified a bifurcation event early in the trajectory when a primitive streak-like cell population segregated into the mesodermal and endodermal lineages. Before this branching point, we could detect the signature of an imminent critical transition: increase in cell heterogeneity and coordination of gene expression. Correlation analysis of gene expression profiles at the tipping point indicates transcription factors that drive the state transition toward each alternative cell fate and their relationships with specific phenotypic readouts. The latter helps us to facilitate small molecule screening for differentiation efficiency. To this end, we set up an analysis of cell population structure at the tipping point after systematic variation of the protocol to bias the differentiation toward mesodermal or endodermal cell lineage. We were able to predict the proportion of cardiomyocytes many days before cells manifest the differentiated phenotype. The analysis of cell populations undergoing a critical state transition thus affords a tool to forecast cell fate outcomes and can be used to optimize differentiation protocols to obtain desired cell populations.

Interactions between behavioral and life-history trade-offs in the evolution of integrated predator-defense plasticity.

PubMed

Cressler, Clayton E; King, Aaron A; Werner, Earl E

2010-09-01

Inducible defense, which is phenotypic plasticity in traits that affect predation risk, is taxonomically widespread and has been shown to have important ecological consequences. However, it remains unclear what factors promote the evolution of qualitatively different defense strategies and when evolution should favor strategies that involve modification of multiple traits. Previous theory suggests that individual-level trade-offs play a key role in defense evolution, but most of this work has assumed that trade-offs are independent. Here we show that the shape of the behavioral trade-off between foraging gain and predation risk determines the interaction between this trade-off and the life-history trade-off between growth and reproduction. The interaction between these fundamental trade-offs determines the optimal investment into behavioral and life-history defenses. Highly nonlinear foraging-predation risk trade-offs favor the evolution of behavioral defenses, while linear trade-offs favor life-history defenses. Between these extremes, integrated defense responses are optimal, with defense expression strongly depending on ontogeny. We suggest that these predictions may be general across qualitatively different defenses. Our results have important implications for theory on the ecological effects of inducible defense, which has not considered how qualitatively different defenses might alter ecological interactions.

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

PubMed

Ross, Mindy K; Yoon, Jinsung; van der Schaar, Auke; van der Schaar, Mihaela

2018-01-01

Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth. Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Four phenotypes were discovered in both datasets: allergic not obese (A + /O - ), obese not allergic (A - /O + ), allergic and obese (A + /O + ), and not allergic not obese (A - /O - ). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A + /O + children's asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo (P = 0.011). The PP algorithm performed significantly better (P < 0.001) than traditional machine learning algorithms for both short- and long-term asthma control prediction. Asthma control and bronchodilator response were the features most predictive of short-term asthma control, regardless of type of controller medication or phenotype. Bronchodilator response and serum eosinophils were the most predictive features of asthma control, regardless of type of controller medication or phenotype. Advanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma.

The Broader Autism Phenotype and Friendships in Non-Clinical Dyads

ERIC Educational Resources Information Center

Wainer, Allison L.; Block, Nicole; Donnellan, M. Brent; Ingersoll, Brooke

2013-01-01

The broader autism phenotype (BAP) is a set of subclinical traits qualitatively similar to those observed in autism spectrum disorders. The current study sought to elucidate the association between self- and informant-reports of the BAP and friendships, in a non-clinical sample of college student dyads. Self-informant agreement of the BAP and…

A Qualitative Analysis of Imitation Performances of Preschoolers with Down Syndrome

ERIC Educational Resources Information Center

Vanvuchelen, Marleen

2016-01-01

A number of studies suggest that imitation is a characteristic strength in children with Down Syndrome (DS). The present study aims to discover whether imitation performances are qualitatively phenotypical in DS. Eight preschoolers with DS were matched on chronological, mental, language and imitation age with 8 preschoolers with intellectual…

Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

PubMed Central

Kou, Peng Meng; Pallassana, Narayanan; Bowden, Rebeca; Cunningham, Barry; Joy, Abraham; Kohn, Joachim; Babensee, Julia E.

2011-01-01

Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response towards the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R2prediction = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R2prediction = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection. PMID:22136715

Correspondence between the CYP2C19 and CYP3A4 genotypes with the inferred metabolizer phenotype by omeprazole administration in Mexican healthy children.

PubMed

Favela-Mendoza, A F; Martínez-Cortes, G; Romero-Prado, M M; Romero-Tejeda, E M; Islas-Carbajal, M C; Sosa-Macias, M; Lares-Asseff, I; Rangel-Villalobos, H

2018-05-07

CYP2C19 genotypes presumably allow the prediction of the metabolizer phenotypes: poor (PMs), extensive (EMs) and ultra-rapid (UMs). However, evidence from previous studies regarding this predictive power is unclear, which is important because the benefits expected by healthcare institutions and patients are based on this premise. Therefore, we aimed to complete a formal evaluation of the diagnostic value of CYP2C19 and CYP3A4 genes for predicting metabolizer phenotypes established by omeprazole (OME) administration in 118 healthy children from Jalisco (western Mexico). The genotypes for CYP3A4*1B and CYP2C19*2, *3, *4, *5 and *17 alleles were determined. CYP2C19 and CYP3A4 phenotypes were obtained after 20 mg OME administration and HPLC quantification in plasma to estimate the Hydroxylation Index (HI = OME/HOME) and Sulfonation Index (SI = OME/SOME), respectively. The distribution of genotypes and phenotypes for CYP2C19 and CYP3A4 was similar to previous studies in Mexico and Latin America. We estimated the CYP2C19 UM, EM and PM phenotype frequency in 0.84%, 96.61% and 2.54%, respectively. Although differences in the HI distribution were observed between CYP2C19 genotypes, they showed a poor diagnostic ability to predict the CYP2C19 metabolizer phenotype. Similarly, the number of CYP2C19 and CYP3A4 functional alleles was correlated with the HI distribution, but also their diagnostic ability to predict the CYP2C19 phenotype was poor. The CYP2C19 phenotype is not predicted by the number of functional alleles of CYP2C19 and CYP3A4 genes. Phenotyping is still the most valuable alternative to dose individualization for CYP2C19 substrate drugs. © 2018 John Wiley & Sons Ltd.

A probabilistic model to predict clinical phenotypic traits from genome sequencing.

PubMed

Chen, Yun-Ching; Douville, Christopher; Wang, Cheng; Niknafs, Noushin; Yeo, Grace; Beleva-Guthrie, Violeta; Carter, Hannah; Stenson, Peter D; Cooper, David N; Li, Biao; Mooney, Sean; Karchin, Rachel

2014-09-01

Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.

Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems.

PubMed

Zhao, Jiangsan; Bodner, Gernot; Rewald, Boris; Leitner, Daniel; Nagel, Kerstin A; Nakhforoosh, Alireza

2017-02-01

Root phenotyping provides trait information for plant breeding. A shortcoming of high-throughput root phenotyping is the limitation to seedling plants and failure to make inferences on mature root systems. We suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem. Sixteen pea genotypes were phenotyped in (i) seedling (Petri dishes) and (ii) mature (sand-filled columns) root phenotyping platforms. The RSA model RootBox was parameterized with seedling traits to simulate the fully developed root systems. Measured and modelled root length, first-order lateral number, and root distribution were compared to determine key traits for model-based prediction. No direct relationship in root traits (tap, lateral length, interbranch distance) was evident between phenotyping systems. RootBox significantly improved the inference over phenotyping platforms. Seedling plant tap and lateral root elongation rates and interbranch distance were sufficient model parameters to predict genotype ranking in total root length with an RSpearman of 0.83. Parameterization including uneven lateral spacing via a scaling function substantially improved the prediction of architectures underlying the differently sized root systems. We conclude that RSA models can solve the inference problem of seedling root phenotyping. RSA models should be included in the phenotyping pipeline to provide reliable information on mature root systems to breeding research. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Phenotype/genotype correlations in Gaucher disease type 1: Clinical and therapeutic implications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sibille, A.; Eng, C.M.; Kim, S.J.

1993-06-01

Gaucher disease is the most frequent lysosomal storage disease and the most prevalent genetic disease among Ashkenazi Jews. Gaucher disease type 1 is characterized by marked variability of the phenotype and by the absence of neuronopathic involvement. To test the hypothesis that this phenotypic variability was due to genetic compounds of several different mutant alleles, 161 symptomatic patients with Gaucher disease type 1 (> 90% Ashkenazi Jewish) were analyzed for clinical involvement, and their genotypes were determined. Qualitative and quantitative measures of disease involvement included age at onset of the disease manifestations, hepatic and splenic volumes, age at splenectomy, andmore » severity of bony disease. High statistically significant differences (P < .005) were found in each clinical parameter in patients with the N370S/N370S genotype compared with those patients with the N370S/84GG, N370S/L444P, and N370/ genotypes. The symptomatic N370S homozygotes had onset of their disease two to three decades later than patients with the other genotypes. In addition, patients with the latter genotypes have much more severely involved livers, spleens, and bones and had a higher incidence of splenectomy at an earlier age. These predictive genotype analyses provide the basis for genetic care delivery and therapeutic recommendations in patients affected with Gaucher disease type 1. 38 refs., 1 fig., 4 tabs.« less

Psychometric Properties of the Spanish Version of the Broad Autism Phenotype Questionnaire: Strengths, Weaknesses, and Future Improvements

ERIC Educational Resources Information Center

Godoy-Giménez, Marta; González-Rodríguez, Antonio; Cañadas, Fernando; Estévez, Angeles F.; Sayans-Jiménez, Pablo

2018-01-01

The Broad autism phenotype (BAP) refers to a set of subclinical behavioural characteristics qualitatively similar to those presented in Autism spectrum disorders (ASDs). The BAP questionnaire (BAPQ) has been widely used to assess the BAP both in relatives of ASD people and within the general population. The current study presents the first Spanish…

A Qualitative Analysis of General Receptive Vocabulary of Adolescents with Down Syndrome

ERIC Educational Resources Information Center

Facon, Bruno; Nuchadee, Marie-Laure; Bollengier, Therese

2012-01-01

This study aimed to discover whether general receptive vocabulary is qualitatively phenotypical in Down syndrome. Sixty-two participants with Down syndrome (M age = 16.74 years, SD = 3.28) were individually matched on general vocabulary raw total score with 62 participants with intellectual disability of undifferentiated etiology (M age = 16.20…

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials.

PubMed

Michel, Sebastian; Ametz, Christian; Gungor, Huseyin; Akgöl, Batuhan; Epure, Doru; Grausgruber, Heinrich; Löschenberger, Franziska; Buerstmayr, Hermann

2017-02-01

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

Phenome-driven disease genetics prediction toward drug discovery

PubMed Central

Chen, Yang; Li, Li; Zhang, Guo-Qiang; Xu, Rong

2015-01-01

Motivation: Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data. Results: To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e−4) and 81.3% (P < e−12) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn’s disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn’s disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn’s disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. Availability and implementation: nlp.case.edu/public/data/DMN Contact: rxx@case.edu PMID:26072493

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.

PubMed

Liu, Mei; Wu, Yonghui; Chen, Yukun; Sun, Jingchun; Zhao, Zhongming; Chen, Xue-wen; Matheny, Michael Edwin; Xu, Hua

2012-06-01

Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.

PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.

PubMed

Kahanda, Indika; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa

2015-01-01

The human phenotype ontology (HPO) was recently developed as a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. At present, only a small fraction of human protein coding genes have HPO annotations. But, researchers believe that a large portion of currently unannotated genes are related to disease phenotypes. Therefore, it is important to predict gene-HPO term associations using accurate computational methods. In this work we demonstrate the performance advantage of the structured SVM approach which was shown to be highly effective for Gene Ontology term prediction in comparison to several baseline methods. Furthermore, we highlight a collection of informative data sources suitable for the problem of predicting gene-HPO associations, including large scale literature mining data.

Environmental and genetic modulation of the phenotypic expression of antibiotic resistance

PubMed Central

Andersson, Dan I

2017-01-01

Abstract Antibiotic resistance can be acquired by mutation or horizontal transfer of a resistance gene, and generally an acquired mechanism results in a predictable increase in phenotypic resistance. However, recent findings suggest that the environment and/or the genetic context can modify the phenotypic expression of specific resistance genes/mutations. An important implication from these findings is that a given genotype does not always result in the expected phenotype. This dissociation of genotype and phenotype has important consequences for clinical bacteriology and for our ability to predict resistance phenotypes from genetics and DNA sequences. A related problem concerns the degree to which the genes/mutations currently identified in vitro can fully explain the in vivo resistance phenotype, or whether there is a significant additional amount of presently unknown mutations/genes (genetic ‘dark matter’) that could contribute to resistance in clinical isolates. Finally, a very important question is whether/how we can identify the genetic features that contribute to making a successful pathogen, and predict why some resistant clones are very successful and spread globally? In this review, we describe different environmental and genetic factors that influence phenotypic expression of antibiotic resistance genes/mutations and how this information is needed to understand why particular resistant clones spread worldwide and to what extent we can use DNA sequences to predict evolutionary success. PMID:28333270

Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.

PubMed

Aridhi, Sabeur; Sghaier, Haïtham; Zoghlami, Manel; Maddouri, Mondher; Nguifo, Engelbert Mephu

2016-01-01

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

Identification of Type 2 Diabetes Risk Factors Using Phenotypes Consisting of Anthropometry and Triglycerides based on Machine Learning.

PubMed

Lee, Bum Ju; Kim, Jong Yeol

2016-01-01

The hypertriglyceridemic waist (HW) phenotype is strongly associated with type 2 diabetes; however, to date, no study has assessed the predictive power of phenotypes based on individual anthropometric measurements and triglyceride (TG) levels. The aims of the present study were to assess the association between the HW phenotype and type 2 diabetes in Korean adults and to evaluate the predictive power of various phenotypes consisting of combinations of individual anthropometric measurements and TG levels. Between November 2006 and August 2013, 11,937 subjects participated in this retrospective cross-sectional study. We measured fasting plasma glucose and TG levels and performed anthropometric measurements. We employed binary logistic regression (LR) to examine statistically significant differences between normal subjects and those with type 2 diabetes using HW and individual anthropometric measurements. For more reliable prediction results, two machine learning algorithms, naive Bayes (NB) and LR, were used to evaluate the predictive power of various phenotypes. All prediction experiments were performed using a tenfold cross validation method. Among all of the variables, the presence of HW was most strongly associated with type 2 diabetes (p < 0.001, adjusted odds ratio (OR) = 2.07 [95% CI, 1.72-2.49] in men; p < 0.001, adjusted OR = 2.09 [1.79-2.45] in women). When comparing waist circumference (WC) and TG levels as components of the HW phenotype, the association between WC and type 2 diabetes was greater than the association between TG and type 2 diabetes. The phenotypes tended to have higher predictive power in women than in men. Among the phenotypes, the best predictors of type 2 diabetes were waist-to-hip ratio + TG in men (AUC by NB = 0.653, AUC by LR = 0.661) and rib-to-hip ratio + TG in women (AUC by NB = 0.73, AUC by LR = 0.735). Although the presence of HW demonstrated the strongest association with type 2 diabetes, the predictive power of the combined measurements of the actual WC and TG values may not be the best manner of predicting type 2 diabetes. Our findings may provide clinical information concerning the development of clinical decision support systems for the initial screening of type 2 diabetes.

Adaptive phenotypic plasticity in the Midas cichlid fish pharyngeal jaw and its relevance in adaptive radiation

PubMed Central

2011-01-01

Background Phenotypic evolution and its role in the diversification of organisms is a central topic in evolutionary biology. A neglected factor during the modern evolutionary synthesis, adaptive phenotypic plasticity, more recently attracted the attention of many evolutionary biologists and is now recognized as an important ingredient in both population persistence and diversification. The traits and directions in which an ancestral source population displays phenotypic plasticity might partly determine the trajectories in morphospace, which are accessible for an adaptive radiation, starting from the colonization of a novel environment. In the case of repeated colonizations of similar environments from the same source population this "flexible stem" hypothesis predicts similar phenotypes to arise in repeated subsequent radiations. The Midas Cichlid (Amphilophus spp.) in Nicaragua has radiated in parallel in several crater-lakes seeded by populations originating from the Nicaraguan Great Lakes. Here, we tested phenotypic plasticity in the pharyngeal jaw of Midas Cichlids. The pharyngeal jaw apparatus of cichlids, a second set of jaws functionally decoupled from the oral ones, is known to mediate ecological specialization and often differs strongly between sister-species. Results We performed a common garden experiment raising three groups of Midas cichlids on food differing in hardness and calcium content. Analyzing the lower pharyngeal jaw-bones we find significant differences between diet groups qualitatively resembling the differences found between specialized species. Observed differences in pharyngeal jaw expression between groups were attributable to the diet's mechanical resistance, whereas surplus calcium in the diet was not found to be of importance. Conclusions The pharyngeal jaw apparatus of Midas Cichlids can be expressed plastically if stimulated mechanically during feeding. Since this trait is commonly differentiated - among other traits - between Midas Cichlid species, its plasticity might be an important factor in Midas Cichlid speciation. The prevalence of pharyngeal jaw differentiation across the Cichlidae further suggests that adaptive phenotypic plasticity in this trait could play an important role in cichlid speciation in general. We discuss several possibilities how the adaptive radiation of Midas Cichlids might have been influenced in this respect. PMID:21529367

Adaptive phenotypic plasticity in the Midas cichlid fish pharyngeal jaw and its relevance in adaptive radiation.

PubMed

Muschick, Moritz; Barluenga, Marta; Salzburger, Walter; Meyer, Axel

2011-04-30

Phenotypic evolution and its role in the diversification of organisms is a central topic in evolutionary biology. A neglected factor during the modern evolutionary synthesis, adaptive phenotypic plasticity, more recently attracted the attention of many evolutionary biologists and is now recognized as an important ingredient in both population persistence and diversification. The traits and directions in which an ancestral source population displays phenotypic plasticity might partly determine the trajectories in morphospace, which are accessible for an adaptive radiation, starting from the colonization of a novel environment. In the case of repeated colonizations of similar environments from the same source population this "flexible stem" hypothesis predicts similar phenotypes to arise in repeated subsequent radiations. The Midas Cichlid (Amphilophus spp.) in Nicaragua has radiated in parallel in several crater-lakes seeded by populations originating from the Nicaraguan Great Lakes. Here, we tested phenotypic plasticity in the pharyngeal jaw of Midas Cichlids. The pharyngeal jaw apparatus of cichlids, a second set of jaws functionally decoupled from the oral ones, is known to mediate ecological specialization and often differs strongly between sister-species. We performed a common garden experiment raising three groups of Midas cichlids on food differing in hardness and calcium content. Analyzing the lower pharyngeal jaw-bones we find significant differences between diet groups qualitatively resembling the differences found between specialized species. Observed differences in pharyngeal jaw expression between groups were attributable to the diet's mechanical resistance, whereas surplus calcium in the diet was not found to be of importance. The pharyngeal jaw apparatus of Midas Cichlids can be expressed plastically if stimulated mechanically during feeding. Since this trait is commonly differentiated--among other traits--between Midas Cichlid species, its plasticity might be an important factor in Midas Cichlid speciation. The prevalence of pharyngeal jaw differentiation across the Cichlidae further suggests that adaptive phenotypic plasticity in this trait could play an important role in cichlid speciation in general. We discuss several possibilities how the adaptive radiation of Midas Cichlids might have been influenced in this respect.

Genome wide selection in Citrus breeding.

PubMed

Gois, I B; Borém, A; Cristofani-Yaly, M; de Resende, M D V; Azevedo, C F; Bastianel, M; Novelli, V M; Machado, M A

2016-10-17

Genome wide selection (GWS) is essential for the genetic improvement of perennial species such as Citrus because of its ability to increase gain per unit time and to enable the efficient selection of characteristics with low heritability. This study assessed GWS efficiency in a population of Citrus and compared it with selection based on phenotypic data. A total of 180 individual trees from a cross between Pera sweet orange (Citrus sinensis Osbeck) and Murcott tangor (Citrus sinensis Osbeck x Citrus reticulata Blanco) were evaluated for 10 characteristics related to fruit quality. The hybrids were genotyped using 5287 DArT_seq TM (diversity arrays technology) molecular markers and their effects on phenotypes were predicted using the random regression - best linear unbiased predictor (rr-BLUP) method. The predictive ability, prediction bias, and accuracy of GWS were estimated to verify its effectiveness for phenotype prediction. The proportion of genetic variance explained by the markers was also computed. The heritability of the traits, as determined by markers, was 16-28%. The predictive ability of these markers ranged from 0.53 to 0.64, and the regression coefficients between predicted and observed phenotypes were close to unity. Over 35% of the genetic variance was accounted for by the markers. Accuracy estimates with GWS were lower than those obtained by phenotypic analysis; however, GWS was superior in terms of genetic gain per unit time. Thus, GWS may be useful for Citrus breeding as it can predict phenotypes early and accurately, and reduce the length of the selection cycle. This study demonstrates the feasibility of genomic selection in Citrus.

Explaining the disease phenotype of intergenic SNP through predicted long range regulation

PubMed Central

Chen, Jingqi; Tian, Weidong

2016-01-01

Thousands of disease-associated SNPs (daSNPs) are located in intergenic regions (IGR), making it difficult to understand their association with disease phenotypes. Recent analysis found that non-coding daSNPs were frequently located in or approximate to regulatory elements, inspiring us to try to explain the disease phenotypes of IGR daSNPs through nearby regulatory sequences. Hence, after locating the nearest distal regulatory element (DRE) to a given IGR daSNP, we applied a computational method named INTREPID to predict the target genes regulated by the DRE, and then investigated their functional relevance to the IGR daSNP's disease phenotypes. 36.8% of all IGR daSNP-disease phenotype associations investigated were possibly explainable through the predicted target genes, which were enriched with, were functionally relevant to, or consisted of the corresponding disease genes. This proportion could be further increased to 60.5% if the LD SNPs of daSNPs were also considered. Furthermore, the predicted SNP-target gene pairs were enriched with known eQTL/mQTL SNP-gene relationships. Overall, it's likely that IGR daSNPs may contribute to disease phenotypes by interfering with the regulatory function of their nearby DREs and causing abnormal expression of disease genes. PMID:27280978

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome

PubMed Central

Desrosiers, Christian; Hassan, Lama; Tanougast, Camel

2016-01-01

Objective: Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM. Methods: The data of 40 patients with GBM were obtained from the Cancer Genome Atlas and Cancer Imaging Archive. The T1 weighted post-contrast and fluid-attenuated inversion-recovery volumes of patients were co-registered and segmented into delineate regions corresponding to three GBM phenotypes: necrosis, active tumour and oedema/invasion. A set of two-dimensional shape features were then extracted slicewise from each phenotype region and combined over slices to describe the three-dimensional shape of these phenotypes. Thereafter, a Kruskal–Wallis test was employed to identify shape features with significantly different distributions across phenotypes. Moreover, a Kaplan–Meier analysis was performed to find features strongly associated with GBM survival. Finally, a multivariate analysis based on the random forest model was used for predicting the survival group of patients with GBM. Results: Our analysis using the Kruskal–Wallis test showed that all but one shape feature had statistically significant differences across phenotypes, with p-value < 0.05, following Holm–Bonferroni correction, justifying the analysis of GBM tumour shapes on a per-phenotype basis. Furthermore, the survival analysis based on the Kaplan–Meier estimator identified three features derived from necrotic regions (i.e. Eccentricity, Extent and Solidity) that were significantly correlated with overall survival (corrected p-value < 0.05; hazard ratios between 1.68 and 1.87). In the multivariate analysis, features from necrotic regions gave the highest accuracy in predicting the survival group of patients, with a mean area under the receiver-operating characteristic curve (AUC) of 63.85%. Combining the features of all three phenotypes increased the mean AUC to 66.99%, suggesting that shape features from different phenotypes can be used in a synergic manner to predict GBM survival. Conclusion: Results show that shape features, in particular those extracted from necrotic regions, can be used effectively to characterize GBM tumours and predict the overall survival of patients with GBM. Advances in knowledge: Simple volumetric features have been largely used to characterize the different phenotypes of a GBM tumour (i.e. active tumour, oedema and necrosis). This study extends previous work by considering a wide range of shape features, extracted in different phenotypes, for the prediction of survival in patients with GBM. PMID:27781499

Behavioural phenotypes predict disease susceptibility and infectiousness

PubMed Central

Araujo, Alessandra; Kirschman, Lucas

2016-01-01

Behavioural phenotypes may provide a means for identifying individuals that disproportionally contribute to disease spread and epizootic outbreaks. For example, bolder phenotypes may experience greater exposure and susceptibility to pathogenic infection because of distinct interactions with conspecifics and their environment. We tested the value of behavioural phenotypes in larval amphibians for predicting ranavirus transmission in experimental trials. We found that behavioural phenotypes characterized by latency-to-food and swimming profiles were predictive of disease susceptibility and infectiousness defined as the capacity of an infected host to transmit an infection by contacts. While viral shedding rates were positively associated with transmission, we also found an inverse relationship between contacts and infections. Together these results suggest intrinsic traits that influence behaviour and the quantity of pathogens shed during conspecific interactions may be an important contributor to ranavirus transmission. These results suggest that behavioural phenotypes provide a means to identify individuals more likely to spread disease and thus give insights into disease outbreaks that threaten wildlife and humans. PMID:27555652

Behavioural phenotypes predict disease susceptibility and infectiousness.

PubMed

Araujo, Alessandra; Kirschman, Lucas; Warne, Robin W

2016-08-01

Behavioural phenotypes may provide a means for identifying individuals that disproportionally contribute to disease spread and epizootic outbreaks. For example, bolder phenotypes may experience greater exposure and susceptibility to pathogenic infection because of distinct interactions with conspecifics and their environment. We tested the value of behavioural phenotypes in larval amphibians for predicting ranavirus transmission in experimental trials. We found that behavioural phenotypes characterized by latency-to-food and swimming profiles were predictive of disease susceptibility and infectiousness defined as the capacity of an infected host to transmit an infection by contacts. While viral shedding rates were positively associated with transmission, we also found an inverse relationship between contacts and infections. Together these results suggest intrinsic traits that influence behaviour and the quantity of pathogens shed during conspecific interactions may be an important contributor to ranavirus transmission. These results suggest that behavioural phenotypes provide a means to identify individuals more likely to spread disease and thus give insights into disease outbreaks that threaten wildlife and humans. © 2016 The Author(s).

Prediction accuracy of direct and indirect approaches, and their relationships with prediction ability of calibration models.

PubMed

Belay, T K; Dagnachew, B S; Boison, S A; Ådnøy, T

2018-03-28

Milk infrared spectra are routinely used for phenotyping traits of interest through links developed between the traits and spectra. Predicted individual traits are then used in genetic analyses for estimated breeding value (EBV) or for phenotypic predictions using a single-trait mixed model; this approach is referred to as indirect prediction (IP). An alternative approach [direct prediction (DP)] is a direct genetic analysis of (a reduced dimension of) the spectra using a multitrait model to predict multivariate EBV of the spectral components and, ultimately, also to predict the univariate EBV or phenotype for the traits of interest. We simulated 3 traits under different genetic (low: 0.10 to high: 0.90) and residual (zero to high: ±0.90) correlation scenarios between the 3 traits and assumed the first trait is a linear combination of the other 2 traits. The aim was to compare the IP and DP approaches for predictions of EBV and phenotypes under the different correlation scenarios. We also evaluated relationships between performances of the 2 approaches and the accuracy of calibration equations. Moreover, the effect of using different regression coefficients estimated from simulated phenotypes (β p ), true breeding values (β g ), and residuals (β r ) on performance of the 2 approaches were evaluated. The simulated data contained 2,100 parents (100 sires and 2,000 cows) and 8,000 offspring (4 offspring per cow). Of the 8,000 observations, 2,000 were randomly selected and used to develop links between the first and the other 2 traits using partial least square (PLS) regression analysis. The different PLS regression coefficients, such as β p , β g , and β r , were used in subsequent predictions following the IP and DP approaches. We used BLUP analyses for the remaining 6,000 observations using the true (co)variance components that had been used for the simulation. Accuracy of prediction (of EBV and phenotype) was calculated as a correlation between predicted and true values from the simulations. The results showed that accuracies of EBV prediction were higher in the DP than in the IP approach. The reverse was true for accuracy of phenotypic prediction when using β p but not when using β g and β r , where accuracy of phenotypic prediction in the DP was slightly higher than in the IP approach. Within the DP approach, accuracies of EBV when using β g were higher than when using β p only at the low genetic correlation scenario. However, we found no differences in EBV prediction accuracy between the β p and β g in the IP approach. Accuracy of the calibration models increased with an increase in genetic and residual correlations between the traits. Performance of both approaches increased with an increase in accuracy of the calibration models. In conclusion, the DP approach is a good strategy for EBV prediction but not for phenotypic prediction, where the classical PLS regression-based equations or the IP approach provided better results. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Phenotypes from ancient DNA: approaches, insights and prospects.

PubMed

Fortes, Gloria G; Speller, Camilla F; Hofreiter, Michael; King, Turi E

2013-08-01

The great majority of phenotypic characteristics are complex traits, complicating the identification of the genes underlying their expression. However, both methodological and theoretical progress in genome-wide association studies have resulted in a much better understanding of the underlying genetics of many phenotypic traits, including externally visible characteristics (EVCs) such as eye and hair color. Consequently, it has become possible to predict EVCs from human samples lacking phenotypic information. Predicting EVCs from genetic evidence is clearly appealing for forensic applications involving the personal identification of human remains. Now, a recent paper has reported the genetic determination of eye and hair color in samples up to 800 years old. The ability to predict EVCs from ancient human remains opens up promising perspectives for ancient DNA research, as this could allow studies to directly address archaeological and evolutionary questions related to the temporal and geographical origins of the genetic variants underlying phenotypes. © 2013 WILEY Periodicals, Inc.

Upper gastrointestinal tract phenotype of Crohn's disease is associated with early surgery and further hospitalization.

PubMed

Chow, Dorothy K L; Sung, Joseph J Y; Wu, Justin C Y; Tsoi, Kelvin K F; Leong, Rupert W L; Chan, Francis K L

2009-04-01

According to the Montreal Classification, upper gastrointestinal tract phenotype L4 is uncommon in Caucasian patients with Crohn's disease (CD) but carries excess risk of recurrence. We studied the clinical course of CD in Chinese patients presenting with the L4 phenotype and factors predicting its occurrence upon longitudinal follow-up. This prospective cohort study included 132 Chinese CD patients (median age at diagnosis, 30.0 years, range: 14.0-77.0 years) who were followed for 770 person-years. Demographic data including disease behavior and location, details of surgery, and hospitalization were collected. The Kaplan-Meier method was used to estimate the probabilities of further hospitalization and major surgery followed by Cox proportional hazards regression to determine if clinical variables independently predicted the endpoints. The L4 phenotype was found in 30 (22.7%) patients at presentation. There were significantly more stricturing (46.7% versus 18.6%) and penetrating (30.0% versus 3.9%) phenotypes in the L4 group than in the non-L4 group (P < 0.0001). The 3-year cumulative probability of further hospitalization was 86.9% (95% confidence interval [CI]: 73.8%-100.0%) in the L4 group as compared with 49.3% (95% CI: 39.3%-59.3%) in the non-L4 group (log-rank test, P < 0.0001). The L4 phenotype independently predicted further hospitalization (adjusted hazards ratio [HR]: 2.1; 95% CI: 1.3-3.5). The cumulative probability of major surgery was significantly higher in the L4 than in the non-L4 group (P < 0.0001). Eighteen (17.6%) patients developed the L4 phenotype on follow-up and the stricturing phenotype predicted its occurrence (adjusted HR: 5.5; 95% CI: 2.2-14.0). Chinese CD patients more often had the L4 phenotype, which predicted the need of subsequent hospitalization.

Genome-wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection.

PubMed

Bandillo, Nonoy B; Lorenz, Aaron J; Graef, George L; Jarquin, Diego; Hyten, David L; Nelson, Randall L; Specht, James E

2017-07-01

Genome-wide association (GWA) has been used as a tool for dissecting the genetic architecture of quantitatively inherited traits. We demonstrate here that GWA can also be highly useful for detecting many major genes governing categorically defined phenotype variants that exist for qualitatively inherited traits in a germplasm collection. Genome-wide association mapping was applied to categorical phenotypic data available for 10 descriptive traits in a collection of ∼13,000 soybean [ (L.) Merr.] accessions that had been genotyped with a 50,000 single nucleotide polymorphism (SNP) chip. A GWA on a panel of accessions of this magnitude can offer substantial statistical power and mapping resolution, and we found that GWA mapping resulted in the identification of strong SNP signals for 24 classical genes as well as several heretofore unknown genes controlling the phenotypic variants in those traits. Because some of these genes had been cloned, we were able to show that the narrow GWA mapping SNP signal regions that we detected for the phenotypic variants had chromosomal bp spans that, with just one exception, overlapped the bp region of the cloned genes, despite local variation in SNP number and nonuniform SNP distribution in the chip set. Copyright © 2017 Crop Science Society of America.

Prediction of whole-genome risk for selection and management of hyperketonemia in Holstein dairy cattle.

PubMed

Weigel, K A; Pralle, R S; Adams, H; Cho, K; Do, C; White, H M

2017-06-01

Hyperketonemia (HYK), a common early postpartum health disorder characterized by elevated blood concentrations of β-hydroxybutyrate (BHB), affects millions of dairy cows worldwide and leads to significant economic losses and animal welfare concerns. In this study, blood concentrations of BHB were assessed for 1,453 Holstein cows using electronic handheld meters at four time points between 5 and 18 days postpartum. Incidence rates of subclinical (1.2 ≤ maximum BHB ≤ 2.9 mmol/L) and clinical ketosis (maximum BHB ≥ 3.0 mmol/L) were 24.0 and 2.4%, respectively. Variance components, estimated breeding values, and predicted HYK phenotypes were computed on the original, square-root, and binary scales. Heritability estimates for HYK ranged from 0.058 to 0.072 in pedigree-based analyses, as compared to estimates that ranged from 0.071 to 0.093 when pedigrees were augmented with 60,671 single nucleotide polymorphism genotypes of 959 cows and 801 male ancestors. On average, predicted HYK phenotypes from the genome-enhanced analysis ranged from 0.55 mmol/L for first-parity cows in the best contemporary group to 1.40 mmol/L for fourth-parity cows in the worst contemporary group. Genome-enhanced predictions of HYK phenotypes were more closely associated with actual phenotypes than pedigree-based predictions in five-fold cross-validation, and transforming phenotypes to reduce skewness and kurtosis also improved predictive ability. This study demonstrates the feasibility of using repeated cowside measurement of blood BHB concentration in early lactation to construct a reference population that can be used to estimate HYK breeding values for genomic selection programmes and predict HYK phenotypes for genome-guided management decisions. © 2017 Blackwell Verlag GmbH.

A Qualitative Analysis of Imitation Performances of Preschoolers With Down Syndrome.

PubMed

Vanvuchelen, Marleen

2016-05-01

A number of studies suggest that imitation is a characteristic strength in children with Down Syndrome (DS). The present study aims to discover whether imitation performances are qualitatively phenotypical in DS. Eight preschoolers with DS were matched on chronological, mental, language and imitation age with 8 preschoolers with intellectual disability of undifferentiated etiology (ID-UND). Imitation performances on the Preschool Imitation and Praxis Scale were videotaped for blind scoring on 30 possible errors. Children with DS made fewer production errors (synkinesias, OR 0.3 [0.1-0.7]), but more conceptual errors (substitution, OR 2.5 [1.6-3.9]) compared to children with ID-UND. This finding is in line with the view of a cognitive phenotype in DS, which is characterized by preserved visuospatial and impaired language abilities.

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

PubMed

Notaro, Marco; Schubach, Max; Robinson, Peter N; Valentini, Giorgio

2017-10-12

The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

Single nucleotide polymorphism coverage and inference of N-acetyltransferase-2 acetylator phenotypes in wordwide population groups.

PubMed

Suarez-Kurtz, Guilherme; Fuchshuber-Moraes, Mateus; Struchiner, Claudio J; Parra, Esteban J

2016-08-01

Several algorithms have been proposed to reduce the genotyping effort and cost, while retaining the accuracy of N-acetyltransferase-2 (NAT2) phenotype prediction. Data from the 1000 Genomes (1KG) project and an admixed cohort of Black Brazilians were used to assess the accuracy of NAT2 phenotype prediction using algorithms based on paired single nucleotide polymorphisms (SNPs) (rs1041983 and rs1801280) or a tag SNP (rs1495741). NAT2 haplotypes comprising SNPs rs1801279, rs1041983, rs1801280, rs1799929, rs1799930, rs1208 and rs1799931 were assigned according to the arylamine N-acetyltransferases database. Contingency tables were used to visualize the agreement between the NAT2 acetylator phenotypes on the basis of these haplotypes versus phenotypes inferred by the prediction algorithms. The paired and tag SNP algorithms provided more than 96% agreement with the 7-SNP derived phenotypes in Europeans, East Asians, South Asians and Admixed Americans, but discordance of phenotype prediction occurred in 30.2 and 24.8% 1KG Africans and in 14.4 and 18.6% Black Brazilians, respectively. Paired SNP panel misclassification occurs in carriers of NATs haplotypes *13A (282T alone), *12B (282T and 803G), *6B (590A alone) and *14A (191A alone), whereas haplotype *14, defined by the 191A allele, is the major culprit of misclassification by the tag allele. Both the paired SNP and the tag SNP algorithms may be used, with economy of scale, to infer NAT2 acetylator phenotypes, including the ultra-slow phenotype, in European, East Asian, South Asian and American populations represented in the 1KG cohort. Both algorithms, however, perform poorly in populations of predominant African descent, including admixed African-Americans, African Caribbeans and Black Brazilians.

Explaining the disease phenotype of intergenic SNP through predicted long range regulation.

PubMed

Chen, Jingqi; Tian, Weidong

2016-10-14

Thousands of disease-associated SNPs (daSNPs) are located in intergenic regions (IGR), making it difficult to understand their association with disease phenotypes. Recent analysis found that non-coding daSNPs were frequently located in or approximate to regulatory elements, inspiring us to try to explain the disease phenotypes of IGR daSNPs through nearby regulatory sequences. Hence, after locating the nearest distal regulatory element (DRE) to a given IGR daSNP, we applied a computational method named INTREPID to predict the target genes regulated by the DRE, and then investigated their functional relevance to the IGR daSNP's disease phenotypes. 36.8% of all IGR daSNP-disease phenotype associations investigated were possibly explainable through the predicted target genes, which were enriched with, were functionally relevant to, or consisted of the corresponding disease genes. This proportion could be further increased to 60.5% if the LD SNPs of daSNPs were also considered. Furthermore, the predicted SNP-target gene pairs were enriched with known eQTL/mQTL SNP-gene relationships. Overall, it's likely that IGR daSNPs may contribute to disease phenotypes by interfering with the regulatory function of their nearby DREs and causing abnormal expression of disease genes. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network.

PubMed

Axelrod, Kevin; Sanchez, Alvaro; Gore, Jeff

2015-08-24

Microorganisms often exhibit a history-dependent phenotypic response after exposure to a stimulus which can be imperative for proper function. However, cells frequently experience unexpected environmental perturbations that might induce phenotypic switching. How cells maintain phenotypic states in the face of environmental fluctuations remains an open question. Here, we use environmental perturbations to characterize the resilience of phenotypic states in a synthetic gene network near a critical transition. We find that far from the critical transition an environmental perturbation may induce little to no phenotypic switching, whereas close to the critical transition the same perturbation can cause many cells to switch phenotypic states. This loss of resilience was observed for perturbations that interact directly with the gene circuit as well as for a variety of generic perturbations-such as salt, ethanol, or temperature shocks-that alter the state of the cell more broadly. We obtain qualitatively similar findings in natural gene circuits, such as the yeast GAL network. Our findings illustrate how phenotypic memory can become destabilized by environmental variability near a critical transition.

Evaluation of breeding strategies for polledness in dairy cattle using a newly developed simulation framework for quantitative and Mendelian traits.

PubMed

Scheper, Carsten; Wensch-Dorendorf, Monika; Yin, Tong; Dressel, Holger; Swalve, Herrmann; König, Sven

2016-06-29

Intensified selection of polled individuals has recently gained importance in predominantly horned dairy cattle breeds as an alternative to routine dehorning. The status quo of the current polled breeding pool of genetically-closely related artificial insemination sires with lower breeding values for performance traits raises questions regarding the effects of intensified selection based on this founder pool. We developed a stochastic simulation framework that combines the stochastic simulation software QMSim and a self-designed R program named QUALsim that acts as an external extension. Two traits were simulated in a dairy cattle population for 25 generations: one quantitative (QMSim) and one qualitative trait with Mendelian inheritance (i.e. polledness, QUALsim). The assignment scheme for qualitative trait genotypes initiated realistic initial breeding situations regarding allele frequencies, true breeding values for the quantitative trait and genetic relatedness. Intensified selection for polled cattle was achieved using an approach that weights estimated breeding values in the animal best linear unbiased prediction model for the quantitative trait depending on genotypes or phenotypes for the polled trait with a user-defined weighting factor. Selection response for the polled trait was highest in the selection scheme based on genotypes. Selection based on phenotypes led to significantly lower allele frequencies for polled. The male selection path played a significantly greater role for a fast dissemination of polled alleles compared to female selection strategies. Fixation of the polled allele implies selection based on polled genotypes among males. In comparison to a base breeding scenario that does not take polledness into account, intensive selection for polled substantially reduced genetic gain for this quantitative trait after 25 generations. Reducing selection intensity for polled males while maintaining strong selection intensity among females, simultaneously decreased losses in genetic gain and achieved a final allele frequency of 0.93 for polled. A fast transition to a completely polled population through intensified selection for polled was in contradiction to the preservation of high genetic gain for the quantitative trait. Selection on male polled genotypes with moderate weighting, and selection on female polled phenotypes with high weighting, could be a suitable compromise regarding all important breeding aspects.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

PubMed

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

PubMed Central

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

Literature-based prediction of novel drug indications considering relationships between entities.

PubMed

Jang, Giup; Lee, Taekeon; Lee, Byung Mun; Yoon, Youngmi

2017-06-27

There have been many attempts to identify and develop new uses for existing drugs, which is known as drug repositioning. Among these efforts, text mining is an effective means of discovering novel knowledge from a large amount of literature data. We identify a gene regulation by a drug and a phenotype based on the biomedical literature. Drugs or phenotypes can activate or inhibit gene regulation. We calculate the therapeutic possibility that a drug acts on a phenotype by means of these two types of regulation. We assume that a drug treats a phenotype if the genes regulated by the phenotype are inversely correlated with the genes regulated by the drug. Based on this hypothesis, we identify drug-phenotype associations with therapeutic possibility. To validate the drug-phenotype associations predicted by our method, we make an enrichment comparison with known drug-phenotype associations. We also identify candidate drugs for drug repositioning from novel associations and thus reveal that our method is a novel approach to drug repositioning.

Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis.

PubMed

Bos, L D; Schouten, L R; van Vught, L A; Wiewel, M A; Ong, D S Y; Cremer, O; Artigas, A; Martin-Loeches, I; Hoogendijk, A J; van der Poll, T; Horn, J; Juffermans, N; Calfee, C S; Schultz, M J

2017-10-01

We hypothesised that patients with acute respiratory distress syndrome (ARDS) can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality. Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking any outcome data into account. Logistic regression with backward selection was used to select the most predictive biomarkers, and these predicted phenotypes were validated in a separate cohort. Multivariable logistic regression was used to quantify the independent association with mortality. Two phenotypes were identified in 454 patients, which we named 'uninflamed' (N=218) and 'reactive' (N=236). A selection of four biomarkers (interleukin-6, interferon gamma, angiopoietin 1/2 and plasminogen activator inhibitor-1) could be used to accurately predict the phenotype in the training cohort (area under the receiver operating characteristics curve: 0.98, 95% CI 0.97 to 0.99). Mortality rates were 15.6% and 36.4% (p<0.001) in the training cohort and 13.6% and 37.5% (p<0.001) in the validation cohort (N=207). The 'reactive phenotype' was independent from confounders associated with intensive care unit mortality (training cohort: OR 1.13, 95% CI 1.04 to 1.23; validation cohort: OR 1.18, 95% CI 1.06 to 1.31). Patients with ARDS can be clustered into two biological phenotypes, with different mortality rates. Four biomarkers can be used to predict the phenotype with high accuracy. The phenotypes were very similar to those found in cohorts derived from randomised controlled trials, and these results may improve patient selection for future clinical trials targeting host response in patients with ARDS. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: A new mammalian circadian oscillator model including the cAMP module

NASA Astrophysics Data System (ADS)

Wang, Jun-Wei; Zhou, Tian-Shou

2009-12-01

In this paper, we develop a new mathematical model for the mammalian circadian clock, which incorporates both transcriptional/translational feedback loops (TTFLs) and a cAMP-mediated feedback loop. The model shows that TTFLs and cAMP signalling cooperatively drive the circadian rhythms. It reproduces typical experimental observations with qualitative similarities, e.g. circadian oscillations in constant darkness and entrainment to light-dark cycles. In addition, it can explain the phenotypes of cAMP-mutant and Rev-erbα-/--mutant mice, and help us make an experimentally-testable prediction: oscillations may be rescued when arrhythmic mice with constitutively low concentrations of cAMP are crossed with Rev-erbα-/- mutant mice. The model enhances our understanding of the mammalian circadian clockwork from the viewpoint of the entire cell.

Local Fitness Landscapes Predict Yeast Evolutionary Dynamics in Directionally Changing Environments.

PubMed

Gorter, Florien A; Aarts, Mark G M; Zwaan, Bas J; de Visser, J Arjan G M

2018-01-01

The fitness landscape is a concept that is widely used for understanding and predicting evolutionary adaptation. The topography of the fitness landscape depends critically on the environment, with potentially far-reaching consequences for evolution under changing conditions. However, few studies have assessed directly how empirical fitness landscapes change across conditions, or validated the predicted consequences of such change. We previously evolved replicate yeast populations in the presence of either gradually increasing, or constant high, concentrations of the heavy metals cadmium (Cd), nickel (Ni), and zinc (Zn), and analyzed their phenotypic and genomic changes. Here, we reconstructed the local fitness landscapes underlying adaptation to each metal by deleting all repeatedly mutated genes both by themselves and in combination. Fitness assays revealed that the height, and/or shape, of each local fitness landscape changed considerably across metal concentrations, with distinct qualitative differences between unconditionally (Cd) and conditionally toxic metals (Ni and Zn). This change in topography had particularly crucial consequences in the case of Ni, where a substantial part of the individual mutational fitness effects changed in sign across concentrations. Based on the Ni landscape analyses, we made several predictions about which mutations had been selected when during the evolution experiment. Deep sequencing of population samples from different time points generally confirmed these predictions, demonstrating the power of landscape reconstruction analyses for understanding and ultimately predicting evolutionary dynamics, even under complex scenarios of environmental change. Copyright © 2018 by the Genetics Society of America.

Genome-enabled prediction models for yield related traits in chickpea

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped...

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

PubMed

Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

2015-02-01

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer. Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Large-Scale Comparative Phenotypic and Genomic Analyses Reveal Ecological Preferences of Shewanella Species and Identify Metabolic Pathways Conserved at the Genus Level ▿ †

PubMed Central

Rodrigues, Jorge L. M.; Serres, Margrethe H.; Tiedje, James M.

2011-01-01

The use of comparative genomics for the study of different microbiological species has increased substantially as sequence technologies become more affordable. However, efforts to fully link a genotype to its phenotype remain limited to the development of one mutant at a time. In this study, we provided a high-throughput alternative to this limiting step by coupling comparative genomics to the use of phenotype arrays for five sequenced Shewanella strains. Positive phenotypes were obtained for 441 nutrients (C, N, P, and S sources), with N-based compounds being the most utilized for all strains. Many genes and pathways predicted by genome analyses were confirmed with the comparative phenotype assay, and three degradation pathways believed to be missing in Shewanella were confirmed as missing. A number of previously unknown gene products were predicted to be parts of pathways or to have a function, expanding the number of gene targets for future genetic analyses. Ecologically, the comparative high-throughput phenotype analysis provided insights into niche specialization among the five different strains. For example, Shewanella amazonensis strain SB2B, isolated from the Amazon River delta, was capable of utilizing 60 C compounds, whereas Shewanella sp. strain W3-18-1, isolated from deep marine sediment, utilized only 25 of them. In spite of the large number of nutrient sources yielding positive results, our study indicated that except for the N sources, they were not sufficiently informative to predict growth phenotypes from increasing evolutionary distances. Our results indicate the importance of phenotypic evaluation for confirming genome predictions. This strategy will accelerate the functional discovery of genes and provide an ecological framework for microbial genome sequencing projects. PMID:21642407

Correlation of RNA secondary structure and attenuation of Sabin vaccine strains of poliovirus in tissue culture.

PubMed

Macadam, A J; Ferguson, G; Burlison, J; Stone, D; Skuce, R; Almond, J W; Minor, P D

1992-08-01

Part of the 5' noncoding regions of all three Sabin vaccine strains of poliovirus contains determinants of attenuation that are shown here to influence the ability of these strains to grow at elevated temperatures in BGM cells. The predicted RNA secondary structure of this region (nt 464-542 in P3/Sabin) suggests that both phenotypes are due to perturbation of base-paired stems. Ts phenotypes of site-directed mutants with defined changes in this region correlated well with predicted secondary structure stabilities. Reversal of base-pair orientation had little effect whereas stem disruption led to marked increases in temperature sensitivity. Phenotypic revertants of such viruses displayed mutations on either side of the stem. Mutations destabilizing stems led to intermediate phenotypes. These results provided evidence for the biological significance of the predicted RNA secondary structure.

On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs.

PubMed

Bastin, C; Théron, L; Lainé, A; Gengler, N

2016-05-01

Fertility and health traits are of prime importance in dairy breeding programs. However, these traits are generally complex, difficult to record, and lowly heritable (<0.10), thereby hampering genetic improvement in disease resistance and fertility. Hence, indicators are useful in the prediction of genetic merit for fertility and health traits as long as they are easier to measure than direct fitness traits, heritable, and genetically correlated. Considering that changes in (fine) milk composition over a lactation reflect the physiological status of the cow, mid-infrared (MIR) analysis of milk opens the door to a wide range of potential indicator traits of fertility and health. Previous studies investigated the phenotypic and genetic relationships between fertility and MIR-predicted phenotypes, most being related to negative postpartum energy balance and body fat mobilization (e.g., fat:protein ratio, urea, fatty acids profile). Results showed that a combination of various fatty acid traits (e.g., C18:1 cis-9 and C10:0) could be used to improve fertility. Furthermore, occurrence of (sub)clinical ketosis has been related to milk-based phenotypes such as fat:protein ratio, fatty acids, and ketone bodies. Hence, MIR-predicted acetone and β-hydroxybutyrate contents in milk could be useful for breeding cows less susceptible to ketosis. Although studies investigating the genetic association among mastitis and MIR-predicted phenotypes are scarce, a wide range of traits, potentially predicted by MIR spectrometry, are worthy of consideration. These include traits related to the disease response of the cow (e.g., lactoferrin), reduced secretory activity (e.g., casein), and the alteration of the blood-milk barrier (e.g., minerals). Moreover, direct MIR prediction of fertility and health traits should be further considered. To conclude, MIR-predicted phenotypes have a role to play in the improvement of dairy cow fertility and health. However, further studies are warranted to (1) grasp underlying associations among MIR-predicted indicator and fitness traits, (2) estimate the genetic parameters, and (3) include these traits in broader breeding strategies. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Self-reported pigmentary phenotypes and race are significant but incomplete predictors of Fitzpatrick skin phototype in an ethnically diverse population.

PubMed

He, Steven Y; McCulloch, Charles E; Boscardin, W John; Chren, Mary-Margaret; Linos, Eleni; Arron, Sarah T

2014-10-01

Fitzpatrick skin phototype (FSPT) is the most common method used to assess sunburn risk and is an independent predictor of skin cancer risk. Because of a conventional assumption that FSPT is predictable based on pigmentary phenotypes, physicians frequently estimate FSPT based on patient appearance. We sought to determine the degree to which self-reported race and pigmentary phenotypes are predictive of FSPT in a large, ethnically diverse population. A cross-sectional survey collected responses from 3386 individuals regarding self-reported FSPT, pigmentary phenotypes, race, age, and sex. Univariate and multivariate logistic regression analyses were performed to determine variables that significantly predict FSPT. Race, sex, skin color, eye color, and hair color are significant but weak independent predictors of FSPT (P<.0001). A multivariate model constructed using all independent predictors of FSPT only accurately predicted FSPT to within 1 point on the Fitzpatrick scale with 92% accuracy (weighted kappa statistic 0.53). Our study enriched for responses from ethnic minorities and does not fully represent the demographics of the US population. Patient self-reported race and pigmentary phenotypes are inaccurate predictors of sun sensitivity as defined by FSPT. There are limitations to using patient-reported race and appearance in predicting individual sunburn risk. Copyright © 2014 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Accuracy of genomic prediction using deregressed breeding values estimated from purebred and crossbred offspring phenotypes in pigs.

PubMed

Hidalgo, A M; Bastiaansen, J W M; Lopes, M S; Veroneze, R; Groenen, M A M; de Koning, D-J

2015-07-01

Genomic selection is applied to dairy cattle breeding to improve the genetic progress of purebred (PB) animals, whereas in pigs and poultry the target is a crossbred (CB) animal for which a different strategy appears to be needed. The source of information used to estimate the breeding values, i.e., using phenotypes of CB or PB animals, may affect the accuracy of prediction. The objective of our study was to assess the direct genomic value (DGV) accuracy of CB and PB pigs using different sources of phenotypic information. Data used were from 3 populations: 2,078 Dutch Landrace-based, 2,301 Large White-based, and 497 crossbreds from an F1 cross between the 2 lines. Two female reproduction traits were analyzed: gestation length (GLE) and total number of piglets born (TNB). Phenotypes used in the analyses originated from offspring of genotyped individuals. Phenotypes collected on CB and PB animals were analyzed as separate traits using a single-trait model. Breeding values were estimated separately for each trait in a pedigree BLUP analysis and subsequently deregressed. Deregressed EBV for each trait originating from different sources (CB or PB offspring) were used to study the accuracy of genomic prediction. Accuracy of prediction was computed as the correlation between DGV and the DEBV of the validation population. Accuracy of prediction within PB populations ranged from 0.43 to 0.62 across GLE and TNB. Accuracies to predict genetic merit of CB animals with one PB population in the training set ranged from 0.12 to 0.28, with the exception of using the CB offspring phenotype of the Dutch Landrace that resulted in an accuracy estimate around 0 for both traits. Accuracies to predict genetic merit of CB animals with both parental PB populations in the training set ranged from 0.17 to 0.30. We conclude that prediction within population and trait had good predictive ability regardless of the trait being the PB or CB performance, whereas using PB population(s) to predict genetic merit of CB animals had zero to moderate predictive ability. We observed that the DGV accuracy of CB animals when training on PB data was greater than or equal to training on CB data. However, when results are corrected for the different levels of reliabilities in the PB and CB training data, we showed that training on CB data does outperform PB data for the prediction of CB genetic merit, indicating that more CB animals should be phenotyped to increase the reliability and, consequently, accuracy of DGV for CB genetic merit.

Text-based phenotypic profiles incorporating biochemical phenotypes of inborn errors of metabolism improve phenomics-based diagnosis.

PubMed

Lee, Jessica J Y; Gottlieb, Michael M; Lever, Jake; Jones, Steven J M; Blau, Nenad; van Karnebeek, Clara D M; Wasserman, Wyeth W

2018-05-01

Phenomics is the comprehensive study of phenotypes at every level of biology: from metabolites to organisms. With high throughput technologies increasing the scope of biological discoveries, the field of phenomics has been developing rapid and precise methods to collect, catalog, and analyze phenotypes. Such methods have allowed phenotypic data to be widely used in medical applications, from assisting clinical diagnoses to prioritizing genomic diagnoses. To channel the benefits of phenomics into the field of inborn errors of metabolism (IEM), we have recently launched IEMbase, an expert-curated knowledgebase of IEM and their disease-characterizing phenotypes. While our efforts with IEMbase have realized benefits, taking full advantage of phenomics requires a comprehensive curation of IEM phenotypes in core phenomics projects, which is dependent upon contributions from the IEM clinical and research community. Here, we assess the inclusion of IEM biochemical phenotypes in a core phenomics project, the Human Phenotype Ontology. We then demonstrate the utility of biochemical phenotypes using a text-based phenomics method to predict gene-disease relationships, showing that the prediction of IEM genes is significantly better using biochemical rather than clinical profiles. The findings herein provide a motivating goal for the IEM community to expand the computationally accessible descriptions of biochemical phenotypes associated with IEM in phenomics resources.

Logistics for Working Together to Facilitate Genomic/Quantitative Genetic Prediction

USDA-ARS?s Scientific Manuscript database

The incorporation of DNA tests into the national cattle evaluation system will require estimation of variances of and covariances among the additive genetic components of the DNA tests and the phenotypic traits they are intended to predict. Populations with both DNA test results and phenotypes will ...

Demographic and phenotypic responses of juvenile steelhead trout to spatial predictability of food resources

Treesearch

Matthew R. Sloat; Gordon H. Reeves

2014-01-01

We manipulated food inputs among patches within experimental streams to determine how variation in foraging behavior influenced demographic and phenotypic responses of juvenile steelhead trout (Oncorhynchus mykiss) to the spatial predictability of food resources. Demographic responses included compensatory adjustments in fish abundance, mean fish...

Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network

PubMed Central

Axelrod, Kevin; Sanchez, Alvaro; Gore, Jeff

2015-01-01

Microorganisms often exhibit a history-dependent phenotypic response after exposure to a stimulus which can be imperative for proper function. However, cells frequently experience unexpected environmental perturbations that might induce phenotypic switching. How cells maintain phenotypic states in the face of environmental fluctuations remains an open question. Here, we use environmental perturbations to characterize the resilience of phenotypic states in a synthetic gene network near a critical transition. We find that far from the critical transition an environmental perturbation may induce little to no phenotypic switching, whereas close to the critical transition the same perturbation can cause many cells to switch phenotypic states. This loss of resilience was observed for perturbations that interact directly with the gene circuit as well as for a variety of generic perturbations-such as salt, ethanol, or temperature shocks-that alter the state of the cell more broadly. We obtain qualitatively similar findings in natural gene circuits, such as the yeast GAL network. Our findings illustrate how phenotypic memory can become destabilized by environmental variability near a critical transition. DOI: http://dx.doi.org/10.7554/eLife.07935.001 PMID:26302311

Enabling phenotypic big data with PheNorm.

PubMed

Yu, Sheng; Ma, Yumeng; Gronsbell, Jessica; Cai, Tianrun; Ananthakrishnan, Ashwin N; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Liao, Katherine P; Cai, Tianxi

2018-01-01

Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference. The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants

PubMed Central

Weston, David J; Gunter, Lee E; Rogers, Alistair; Wullschleger, Stan D

2008-01-01

Background One of the eminent opportunities afforded by modern genomic technologies is the potential to provide a mechanistic understanding of the processes by which genetic change translates to phenotypic variation and the resultant appearance of distinct physiological traits. Indeed much progress has been made in this area, particularly in biomedicine where functional genomic information can be used to determine the physiological state (e.g., diagnosis) and predict phenotypic outcome (e.g., patient survival). Ecology currently lacks an analogous approach where genomic information can be used to diagnose the presence of a given physiological state (e.g., stress response) and then predict likely phenotypic outcomes (e.g., stress duration and tolerance, fitness). Results Here, we demonstrate that a compendium of genomic signatures can be used to classify the plant abiotic stress phenotype in Arabidopsis according to the architecture of the transcriptome, and then be linked with gene coexpression network analysis to determine the underlying genes governing the phenotypic response. Using this approach, we confirm the existence of known stress responsive pathways and marker genes, report a common abiotic stress responsive transcriptome and relate phenotypic classification to stress duration. Conclusion Linking genomic signatures to gene coexpression analysis provides a unique method of relating an observed plant phenotype to changes in gene expression that underlie that phenotype. Such information is critical to current and future investigations in plant biology and, in particular, to evolutionary ecology, where a mechanistic understanding of adaptive physiological responses to abiotic stress can provide researchers with a tool of great predictive value in understanding species and population level adaptation to climate change. PMID:18248680

Forensic DNA Phenotyping: Predicting human appearance from crime scene material for investigative purposes.

PubMed

Kayser, Manfred

2015-09-01

Forensic DNA Phenotyping refers to the prediction of appearance traits of unknown sample donors, or unknown deceased (missing) persons, directly from biological materials found at the scene. "Biological witness" outcomes of Forensic DNA Phenotyping can provide investigative leads to trace unknown persons, who are unidentifiable with current comparative DNA profiling. This intelligence application of DNA marks a substantially different forensic use of genetic material rather than that of current DNA profiling presented in the courtroom. Currently, group-specific pigmentation traits are already predictable from DNA with reasonably high accuracies, while several other externally visible characteristics are under genetic investigation. Until individual-specific appearance becomes accurately predictable from DNA, conventional DNA profiling needs to be performed subsequent to appearance DNA prediction. Notably, and where Forensic DNA Phenotyping shows great promise, this is on a (much) smaller group of potential suspects, who match the appearance characteristics DNA-predicted from the crime scene stain or from the deceased person's remains. Provided sufficient funding being made available, future research to better understand the genetic basis of human appearance will expectedly lead to a substantially more detailed description of an unknown person's appearance from DNA, delivering increased value for police investigations in criminal and missing person cases involving unknowns. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Do recognizable lifetime eating disorder phenotypes naturally occur in a culturally asian population? A combined latent profile and taxometric approach.

PubMed

Thomas, Jennifer J; Eddy, Kamryn T; Ruscio, John; Ng, King Lam; Casale, Kristen E; Becker, Anne E; Lee, Sing

2015-05-01

We examined whether empirically derived eating disorder (ED) categories in Hong Kong Chinese patients (N = 454) would be consistent with recognizable lifetime ED phenotypes derived from latent structure models of European and American samples. We performed latent profile analysis (LPA) using indicator variables from data collected during routine assessment, and then applied taxometric analysis to determine whether latent classes were qualitatively versus quantitatively distinct. Latent profile analysis identified four classes: (i) binge/purge (47%); (ii) non-fat-phobic low-weight (34%); (iii) fat-phobic low-weight (12%); and (iv) overweight disordered eating (6%). Taxometric analysis identified qualitative (categorical) distinctions between the binge/purge and non-fat-phobic low-weight classes, and also between the fat-phobic and non-fat-phobic low-weight classes. Distinctions between the fat-phobic low-weight and binge/purge classes were indeterminate. Empirically derived categories in Hong Kong showed recognizable correspondence with recognizable lifetime ED phenotypes. Although taxometric findings support two distinct classes of low weight EDs, LPA findings also support heterogeneity among non-fat-phobic individuals. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus

NASA Astrophysics Data System (ADS)

Lobo, Daniel; Lobikin, Maria; Levin, Michael

2017-01-01

Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation. We developed a Machine Learning method which inferred a model explaining this complex, stochastic all-or-none dataset. We then used this model to ask how a new phenotype could be generated: animals in which only some of the melanocytes converted. Systematically performing in silico perturbations, the model predicted that a combination of altanserin (5HTR2 inhibitor), reserpine (VMAT inhibitor), and VP16-XlCreb1 (constitutively active CREB) would break the all-or-none concordance. Remarkably, applying the predicted combination of three reagents in vivo revealed precisely the expected novel outcome, resulting in partial conversion of melanocytes within individuals. This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.

The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants.

PubMed

Al-Numair, Nouf S; Lopes, Luis; Syrris, Petros; Monserrat, Lorenzo; Elliott, Perry; Martin, Andrew C R

2016-10-01

High-throughput sequencing platforms are increasingly used to screen patients with genetic disease for pathogenic mutations, but prediction of the effects of mutations remains challenging. Previously we developed SAAPdap (Single Amino Acid Polymorphism Data Analysis Pipeline) and SAAPpred (Single Amino Acid Polymorphism Predictor) that use a combination of rule-based structural measures to predict whether a missense genetic variant is pathogenic. Here we investigate whether the same methodology can be used to develop a differential phenotype predictor, which, once a mutation has been predicted as pathogenic, is able to distinguish between phenotypes-in this case the two major clinical phenotypes (hypertrophic cardiomyopathy, HCM and dilated cardiomyopathy, DCM) associated with mutations in the beta-myosin heavy chain (MYH7) gene product (Myosin-7). A random forest predictor trained on rule-based structural analyses together with structural clustering data gave a Matthews' correlation coefficient (MCC) of 0.53 (accuracy, 75%). A post hoc removal of machine learning models that performed particularly badly, increased the performance (MCC = 0.61, Acc = 79%). This proof of concept suggests that methods used for pathogenicity prediction can be extended for use in differential phenotype prediction. Analyses were implemented in Perl and C and used the Java-based Weka machine learning environment. Please contact the authors for availability. andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk Supplementary data are available at Bioinformatics online. © The Authors 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

PubMed Central

Howard, Réka; Carriquiry, Alicia L.; Beavis, William D.

2014-01-01

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE. PMID:24727289

Integrating Milk Metabolite Profile Information for the Prediction of Traditional Milk Traits Based on SNP Information for Holstein Cows

PubMed Central

Melzer, Nina; Wittenburg, Dörte; Repsilber, Dirk

2013-01-01

In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype). PMID:23990900

Comparing the predictive abilities of phenotypic and marker-assisted selection methods in a biparental lettuce population

USDA-ARS?s Scientific Manuscript database

Breeding and selection for the traits with polygenic inheritance is a challenging task that can be done by phenotypic selection, by marker-assisted selection or by genome wide selection. We tested predictive ability of four selection models in a biparental population genotyped with 95 SNP markers an...

Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield

USDA-ARS?s Scientific Manuscript database

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat (Triticum aestivum L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect s...

Non-additive genetic variation in growth, carcass and fertility traits of beef cattle.

PubMed

Bolormaa, Sunduimijid; Pryce, Jennie E; Zhang, Yuandan; Reverter, Antonio; Barendse, William; Hayes, Ben J; Goddard, Michael E

2015-04-02

A better understanding of non-additive variance could lead to increased knowledge on the genetic control and physiology of quantitative traits, and to improved prediction of the genetic value and phenotype of individuals. Genome-wide panels of single nucleotide polymorphisms (SNPs) have been mainly used to map additive effects for quantitative traits, but they can also be used to investigate non-additive effects. We estimated dominance and epistatic effects of SNPs on various traits in beef cattle and the variance explained by dominance, and quantified the increase in accuracy of phenotype prediction by including dominance deviations in its estimation. Genotype data (729 068 real or imputed SNPs) and phenotypes on up to 16 traits of 10 191 individuals from Bos taurus, Bos indicus and composite breeds were used. A genome-wide association study was performed by fitting the additive and dominance effects of single SNPs. The dominance variance was estimated by fitting a dominance relationship matrix constructed from the 729 068 SNPs. The accuracy of predicted phenotypic values was evaluated by best linear unbiased prediction using the additive and dominance relationship matrices. Epistatic interactions (additive × additive) were tested between each of the 28 SNPs that are known to have additive effects on multiple traits, and each of the other remaining 729 067 SNPs. The number of significant dominance effects was greater than expected by chance and most of them were in the direction that is presumed to increase fitness and in the opposite direction to inbreeding depression. Estimates of dominance variance explained by SNPs varied widely between traits, but had large standard errors. The median dominance variance across the 16 traits was equal to 5% of the phenotypic variance. Including a dominance deviation in the prediction did not significantly increase its accuracy for any of the phenotypes. The number of additive × additive epistatic effects that were statistically significant was greater than expected by chance. Significant dominance and epistatic effects occur for growth, carcass and fertility traits in beef cattle but they are difficult to estimate precisely and including them in phenotype prediction does not increase its accuracy.

Predicting plant biomass accumulation from image-derived parameters

PubMed Central

Chen, Dijun; Shi, Rongli; Pape, Jean-Michel; Neumann, Kerstin; Graner, Andreas; Chen, Ming; Klukas, Christian

2018-01-01

Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species. PMID:29346559

Genomic selection in a commercial winter wheat population.

PubMed

He, Sang; Schulthess, Albert Wilhelm; Mirdita, Vilson; Zhao, Yusheng; Korzun, Viktor; Bothe, Reiner; Ebmeyer, Erhard; Reif, Jochen C; Jiang, Yong

2016-03-01

Genomic selection models can be trained using historical data and filtering genotypes based on phenotyping intensity and reliability criterion are able to increase the prediction ability. We implemented genomic selection based on a large commercial population incorporating 2325 European winter wheat lines. Our objectives were (1) to study whether modeling epistasis besides additive genetic effects results in enhancement on prediction ability of genomic selection, (2) to assess prediction ability when training population comprised historical or less-intensively phenotyped lines, and (3) to explore the prediction ability in subpopulations selected based on the reliability criterion. We found a 5 % increase in prediction ability when shifting from additive to additive plus epistatic effects models. In addition, only a marginal loss from 0.65 to 0.50 in accuracy was observed using the data collected from 1 year to predict genotypes of the following year, revealing that stable genomic selection models can be accurately calibrated to predict subsequent breeding stages. Moreover, prediction ability was maximized when the genotypes evaluated in a single location were excluded from the training set but subsequently decreased again when the phenotyping intensity was increased above two locations, suggesting that the update of the training population should be performed considering all the selected genotypes but excluding those evaluated in a single location. The genomic prediction ability was substantially higher in subpopulations selected based on the reliability criterion, indicating that phenotypic selection for highly reliable individuals could be directly replaced by applying genomic selection to them. We empirically conclude that there is a high potential to assist commercial wheat breeding programs employing genomic selection approaches.

Biomedical engineering strategies in system design space.

PubMed

Savageau, Michael A

2011-04-01

Modern systems biology and synthetic bioengineering face two major challenges in relating properties of the genetic components of a natural or engineered system to its integrated behavior. The first is the fundamental unsolved problem of relating the digital representation of the genotype to the analog representation of the parameters for the molecular components. For example, knowing the DNA sequence does not allow one to determine the kinetic parameters of an enzyme. The second is the fundamental unsolved problem of relating the parameters of the components and the environment to the phenotype of the global system. For example, knowing the parameters does not tell one how many qualitatively distinct phenotypes are in the organism's repertoire or the relative fitness of the phenotypes in different environments. These also are challenges for biomedical engineers as they attempt to develop therapeutic strategies to treat pathology or to redirect normal cellular functions for biotechnological purposes. In this article, the second of these fundamental challenges will be addressed, and the notion of a "system design space" for relating the parameter space of components to the phenotype space of bioengineering systems will be focused upon. First, the concept of a system design space will be motivated by introducing one of its key components from an intuitive perspective. Second, a simple linear example will be used to illustrate a generic method for constructing the design space in which qualitatively distinct phenotypes can be identified and counted, their fitness analyzed and compared, and their tolerance to change measured. Third, two examples of nonlinear systems from different areas of biomedical engineering will be presented. Finally, after giving reference to a few other applications that have made use of the system design space approach to reveal important design principles, some concluding remarks concerning challenges and opportunities for further development will be made.

Tumor evolution in space: the effects of competition colonization tradeoffs on tumor invasion dynamics.

PubMed

Orlando, Paul A; Gatenby, Robert A; Brown, Joel S

2013-01-01

We apply competition colonization tradeoff models to tumor growth and invasion dynamics to explore the hypothesis that varying selection forces will result in predictable phenotypic differences in cells at the tumor invasive front compared to those in the core. Spatially, ecologically, and evolutionarily explicit partial differential equation models of tumor growth confirm that spatial invasion produces selection pressure for motile phenotypes. The effects of the invasive phenotype on normal adjacent tissue determine the patterns of growth and phenotype distribution. If tumor cells do not destroy their environment, colonizer and competitive phenotypes coexist with the former localized at the invasion front and the latter, to the tumor interior. If tumors cells do destroy their environment, then cell motility is strongly selected resulting in accelerated invasion speed with time. Our results suggest that the widely observed genetic heterogeneity within cancers may not be the stochastic effect of random mutations. Rather, it may be the consequence of predictable variations in environmental selection forces and corresponding phenotypic adaptations.

Tumor Evolution in Space: The Effects of Competition Colonization Tradeoffs on Tumor Invasion Dynamics

PubMed Central

Orlando, Paul A.; Gatenby, Robert A.; Brown, Joel S.

2013-01-01

We apply competition colonization tradeoff models to tumor growth and invasion dynamics to explore the hypothesis that varying selection forces will result in predictable phenotypic differences in cells at the tumor invasive front compared to those in the core. Spatially, ecologically, and evolutionarily explicit partial differential equation models of tumor growth confirm that spatial invasion produces selection pressure for motile phenotypes. The effects of the invasive phenotype on normal adjacent tissue determine the patterns of growth and phenotype distribution. If tumor cells do not destroy their environment, colonizer and competitive phenotypes coexist with the former localized at the invasion front and the latter, to the tumor interior. If tumors cells do destroy their environment, then cell motility is strongly selected resulting in accelerated invasion speed with time. Our results suggest that the widely observed genetic heterogeneity within cancers may not be the stochastic effect of random mutations. Rather, it may be the consequence of predictable variations in environmental selection forces and corresponding phenotypic adaptations. PMID:23508890

Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics | Office of Cancer Genomics

Cancer.gov

High cancer death rates indicate the need for new anticancer therapeutic agents. Approaches to discovering new cancer drugs include target-based drug discovery and phenotypic screening. Here, we identified phosphodiesterase 3A modulators as cell-selective cancer cytotoxic compounds through phenotypic compound library screening and target deconvolution by predictive chemogenomics.

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

PubMed Central

Hartman, John L.; Stisher, Chandler; Outlaw, Darryl A.; Guo, Jingyu; Shah, Najaf A.; Tian, Dehua; Santos, Sean M.; Rodgers, John W.; White, Richard A.

2015-01-01

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease. PMID:25668739

Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments

PubMed Central

Kumar, Satish; Molloy, Claire; Muñoz, Patricio; Daetwyler, Hans; Chagné, David; Volz, Richard

2015-01-01

The nonadditive genetic effects may have an important contribution to total genetic variation of phenotypes, so estimates of both the additive and nonadditive effects are desirable for breeding and selection purposes. Our main objectives were to: estimate additive, dominance and epistatic variances of apple (Malus × domestica Borkh.) phenotypes using relationship matrices constructed from genome-wide dense single nucleotide polymorphism (SNP) markers; and compare the accuracy of genomic predictions using genomic best linear unbiased prediction models with or without including nonadditive genetic effects. A set of 247 clonally replicated individuals was assessed for six fruit quality traits at two sites, and also genotyped using an Illumina 8K SNP array. Across several fruit quality traits, the additive, dominance, and epistatic effects contributed about 30%, 16%, and 19%, respectively, to the total phenotypic variance. Models ignoring nonadditive components yielded upwardly biased estimates of additive variance (heritability) for all traits in this study. The accuracy of genomic predicted genetic values (GEGV) varied from about 0.15 to 0.35 for various traits, and these were almost identical for models with or without including nonadditive effects. However, models including nonadditive genetic effects further reduced the bias of GEGV. Between-site genotypic correlations were high (>0.85) for all traits, and genotype-site interaction accounted for <10% of the phenotypic variability. The accuracy of prediction, when the validation set was present only at one site, was generally similar for both sites, and varied from about 0.50 to 0.85. The prediction accuracies were strongly influenced by trait heritability, and genetic relatedness between the training and validation families. PMID:26497141

The alignment between phenotypic plasticity, the major axis of genetic variation and the response to selection.

PubMed

Lind, Martin I; Yarlett, Kylie; Reger, Julia; Carter, Mauricio J; Beckerman, Andrew P

2015-10-07

Phenotypic plasticity is the ability of a genotype to produce more than one phenotype in order to match the environment. Recent theory proposes that the major axis of genetic variation in a phenotypically plastic population can align with the direction of selection. Therefore, theory predicts that plasticity directly aids adaptation by increasing genetic variation in the direction favoured by selection and reflected in plasticity. We evaluated this theory in the freshwater crustacean Daphnia pulex, facing predation risk from two contrasting size-selective predators. We estimated plasticity in several life-history traits, the G matrix of these traits, the selection gradients on reproduction and survival, and the predicted responses to selection. Using these data, we tested whether the genetic lines of least resistance and the predicted response to selection aligned with plasticity. We found predator environment-specific G matrices, but shared genetic architecture across environments resulted in more constraint in the G matrix than in the plasticity of the traits, sometimes preventing alignment of the two. However, as the importance of survival selection increased, the difference between environments in their predicted response to selection increased and resulted in closer alignment between the plasticity and the predicted selection response. Therefore, plasticity may indeed aid adaptation to new environments. © 2015 The Authors.

Mechanobiological simulations of peri-acetabular bone ingrowth: a comparative analysis of cell-phenotype specific and phenomenological algorithms.

PubMed

Mukherjee, Kaushik; Gupta, Sanjay

2017-03-01

Several mechanobiology algorithms have been employed to simulate bone ingrowth around porous coated implants. However, there is a scarcity of quantitative comparison between the efficacies of commonly used mechanoregulatory algorithms. The objectives of this study are: (1) to predict peri-acetabular bone ingrowth using cell-phenotype specific algorithm and to compare these predictions with those obtained using phenomenological algorithm and (2) to investigate the influences of cellular parameters on bone ingrowth. The variation in host bone material property and interfacial micromotion of the implanted pelvis were mapped onto the microscale model of implant-bone interface. An overall variation of 17-88 % in peri-acetabular bone ingrowth was observed. Despite differences in predicted tissue differentiation patterns during the initial period, both the algorithms predicted similar spatial distribution of neo-tissue layer, after attainment of equilibrium. Results indicated that phenomenological algorithm, being computationally faster than the cell-phenotype specific algorithm, might be used to predict peri-prosthetic bone ingrowth. The cell-phenotype specific algorithm, however, was found to be useful in numerically investigating the influence of alterations in cellular activities on bone ingrowth, owing to biologically related factors. Amongst the host of cellular activities, matrix production rate of bone tissue was found to have predominant influence on peri-acetabular bone ingrowth.

Prospects for Genomic Selection in Cassava Breeding.

PubMed

Wolfe, Marnin D; Del Carpio, Dunia Pino; Alabi, Olumide; Ezenwaka, Lydia C; Ikeogu, Ugochukwu N; Kayondo, Ismail S; Lozano, Roberto; Okeke, Uche G; Ozimati, Alfred A; Williams, Esuma; Egesi, Chiedozie; Kawuki, Robert S; Kulakow, Peter; Rabbi, Ismail Y; Jannink, Jean-Luc

2017-11-01

Cassava ( Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden. Copyright © 2017 Crop Science Society of America.

Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems

PubMed Central

Lomnitz, Jason G.; Savageau, Michael A.

2016-01-01

Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits. PMID:27462346

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems.

PubMed

Yu, Michael Ku; Kramer, Michael; Dutkowski, Janusz; Srivas, Rohith; Licon, Katherine; Kreisberg, Jason; Ng, Cherie T; Krogan, Nevan; Sharan, Roded; Ideker, Trey

2016-02-24

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

The common occurrence of epistasis in the determination of human pigmentation and its impact on DNA-based pigmentation phenotype prediction.

PubMed

Pośpiech, Ewelina; Wojas-Pelc, Anna; Walsh, Susan; Liu, Fan; Maeda, Hitoshi; Ishikawa, Takaki; Skowron, Małgorzata; Kayser, Manfred; Branicki, Wojciech

2014-07-01

The role of epistatic effects in the determination of complex traits is often underlined but its significance in the prediction of pigmentation phenotypes has not been evaluated so far. The prediction of pigmentation from genetic data can be useful in forensic science to describe the physical appearance of an unknown offender, victim, or missing person who cannot be identified via conventional DNA profiling. Available forensic DNA prediction systems enable the reliable prediction of several eye and hair colour categories. However, there is still space for improvement. Here we verified the association of 38 candidate DNA polymorphisms from 13 genes and explored the extent to which interactions between them may be involved in human pigmentation and their impact on forensic DNA prediction in particular. The model-building set included 718 Polish samples and the model-verification set included 307 independent Polish samples and additional 72 samples from Japan. In total, 29 significant SNP-SNP interactions were found with 5 of them showing an effect on phenotype prediction. For predicting green eye colour, interactions between HERC2 rs12913832 and OCA2 rs1800407 as well as TYRP1 rs1408799 raised the prediction accuracy expressed by AUC from 0.667 to 0.697 and increased the prediction sensitivity by >3%. Interaction between MC1R 'R' variants and VDR rs731236 increased the sensitivity for light skin by >1% and by almost 3% for dark skin colour prediction. Interactions between VDR rs1544410 and TYR rs1042602 as well as between MC1R 'R' variants and HERC2 rs12913832 provided an increase in red/non-red hair prediction accuracy from an AUC of 0.902-0.930. Our results thus underline epistasis as a common phenomenon in human pigmentation genetics and demonstrate that considering SNP-SNP interactions in forensic DNA phenotyping has little impact on eye, hair and skin colour prediction. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Experimental evolution in budding yeast

NASA Astrophysics Data System (ADS)

Murray, Andrew

2012-02-01

I will discuss our progress in analyzing evolution in the budding yeast, Saccharomyces cerevisiae. We take two basic approaches. The first is to try and examine quantitative aspects of evolution, for example by determining how the rate of evolution depends on the mutation rate and the population size or asking whether the rate of mutation is uniform throughout the genome. The second is to try to evolve qualitatively novel, cell biologically interesting phenotypes and track the mutations that are responsible for the phenotype. Our efforts include trying to alter cell morphology, evolve multicellularity, and produce a biological oscillator.

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat

PubMed Central

Rutkoski, Jessica; Poland, Jesse; Mondal, Suchismita; Autrique, Enrique; Pérez, Lorena González; Crossa, José; Reynolds, Matthew; Singh, Ravi

2016-01-01

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots. PMID:27402362

Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction.

PubMed

Zhou, Yao; Vales, M Isabel; Wang, Aoxue; Zhang, Zhiwu

2017-09-01

Accuracy of genomic prediction is commonly calculated as the Pearson correlation coefficient between the predicted and observed phenotypes in the inference population by using cross-validation analysis. More frequently than expected, significant negative accuracies of genomic prediction have been reported in genomic selection studies. These negative values are surprising, given that the minimum value for prediction accuracy should hover around zero when randomly permuted data sets are analyzed. We reviewed the two common approaches for calculating the Pearson correlation and hypothesized that these negative accuracy values reflect potential bias owing to artifacts caused by the mathematical formulas used to calculate prediction accuracy. The first approach, Instant accuracy, calculates correlations for each fold and reports prediction accuracy as the mean of correlations across fold. The other approach, Hold accuracy, predicts all phenotypes in all fold and calculates correlation between the observed and predicted phenotypes at the end of the cross-validation process. Using simulated and real data, we demonstrated that our hypothesis is true. Both approaches are biased downward under certain conditions. The biases become larger when more fold are employed and when the expected accuracy is low. The bias of Instant accuracy can be corrected using a modified formula. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

PubMed

Thorwarth, Patrick; Yousef, Eltohamy A A; Schmid, Karl J

2018-02-02

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower ( Brassica oleracea var. botrytis ) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Copyright © 2018 Thorwarth et al.

Antimicrobial Resistance Prediction in PATRIC and RAST.

PubMed

Davis, James J; Boisvert, Sébastien; Brettin, Thomas; Kenyon, Ronald W; Mao, Chunhong; Olson, Robert; Overbeek, Ross; Santerre, John; Shukla, Maulik; Wattam, Alice R; Will, Rebecca; Xia, Fangfang; Stevens, Rick

2016-06-14

The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88-99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71-88%. This set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.

Classification of cassava genotypes based on qualitative and quantitative data.

PubMed

Oliveira, E J; Oliveira Filho, O S; Santos, V S

2015-02-02

We evaluated the genetic variation of cassava accessions based on qualitative (binomial and multicategorical) and quantitative traits (continuous). We characterized 95 accessions obtained from the Cassava Germplasm Bank of Embrapa Mandioca e Fruticultura; we evaluated these accessions for 13 continuous, 10 binary, and 25 multicategorical traits. First, we analyzed the accessions based only on quantitative traits; next, we conducted joint analysis (qualitative and quantitative traits) based on the Ward-MLM method, which performs clustering in two stages. According to the pseudo-F, pseudo-t2, and maximum likelihood criteria, we identified five and four groups based on quantitative trait and joint analysis, respectively. The smaller number of groups identified based on joint analysis may be related to the nature of the data. On the other hand, quantitative data are more subject to environmental effects in the phenotype expression; this results in the absence of genetic differences, thereby contributing to greater differentiation among accessions. For most of the accessions, the maximum probability of classification was >0.90, independent of the trait analyzed, indicating a good fit of the clustering method. Differences in clustering according to the type of data implied that analysis of quantitative and qualitative traits in cassava germplasm might explore different genomic regions. On the other hand, when joint analysis was used, the means and ranges of genetic distances were high, indicating that the Ward-MLM method is very useful for clustering genotypes when there are several phenotypic traits, such as in the case of genetic resources and breeding programs.

Phenotypic interactions between tree hosts and invasive forest pathogens in the light of globalization and climate change

PubMed Central

2016-01-01

Invasive pathogens can cause considerable damage to forest ecosystems. Lack of coevolution is generally thought to enable invasive pathogens to bypass the defence and/or recognition systems in the host. Although mostly true, this argument fails to predict intermittent outcomes in space and time, underlining the need to include the roles of the environment and the phenotype in host–pathogen interactions when predicting disease impacts. We emphasize the need to consider host–tree imbalances from a phenotypic perspective, considering the lack of coevolutionary and evolutionary history with the pathogen and the environment, respectively. We describe how phenotypic plasticity and plastic responses to environmental shifts may become maladaptive when hosts are faced with novel pathogens. The lack of host–pathogen and environmental coevolution are aligned with two global processes currently driving forest damage: globalization and climate change, respectively. We suggest that globalization and climate change act synergistically, increasing the chances of both genotypic and phenotypic imbalances. Short moves on the same continent are more likely to be in balance than if the move is from another part of the world. We use Gremmeniella abietina outbreaks in Sweden to exemplify how host–pathogen phenotypic interactions can help to predict the impacts of specific invasive and emergent diseases. This article is part of the themed issue ‘Tackling emerging fungal threats to animal health, food security and ecosystem resilience’. PMID:28080981

Phenotypic interactions between tree hosts and invasive forest pathogens in the light of globalization and climate change.

PubMed

Stenlid, Jan; Oliva, Jonàs

2016-12-05

Invasive pathogens can cause considerable damage to forest ecosystems. Lack of coevolution is generally thought to enable invasive pathogens to bypass the defence and/or recognition systems in the host. Although mostly true, this argument fails to predict intermittent outcomes in space and time, underlining the need to include the roles of the environment and the phenotype in host-pathogen interactions when predicting disease impacts. We emphasize the need to consider host-tree imbalances from a phenotypic perspective, considering the lack of coevolutionary and evolutionary history with the pathogen and the environment, respectively. We describe how phenotypic plasticity and plastic responses to environmental shifts may become maladaptive when hosts are faced with novel pathogens. The lack of host-pathogen and environmental coevolution are aligned with two global processes currently driving forest damage: globalization and climate change, respectively. We suggest that globalization and climate change act synergistically, increasing the chances of both genotypic and phenotypic imbalances. Short moves on the same continent are more likely to be in balance than if the move is from another part of the world. We use Gremmeniella abietina outbreaks in Sweden to exemplify how host-pathogen phenotypic interactions can help to predict the impacts of specific invasive and emergent diseases.This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'. © 2016 The Author(s).

Transgressive Hybrids as Hopeful Monsters.

PubMed

Dittrich-Reed, Dylan R; Fitzpatrick, Benjamin M

2013-06-01

The origin of novelty is a critical subject for evolutionary biologists. Early geneticists speculated about the sudden appearance of new species via special macromutations, epitomized by Goldschmidt's infamous "hopeful monster". Although these ideas were easily dismissed by the insights of the Modern Synthesis, a lingering fascination with the possibility of sudden, dramatic change has persisted. Recent work on hybridization and gene exchange suggests an underappreciated mechanism for the sudden appearance of evolutionary novelty that is entirely consistent with the principles of modern population genetics. Genetic recombination in hybrids can produce transgressive phenotypes, "monstrous" phenotypes beyond the range of parental populations. Transgressive phenotypes can be products of epistatic interactions or additive effects of multiple recombined loci. We compare several epistatic and additive models of transgressive segregation in hybrids and find that they are special cases of a general, classic quantitative genetic model. The Dobzhansky-Muller model predicts "hopeless" monsters, sterile and inviable transgressive phenotypes. The Bateson model predicts "hopeful" monsters with fitness greater than either parental population. The complementation model predicts both. Transgressive segregation after hybridization can rapidly produce novel phenotypes by recombining multiple loci simultaneously. Admixed populations will also produce many similar recombinant phenotypes at the same time, increasing the probability that recombinant "hopeful monsters" will establish true-breeding evolutionary lineages. Recombination is not the only (or even most common) process generating evolutionary novelty, but might be the most credible mechanism for sudden appearance of new forms.

Inappropriate analysis does not reveal the ecological causes of evolution of stickleback armour: a critique of Spence et al. 2013.

PubMed

MacColl, Andrew D C; Aucott, Beth

2014-09-01

In a recent paper in this journal, Spence et al. (2013) sought to identify the ecological causes of morphological evolution in three-spined sticklebacks Gasterosteus aculeatus, by examining phenotypic and environmental variation between populations on the island of North Uist, Scotland. However, by using simple qualitative assessments of phenotype and inappropriate measures of environmental variation, Spence et al. have come to a conclusion that is diametrically opposite to that which we have arrived at in studying the same populations. Our criticisms of their paper are threefold: (1) using a binomial qualitative measure of the variation in stickleback armour ("low" versus "minimal" (i.e., "normal" low-plated freshwater sticklebacks versus spineless and/or plateless fish)) does not represent the full range of phenotypes that can be described by quantitative measures of the individual elements of armour. (2) Their use of unspecified test kits, with a probable accuracy of 4 ppm, may not be accurate in the range of water chemistry on North Uist (1 to 30 ppm calcium). (3) Their qualitative assessment of the abundance of brown trout Salmo trutta as the major predator of sticklebacks does not accurately describe the variation in brown trout abundance that is revealed by catch-per-unit-effort statistics. Repeating Spence et al.'s analysis using our own measurements, we find, in direct contradiction to them, that variation in stickleback bony armour is strongly correlated with variation in trout abundance, and unrelated to variation in the concentration of calcium in the lochs in which they live. Field studies in ecology and evolution seldom address the same question in the same system at the same time, and it is salutary that in this rare instance two such studies arrived at diametrically opposite answers.

Extent of QTL Reuse During Repeated Phenotypic Divergence of Sympatric Threespine Stickleback.

PubMed

Conte, Gina L; Arnegard, Matthew E; Best, Jacob; Chan, Yingguang Frank; Jones, Felicity C; Kingsley, David M; Schluter, Dolph; Peichel, Catherine L

2015-11-01

How predictable is the genetic basis of phenotypic adaptation? Answering this question begins by estimating the repeatability of adaptation at the genetic level. Here, we provide a comprehensive estimate of the repeatability of the genetic basis of adaptive phenotypic evolution in a natural system. We used quantitative trait locus (QTL) mapping to discover genomic regions controlling a large number of morphological traits that have diverged in parallel between pairs of threespine stickleback (Gasterosteus aculeatus species complex) in Paxton and Priest lakes, British Columbia. We found that nearly half of QTL affected the same traits in the same direction in both species pairs. Another 40% influenced a parallel phenotypic trait in one lake but not the other. The remaining 10% of QTL had phenotypic effects in opposite directions in the two species pairs. Similarity in the proportional contributions of all QTL to parallel trait differences was about 0.4. Surprisingly, QTL reuse was unrelated to phenotypic effect size. Our results indicate that repeated use of the same genomic regions is a pervasive feature of parallel phenotypic adaptation, at least in sticklebacks. Identifying the causes of this pattern would aid prediction of the genetic basis of phenotypic evolution. Copyright © 2015 by the Genetics Society of America.

Adaptation to local ultraviolet radiation conditions among neighbouring Daphnia populations

PubMed Central

Miner, Brooks E.; Kerr, Benjamin

2011-01-01

Understanding the historical processes that generated current patterns of phenotypic diversity in nature is particularly challenging in subdivided populations. Populations often exhibit heritable genetic differences that correlate with environmental variables, but the non-independence among neighbouring populations complicates statistical inference of adaptation. To understand the relative influence of adaptive and non-adaptive processes in generating phenotypes requires joint evaluation of genetic and phenotypic divergence in an integrated and statistically appropriate analysis. We investigated phenotypic divergence, population-genetic structure and potential fitness trade-offs in populations of Daphnia melanica inhabiting neighbouring subalpine ponds of widely differing transparency to ultraviolet radiation (UVR). Using a combination of experimental, population-genetic and statistical techniques, we separated the effects of shared population ancestry and environmental variables in predicting phenotypic divergence among populations. We found that native water transparency significantly predicted divergence in phenotypes among populations even after accounting for significant population structure. This result demonstrates that environmental factors such as UVR can at least partially account for phenotypic divergence. However, a lack of evidence for a hypothesized trade-off between UVR tolerance and growth rates in the absence of UVR prevents us from ruling out the possibility that non-adaptive processes are partially responsible for phenotypic differentiation in this system. PMID:20943691

Which ante mortem clinical features predict progressive supranuclear palsy pathology?

PubMed

Respondek, Gesine; Kurz, Carolin; Arzberger, Thomas; Compta, Yaroslau; Englund, Elisabet; Ferguson, Leslie W; Gelpi, Ellen; Giese, Armin; Irwin, David J; Meissner, Wassilios G; Nilsson, Christer; Pantelyat, Alexander; Rajput, Alex; van Swieten, John C; Troakes, Claire; Josephs, Keith A; Lang, Anthony E; Mollenhauer, Brit; Müller, Ulrich; Whitwell, Jennifer L; Antonini, Angelo; Bhatia, Kailash P; Bordelon, Yvette; Corvol, Jean-Christophe; Colosimo, Carlo; Dodel, Richard; Grossman, Murray; Kassubek, Jan; Krismer, Florian; Levin, Johannes; Lorenzl, Stefan; Morris, Huw; Nestor, Peter; Oertel, Wolfgang H; Rabinovici, Gil D; Rowe, James B; van Eimeren, Thilo; Wenning, Gregor K; Boxer, Adam; Golbe, Lawrence I; Litvan, Irene; Stamelou, Maria; Höglinger, Günter U

2017-07-01

Progressive supranuclear palsy (PSP) is a neuropathologically defined disease presenting with a broad spectrum of clinical phenotypes. To identify clinical features and investigations that predict or exclude PSP pathology during life, aiming at an optimization of the clinical diagnostic criteria for PSP. We performed a systematic review of the literature published since 1996 to identify clinical features and investigations that may predict or exclude PSP pathology. We then extracted standardized data from clinical charts of patients with pathologically diagnosed PSP and relevant disease controls and calculated the sensitivity, specificity, and positive predictive value of key clinical features for PSP in this cohort. Of 4166 articles identified by the database inquiry, 269 met predefined standards. The literature review identified clinical features predictive of PSP, including features of the following 4 functional domains: ocular motor dysfunction, postural instability, akinesia, and cognitive dysfunction. No biomarker or genetic feature was found reliably validated to predict definite PSP. High-quality original natural history data were available from 206 patients with pathologically diagnosed PSP and from 231 pathologically diagnosed disease controls (54 corticobasal degeneration, 51 multiple system atrophy with predominant parkinsonism, 53 Parkinson's disease, 73 behavioral variant frontotemporal dementia). We identified clinical features that predicted PSP pathology, including phenotypes other than Richardson's syndrome, with varying sensitivity and specificity. Our results highlight the clinical variability of PSP and the high prevalence of phenotypes other than Richardson's syndrome. The features of variant phenotypes with high specificity and sensitivity should serve to optimize clinical diagnosis of PSP. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.

Systematic Association of Genes to Phenotypes by Genome and Literature Mining

PubMed Central

Jensen, Lars J; Perez-Iratxeta, Carolina; Kaczanowski, Szymon; Hooper, Sean D; Andrade, Miguel A

2005-01-01

One of the major challenges of functional genomics is to unravel the connection between genotype and phenotype. So far no global analysis has attempted to explore those connections in the light of the large phenotypic variability seen in nature. Here, we use an unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis. We first mine the MEDLINE literature database for terms that reflect phenotypic similarities of species. Subsequently we predict the likely genomic determinants: genes specifically present in the respective genomes. In a global analysis involving 92 prokaryotic genomes we retrieve 323 clusters containing a total of 2,700 significant gene–phenotype associations. Some clusters contain mostly known relationships, such as genes involved in motility or plant degradation, often with additional hypothetical proteins associated with those phenotypes. Other clusters comprise unexpected associations; for example, a group of terms related to food and spoilage is linked to genes predicted to be involved in bacterial food poisoning. Among the clusters, we observe an enrichment of pathogenicity-related associations, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases. PMID:15799710

Circadian Phenotype Composition is a Major Predictor of Diurnal Physical Performance in Teams.

PubMed

Facer-Childs, Elise; Brandstaetter, Roland

2015-01-01

Team performance is a complex phenomenon involving numerous influencing factors including physiology, psychology, and management. Biological rhythms and the impact of circadian phenotype have not been studied for their contribution to this array of factors so far despite our knowledge of the circadian regulation of key physiological processes involved in physical and mental performance. This study involved 216 individuals from 12 different teams who were categorized into circadian phenotypes using the novel RBUB chronometric test. The composition of circadian phenotypes within each team was used to model predicted daily team performance profiles based on physical performance tests. Our results show that the composition of circadian phenotypes within teams is variable and unpredictable. Predicted physical peak performance ranged from 1:52 to 8:59 p.m. with performance levels fluctuating by up to 14.88% over the course of the day. The major predictor for peak performance time in the course of a day in a team is the occurrence of late circadian phenotypes. We conclude that circadian phenotype is a performance indicator in teams that allows new insight and a better understanding of team performance variation in the course of a day as often observed in different groupings of individuals.

Circadian Phenotype Composition is a Major Predictor of Diurnal Physical Performance in Teams

PubMed Central

Facer-Childs, Elise; Brandstaetter, Roland

2015-01-01

Team performance is a complex phenomenon involving numerous influencing factors including physiology, psychology, and management. Biological rhythms and the impact of circadian phenotype have not been studied for their contribution to this array of factors so far despite our knowledge of the circadian regulation of key physiological processes involved in physical and mental performance. This study involved 216 individuals from 12 different teams who were categorized into circadian phenotypes using the novel RBUB chronometric test. The composition of circadian phenotypes within each team was used to model predicted daily team performance profiles based on physical performance tests. Our results show that the composition of circadian phenotypes within teams is variable and unpredictable. Predicted physical peak performance ranged from 1:52 to 8:59 p.m. with performance levels fluctuating by up to 14.88% over the course of the day. The major predictor for peak performance time in the course of a day in a team is the occurrence of late circadian phenotypes. We conclude that circadian phenotype is a performance indicator in teams that allows new insight and a better understanding of team performance variation in the course of a day as often observed in different groupings of individuals. PMID:26483754

Predictable Phenotypes of Antibiotic Resistance Mutations.

PubMed

Knopp, M; Andersson, D I

2018-05-15

Antibiotic-resistant bacteria represent a major threat to our ability to treat bacterial infections. Two factors that determine the evolutionary success of antibiotic resistance mutations are their impact on resistance level and the fitness cost. Recent studies suggest that resistance mutations commonly show epistatic interactions, which would complicate predictions of their stability in bacterial populations. We analyzed 13 different chromosomal resistance mutations and 10 host strains of Salmonella enterica and Escherichia coli to address two main questions. (i) Are there epistatic interactions between different chromosomal resistance mutations? (ii) How does the strain background and genetic distance influence the effect of chromosomal resistance mutations on resistance and fitness? Our results show that the effects of combined resistance mutations on resistance and fitness are largely predictable and that epistasis remains rare even when up to four mutations were combined. Furthermore, a majority of the mutations, especially target alteration mutations, demonstrate strain-independent phenotypes across different species. This study extends our understanding of epistasis among resistance mutations and shows that interactions between different resistance mutations are often predictable from the characteristics of the individual mutations. IMPORTANCE The spread of antibiotic-resistant bacteria imposes an urgent threat to public health. The ability to forecast the evolutionary success of resistant mutants would help to combat dissemination of antibiotic resistance. Previous studies have shown that the phenotypic effects (fitness and resistance level) of resistance mutations can vary substantially depending on the genetic context in which they occur. We conducted a broad screen using many different resistance mutations and host strains to identify potential epistatic interactions between various types of resistance mutations and to determine the effect of strain background on resistance phenotypes. Combinations of several different mutations showed a large amount of phenotypic predictability, and the majority of the mutations displayed strain-independent phenotypes. However, we also identified a few outliers from these patterns, illustrating that the choice of host organism can be critically important when studying antibiotic resistance mutations. Copyright © 2018 Knopp and Andersson.

Breeding and Genetics Symposium: networks and pathways to guide genomic selection.

PubMed

Snelling, W M; Cushman, R A; Keele, J W; Maltecca, C; Thomas, M G; Fortes, M R S; Reverter, A

2013-02-01

Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by functionally informed genomic predictions being more accurate than naive whole-genome predictions of beef tenderness, coupling evidence from livestock genotypes, phenotypes, gene expression, and genomic variants with existing knowledge of gene functions and interactions may provide greater insight into the genes and genomic mechanisms affecting polygenic traits and facilitate functional genomic selection for economically important traits.

Current V3 genotyping algorithms are inadequate for predicting X4 co-receptor usage in clinical isolates.

PubMed

Low, Andrew J; Dong, Winnie; Chan, Dennison; Sing, Tobias; Swanstrom, Ronald; Jensen, Mark; Pillai, Satish; Good, Benjamin; Harrigan, P Richard

2007-09-12

Integrating CCR5 antagonists into clinical practice would benefit from accurate assays of co-receptor usage (CCR5 versus CXCR4) with fast turnaround and low cost. Published HIV V3-loop based predictors of co-receptor usage were compared with actual phenotypic tropism results in a large cohort of antiretroviral naive individuals to determine accuracy on clinical samples and identify areas for improvement. Aligned HIV envelope V3 loop sequences (n = 977), derived by bulk sequencing were analyzed by six methods: the 11/25 rule; a neural network (NN), two support vector machines, and two subtype-B position specific scoring matrices (PSSM). Co-receptor phenotype results (Trofile Co-receptor Phenotype Assay; Monogram Biosciences) were stratified by CXCR4 relative light unit (RLU) readout and CD4 cell count. Co-receptor phenotype was available for 920 clinical samples with V3 genotypes having fewer than seven amino acid mixtures (n = 769 R5; n = 151 X4-capable). Sensitivity and specificity for predicting X4 capacity were evaluated for the 11/25 rule (30% sensitivity/93% specificity), NN (44%/88%), PSSM(sinsi) (34%/96%), PSSM(x4r5) (24%/97%), SVMgenomiac (22%/90%) and SVMgeno2pheno (50%/89%). Quantitative increases in sensitivity could be obtained by optimizing the cut-off for methods with continuous output (PSSM methods), and/or integrating clinical data (CD4%). Sensitivity was directly proportional to strength of X4 signal in the phenotype assay (P < 0.05). Current default implementations of co-receptor prediction algorithms are inadequate for predicting HIV X4 co-receptor usage in clinical samples, particularly those X4 phenotypes with low CXCR4 RLU signals. Significant improvements can be made to genotypic predictors, including training on clinical samples, using additional data to improve predictions and optimizing cutoffs and increasing genotype sensitivity.

Co-clustering phenome–genome for phenotype classification and disease gene discovery

PubMed Central

Hwang, TaeHyun; Atluri, Gowtham; Xie, MaoQiang; Dey, Sanjoy; Hong, Changjin; Kumar, Vipin; Kuang, Rui

2012-01-01

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways. PMID:22735708

Relaxed selection is a precursor to the evolution of phenotypic plasticity.

PubMed

Hunt, Brendan G; Ometto, Lino; Wurm, Yannick; Shoemaker, DeWayne; Yi, Soojin V; Keller, Laurent; Goodisman, Michael A D

2011-09-20

Phenotypic plasticity allows organisms to produce alternative phenotypes under different conditions and represents one of the most important ways by which organisms adaptively respond to the environment. However, the relationship between phenotypic plasticity and molecular evolution remains poorly understood. We addressed this issue by investigating the evolution of genes associated with phenotypically plastic castes, sexes, and developmental stages of the fire ant Solenopsis invicta. We first determined if genes associated with phenotypic plasticity in S. invicta evolved at a rapid rate, as predicted under theoretical models. We found that genes differentially expressed between S. invicta castes, sexes, and developmental stages all exhibited elevated rates of evolution compared with ubiquitously expressed genes. We next investigated the evolutionary history of genes associated with the production of castes. Surprisingly, we found that orthologs of caste-biased genes in S. invicta and the social bee Apis mellifera evolved rapidly in lineages without castes. Thus, in contrast to some theoretical predictions, our results suggest that rapid rates of molecular evolution may not arise primarily as a consequence of phenotypic plasticity. Instead, genes evolving under relaxed purifying selection may more readily adopt new forms of biased expression during the evolution of alternate phenotypes. These results suggest that relaxed selective constraint on protein-coding genes is an important and underappreciated element in the evolutionary origin of phenotypic plasticity.

Prognosis of Pain and Physical Functioning in Patients With Knee Osteoarthritis: A Systematic Review and Meta-Analysis.

PubMed

de Rooij, Mariëtte; van der Leeden, Marike; Heymans, Martijn W; Holla, Jasmijn F M; Häkkinen, Arja; Lems, Willem F; Roorda, Leo D; Veenhof, Cindy; Sanchez-Ramirez, Diana C; de Vet, Henrica C W; Dekker, Joost

2016-04-01

To systematically summarize the literature on the course of pain in patients with knee osteoarthritis (OA), prognostic factors that predict deterioration of pain, the course of physical functioning, and prognostic factors that predict deterioration of physical functioning in persons with knee OA. A search was conducted in PubMed, CINAHL, Embase, Psych-INFO, and SPORTDiscus up to January 2014. A meta-analysis and a qualitative data synthesis were performed. Of the 58 studies included, 39 were of high quality. High heterogeneity across studies (I(2)  >90%) and within study populations (reflected by large SDs of change scores) was found. Therefore, the course of pain and physical functioning was interpreted to be indistinct. We found strong evidence for a number of prognostic factors predicting deterioration in pain (e.g., higher knee pain at baseline, bilateral knee symptoms, and depressive symptoms). We also found strong evidence for a number of prognostic factors predicting deterioration in physical functioning (e.g., worsening in radiographic OA, worsening of knee pain, lower knee extension muscle strength, lower walking speed, and higher comorbidity count). Because of high heterogeneity across studies and within study populations, no conclusions can be drawn with regard to the course of pain and physical functioning. These findings support current research efforts to define subgroups or phenotypes within knee OA populations. Strong evidence was found for knee characteristics, clinical factors, and psychosocial factors as prognostics of deterioration of pain and physical functioning. © 2016, American College of Rheumatology.

Geographic atrophy phenotype identification by cluster analysis.

PubMed

Monés, Jordi; Biarnés, Marc

2018-03-01

To identify ocular phenotypes in patients with geographic atrophy secondary to age-related macular degeneration (GA) using a data-driven cluster analysis. This was a retrospective analysis of data from a prospective, natural history study of patients with GA who were followed for ≥6 months. Cluster analysis was used to identify subgroups within the population based on the presence of several phenotypic features: soft drusen, reticular pseudodrusen (RPD), primary foveal atrophy, increased fundus autofluorescence (FAF), greyish FAF appearance and subfoveal choroidal thickness (SFCT). A comparison of features between the subgroups was conducted, and a qualitative description of the new phenotypes was proposed. The atrophy growth rate between phenotypes was then compared. Data were analysed from 77 eyes of 77 patients with GA. Cluster analysis identified three groups: phenotype 1 was characterised by high soft drusen load, foveal atrophy and slow growth; phenotype 3 showed high RPD load, extrafoveal and greyish FAF appearance and thin SFCT; the characteristics of phenotype 2 were midway between phenotypes 1 and 3. Phenotypes differed in all measured features (p≤0.013), with decreases in the presence of soft drusen, foveal atrophy and SFCT seen from phenotypes 1 to 3 and corresponding increases in high RPD load, high FAF and greyish FAF appearance. Atrophy growth rate differed between phenotypes 1, 2 and 3 (0.63, 1.91 and 1.73 mm 2 /year, respectively, p=0.0005). Cluster analysis identified three distinct phenotypes in GA. One of them showed a particularly slow growth pattern. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The GP problem: quantifying gene-to-phenotype relationships.

PubMed

Cooper, Mark; Chapman, Scott C; Podlich, Dean W; Hammer, Graeme L

2002-01-01

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

Duffy blood group phenotype-genotype correlations using high-resolution melting analysis PCR and microarray reveal complex cases including a new null FY*A allele: the role for sequencing in genotyping algorithms.

PubMed

Lopez, G H; Morrison, J; Condon, J A; Wilson, B; Martin, J R; Liew, Y-W; Flower, R L; Hyland, C A

2015-10-01

Duffy blood group phenotypes can be predicted by genotyping for single nucleotide polymorphisms (SNPs) responsible for the Fy(a) /Fy(b) polymorphism, for weak Fy(b) antigen, and for the red cell null Fy(a-b-) phenotype. This study correlates Duffy phenotype predictions with serotyping to assess the most reliable procedure for typing. Samples, n = 155 (135 donors and 20 patients), were genotyped by high-resolution melt PCR and by microarray. Samples were in three serology groups: 1) Duffy patterns expected n = 79, 2) weak and equivocal Fy(b) patterns n = 29 and 3) Fy(a-b-) n = 47 (one with anti-Fy3 antibody). Discrepancies were observed for five samples. For two, SNP genotyping predicted weak Fy(b) expression discrepant with Fy(b-) (Group 1 and 3). For three, SNP genotyping predicted Fy(a) , discrepant with Fy(a-b-) (Group 3). DNA sequencing identified silencing mutations in these FY*A alleles. One was a novel FY*A 719delG. One, the sample with the anti-Fy3, was homozygous for a 14-bp deletion (FY*01N.02); a true null. Both the high-resolution melting analysis and SNP microarray assays were concordant and showed genotyping, as well as phenotyping, is essential to ensure 100% accuracy for Duffy blood group assignments. Sequencing is important to resolve phenotype/genotype conflicts which here identified alleles, one novel, that carry silencing mutations. The risk of alloimmunisation may be dependent on this zygosity status. © 2015 International Society of Blood Transfusion.

Association of various blood pressure variables and vascular phenotypes with coronary, stroke and renal deaths: Potential implications for prevention.

PubMed

Harbaoui, Brahim; Courand, Pierre-Yves; Milon, Hughes; Fauvel, Jean-Pierre; Khettab, Fouad; Mechtouff, Laura; Cassar, Emmanuel; Girerd, Nicolas; Lantelme, Pierre

2015-11-01

The relationship between blood pressure (BP) and cardiovascular diseases has been extensively documented. However, the benefit of anti-hypertensive drugs differs according to the type of cardiovascular event. Aortic stiffness is tightly intertwined with BP and aorta cross-talk with small arteries. We endeavored to elucidate which BP component and type of vessel remodeling was predictive of the following outcomes: fatal myocardial infarction (MI), fatal stroke, renal -, coronary- or cerebrovascular-related deaths. Large vessel remodeling was estimated by an aortography-based aortic atherosclerosis score (ATS) while small vessel disease was documented by the presence of a hypertensive retinopathy. We included 1031 subjects referred for hypertension workup and assessed outcomes 30 years later. After adjustment for major risk factors, ATS and pulse pressure (PP) were predictive of coronary events while mean BP (MBP) and retinopathy were not. On the contrary, MBP was predictive of cerebrovascular and renal related deaths while ATS and PP were not. Retinopathy was only predictive of cerebrovascular related deaths. Lastly, the aortic atherosclerosis phenotype and increased PP identified patients prone to develop fatal MI whereas the retinopathy phenotype and increased MBP identified patients at higher risk of fatal stroke. These results illustrate the particular feature of the resistive coronary circulation comparatively to the brain and kidneys' low-resistance circulation. Our results advocate for a rational preventive strategy based on the identification of distinct clinical phenotypes. Accordingly, decreasing MBP levels could help preventing stroke in retinopathy phenotypes whereas targeting PP is possibly more efficient in preventing MI in atherosclerotic phenotypes. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Use of qualitative environmental and phenotypic variables in the context of allele distribution models: detecting signatures of selection in the genome of Lake Victoria cichlids.

PubMed

Joost, Stéphane; Kalbermatten, Michael; Bezault, Etienne; Seehausen, Ole

2012-01-01

When searching for loci possibly under selection in the genome, an alternative to population genetics theoretical models is to establish allele distribution models (ADM) for each locus to directly correlate allelic frequencies and environmental variables such as precipitation, temperature, or sun radiation. Such an approach implementing multiple logistic regression models in parallel was implemented within a computing program named MATSAM: . Recently, this application was improved in order to support qualitative environmental predictors as well as to permit the identification of associations between genomic variation and individual phenotypes, allowing the detection of loci involved in the genetic architecture of polymorphic characters. Here, we present the corresponding methodological developments and compare the results produced by software implementing population genetics theoretical models (DFDIST: and BAYESCAN: ) and ADM (MATSAM: ) in an empirical context to detect signatures of genomic divergence associated with speciation in Lake Victoria cichlid fishes.

Genotypic richness predicts phenotypic variation in an endangered clonal plant

PubMed Central

Sinclair, Elizabeth A.; Poore, Alistair G.B.; Bain, Keryn F.; Vergés, Adriana

2016-01-01

Declines in genetic diversity within a species can affect the stability and functioning of populations. The conservation of genetic diversity is thus a priority, especially for threatened or endangered species. The importance of genetic variation, however, is dependent on the degree to which it translates into phenotypic variation for traits that affect individual performance and ecological processes. This is especially important for predominantly clonal species, as no single clone is likely to maximise all aspects of performance. Here we show that intraspecific genotypic diversity as measured using microsatellites is a strong predictor of phenotypic variation in morphological traits and shoot productivity of the threatened, predominantly clonal seagrass Posidonia australis, on the east coast of Australia. Biomass and surface area variation was most strongly predicted by genotypic richness, while variation in leaf chemistry (phenolics and nitrogen) was unrelated to genotypic richness. Genotypic richness did not predict tissue loss to herbivores or epiphyte load, however we did find that increased herbivore damage was positively correlated with allelic richness. Although there was no clear relationship between higher primary productivity and genotypic richness, variation in shoot productivity within a meadow was significantly greater in more genotypically diverse meadows. The proportion of phenotypic variation explained by environmental conditions varied among different genotypes, and there was generally no variation in phenotypic traits among genotypes present in the same meadows. Our results show that genotypic richness as measured through the use of presumably neutral DNA markers does covary with phenotypic variation in functionally relevant traits such as leaf morphology and shoot productivity. The remarkably long lifespan of individual Posidonia plants suggests that plasticity within genotypes has played an important role in the longevity of the species. However, the strong link between genotypic and phenotypic variation suggests that a range of genotypes is still the best case scenario for adaptation to and recovery from predicted environmental change. PMID:26925313

Envirotyping for deciphering environmental impacts on crop plants.

PubMed

Xu, Yunbi

2016-04-01

Global climate change imposes increasing impacts on our environments and crop production. To decipher environmental impacts on crop plants, the concept "envirotyping" is proposed, as a third "typing" technology, complementing with genotyping and phenotyping. Environmental factors can be collected through multiple environmental trials, geographic and soil information systems, measurement of soil and canopy properties, and evaluation of companion organisms. Envirotyping contributes to crop modeling and phenotype prediction through its functional components, including genotype-by-environment interaction (GEI), genes responsive to environmental signals, biotic and abiotic stresses, and integrative phenotyping. Envirotyping, driven by information and support systems, has a wide range of applications, including environmental characterization, GEI analysis, phenotype prediction, near-iso-environment construction, agronomic genomics, precision agriculture and breeding, and development of a four-dimensional profile of crop science involving genotype (G), phenotype (P), envirotype (E) and time (T) (developmental stage). In the future, envirotyping needs to zoom into specific experimental plots and individual plants, along with the development of high-throughput and precision envirotyping platforms, to integrate genotypic, phenotypic and envirotypic information for establishing a high-efficient precision breeding and sustainable crop production system based on deciphered environmental impacts.

Constraint-based modeling in microbial food biotechnology

PubMed Central

Rau, Martin H.

2018-01-01

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM. PMID:29588387

Photorespiratory Bypasses Lead to Increased Growth in Arabidopsis thaliana: Are Predictions Consistent with Experimental Evidence?

PubMed Central

Basler, Georg; Küken, Anika; Fernie, Alisdair R.; Nikoloski, Zoran

2016-01-01

Arguably, the biggest challenge of modern plant systems biology lies in predicting the performance of plant species, and crops in particular, upon different intracellular and external perturbations. Recently, an increased growth of Arabidopsis thaliana plants was achieved by introducing two different photorespiratory bypasses via metabolic engineering. Here, we investigate the extent to which these findings match the predictions from constraint-based modeling. To determine the effect of the employed metabolic network model on the predictions, we perform a comparative analysis involving three state-of-the-art metabolic reconstructions of A. thaliana. In addition, we investigate three scenarios with respect to experimental findings on the ratios of the carboxylation and oxygenation reactions of Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). We demonstrate that the condition-dependent growth phenotypes of one of the engineered bypasses can be qualitatively reproduced by each reconstruction, particularly upon considering the additional constraints with respect to the ratio of fluxes for the RuBisCO reactions. Moreover, our results lend support for the hypothesis of a reduced photorespiration in the engineered plants, and indicate that specific changes in CO2 exchange as well as in the proxies for co-factor turnover are associated with the predicted growth increase in the engineered plants. We discuss our findings with respect to the structure of the used models, the modeling approaches taken, and the available experimental evidence. Our study sets the ground for investigating other strategies for increase of plant biomass by insertion of synthetic reactions. PMID:27092301

Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer.

PubMed

Li, Site; Zhu, Xiaomei; Liu, Bingya; Wang, Gaowei; Ao, Ping

2015-05-30

Intratumor heterogeneity is a common phenomenon and impedes cancer therapy and research. Gastric cancer (GC) cells have generally been classified into two heterogeneous cellular phenotypes, the gastric and intestinal types, yet the mechanisms of maintaining two phenotypes and controlling phenotypic transition are largely unknown. A qualitative systematic framework, the endogenous molecular network hypothesis, has recently been proposed to understand cancer genesis and progression. Here, a minimal network corresponding to such framework was found for GC and was quantified via a stochastic nonlinear dynamical system. We then further extended the framework to address the important question of intratumor heterogeneity quantitatively. The working network characterized main known features of normal gastric epithelial and GC cell phenotypes. Our results demonstrated that four positive feedback loops in the network are critical for GC cell phenotypes. Moreover, two mechanisms that contribute to GC cell heterogeneity were identified: particular positive feedback loops are responsible for the maintenance of intestinal and gastric phenotypes; GC cell progression routes that were revealed by the dynamical behaviors of individual key components are heterogeneous. In this work, we constructed an endogenous molecular network of GC that can be expanded in the future and would broaden the known mechanisms of intratumor heterogeneity.

Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer

PubMed Central

Li, Site; Zhu, Xiaomei; Liu, Bingya; Wang, Gaowei; Ao, Ping

2015-01-01

Intratumor heterogeneity is a common phenomenon and impedes cancer therapy and research. Gastric cancer (GC) cells have generally been classified into two heterogeneous cellular phenotypes, the gastric and intestinal types, yet the mechanisms of maintaining two phenotypes and controlling phenotypic transition are largely unknown. A qualitative systematic framework, the endogenous molecular network hypothesis, has recently been proposed to understand cancer genesis and progression. Here, a minimal network corresponding to such framework was found for GC and was quantified via a stochastic nonlinear dynamical system. We then further extended the framework to address the important question of intratumor heterogeneity quantitatively. The working network characterized main known features of normal gastric epithelial and GC cell phenotypes. Our results demonstrated that four positive feedback loops in the network are critical for GC cell phenotypes. Moreover, two mechanisms that contribute to GC cell heterogeneity were identified: particular positive feedback loops are responsible for the maintenance of intestinal and gastric phenotypes; GC cell progression routes that were revealed by the dynamical behaviors of individual key components are heterogeneous. In this work, we constructed an endogenous molecular network of GC that can be expanded in the future and would broaden the known mechanisms of intratumor heterogeneity. PMID:25962957

Childhood CBCL Bipolar Profile and Adolescent/Young Adult Personality Disorders: A 9-year Follow-up

PubMed Central

Halperin, Jeffrey M.; Rucklidge, Julia J.; Powers, Robyn L.; Miller, Carlin J.; Newcorn, Jeffrey H.

2010-01-01

Background To assess the late adolescent psychiatric outcomes associated with a positive Child Behavior Checklist – Juvenile Bipolar Disorder Phenotype (CBCL-JBD) in children diagnosed with ADHD and followed over a 9-year period. Methods Parents of 152 children diagnosed as ADHD (ages 7–11 years) completed the CBCL. Ninety of these parents completed it again 9 years later as part of a comprehensive evaluation of Axis I and II diagnoses as assessed using semi-structured interviews. As previously proposed, the CBCL-JBD phenotype was defined as T-scores of 70 or greater on the Attention Problems, Aggression, and Anxiety/Depression subscales. Results The CBCL-JBD phenotype was found in 31% of those followed but only 4.9% of the sample continued to meet the phenotype criteria at follow up. Only two of the sample developed Bipolar Disorder by late adolescence and only one of those had the CBCL-JBD profile in childhood. The proxy did not predict any Axis I disorders. However, the CBCL-JBD proxy was highly predictive of later personality disorders. Limitations Only a subgroup of the original childhood sample was followed. Given this sample was confined to children with ADHD, it is not known whether the prediction of personality disorders from CBCL scores would generalize to a wider community or clinical population Conclusions A positive CBCL-JBD phenotype profile in childhood does not predict Axis I Disorders in late adolescence; however, it may be prognostic of the emergence of personality disorders. PMID:21056910

Similar predictions of etravirine sensitivity regardless of genotypic testing method used: comparison of available scoring systems.

PubMed

Vingerhoets, Johan; Nijs, Steven; Tambuyzer, Lotke; Hoogstoel, Annemie; Anderson, David; Picchio, Gaston

2012-01-01

The aims of this study were to compare various genotypic scoring systems commonly used to predict virological outcome to etravirine, and examine their concordance with etravirine phenotypic susceptibility. Six etravirine genotypic scoring systems were assessed: Tibotec 2010 (based on 20 mutations; TBT 20), Monogram, Stanford HIVdb, ANRS, Rega (based on 37, 30, 27 and 49 mutations, respectively) and virco(®)TYPE HIV-1 (predicted fold change based on genotype). Samples from treatment-experienced patients who participated in the DUET trials and with both genotypic and phenotypic data (n=403) were assessed using each scoring system. Results were retrospectively correlated with virological response in DUET. κ coefficients were calculated to estimate the degree of correlation between the different scoring systems. Correlation between the five scoring systems and the TBT 20 system was approximately 90%. Virological response by etravirine susceptibility was comparable regardless of which scoring system was utilized, with 70-74% of DUET patients determined as susceptible to etravirine by the different scoring systems achieving plasma viral load <50 HIV-1 RNA copies/ml. In samples classed as phenotypically susceptible to etravirine (fold change in 50% effective concentration ≤3), correlations with genotypic score were consistently high across scoring systems (≥70%). In general, the etravirine genotypic scoring systems produced similar results, and genotype-phenotype concordance was high. As such, phenotypic interpretations, and in their absence all genotypic scoring systems investigated, may be used to reliably predict the activity of etravirine.

Potential fitness benefits of the half-pounder life history in Klamath River steelhead

USGS Publications Warehouse

Hodge, Brian W.; Wilzbach, Peggy; Duffy, Walter G.

2014-01-01

Steelhead Oncorhynchus mykiss from several of the world's rivers display the half-pounder life history, a variant characterized by an amphidromous (and, less often, anadromous) return to freshwater in the year of initial ocean entry. We evaluated factors related to expression of the half-pounder life history in wild steelhead from the lower Klamath River basin, California. We also evaluated fitness consequences of the half-pounder phenotype using a simple life history model that was parameterized with our empirical data and outputs from a regional survival equation. The incidence of the half-pounder life history differed among subbasins of origin and smolt ages. Precocious maturation occurred in approximately 8% of half-pounders and was best predicted by individual length in freshwater preceding ocean entry. Adult steelhead of the half-pounder phenotype were smaller and less fecund at age than adult steelhead of the alternative (ocean contingent) phenotype. However, our data suggest that fish of the half-pounder phenotype are more likely to spawn repeatedly than are fish of the ocean contingent phenotype. Models predicted that if lifetime survivorship were equal between phenotypes, the fitness of the half-pounder phenotype would be 17–28% lower than that of the ocean contingent phenotype. To meet the condition of equal fitness between phenotypes would require that first-year ocean survival be 21–40% higher among half-pounders in freshwater than among their cohorts at sea. We concluded that continued expression of the half-pounder phenotype is favored by precocious maturation and increased survival relative to that of the ocean contingent phenotype.

Hybrid multiscale modeling and prediction of cancer cell behavior

PubMed Central

Habibi, Jafar

2017-01-01

Background Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. Methods In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Results Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Conclusion Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset. PMID:28846712

Transferrin saturation phenotype and HFE genotype screening for hemochromatosis and primary iron overload: predictions from a model based on national, racial, and ethnic group composition in central Alabama.

PubMed

Barton, J C; Acton, R T

2000-01-01

There is interest in general population screening for hemochromatosis and other primary iron overload disorders, although not all persons are at equal risk. We developed a model to estimate the numbers of persons in national, racial, or ethnic population subgroups in Jefferson County, Alabama, who would be detected using transferrin saturation (phenotype) or HFE mutation analysis (genotype) screening. Approximately 62% are Caucasians, 37% are African Americans, and the remainder are Hispanics, Asians, or Native Americans. The predicted phenotype frequencies are greatest in a Caucasian subgroup, ethnicity unspecified, which consists predominantly of persons of Scotch and Irish descent (0.0065 men, 0.0046 women), and in African Americans (0.0089 men, 0.0085 women). Frequencies of the HFE genotype C282Y/C282Y > or = 0.0001 are predicted to occur only among Caucasians; the greatest frequency (0.0080) was predicted to occur in the ethnicity-unspecified Caucasian population. C282Y/C282Y frequency estimates were lower in Italian, Greek, and Jewish subgroups. There is excellent agreement in the numbers of the ethnicity-unspecified Caucasians who would be detected using phenotype and genotype criteria. Our model also indicates that phenotyping would identify more persons with primary iron overload than would genotyping in our Italian Caucasian, Hispanic, and African American subgroups. This is consistent with previous observations that indicate that primary iron overload disorders in persons of southern Italian descent and African Americans are largely attributable to non-HFE alleles. Because the proportions of population subgroups and their genetic constitution may differ significantly in other geographic regions, we suggest that models similar to the present one be constructed to predict optimal screening strategies for primary iron overload disorders.

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling.

PubMed

Povey, Jane F; O'Malley, Christopher J; Root, Tracy; Martin, Elaine B; Montague, Gary A; Feary, Marc; Trim, Carol; Lang, Dietmar A; Alldread, Richard; Racher, Andrew J; Smales, C Mark

2014-08-20

Despite many advances in the generation of high producing recombinant mammalian cell lines over the last few decades, cell line selection and development is often slowed by the inability to predict a cell line's phenotypic characteristics (e.g. growth or recombinant protein productivity) at larger scale (large volume bioreactors) using data from early cell line construction at small culture scale. Here we describe the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data are used to predict the phenotype of mammalian cell lines at larger culture scale using a Partial Least Squares Discriminant Analysis (PLS-DA) model. Using MALDI-ToF mass spectrometry, a library of mass spectrometry fingerprints was generated for individual cell lines at the 96 deep well plate stage of cell line development. The growth and productivity of these cell lines were evaluated in a 10L bioreactor model of Lonza's large-scale (up to 20,000L) fed-batch cell culture processes. Using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10L bioreactor scale a PLS-DA model was developed to predict the productivity of unknown cell lines at the 10L scale based upon their MALDI-ToF fingerprint at the 96 deep well plate scale. This approach provides the basis for the very early prediction of cell lines' performance in cGMP manufacturing-scale bioreactors and the foundation for methods and models for predicting other mammalian cell phenotypes from rapid, intact-cell mass spectrometry based measurements. Copyright © 2014 Elsevier B.V. All rights reserved.

The evolution of trade-offs: testing predictions on response to selection and environmental variation.

PubMed

Roff, Derek A; Mostowy, Serge; Fairbairn, Daphne J

2002-01-01

The concept of phenotypic trade-offs is a central element in evolutionary theory. In general, phenotypic models assume a fixed trade-off function, whereas quantitative genetic theory predicts that the trade-off function will change as a result of selection. For a linear trade-off function selection will readily change the intercept but will have to be relatively stronger to change the slope. We test these predictions by examining the trade-off between fecundity and flight capability, as measured by dorso-longitudinal muscle mass, in four different populations of the sand cricket, Gryllus firmus. Three populations were recently derived from the wild, and the fourth had been in the laboratory for 19 years. We hypothesized that the laboratory population had most likely undergone more and different selection from the three wild populations and therefore should differ from these in respect to both slope and intercept. Because of geographic variation in selection, we predicted a general difference in intercept among the four populations. We further tested the hypothesis that this intercept will be correlated with proportion macropterous and that this relationship will itself vary with environmental conditions experienced during both the nymphal and adult period. Observed variation in the phenotypic trade-off was consistent with the predictions of the quantitative genetic model. These results point to the importance of modeling trade-offs as dynamic rather than static relationships. We discuss how phenotypic models can incorporate such variation. The phenotypic trade-off between fecundity and dorso-longitudinal muscle mass is determined in part by variation in body size, illustrating the necessity of considering trade-offs to be multi factorial rather than simply bivariate relationships.

Hybrid multiscale modeling and prediction of cancer cell behavior.

PubMed

Zangooei, Mohammad Hossein; Habibi, Jafar

2017-01-01

Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.

Facial morphology predicts male fitness and rank but not survival in Second World War Finnish soldiers.

PubMed

Loehr, John; O'Hara, Robert B

2013-08-23

We investigated fitness, military rank and survival of facial phenotypes in large-scale warfare using 795 Finnish soldiers who fought in the Winter War (1939-1940). We measured facial width-to-height ratio-a trait known to predict aggressive behaviour in males-and assessed whether facial morphology could predict survival, lifetime reproductive success (LRS) and social status. We found no difference in survival along the phenotypic gradient, however, wider-faced individuals had greater LRS, but achieved a lower military rank.

Using cluster analysis to identify phenotypes and validation of mortality in men with COPD.

PubMed

Chen, Chiung-Zuei; Wang, Liang-Yi; Ou, Chih-Ying; Lee, Cheng-Hung; Lin, Chien-Chung; Hsiue, Tzuen-Ren

2014-12-01

Cluster analysis has been proposed to examine phenotypic heterogeneity in chronic obstructive pulmonary disease (COPD). The aim of this study was to use cluster analysis to define COPD phenotypes and validate them by assessing their relationship with mortality. Male subjects with COPD were recruited to identify and validate COPD phenotypes. Seven variables were assessed for their relevance to COPD, age, FEV(1) % predicted, BMI, history of severe exacerbations, mMRC, SpO(2), and Charlson index. COPD groups were identified by cluster analysis and validated prospectively against mortality during a 4-year follow-up. Analysis of 332 COPD subjects identified five clusters from cluster A to cluster E. Assessment of the predictive validity of these clusters of COPD showed that cluster E patients had higher all cause mortality (HR 18.3, p < 0.0001), and respiratory cause mortality (HR 21.5, p < 0.0001) than those in the other four groups. Cluster E patients also had higher all cause mortality (HR 14.3, p = 0.0002) and respiratory cause mortality (HR 10.1, p = 0.0013) than patients in cluster D alone. COPD patient with severe airflow limitation, many symptoms, and a history of frequent severe exacerbations was a novel and distinct clinical phenotype predicting mortality in men with COPD.

Ecological transition predictably associated with gene degeneration.

PubMed

Wessinger, Carolyn A; Rausher, Mark D

2015-02-01

Gene degeneration or loss can significantly contribute to phenotypic diversification, but may generate genetic constraints on future evolutionary trajectories, potentially restricting phenotypic reversal. Such constraints may manifest as directional evolutionary trends when parallel phenotypic shifts consistently involve gene degeneration or loss. Here, we demonstrate that widespread parallel evolution in Penstemon from blue to red flowers predictably involves the functional inactivation and degeneration of the enzyme flavonoid 3',5'-hydroxylase (F3'5'H), an anthocyanin pathway enzyme required for the production of blue floral pigments. Other types of genetic mutations do not consistently accompany this phenotypic shift. This pattern may be driven by the relatively large mutational target size of degenerative mutations to this locus and the apparent lack of associated pleiotropic effects. The consistent degeneration of F3'5'H may provide a mechanistic explanation for the observed asymmetry in the direction of flower color evolution in Penstemon: Blue to red transitions are common, but reverse transitions have not been observed. Although phenotypic shifts in this system are likely driven by natural selection, internal constraints may generate predictable genetic outcomes and may restrict future evolutionary trajectories. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Landscape genomic prediction for restoration of a Eucalyptus foundation species under climate change.

PubMed

Supple, Megan Ann; Bragg, Jason G; Broadhurst, Linda M; Nicotra, Adrienne B; Byrne, Margaret; Andrew, Rose L; Widdup, Abigail; Aitken, Nicola C; Borevitz, Justin O

2018-04-24

As species face rapid environmental change, we can build resilient populations through restoration projects that incorporate predicted future climates into seed sourcing decisions. Eucalyptus melliodora is a foundation species of a critically endangered community in Australia that is a target for restoration. We examined genomic and phenotypic variation to make empirical based recommendations for seed sourcing. We examined isolation by distance and isolation by environment, determining high levels of gene flow extending for 500 km and correlations with climate and soil variables. Growth experiments revealed extensive phenotypic variation both within and among sampling sites, but no site-specific differentiation in phenotypic plasticity. Model predictions suggest that seed can be sourced broadly across the landscape, providing ample diversity for adaptation to environmental change. Application of our landscape genomic model to E. melliodora restoration projects can identify genomic variation suitable for predicted future climates, thereby increasing the long term probability of successful restoration. © 2018, Supple et al.

Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

PubMed

Wu, Mon-Ju; Mwangi, Benson; Bauer, Isabelle E; Passos, Ives C; Sanches, Marsal; Zunta-Soares, Giovana B; Meyer, Thomas D; Hasan, Khader M; Soares, Jair C

2017-01-15

Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders. Copyright © 2016 Elsevier Inc. All rights reserved.

Phenylalanine hydroxylase deficiency in Mexico: genotype-phenotype correlations, BH4 responsiveness and evidence of a founder effect.

PubMed

Vela-Amieva, M; Abreu-González, M; González-del Angel, A; Ibarra-González, I; Fernández-Lainez, C; Barrientos-Ríos, R; Monroy-Santoyo, S; Guillén-López, S; Alcántara-Ortigoza, M A

2015-07-01

The mutational spectrum of the phenylalanine hydroxylase gene (PAH) in Mexico is unknown, although it has been suggested that PKU variants could have a differential geographical distribution. Genotype-phenotype correlations and genotype-based predictions of responsiveness to tetrahydrobiopterin (BH4 ) have never been performed. We sequenced the PAH gene and determined the geographic origin of each allele, mini-haplotype associated, genotype-phenotype correlations and genotype-based prediction of BH4 responsiveness in 48 Mexican patients. The mutational spectrum included 34 variants with c.60+5G>T being the most frequent (20.8%) and linked to haplotype 4.3 possibly because of a founder effect and/or genetic drift. Two new variants were found c.1A>T and c.969+6T>C. The genotype-phenotype correlation was concordant in 70.8%. The genotype-based prediction to BH4 -responsiveness was 41.7%, this information could be useful for the rational selection of candidates for BH4 testing and therapy. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Disease Modeling via Large-Scale Network Analysis

DTIC Science & Technology

2015-05-20

SECURITY CLASSIFICATION OF: A central goal of genetics is to learn how the genotype of an organism determines its phenotype. We address the implicit...guarantees for the methods. In the past, we have developed predictive methods general enough to apply to potentially any genetic trait, varying from... genetics is to learn how the genotype of an organism determines its phenotype. We address the implicit problem of predicting the association of genes with

Antimicrobial resistance prediction in PATRIC and RAST

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davis, James J.; Boisvert, Sebastien; Brettin, Thomas

The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned bymore » their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88–99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71–88%. Lastly, this set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.« less

Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour.

PubMed

Wollstein, Andreas; Walsh, Susan; Liu, Fan; Chakravarthy, Usha; Rahu, Mati; Seland, Johan H; Soubrane, Gisèle; Tomazzoli, Laura; Topouzis, Fotis; Vingerling, Johannes R; Vioque, Jesus; Böhringer, Stefan; Fletcher, Astrid E; Kayser, Manfred

2017-02-27

Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing.

Causal Genetic Variation Underlying Metabolome Differences.

PubMed

Swain-Lenz, Devjanee; Nikolskiy, Igor; Cheng, Jiye; Sudarsanam, Priya; Nayler, Darcy; Staller, Max V; Cohen, Barak A

2017-08-01

An ongoing challenge in biology is to predict the phenotypes of individuals from their genotypes. Genetic variants that cause disease often change an individual's total metabolite profile, or metabolome. In light of our extensive knowledge of metabolic pathways, genetic variants that alter the metabolome may help predict novel phenotypes. To link genetic variants to changes in the metabolome, we studied natural variation in the yeast Saccharomyces cerevisiae We used an untargeted mass spectrometry method to identify dozens of metabolite Quantitative Trait Loci (mQTL), genomic regions containing genetic variation that control differences in metabolite levels between individuals. We mapped differences in urea cycle metabolites to genetic variation in specific genes known to regulate amino acid biosynthesis. Our functional assays reveal that genetic variation in two genes, AUA1 and ARG81 , cause the differences in the abundance of several urea cycle metabolites. Based on knowledge of the urea cycle, we predicted and then validated a new phenotype: sensitivity to a particular class of amino acid isomers. Our results are a proof-of-concept that untargeted mass spectrometry can reveal links between natural genetic variants and metabolome diversity. The interpretability of our results demonstrates the promise of using genetic variants underlying natural differences in the metabolome to predict novel phenotypes from genotype. Copyright © 2017 by the Genetics Society of America.

Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour

PubMed Central

Wollstein, Andreas; Walsh, Susan; Liu, Fan; Chakravarthy, Usha; Rahu, Mati; Seland, Johan H.; Soubrane, Gisèle; Tomazzoli, Laura; Topouzis, Fotis; Vingerling, Johannes R.; Vioque, Jesus; Böhringer, Stefan; Fletcher, Astrid E.; Kayser, Manfred

2017-01-01

Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing. PMID:28240252

Antimicrobial resistance prediction in PATRIC and RAST

DOE PAGES

Davis, James J.; Boisvert, Sebastien; Brettin, Thomas; ...

2016-06-14

The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned bymore » their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii, methicillin resistance in Staphylococcus aureus, and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88–99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis, achieving accuracies ranging from 71–88%. Lastly, this set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.« less

Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-based Algorithm.

PubMed

Cuthbertson, Carmen C; Kucharska-Newton, Anna; Faurot, Keturah R; Stürmer, Til; Jonsson Funk, Michele; Palta, Priya; Windham, B Gwen; Thai, Sydney; Lund, Jennifer L

2018-07-01

Frailty is a geriatric syndrome characterized by weakness and weight loss and is associated with adverse health outcomes. It is often an unmeasured confounder in pharmacoepidemiologic and comparative effectiveness studies using administrative claims data. Among the Atherosclerosis Risk in Communities (ARIC) Study Visit 5 participants (2011-2013; n = 3,146), we conducted a validation study to compare a Medicare claims-based algorithm of dependency in activities of daily living (or dependency) developed as a proxy for frailty with a reference standard measure of phenotypic frailty. We applied the algorithm to the ARIC participants' claims data to generate a predicted probability of dependency. Using the claims-based algorithm, we estimated the C-statistic for predicting phenotypic frailty. We further categorized participants by their predicted probability of dependency (<5%, 5% to <20%, and ≥20%) and estimated associations with difficulties in physical abilities, falls, and mortality. The claims-based algorithm showed good discrimination of phenotypic frailty (C-statistic = 0.71; 95% confidence interval [CI] = 0.67, 0.74). Participants classified with a high predicted probability of dependency (≥20%) had higher prevalence of falls and difficulty in physical ability, and a greater risk of 1-year all-cause mortality (hazard ratio = 5.7 [95% CI = 2.5, 13]) than participants classified with a low predicted probability (<5%). Sensitivity and specificity varied across predicted probability of dependency thresholds. The Medicare claims-based algorithm showed good discrimination of phenotypic frailty and high predictive ability with adverse health outcomes. This algorithm can be used in future Medicare claims analyses to reduce confounding by frailty and improve study validity.

Testing computational prediction of missense mutation phenotypes: Functional characterization of 204 mutations of human cystathionine beta synthase

PubMed Central

Wei, Qiong; Wang, Liqun; Wang, Qiang; Kruger, Warren D.; Dunbrack, Roland L.

2010-01-01

Predicting the phenotypes of missense mutations uncovered by large-scale sequencing projects is an important goal in computational biology. High-confidence predictions can be an aid in focusing experimental and association studies on those mutations most likely to be associated with causative relationships between mutation and disease. As an aid in developing these methods further, we have derived a set of random mutations of the enzymatic domains of human cystathionine beta synthase. This enzyme is a dimeric protein that catalyzes the condensation of serine and homocysteine to produce cystathionine. Yeast missing this enzyme cannot grow on medium lacking a source of cysteine, while transfection of functional human CBS into yeast strains missing endogenous enzyme can successfully complement for the missing gene. We used PCR mutagenesis with error-prone Taq polymerase to produce 948 colonies, and compared cell growth in the presence or absence of a cysteine source as a measure of CBS function. We were able to infer the phenotypes of 204 single-site mutants, 79 of them deleterious and 125 neutral. This set was used to test the accuracy of six publicly available prediction methods for phenotype prediction of missense mutations: SIFT, PolyPhen, PMut, SNPs3D, PhD-SNP, and nsSNPAnalyzer. The top methods are PolyPhen, SIFT, and nsSNPAnalyzer, which have similar performance. Using kernel discriminant functions, we found that the difference in position-specific scoring matrix values is more predictive than the wild-type PSSM score alone, and that the relative surface area in the biologically relevant complex is more predictive than that of the monomeric proteins. PMID:20455263

Prediction of heterosis using genome-wide SNP-marker data: application to egg production traits in white Leghorn crosses.

PubMed

Amuzu-Aweh, E N; Bijma, P; Kinghorn, B P; Vereijken, A; Visscher, J; van Arendonk, J Am; Bovenhuis, H

2013-12-01

Prediction of heterosis has a long history with mixed success, partly due to low numbers of genetic markers and/or small data sets. We investigated the prediction of heterosis for egg number, egg weight and survival days in domestic white Leghorns, using ∼400 000 individuals from 47 crosses and allele frequencies on ∼53 000 genome-wide single nucleotide polymorphisms (SNPs). When heterosis is due to dominance, and dominance effects are independent of allele frequencies, heterosis is proportional to the squared difference in allele frequency (SDAF) between parental pure lines (not necessarily homozygous). Under these assumptions, a linear model including regression on SDAF partitions crossbred phenotypes into pure-line values and heterosis, even without pure-line phenotypes. We therefore used models where phenotypes of crossbreds were regressed on the SDAF between parental lines. Accuracy of prediction was determined using leave-one-out cross-validation. SDAF predicted heterosis for egg number and weight with an accuracy of ∼0.5, but did not predict heterosis for survival days. Heterosis predictions allowed preselection of pure lines before field-testing, saving ∼50% of field-testing cost with only 4% loss in heterosis. Accuracies from cross-validation were lower than from the model-fit, suggesting that accuracies previously reported in literature are overestimated. Cross-validation also indicated that dominance cannot fully explain heterosis. Nevertheless, the dominance model had considerable accuracy, clearly greater than that of a general/specific combining ability model. This work also showed that heterosis can be modelled even when pure-line phenotypes are unavailable. We concluded that SDAF is a useful predictor of heterosis in commercial layer breeding.

Genome-wide association mapping of qualitatively inherited traits in a germplasm collection

USDA-ARS?s Scientific Manuscript database

Genome-wide association (GWA) has been used as a tool for dissecting the genetic architecture of quantitatively inherited traits. We demonstrate here that GWA can also be highly useful for detecting the genomic locations of major genes governing categorically defined phenotype variants that exist fo...

Mapping QTL for popping expansion volume in popcorn with simple sequence repeat markers.

PubMed

Lu, H-J; Bernardo, R; Ohm, H W

2003-02-01

Popping expansion volume is the most important quality trait in popcorn ( Zea mays L.), but its genetics is not well understood. The objectives of this study were to map quantitative trait loci (QTLs) responsible for popping expansion volume in a popcorn x dent corn cross, and to compare the predicted efficiencies of phenotypic selection, marker-based selection, and marker-assisted selection for popping expansion volume. Of 259 simple sequence repeat (SSR) primer pairs screened, 83 pairs were polymorphic between the H123 (dent corn) and AG19 (popcorn) parental inbreds. Popping test data were obtained for 160 S(1) families developed from the [AG19(H123 x AG19)] BC(1) population. The heritability ( h(2)) for popping expansion volume on an S(1) family mean basis was 0.73. The presence of the gametophyte factor Ga1(s) in popcorn complicates the analysis of popcorn x dent corn crosses. But, from a practical perspective, the linkage between a favorable QTL allele and Ga1(s) in popcorn will lead to selection for the favorable QTL allele. Four QTLs, on chromosomes 1S, 3S, 5S and 5L, jointly explained 45% of the phenotypic variation. Marker-based selection for popping expansion volume would require less time and work than phenotypic selection. But due to the high h(2) of popping expansion volume, marker-based selection was predicted to be only 92% as efficient as phenotypic selection. Marker-assisted selection, which comprises index selection on phenotypic and marker scores, was predicted to be 106% as efficient as phenotypic selection. Overall, our results suggest that phenotypic selection will remain the preferred method for selection in popcorn x dent corn crosses.

Computer vision and machine learning for robust phenotyping in genome-wide studies

PubMed Central

Zhang, Jiaoping; Naik, Hsiang Sing; Assefa, Teshale; Sarkar, Soumik; Reddy, R. V. Chowda; Singh, Arti; Ganapathysubramanian, Baskar; Singh, Asheesh K.

2017-01-01

Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems. PMID:28272456

Wine Expertise Predicts Taste Phenotype

PubMed Central

Hayes, John E; Pickering, Gary J

2011-01-01

Taste phenotypes have long been studied in relation to alcohol intake, dependence, and family history, with contradictory findings. However, on balance – with appropriate caveats about populations tested, outcomes measured and psychophysical methods used – an association between variation in taste responsiveness and some alcohol behaviors is supported. Recent work suggests super-tasting (operationalized via propylthiouracil (PROP) bitterness) not only associates with heightened response but also with more acute discrimination between stimuli. Here, we explore relationships between food and beverage adventurousness and taste phenotype. A convenience sample of wine drinkers (n=330) were recruited in Ontario and phenotyped for PROP bitterness via filter paper disk. They also filled out a short questionnaire regarding willingness to try new foods, alcoholic beverages and wines as well as level of wine involvement, which was used to classify them as a wine expert (n=110) or wine consumer (n=220). In univariate logisitic models, food adventurousness predicted trying new wines and beverages but not expertise. Likewise, wine expertise predicted willingness to try new wines and beverages but not foods. In separate multivariate logistic models, willingness to try new wines and beverages was predicted by expertise and food adventurousness but not PROP. However, mean PROP bitterness was higher among wine experts than wine consumers, and the conditional distribution functions differed between experts and consumers. In contrast, PROP means and distributions did not differ with food adventurousness. These data suggest individuals may self-select for specific professions based on sensory ability (i.e., an active gene-environment correlation) but phenotype does not explain willingness to try new stimuli. PMID:22888174

Wine Expertise Predicts Taste Phenotype.

PubMed

Hayes, John E; Pickering, Gary J

2012-03-01

Taste phenotypes have long been studied in relation to alcohol intake, dependence, and family history, with contradictory findings. However, on balance - with appropriate caveats about populations tested, outcomes measured and psychophysical methods used - an association between variation in taste responsiveness and some alcohol behaviors is supported. Recent work suggests super-tasting (operationalized via propylthiouracil (PROP) bitterness) not only associates with heightened response but also with more acute discrimination between stimuli. Here, we explore relationships between food and beverage adventurousness and taste phenotype. A convenience sample of wine drinkers (n=330) were recruited in Ontario and phenotyped for PROP bitterness via filter paper disk. They also filled out a short questionnaire regarding willingness to try new foods, alcoholic beverages and wines as well as level of wine involvement, which was used to classify them as a wine expert (n=110) or wine consumer (n=220). In univariate logisitic models, food adventurousness predicted trying new wines and beverages but not expertise. Likewise, wine expertise predicted willingness to try new wines and beverages but not foods. In separate multivariate logistic models, willingness to try new wines and beverages was predicted by expertise and food adventurousness but not PROP. However, mean PROP bitterness was higher among wine experts than wine consumers, and the conditional distribution functions differed between experts and consumers. In contrast, PROP means and distributions did not differ with food adventurousness. These data suggest individuals may self-select for specific professions based on sensory ability (i.e., an active gene-environment correlation) but phenotype does not explain willingness to try new stimuli.

Genome-environment associations in sorghum landraces predict adaptive traits

PubMed Central

Lasky, Jesse R.; Upadhyaya, Hari D.; Ramu, Punna; Deshpande, Santosh; Hash, C. Tom; Bonnette, Jason; Juenger, Thomas E.; Hyma, Katie; Acharya, Charlotte; Mitchell, Sharon E.; Buckler, Edward S.; Brenton, Zachary; Kresovich, Stephen; Morris, Geoffrey P.

2015-01-01

Improving environmental adaptation in crops is essential for food security under global change, but phenotyping adaptive traits remains a major bottleneck. If associations between single-nucleotide polymorphism (SNP) alleles and environment of origin in crop landraces reflect adaptation, then these could be used to predict phenotypic variation for adaptive traits. We tested this proposition in the global food crop Sorghum bicolor, characterizing 1943 georeferenced landraces at 404,627 SNPs and quantifying allelic associations with bioclimatic and soil gradients. Environment explained a substantial portion of SNP variation, independent of geographical distance, and genic SNPs were enriched for environmental associations. Further, environment-associated SNPs predicted genotype-by-environment interactions under experimental drought stress and aluminum toxicity. Our results suggest that genomic signatures of environmental adaptation may be useful for crop improvement, enhancing germplasm identification and marker-assisted selection. Together, genome-environment associations and phenotypic analyses may reveal the basis of environmental adaptation. PMID:26601206

The pharmacogenetics of body odor: as easy as ABCC?

PubMed

Brown, Sara

2013-07-01

ABCC11 genotype affects apocrine secretory cell function and determines individual body odor phenotype. Rodriguez et al. have applied genetic epidemiology using predetermined phenotype data to demonstrate an association between a single-nucleotide polymorphism (rs17822931) and the human behavior of deodorant application. Individuals with the ABCC11 genotype predicting a nonodorous phenotype report a significantly lower frequency of deodorant use.

Chaos and unpredictability in evolution.

PubMed

Doebeli, Michael; Ispolatov, Iaroslav

2014-05-01

The possibility of complicated dynamic behavior driven by nonlinear feedbacks in dynamical systems has revolutionized science in the latter part of the last century. Yet despite examples of complicated frequency dynamics, the possibility of long-term evolutionary chaos is rarely considered. The concept of "survival of the fittest" is central to much evolutionary thinking and embodies a perspective of evolution as a directional optimization process exhibiting simple, predictable dynamics. This perspective is adequate for simple scenarios, when frequency-independent selection acts on scalar phenotypes. However, in most organisms many phenotypic properties combine in complicated ways to determine ecological interactions, and hence frequency-dependent selection. Therefore, it is natural to consider models for evolutionary dynamics generated by frequency-dependent selection acting simultaneously on many different phenotypes. Here we show that complicated, chaotic dynamics of long-term evolutionary trajectories in phenotype space is very common in a large class of such models when the dimension of phenotype space is large, and when there are selective interactions between the phenotypic components. Our results suggest that the perspective of evolution as a process with simple, predictable dynamics covers only a small fragment of long-term evolution. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Bridging the gap between genome analysis and precision breeding in potato.

PubMed

Gebhardt, Christiane

2013-04-01

Efficiency and precision in plant breeding can be enhanced by using diagnostic DNA-based markers for the selection of superior cultivars. This technique has been applied to many crops, including potatoes. The first generation of diagnostic DNA-based markers useful in potato breeding were enabled by several developments: genetic linkage maps based on DNA polymorphisms, linkage mapping of qualitative and quantitative agronomic traits, cloning and functional analysis of genes for pathogen resistance and genes controlling plant metabolism, and association genetics in collections of tetraploid varieties and advanced breeding clones. Although these have led to significant improvements in potato genetics, the prediction of most, if not all, natural variation in agronomic traits by diagnostic markers ultimately requires the identification of the causal genes and their allelic variants. This objective will be facilitated by new genomic tools, such as genomic resequencing and comparative profiling of the proteome, transcriptome, and metabolome in combination with phenotyping genetic materials relevant for variety development. Copyright © 2012 Elsevier Ltd. All rights reserved.

Mouse Models as Predictors of Human Responses: Evolutionary Medicine.

PubMed

Uhl, Elizabeth W; Warner, Natalie J

Mice offer a number of advantages and are extensively used to model human diseases and drug responses. Selective breeding and genetic manipulation of mice have made many different genotypes and phenotypes available for research. However, in many cases, mouse models have failed to be predictive. Important sources of the prediction problem have been the failure to consider the evolutionary basis for species differences, especially in drug metabolism, and disease definitions that do not reflect the complexity of gene expression underlying disease phenotypes. Incorporating evolutionary insights into mouse models allow for unique opportunities to characterize the effects of diet, different gene expression profiles, and microbiomics underlying human drug responses and disease phenotypes.

Evolution of increased phenotypic diversity enhances population performance by reducing sexual harassment in damselflies.

PubMed

Takahashi, Yuma; Kagawa, Kotaro; Svensson, Erik I; Kawata, Masakado

2014-07-18

The effect of evolutionary changes in traits and phenotypic/genetic diversity on ecological dynamics has received much theoretical attention; however, the mechanisms and ecological consequences are usually unknown. Female-limited colour polymorphism in damselflies is a counter-adaptation to male mating harassment, and thus, is expected to alter population dynamics through relaxing sexual conflict. Here we show the side effect of the evolution of female morph diversity on population performance (for example, population productivity and sustainability) in damselflies. Our theoretical model incorporating key features of the sexual interaction predicts that the evolution of increased phenotypic diversity will reduce overall fitness costs to females from sexual conflict, which in turn will increase productivity, density and stability of a population. Field data and mesocosm experiments support these model predictions. Our study suggests that increased phenotypic diversity can enhance population performance that can potentially reduce extinction rates and thereby influence macroevolutionary processes.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

PubMed

Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

2016-01-01

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

PubMed

Afshar, Majid; Press, Valerie G; Robison, Rachel G; Kho, Abel N; Bandi, Sindhura; Biswas, Ashvini; Avila, Pedro C; Kumar, Harsha Vardhan Madan; Yu, Byung; Naureckas, Edward T; Nyenhuis, Sharmilee M; Codispoti, Christopher D

2017-10-13

Comprehensive, rapid, and accurate identification of patients with asthma for clinical care and engagement in research efforts is needed. The original development and validation of a computable phenotype for asthma case identification occurred at a single institution in Chicago and demonstrated excellent test characteristics. However, its application in a diverse payer mix, across different health systems and multiple electronic health record vendors, and in both children and adults was not examined. The objective of this study is to externally validate the computable phenotype across diverse Chicago institutions to accurately identify pediatric and adult patients with asthma. A cohort of 900 asthma and control patients was identified from the electronic health record between January 1, 2012 and November 30, 2014. Two physicians at each site independently reviewed the patient chart to annotate cases. The inter-observer reliability between the physician reviewers had a κ-coefficient of 0.95 (95% CI 0.93-0.97). The accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the computable phenotype were all above 94% in the full cohort. The excellent positive and negative predictive values in this multi-center external validation study establish a useful tool to identify asthma cases in in the electronic health record for research and care. This computable phenotype could be used in large-scale comparative-effectiveness trials.

Single and multiple phenotype QTL analyses of downy mildew resistance in interspecific grapevines.

PubMed

Divilov, Konstantin; Barba, Paola; Cadle-Davidson, Lance; Reisch, Bruce I

2018-05-01

Downy mildew resistance across days post-inoculation, experiments, and years in two interspecific grapevine F 1 families was investigated using linear mixed models and Bayesian networks, and five new QTL were identified. Breeding grapevines for downy mildew disease resistance has traditionally relied on qualitative gene resistance, which can be overcome by pathogen evolution. Analyzing two interspecific F 1 families, both having ancestry derived from Vitis vinifera and wild North American Vitis species, across 2 years and multiple experiments, we found multiple loci associated with downy mildew sporulation and hypersensitive response in both families using a single phenotype model. The loci explained between 7 and 17% of the variance for either phenotype, suggesting a complex genetic architecture for these traits in the two families studied. For two loci, we used RNA-Seq to detect differentially transcribed genes and found that the candidate genes at these loci were likely not NBS-LRR genes. Additionally, using a multiple phenotype Bayesian network analysis, we found effects between the leaf trichome density, hypersensitive response, and sporulation phenotypes. Moderate-high heritabilities were found for all three phenotypes, suggesting that selection for downy mildew resistance is an achievable goal by breeding for either physical- or non-physical-based resistance mechanisms, with the combination of the two possibly providing durable resistance.

Genomic selection in sugar beet breeding populations.

PubMed

Würschum, Tobias; Reif, Jochen C; Kraft, Thomas; Janssen, Geert; Zhao, Yusheng

2013-09-18

Genomic selection exploits dense genome-wide marker data to predict breeding values. In this study we used a large sugar beet population of 924 lines representing different germplasm types present in breeding populations: unselected segregating families and diverse lines from more advanced stages of selection. All lines have been intensively phenotyped in multi-location field trials for six agronomically important traits and genotyped with 677 SNP markers. We used ridge regression best linear unbiased prediction in combination with fivefold cross-validation and obtained high prediction accuracies for all except one trait. In addition, we investigated whether a calibration developed based on a training population composed of diverse lines is suited to predict the phenotypic performance within families. Our results show that the prediction accuracy is lower than that obtained within the diverse set of lines, but comparable to that obtained by cross-validation within the respective families. The results presented in this study suggest that a training population derived from intensively phenotyped and genotyped diverse lines from a breeding program does hold potential to build up robust calibration models for genomic selection. Taken together, our results indicate that genomic selection is a valuable tool and can thus complement the genomics toolbox in sugar beet breeding.

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

PubMed

Pesesky, Mitchell W; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burnham, Carey-Ann D; Dantas, Gautam

2016-01-01

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and incomplete genome assembly confounded the rules-based algorithm, resulting in predictions based on gene family, rather than on knowledge of the specific variant found. Low-frequency resistance caused errors in the machine-learning algorithm because those genes were not seen or seen infrequently in the test set. We also identified an example of variability in the phenotype-based results that led to disagreement with both genotype-based methods. Genotype-based antimicrobial susceptibility testing shows great promise as a diagnostic tool, and we outline specific research goals to further refine this methodology.

Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Breast Cancer Pathologic Stage

PubMed Central

Burnside, Elizabeth S.; Drukker, Karen; Li, Hui; Bonaccio, Ermelinda; Zuley, Margarita; Ganott, Marie; Net, Jose M.; Sutton, Elizabeth; Brandt, Kathleen R.; Whitman, Gary; Conzen, Suzanne; Lan, Li; Ji, Yuan; Zhu, Yitan; Jaffe, Carl; Huang, Erich; Freymann, John; Kirby, Justin; Morris, Elizabeth; Giger, Maryellen

2015-01-01

Background To demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on MRI can accurately predict pathologic stage. Methods We used a dataset of de-identified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. We analyzed 91 biopsy-proven breast cancer cases with pathologic stage (stage I = 22; stage II = 58; stage III = 11) and surgically proven nodal status (negative nodes = 46, ≥ 1 positive node = 44, no nodes examined = 1). We characterized tumors by (a) radiologist measured size, and (b) CEIP. We built models combining two CEIPs to predict tumor pathologic stage and lymph node involvement, evaluated them in leave-one-out cross-validation with area under the ROC curve (AUC) as figure of merit. Results Tumor size was the most powerful predictor of pathologic stage but CEIPs capturing biologic behavior also emerged as predictive (e.g. stage I+II vs. III demonstrated AUC = 0.83). No size measure was successful in the prediction of positive lymph nodes but adding a CEIP describing tumor “homogeneity,” significantly improved this discrimination (AUC = 0.62, p=.003) over chance. Conclusions Our results indicate that MRI phenotypes show promise for predicting breast cancer pathologic stage and lymph node status. PMID:26619259

Qualitative radiology assessment of tumor response: does it measure up?

PubMed

Gottlieb, Ronald H; Litwin, Alan; Gupta, Bhavna; Taylor, John; Raczyk, Cheryl; Mashtare, Terry; Wilding, Gregory; Fakih, Marwan

2008-01-01

Our purpose was to assess whether a simpler qualitative evaluation of tumor response by computed tomography is as reproducible and predictive of clinical outcome as the Response Evaluation Criteria in Solid Tumors (RECIST) and World Health Organization (WHO) methods. This study was a two-reader retrospective evaluation in which qualitative assessment resulted in agreement in 21 of 23 patients with metastatic colorectal carcinoma (91.3%, kappa=0.78; 95% CI, 0.51-1.00). Hepatic metastases were classified as increased, decreased, or unchanged, compared with agreement in 20 of 23 patients (87.0%) for RECIST (kappa=0.62; 95% CI, 0.23-1.00) and WHO (kappa=0.67; 95% CI, 0.34-1.00) methods. Patients were placed into partial response, stable disease, and disease progression categories. Time to progression of disease was better predicted qualitatively than by RECIST or WHO. Our pilot data suggest that our qualitative scoring system is more reproducible and predictive of patient clinical outcome than the RECIST and WHO methods.

Breeding nursery tissue collection for possible genomic analysis

USDA-ARS?s Scientific Manuscript database

Phenotyping is considered a major bottleneck in breeding programs. With new genomic technologies, high throughput genotype schemes are constantly being developed. However, every genomic technology requires phenotypic data to inform prediction models generated from the technology. Forage breeders con...

Frailty and the prediction of dependence and mortality in low- and middle-income countries: a 10/66 population-based cohort study.

PubMed

At, Jotheeswaran; Bryce, Renata; Prina, Matthew; Acosta, Daisy; Ferri, Cleusa P; Guerra, Mariella; Huang, Yueqin; Rodriguez, Juan J Llibre; Salas, Aquiles; Sosa, Ana Luisa; Williams, Joseph D; Dewey, Michael E; Acosta, Isaac; Liu, Zhaorui; Beard, John; Prince, Martin

2015-06-10

In countries with high incomes, frailty indicators predict adverse outcomes in older people, despite a lack of consensus on definition or measurement. We tested the predictive validity of physical and multidimensional frailty phenotypes in settings in Latin America, India, and China. Population-based cohort studies were conducted in catchment area sites in Cuba, Dominican Republic, Venezuela, Mexico, Peru, India, and China. Seven frailty indicators, namely gait speed, self-reported exhaustion, weight loss, low energy expenditure, undernutrition, cognitive, and sensory impairment were assessed to estimate frailty phenotypes. Mortality and onset of dependence were ascertained after a median of 3.9 years. Overall, 13,924 older people were assessed at baseline, with 47,438 person-years follow-up for mortality and 30,689 for dependence. Both frailty phenotypes predicted the onset of dependence and mortality, even adjusting for chronic diseases and disability, with little heterogeneity of effect among sites. However, population attributable fractions (PAF) summarising etiologic force were highest for the aggregate effect of the individual indicators, as opposed to either the number of indicators or the dichotomised frailty phenotypes. The aggregate of all seven indicators provided the best overall prediction (weighted mean PAF 41.8 % for dependence and 38.3 % for mortality). While weight loss, underactivity, slow walking speed, and cognitive impairment predicted both outcomes, whereas undernutrition predicted only mortality and sensory impairment only dependence. Exhaustion predicted neither outcome. Simply assessed frailty indicators identify older people at risk of dependence and mortality, beyond information provided by chronic disease diagnoses and disability. Frailty is likely to be multidimensional. A better understanding of the construct and pathways to adverse outcomes could inform multidimensional assessment and intervention to prevent or manage dependence in frail older people, with potential to add life to years, and years to life.

Replaying the tape of life in the twenty-first century.

PubMed

Orgogozo, Virginie

2015-12-06

Should the tape of life be replayed, would it produce similar living beings? A classical answer has long been 'no', but accumulating data are now challenging this view. Repeatability in experimental evolution, in phenotypic evolution of diverse species and in the genes underlying phenotypic evolution indicates that despite unpredictability at the level of basic evolutionary processes (such as apparition of mutations), a certain kind of predictability can emerge at higher levels over long time periods. For instance, a survey of the alleles described in the literature that cause non-deleterious phenotypic differences among animals, plants and yeasts indicates that similar phenotypes have often evolved in distinct taxa through independent mutations in the same genes. Does this mean that the range of possibilities for evolution is limited? Does this mean that we can predict the outcomes of a replayed tape of life? Imagining other possible paths for evolution runs into four important issues: (i) resolving the influence of contingency, (ii) imagining living organisms that are different from the ones we know, (iii) finding the relevant concepts for predicting evolution, and (iv) estimating the probability of occurrence for complex evolutionary events that occurred only once during the evolution of life on earth.

Six-Minute Walk Distance Predictors, Including CT Scan Measures, in the COPDGene Cohort

PubMed Central

Rambod, Mehdi; Porszasz, Janos; Make, Barry J.; Crapo, James D.

2012-01-01

Background: Exercise tolerance in COPD is only moderately well predicted by airflow obstruction assessed by FEV1. We determined whether other phenotypic characteristics, including CT scan measures, are independent predictors of 6-min walk distance (6MWD) in the COPDGene cohort. Methods: COPDGene recruits non-Hispanic Caucasian and African American current and ex-smokers. Phenotyping measures include postbronchodilator FEV1 % predicted and inspiratory and expiratory CT lung scans. We defined % emphysema as the percentage of lung voxels < −950 Hounsfield units on the inspiratory scan and % gas trapping as the percentage of lung voxels < −856 Hounsfield units on the expiratory scan. Results: Data of the first 2,500 participants of the COPDGene cohort were analyzed. Participant age was 61 ± 9 years; 51% were men; 76% were non-Hispanic Caucasians, and 24% were African Americans. Fifty-six percent had spirometrically defined COPD, with 9.3%, 23.4%, 15.0%, and 8.3% in GOLD (Global Initiative for Chronic Obstructive Lung Disease) stages I to IV, respectively. Higher % emphysema and % gas trapping predicted lower 6MWD (P < .001). However, in a given spirometric group, after adjustment for age, sex, race, and BMI, neither % emphysema nor % gas trapping, or their interactions with FEV1 % predicted, remained a significant 6MWD predictor. In a given spirometric group, only 16% to 27% of the variance in 6MWD could be explained by age, male sex, Caucasian race, and lower BMI as significant predictors of higher 6MWD. Conclusions: In this large cohort of smokers in a given spirometric stage, phenotypic characteristics were only modestly predictive of 6MWD. CT scan measures of emphysema and gas trapping were not predictive of 6MWD after adjustment for other phenotypic characteristics. PMID:21960696

Six-minute walk distance predictors, including CT scan measures, in the COPDGene cohort.

PubMed

Rambod, Mehdi; Porszasz, Janos; Make, Barry J; Crapo, James D; Casaburi, Richard

2012-04-01

Exercise tolerance in COPD is only moderately well predicted by airflow obstruction assessed by FEV(1). We determined whether other phenotypic characteristics, including CT scan measures, are independent predictors of 6-min walk distance (6MWD) in the COPDGene cohort. COPDGene recruits non-Hispanic Caucasian and African American current and ex-smokers. Phenotyping measures include postbronchodilator FEV(1) % predicted and inspiratory and expiratory CT lung scans. We defined % emphysema as the percentage of lung voxels < -950 Hounsfield units on the inspiratory scan and % gas trapping as the percentage of lung voxels < -856 Hounsfield units on the expiratory scan. Data of the first 2,500 participants of the COPDGene cohort were analyzed. Participant age was 61 ± 9 years; 51% were men; 76% were non-Hispanic Caucasians, and 24% were African Americans. Fifty-six percent had spirometrically defined COPD, with 9.3%, 23.4%, 15.0%, and 8.3% in GOLD (Global Initiative for Chronic Obstructive Lung Disease) stages I to IV, respectively. Higher % emphysema and % gas trapping predicted lower 6MWD (P < .001). However, in a given spirometric group, after adjustment for age, sex, race, and BMI, neither % emphysema nor % gas trapping, or their interactions with FEV(1) % predicted, remained a significant 6MWD predictor. In a given spirometric group, only 16% to 27% of the variance in 6MWD could be explained by age, male sex, Caucasian race, and lower BMI as significant predictors of higher 6MWD. In this large cohort of smokers in a given spirometric stage, phenotypic characteristics were only modestly predictive of 6MWD. CT scan measures of emphysema and gas trapping were not predictive of 6MWD after adjustment for other phenotypic characteristics.

Context-sensitive network-based disease genetics prediction and its implications in drug discovery

PubMed Central

Chen, Yang; Xu, Rong

2017-01-01

Abstract Motivation: Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. Results: We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach (p

φ-evo: A program to evolve phenotypic models of biological networks.

PubMed

Henry, Adrien; Hemery, Mathieu; François, Paul

2018-06-01

Molecular networks are at the core of most cellular decisions, but are often difficult to comprehend. Reverse engineering of network architecture from their functions has proved fruitful to classify and predict the structure and function of molecular networks, suggesting new experimental tests and biological predictions. We present φ-evo, an open-source program to evolve in silico phenotypic networks performing a given biological function. We include implementations for evolution of biochemical adaptation, adaptive sorting for immune recognition, metazoan development (somitogenesis, hox patterning), as well as Pareto evolution. We detail the program architecture based on C, Python 3, and a Jupyter interface for project configuration and network analysis. We illustrate the predictive power of φ-evo by first recovering the asymmetrical structure of the lac operon regulation from an objective function with symmetrical constraints. Second, we use the problem of hox-like embryonic patterning to show how a single effective fitness can emerge from multi-objective (Pareto) evolution. φ-evo provides an efficient approach and user-friendly interface for the phenotypic prediction of networks and the numerical study of evolution itself.

Dynamical predictors of an imminent phenotypic switch in bacteria

NASA Astrophysics Data System (ADS)

Wang, Huijing; Ray, J. Christian J.

2017-08-01

Single cells can stochastically switch across thresholds imposed by regulatory networks. Such thresholds can act as a tipping point, drastically changing global phenotypic states. In ecology and economics, imminent transitions across such tipping points can be predicted using dynamical early warning indicators. A typical example is ‘flickering’ of a fast variable, predicting a longer-lasting switch from a low to a high state or vice versa. Considering the different timescales between metabolite and protein fluctuations in bacteria, we hypothesized that metabolic early warning indicators predict imminent transitions across a network threshold caused by enzyme saturation. We used stochastic simulations to determine if flickering predicts phenotypic transitions, accounting for a variety of molecular physiological parameters, including enzyme affinity, burstiness of enzyme gene expression, homeostatic feedback, and rates of metabolic precursor influx. In most cases, we found that metabolic flickering rates are robustly peaked near the enzyme saturation threshold. The degree of fluctuation was amplified by product inhibition of the enzyme. We conclude that sensitivity to flickering in fast variables may be a possible natural or synthetic strategy to prepare physiological states for an imminent transition.

Modeling continuum of epithelial mesenchymal transition plasticity.

PubMed

Mandal, Mousumi; Ghosh, Biswajoy; Anura, Anji; Mitra, Pabitra; Pathak, Tanmaya; Chatterjee, Jyotirmoy

2016-02-01

Living systems respond to ambient pathophysiological changes by altering their phenotype, a phenomenon called 'phenotypic plasticity'. This program contains information about adaptive biological dynamism. Epithelial-mesenchymal transition (EMT) is one such process found to be crucial in development, wound healing, and cancer wherein the epithelial cells with restricted migratory potential develop motile functions by acquiring mesenchymal characteristics. In the present study, phase contrast microscopy images of EMT induced HaCaT cells were acquired at 24 h intervals for 96 h. The expression study of relevant pivotal molecules viz. F-actin, vimentin, fibronectin and N-cadherin was carried out to confirm the EMT process. Cells were intuitively categorized into five distinct morphological phenotypes. A population of 500 cells for each temporal point was selected to quantify their frequency of occurrence. The plastic interplay of cell phenotypes from the observations was described as a Markovian process. A model was formulated empirically using simple linear algebra, to depict the possible mechanisms of cellular transformation among the five phenotypes. This work employed qualitative, semi-quantitative and quantitative tools towards illustration and establishment of the EMT continuum. Thus, it provides a newer perspective to understand the embedded plasticity across the EMT spectrum.

Genomic selection using beef commercial carcass phenotypes.

PubMed

Todd, D L; Roughsedge, T; Woolliams, J A

2014-03-01

In this study, an industry terminal breeding goal was used in a deterministic simulation, using selection index methodology, to predict genetic gain in a beef population modelled on the UK pedigree Limousin, when using genomic selection (GS) and incorporating phenotype information from novel commercial carcass traits. The effect of genotype-environment interaction was investigated by including the model variations of the genetic correlation between purebred and commercial cross-bred performance (ρX). Three genomic scenarios were considered: (1) genomic breeding values (GBV)+estimated breeding values (EBV) for existing selection traits; (2) GBV for three novel commercial carcass traits+EBV in existing traits; and (3) GBV for novel and existing traits plus EBV for existing traits. Each of the three scenarios was simulated for a range of training population (TP) sizes and with three values of ρX. Scenarios 2 and 3 predicted substantially higher percentage increases over current selection than Scenario 1. A TP of 2000 sires, each with 20 commercial progeny with carcass phenotypes, and assuming a ρX of 0.7, is predicted to increase gain by 40% over current selection in Scenario 3. The percentage increase in gain over current selection increased with decreasing ρX; however, the effect of varying ρX was reduced at high TP sizes for Scenarios 2 and 3. A further non-genomic scenario (4) was considered simulating a conventional population-wide progeny test using EBV only. With 20 commercial cross-bred progenies per sire, similar gain was predicted to Scenario 3 with TP=5000 and ρX=1.0. The range of increases in genetic gain predicted for terminal traits when using GS are of similar magnitude to those observed after the implementation of BLUP technology in the United Kingdom. It is concluded that implementation of GS in a terminal sire breeding goal, using purebred phenotypes alone, will be sub-optimal compared with the inclusion of novel commercial carcass phenotypes in genomic evaluations.

78 FR 43838 - Airworthiness Directives; Hamilton Sundstrand Corporation Propellers

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-22

... qualitative risk assessment. The data gathered was then used for a more representative quantitative risk analysis. The results from the bond strength tests predicts a significantly lower fleet risk than the prior... predicts a significantly lower fleet risk than the prior qualitative analysis. Accordingly, we withdraw the...

Accounting for the Down syndrome advantage?

PubMed

Esbensen, Anna J; Seltzer, Marsha Mailick

2011-01-01

The authors examined factors that could explain the higher levels of psychosocial well being observed in past research in mothers of individuals with Down syndrome compared with mothers of individuals with other types of intellectual disabilities. The authors studied 155 mothers of adults with Down syndrome, contrasting factors that might validly account for the ?Down syndrome advantage? (behavioral phenotype) with those that have been portrayed in past research as artifactual (maternal age, social supports). The behavioral phenotype predicted less pessimism, more life satisfaction, and a better quality of the mother?child relationship. However, younger maternal age and fewer social supports, as well as the behavioral phenotype, predicted higher levels of caregiving burden. Implications for future research on families of individuals with Down syndrome are discussed.

Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.

PubMed

Aliloo, Hassan; Pryce, Jennie E; González-Recio, Oscar; Cocks, Benjamin G; Hayes, Ben J

2016-02-01

Dominance effects may contribute to genetic variation of complex traits in dairy cattle, especially for traits closely related to fitness such as fertility. However, traditional genetic evaluations generally ignore dominance effects and consider additive genetic effects only. Availability of dense single nucleotide polymorphisms (SNPs) panels provides the opportunity to investigate the role of dominance in quantitative variation of complex traits at both the SNP and animal levels. Including dominance effects in the genomic evaluation of animals could also help to increase the accuracy of prediction of future phenotypes. In this study, we estimated additive and dominance variance components for fertility and milk production traits of genotyped Holstein and Jersey cows in Australia. The predictive abilities of a model that accounts for additive effects only (additive), and a model that accounts for both additive and dominance effects (additive + dominance) were compared in a fivefold cross-validation. Estimates of the proportion of dominance variation relative to phenotypic variation that is captured by SNPs, for production traits, were up to 3.8 and 7.1 % in Holstein and Jersey cows, respectively, whereas, for fertility, they were equal to 1.2 % in Holstein and very close to zero in Jersey cows. We found that including dominance in the model was not consistently advantageous. Based on maximum likelihood ratio tests, the additive + dominance model fitted the data better than the additive model, for milk, fat and protein yields in both breeds. However, regarding the prediction of phenotypes assessed with fivefold cross-validation, including dominance effects in the model improved accuracy only for fat yield in Holstein cows. Regression coefficients of phenotypes on genetic values and mean squared errors of predictions showed that the predictive ability of the additive + dominance model was superior to that of the additive model for some of the traits. In both breeds, dominance effects were significant (P < 0.01) for all milk production traits but not for fertility. Accuracy of prediction of phenotypes was slightly increased by including dominance effects in the genomic evaluation model. Thus, it can help to better identify highly performing individuals and be useful for culling decisions.

Redox Abnormalities as a Vulnerability Phenotype for Autism and Related Alterations in CNS Development

DTIC Science & Technology

2011-10-01

the hypothesis that SJL mice would have impaired neuronal dendrite generation, as has been observed in autism . This was our prediction due to the...phenotype for Autism and related alterations in CNS development PRINCIPAL INVESTIGATOR: Mark D. Noble, Ph.D. CONTRACTING...SUBTITLE Redox abnormalities as a vulnerability phenotype for Autism 5a. CONTRACT NUMBER And related alterations in CNS development 5b. GRANT

Assessing the Efficiency of Phenotyping Early Traits in a Greenhouse Automated Platform for Predicting Drought Tolerance of Soybean in the Field.

PubMed

Peirone, Laura S; Pereyra Irujo, Gustavo A; Bolton, Alejandro; Erreguerena, Ignacio; Aguirrezábal, Luis A N

2018-01-01

Conventional field phenotyping for drought tolerance, the most important factor limiting yield at a global scale, is labor-intensive and time-consuming. Automated greenhouse platforms can increase the precision and throughput of plant phenotyping and contribute to a faster release of drought tolerant varieties. The aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform, for predicting the drought tolerance of field grown soybean genotypes. A group of genotypes was evaluated, which showed variation in their drought susceptibility index (DSI) for final biomass and leaf area. A large number of traits were measured before and after the onset of a water deficit treatment, which were analyzed under several criteria: the significance of the regression with the DSI, phenotyping cost, earliness, and repeatability. The most efficient trait was found to be transpiration efficiency measured at 13 days after emergence. This trait was further tested in a second experiment with different water deficit intensities, and validated using a different set of genotypes against field data from a trial network in a third experiment. The framework applied in this work for assessing traits under different criteria could be helpful for selecting those most efficient for automated phenotyping.

Assessing admixture by multivariate analyses of phenotypic differentiation in the Algerian goat livestock.

PubMed

Ouchene-Khelifi, Nadjet-Amina; Ouchene, Nassim; Maftah, Abderrahman; Da Silva, Anne Blondeau; Lafri, Mohamed

2015-10-01

In Algeria, goat research has been largely neglected, in spite of the economic importance of this domestic species for rural livelihoods. Goat farming is traditional and cross-breeding practices are current. The phenotypic variability of the four main native breeds (Arabia, Makatia, M'zabite and Kabyle), and of two exotic breeds (Alpine and Saanen), was investigated for the first time, using multivariate discriminant analysis. A total of 892 females were sampled in a large area, including the cradle of the native breeds, and phenotyped with 23 quantitative measures and 10 qualitative traits. Our results suggested that cross-breeding practices have ever led to critical consequences, particularly for Makatia and M'zabite. The information reported in this study has to be carefully considered in order to establish governmental plan able to prevent the genetic dilution of the Algerian goat livestock.

Genomic prediction using phenotypes from pedigreed lines with no marker data

USDA-ARS?s Scientific Manuscript database

Until now genomic prediction in plant breeding has only used information from individuals that have been genotyped. In practice, information from non-genotyped relatives of genotyped individuals can be used to improve the genomic prediction accuracy. Single-step genomic prediction integrates all the...

Inflammatory Genes and Psychological Factors Predict Induced Shoulder Pain Phenotype

PubMed Central

George, Steven Z.; Parr, Jeffrey J.; Wallace, Margaret R.; Wu, Samuel S.; Borsa, Paul A.; Dai, Yunfeng; Fillingim, Roger B.

2014-01-01

Purpose The pain experience has multiple influences but little is known about how specific biological and psychological factors interact to influence pain responses. The current study investigated the combined influences of genetic (pro-inflammatory) and psychological factors on several pre-clinical shoulder pain phenotypes. Methods An exercise-induced shoulder injury model was used, and a priori selected genetic (IL1B, TNF/LTA region, IL6 single nucleotide polymorphisms, SNPs) and psychological (anxiety, depressive symptoms, pain catastrophizing, fear of pain, kinesiophobia) factors were included as the predictors of interest. The phenotypes were pain intensity (5-day average and peak reported on numerical rating scale), upper-extremity disability (5-day average and peak reported on the QuickDASH instrument), and duration of shoulder pain (in days). Results After controlling for age, sex, and race, the genetic and psychological predictors were entered separately as main effects and interaction terms in regression models for each pain phenotype. Results from the recruited cohort (n = 190) indicated strong statistical evidence for the interactions between 1) TNF/LTA SNP rs2229094 and depressive symptoms for average pain intensity and duration and 2) IL1B two-SNP diplotype and kinesiophobia for average shoulder pain intensity. Moderate statistical evidence for prediction of additional shoulder pain phenotypes included interactions of kinesiophobia, fear of pain, or depressive symptoms with TNF/LTA rs2229094 and IL1B. Conclusion These findings support the combined predictive ability of specific genetic and psychological factors for shoulder pain phenotypes by revealing novel combinations that may merit further investigation in clinical cohorts, to determine their involvement in the transition from acute to chronic pain conditions. PMID:24598699

Genome-wide prediction of childhood asthma and related phenotypes in a longitudinal birth cohort

PubMed Central

Spycher, Ben D.; Henderson, John; Granell, Raquel; Evans, David M.; Smith, George Davey; Timpson, Nicholas J.; Sterne, Jonathan A. C.

2016-01-01

Background Childhood wheezing and asthma vary greatly in clinical presentation and time course. The extent to which phenotypic variation reflects heterogeneity in disease pathways is unclear. Objective To assess the extent to which single nucleotide polymorphisms (SNPs) associated with childhood asthma in a genome-wide association study are predictive of asthma-related phenotypes. Methods In 8365 children from a population based birth cohort, the Avon Longitudinal Study of Parents and Children, allelic scores were derived based on between 10 and 215,443 SNPs ranked according to inverse of the p-value for their association with physician diagnosed asthma in an independent genome-wide association study (6176 cases and 7111 controls). We assessed the predictive value of allelic scores for asthma-related outcomes at age 7-9 years (physician’s diagnosis, longitudinal wheezing phenotypes, and measurements of pulmonary function, bronchial responsiveness and atopy). Results Scores based on the 46 highest-ranked SNPs were associated with the symptom-based phenotypes persistent (P<10-11, area under ROC curve (AUC)=0.59) and intermediate onset (P<10-3, AUC=0.58) wheeze. Among lower-ranked SNPs (ranks 21,545-46,416), there was evidence for associations with diagnosed asthma (P<10-4, AUC=0.54) and atopy (P<10-5, AUC=0.55). We found little evidence of associations with transient early wheezing, reduced pulmonary function or non-asthma phenotypes. Conclusion The genetic origins of asthma are diverse and: some pathways are specific to wheezing syndromes while others are shared with atopy and bronchial hyper-responsiveness. Out study also provides evidence of aetiological differences among wheezing syndromes. PMID:22846752

A strategy to apply quantitative epistasis analysis on developmental traits.

PubMed

Labocha, Marta K; Yuan, Wang; Aleman-Meza, Boanerges; Zhong, Weiwei

2017-05-15

Genetic interactions are keys to understand complex traits and evolution. Epistasis analysis is an effective method to map genetic interactions. Large-scale quantitative epistasis analysis has been well established for single cells. However, there is a substantial lack of such studies in multicellular organisms and their complex phenotypes such as development. Here we present a method to extend quantitative epistasis analysis to developmental traits. In the nematode Caenorhabditis elegans, we applied RNA interference on mutants to inactivate two genes, used an imaging system to quantitatively measure phenotypes, and developed a set of statistical methods to extract genetic interactions from phenotypic measurement. Using two different C. elegans developmental phenotypes, body length and sex ratio, as examples, we showed that this method could accommodate various metazoan phenotypes with performances comparable to those methods in single cell growth studies. Comparing with qualitative observations, this method of quantitative epistasis enabled detection of new interactions involving subtle phenotypes. For example, several sex-ratio genes were found to interact with brc-1 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively. We confirmed the brc-1 interactions with the following genes in DNA damage response: C34F6.1, him-3 (ortholog of HORMAD1, HORMAD2), sdc-1, and set-2 (ortholog of SETD1A, SETD1B, KMT2C, KMT2D), validating the effectiveness of our method in detecting genetic interactions. We developed a reliable, high-throughput method for quantitative epistasis analysis of developmental phenotypes.

Grip strength as a frailty diagnostic component in geriatric inpatients.

PubMed

Dudzińska-Griszek, Joanna; Szuster, Karolina; Szewieczek, Jan

2017-01-01

Frailty has emerged as a key medical syndrome predictive of comorbidity, disability, institutionalization and death. As a component of the five frailty phenotype diagnostic criteria, patient grip strength deserves attention as a simple and objective measure of the frailty syndrome. The aim of this study was to assess conditions that influence grip strength in geriatric inpatients. The study group consisted of 80 patients aged 78.6±7.0 years [Formula: see text], with 68.8% women, admitted to the Department of Geriatrics. A comprehensive geriatric assessment was complemented with assessment for the frailty phenotype as described by Fried et al for all patients in the study group. Functional assessment included Barthel Index of Activities of Daily Living (Barthel Index), Instrumental Activities of Daily Living Scale and Mini-Mental State Examination. Three or more frailty criteria were positive in 32 patients (40%), while 56 subjects (70%) fulfilled the frailty criterion of weakness (grip strength test). Multivariate linear regression analysis revealed that two independent measures showed positive association with grip strength - Mini-Mental State Examination score (β=0.239; P =0.001) and statin use (β=0.213; P =0.002) - and four independent measures were negatively associated with grip strength - female sex (β=-0.671; P <0.001), C-reactive protein (β=-0.253; P <0.001), prior myocardial infarction (β=-0.190; P =0.006) and use of an antidepressant (β=-0.163; P =0.018). Low physical activity was identified as the only independent qualitative frailty component associated with 2-year mortality in multivariate logistic regression analysis after adjustment for age and sex (odds ratio =6.000; 95% CI =1.357-26.536; P =0.018). Cognitive function, somatic comorbidity and medical treatment affect grip strength as a measure of physical frailty in geriatric inpatients. Grip strength was not predictive of 2-year mortality in this group.

Phenotypic plasticity and specialization in clonal versus non-clonal plants: A data synthesis

NASA Astrophysics Data System (ADS)

Fazlioglu, Fatih; Bonser, Stephen P.

2016-11-01

Reproductive strategies can be associated with ecological specialization and generalization. Clonal plants produce lineages adapted to the maternal habitat that can lead to specialization. However, clonal plants frequently display high phenotypic plasticity (e.g. clonal foraging for resources), factors linked to ecological generalization. Alternately, sexual reproduction can be associated with generalization via increasing genetic variation or specialization through rapid adaptive evolution. Moreover, specializing to high or low quality habitats can determine how phenotypic plasticity is expressed in plants. The specialization hypothesis predicts that specialization to good environments results in high performance trait plasticity and specialization to bad environments results in low performance trait plasticity. The interplay between reproductive strategies, phenotypic plasticity, and ecological specialization is important for understanding how plants adapt to variable environments. However, we currently have a poor understanding of these relationships. In this study, we addressed following questions: 1) Is there a relationship between phenotypic plasticity, specialization, and reproductive strategies in plants? 2) Do good habitat specialists express greater performance trait plasticity than bad habitat specialists? We searched the literature for studies examining plasticity for performance traits and functional traits in clonal and non-clonal plant species from different habitat types. We found that non-clonal (obligate sexual) plants expressed greater performance trait plasticity and functional trait plasticity than clonal plants. That is, non-clonal plants exhibited a specialist strategy where they perform well only in a limited range of habitats. Clonal plants expressed less performance loss across habitats and a more generalist strategy. In addition, specialization to good habitats did not result in greater performance trait plasticity. This result was contrary to the predictions of the specialization hypothesis. Overall, reproductive strategies are associated with ecological specialization or generalization through phenotypic plasticity. While specialization is common in plant populations, the evolution of specialization does not control the nature of phenotypic plasticity as predicted under the specialization hypothesis.

Measuring the effect of inter-study variability on estimating prediction error.

PubMed

Ma, Shuyi; Sung, Jaeyun; Magis, Andrew T; Wang, Yuliang; Geman, Donald; Price, Nathan D

2014-01-01

The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in "batch-effects") and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies. Here we quantify the impact of these combined "study-effects" on a disease signature's predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance. As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification. We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when "sufficient" diversity has been achieved for learning a molecular signature likely to translate without significant loss of accuracy to new clinical settings.

Learning about Ecological Systems by Constructing Qualitative Models with DynaLearn

ERIC Educational Resources Information Center

Leiba, Moshe; Zuzovsky, Ruth; Mioduser, David; Benayahu, Yehuda; Nachmias, Rafi

2012-01-01

A qualitative model of a system is an abstraction that captures ordinal knowledge and predicts the set of qualitatively possible behaviours of the system, given a qualitative description of its structure and initial state. This paper examines an innovative approach to science education using an interactive learning environment that supports…

Genomic selection in sugar beet breeding populations

PubMed Central

2013-01-01

Background Genomic selection exploits dense genome-wide marker data to predict breeding values. In this study we used a large sugar beet population of 924 lines representing different germplasm types present in breeding populations: unselected segregating families and diverse lines from more advanced stages of selection. All lines have been intensively phenotyped in multi-location field trials for six agronomically important traits and genotyped with 677 SNP markers. Results We used ridge regression best linear unbiased prediction in combination with fivefold cross-validation and obtained high prediction accuracies for all except one trait. In addition, we investigated whether a calibration developed based on a training population composed of diverse lines is suited to predict the phenotypic performance within families. Our results show that the prediction accuracy is lower than that obtained within the diverse set of lines, but comparable to that obtained by cross-validation within the respective families. Conclusions The results presented in this study suggest that a training population derived from intensively phenotyped and genotyped diverse lines from a breeding program does hold potential to build up robust calibration models for genomic selection. Taken together, our results indicate that genomic selection is a valuable tool and can thus complement the genomics toolbox in sugar beet breeding. PMID:24047500

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

PubMed Central

Overby, Casey Lynnette; Pathak, Jyotishman; Gottesman, Omri; Haerian, Krystl; Perotte, Adler; Murphy, Sean; Bruce, Kevin; Johnson, Stephanie; Talwalkar, Jayant; Shen, Yufeng; Ellis, Steve; Kullo, Iftikhar; Chute, Christopher; Friedman, Carol; Bottinger, Erwin; Hripcsak, George; Weng, Chunhua

2013-01-01

Objective To describe a collaborative approach for developing an electronic health record (EHR) phenotyping algorithm for drug-induced liver injury (DILI). Methods We analyzed types and causes of differences in DILI case definitions provided by two institutions—Columbia University and Mayo Clinic; harmonized two EHR phenotyping algorithms; and assessed the performance, measured by sensitivity, specificity, positive predictive value, and negative predictive value, of the resulting algorithm at three institutions except that sensitivity was measured only at Columbia University. Results Although these sites had the same case definition, their phenotyping methods differed by selection of liver injury diagnoses, inclusion of drugs cited in DILI cases, laboratory tests assessed, laboratory thresholds for liver injury, exclusion criteria, and approaches to validating phenotypes. We reached consensus on a DILI phenotyping algorithm and implemented it at three institutions. The algorithm was adapted locally to account for differences in populations and data access. Implementations collectively yielded 117 algorithm-selected cases and 23 confirmed true positive cases. Discussion Phenotyping for rare conditions benefits significantly from pooling data across institutions. Despite the heterogeneity of EHRs and varied algorithm implementations, we demonstrated the portability of this algorithm across three institutions. The performance of this algorithm for identifying DILI was comparable with other computerized approaches to identify adverse drug events. Conclusions Phenotyping algorithms developed for rare and complex conditions are likely to require adaptive implementation at multiple institutions. Better approaches are also needed to share algorithms. Early agreement on goals, data sources, and validation methods may improve the portability of the algorithms. PMID:23837993

Transcriptomic and macroevolutionary evidence for phenotypic uncoupling between frog life history phases

PubMed Central

Wollenberg Valero, Katharina C.; Garcia-Porta, Joan; Rodríguez, Ariel; Arias, Mónica; Shah, Abhijeet; Randrianiaina, Roger Daniel; Brown, Jason L.; Glaw, Frank; Amat, Felix; Künzel, Sven; Metzler, Dirk; Isokpehi, Raphael D.; Vences, Miguel

2017-01-01

Anuran amphibians undergo major morphological transitions during development, but the contribution of their markedly different life-history phases to macroevolution has rarely been analysed. Here we generate testable predictions for coupling versus uncoupling of phenotypic evolution of tadpole and adult life-history phases, and for the underlying expression of genes related to morphological feature formation. We test these predictions by combining evidence from gene expression in two distantly related frogs, Xenopus laevis and Mantidactylus betsileanus, with patterns of morphological evolution in the entire radiation of Madagascan mantellid frogs. Genes linked to morphological structure formation are expressed in a highly phase-specific pattern, suggesting uncoupling of phenotypic evolution across life-history phases. This gene expression pattern agrees with uncoupled rates of trait evolution among life-history phases in the mantellids, which we show to have undergone an adaptive radiation. Our results validate a prevalence of uncoupling in the evolution of tadpole and adult phenotypes of frogs. PMID:28504275

Challenges Facing Crop Production And (Some) Potential Solutions

NASA Astrophysics Data System (ADS)

Schnable, P. S.

2017-12-01

To overcome some of the myriad challenges facing sustainable crop production we are seeking to develop statistical models that will predict crop performance in diverse agronomic environments. Crop phenotypes such as yield and drought tolerance are controlled by genotype, environment (considered broadly) and their interaction (GxE). As a consequence of the next generation sequencing revolution genotyping data are now available for a wide diversity of accessions in each of the major crops. The necessary volumes of phenotypic data, however, remain limiting and our understanding of molecular basis of GxE is minimal. To address this limitation, we are collaborating with engineers to construct new sensors and robots to automatically collect large volumes of phenotypic data. Two types of high-throughput, high-resolution, field-based phenotyping systems and new sensors will be described. Some of these technologies will be introduced within the context of the Genomes to Fields Initiative. Progress towards developing predictive models will be briefly summarized. An administrative structure that fosters transdisciplinary collaborations will be briefly described.

Genetic determinants of freckle occurrence in the Spanish population: Towards ephelides prediction from human DNA samples.

PubMed

Hernando, Barbara; Ibañez, Maria Victoria; Deserio-Cuesta, Julio Alberto; Soria-Navarro, Raquel; Vilar-Sastre, Inca; Martinez-Cadenas, Conrado

2018-03-01

Prediction of human pigmentation traits, one of the most differentiable externally visible characteristics among individuals, from biological samples represents a useful tool in the field of forensic DNA phenotyping. In spite of freckling being a relatively common pigmentation characteristic in Europeans, little is known about the genetic basis of this largely genetically determined phenotype in southern European populations. In this work, we explored the predictive capacity of eight freckle and sunlight sensitivity-related genes in 458 individuals (266 non-freckled controls and 192 freckled cases) from Spain. Four loci were associated with freckling (MC1R, IRF4, ASIP and BNC2), and female sex was also found to be a predictive factor for having a freckling phenotype in our population. After identifying the most informative genetic variants responsible for human ephelides occurrence in our sample set, we developed a DNA-based freckle prediction model using a multivariate regression approach. Once developed, the capabilities of the prediction model were tested by a repeated 10-fold cross-validation approach. The proportion of correctly predicted individuals using the DNA-based freckle prediction model was 74.13%. The implementation of sex into the DNA-based freckle prediction model slightly improved the overall prediction accuracy by 2.19% (76.32%). Further evaluation of the newly-generated prediction model was performed by assessing the model's performance in a new cohort of 212 Spanish individuals, reaching a classification success rate of 74.61%. Validation of this prediction model may be carried out in larger populations, including samples from different European populations. Further research to validate and improve this newly-generated freckle prediction model will be needed before its forensic application. Together with DNA tests already validated for eye and hair colour prediction, this freckle prediction model may lead to a substantially more detailed physical description of unknown individuals from DNA found at the crime scene. Copyright © 2017 Elsevier B.V. All rights reserved.

Integrating environmental and genetic effects to predict responses of tree populations to climate.

PubMed

Wang, Tongli; O'Neill, Gregory A; Aitken, Sally N

2010-01-01

Climate is a major environmental factor affecting the phenotype of trees and is also a critical agent of natural selection that has molded among-population genetic variation. Population response functions describe the environmental effect of planting site climates on the performance of a single population, whereas transfer functions describe among-population genetic variation molded by natural selection for climate. Although these approaches are widely used to predict the responses of trees to climate change, both have limitations. We present a novel approach that integrates both genetic and environmental effects into a single "universal response function" (URF) to better predict the influence of climate on phenotypes. Using a large lodgepole pine (Pinus contorta Dougl. ex Loud.) field transplant experiment composed of 140 populations planted on 62 sites to demonstrate the methodology, we show that the URF makes full use of data from provenance trials to: (1) improve predictions of climate change impacts on phenotypes; (2) reduce the size and cost of future provenance trials without compromising predictive power; (3) more fully exploit existing, less comprehensive provenance tests; (4) quantify and compare environmental and genetic effects of climate on population performance; and (5) predict the performance of any population growing in any climate. Finally, we discuss how the last attribute allows the URF to be used as a mechanistic model to predict population and species ranges for the future and to guide assisted migration of seed for reforestation, restoration, or afforestation and genetic conservation in a changing climate.

Prospects of Genomic Prediction in the USDA Soybean Germplasm Collection: Historical Data Creates Robust Models for Enhancing Selection of Accessions.

PubMed

Jarquin, Diego; Specht, James; Lorenz, Aaron

2016-08-09

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K single nucleotide polymorphism data collected on 18,480 domesticated soybean (Glycine max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Second, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7608 nonphenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for nonphenotyped accessions, with no nonphenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions. Copyright © 2016 Jarquin et al.

Data Sources for Trait Databases: Comparing the Phenomic Content of Monographs and Evolutionary Matrices.

PubMed

Dececchi, T Alex; Mabee, Paula M; Blackburn, David C

2016-01-01

Databases of organismal traits that aggregate information from one or multiple sources can be leveraged for large-scale analyses in biology. Yet the differences among these data streams and how well they capture trait diversity have never been explored. We present the first analysis of the differences between phenotypes captured in free text of descriptive publications ('monographs') and those used in phylogenetic analyses ('matrices'). We focus our analysis on osteological phenotypes of the limbs of four extinct vertebrate taxa critical to our understanding of the fin-to-limb transition. We find that there is low overlap between the anatomical entities used in these two sources of phenotype data, indicating that phenotypes represented in matrices are not simply a subset of those found in monographic descriptions. Perhaps as expected, compared to characters found in matrices, phenotypes in monographs tend to emphasize descriptive and positional morphology, be somewhat more complex, and relate to fewer additional taxa. While based on a small set of focal taxa, these qualitative and quantitative data suggest that either source of phenotypes alone will result in incomplete knowledge of variation for a given taxon. As a broader community develops to use and expand databases characterizing organismal trait diversity, it is important to recognize the limitations of the data sources and develop strategies to more fully characterize variation both within species and across the tree of life.

Data Sources for Trait Databases: Comparing the Phenomic Content of Monographs and Evolutionary Matrices

PubMed Central

Dececchi, T. Alex; Mabee, Paula M.; Blackburn, David C.

2016-01-01

Databases of organismal traits that aggregate information from one or multiple sources can be leveraged for large-scale analyses in biology. Yet the differences among these data streams and how well they capture trait diversity have never been explored. We present the first analysis of the differences between phenotypes captured in free text of descriptive publications (‘monographs’) and those used in phylogenetic analyses (‘matrices’). We focus our analysis on osteological phenotypes of the limbs of four extinct vertebrate taxa critical to our understanding of the fin-to-limb transition. We find that there is low overlap between the anatomical entities used in these two sources of phenotype data, indicating that phenotypes represented in matrices are not simply a subset of those found in monographic descriptions. Perhaps as expected, compared to characters found in matrices, phenotypes in monographs tend to emphasize descriptive and positional morphology, be somewhat more complex, and relate to fewer additional taxa. While based on a small set of focal taxa, these qualitative and quantitative data suggest that either source of phenotypes alone will result in incomplete knowledge of variation for a given taxon. As a broader community develops to use and expand databases characterizing organismal trait diversity, it is important to recognize the limitations of the data sources and develop strategies to more fully characterize variation both within species and across the tree of life. PMID:27191170

Local adaptation and the evolution of phenotypic plasticity in Trinidadian guppies (Poecilia reticulata).

PubMed

Torres-Dowdall, Julián; Handelsman, Corey A; Reznick, David N; Ghalambor, Cameron K

2012-11-01

Divergent selection pressures across environments can result in phenotypic differentiation that is due to local adaptation, phenotypic plasticity, or both. Trinidadian guppies exhibit local adaptation to the presence or absence of predators, but the degree to which predator-induced plasticity contributes to population differentiation is less clear. We conducted common garden experiments on guppies obtained from two drainages containing populations adapted to high- and low-predation environments. We reared full-siblings from all populations in treatments simulating the presumed ancestral (predator cues present) and derived (predator cues absent) conditions and measured water column use, head morphology, and size at maturity. When reared in presence of predator cues, all populations had phenotypes that were typical of a high-predation ecotype. However, when reared in the absence of predator cues, guppies from high- and low-predation regimes differed in head morphology and size at maturity; the qualitative nature of these differences corresponded to those that characterize adaptive phenotypes in high- versus low-predation environments. Thus, divergence in plasticity is due to phenotypic differences between high- and low-predation populations when reared in the absence of predator cues. These results suggest that plasticity might initially play an important role during colonization of novel environments, and then evolve as a by-product of adaptation to the derived environment. © 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.

HIV-1 with Multiple CCR5/CXCR4 Chimeric Receptor Use Is Predictive of Immunological Failure in Infected Children

PubMed Central

Cavarelli, Mariangela; Karlsson, Ingrid; Zanchetta, Marisa; Antonsson, Liselotte; Plebani, Anna; Giaquinto, Carlo; Fenyö, Eva Maria; De Rossi, Anita; Scarlatti, Gabriella

2008-01-01

Background HIV-1 R5 viruses are characterized by a large phenotypic variation, that is reflected by the mode of coreceptor use. The ability of R5 HIV-1 to infect target cells expressing chimeric receptors between CCR5 and CXCR4 (R5broad viruses), was shown to correlate with disease stage in HIV-1 infected adults. Here, we ask the question whether phenotypic variation of R5 viruses could play a role also in mother-to-child transmission (MTCT) of HIV-1 and pediatric disease progression. Methodology/Principal Findings Viral isolates obtained from a total of 59 HIV-1 seropositive women (24 transmitting and 35 non transmitting) and 28 infected newborn children, were used to infect U87.CD4 cells expressing wild type or six different CCR5/CXCR4 chimeric receptors. HIV-1 isolates obtained from newborn infants had predominantly R5narrow phenotype (n = 20), but R5broad and R5X4 viruses were also found in seven and one case, respectively. The presence of R5broad and R5X4 phenotypes correlated significantly with a severe decline of the CD4+ T cells (CDC stage 3) or death within 2 years of age. Forty-three percent of the maternal R5 isolates displayed an R5broad phenotype, however, the presence of the R5broad virus was not predictive for MTCT of HIV-1. Of interest, while only 1 of 5 mothers with an R5X4 virus transmitted the dualtropic virus, 5 of 6 mothers carrying R5broad viruses transmitted viruses with a similar broad chimeric coreceptor usage. Thus, the maternal R5broad phenotype was largely preserved during transmission and could be predictive of the phenotype of the newborn's viral variant. Conclusions/Significance Our results show that R5broad viruses are not hampered in transmission. When transmitted, immunological failure occurs earlier than in children infected with HIV-1 of R5narrow phenotype. We believe that this finding is of utmost relevance for therapeutic interventions in pediatric HIV-1 infection. PMID:18820725

HIV-1 with multiple CCR5/CXCR4 chimeric receptor use is predictive of immunological failure in infected children.

PubMed

Cavarelli, Mariangela; Karlsson, Ingrid; Zanchetta, Marisa; Antonsson, Liselotte; Plebani, Anna; Giaquinto, Carlo; Fenyö, Eva Maria; De Rossi, Anita; Scarlatti, Gabriella

2008-09-29

HIV-1 R5 viruses are characterized by a large phenotypic variation, that is reflected by the mode of coreceptor use. The ability of R5 HIV-1 to infect target cells expressing chimeric receptors between CCR5 and CXCR4 (R5(broad) viruses), was shown to correlate with disease stage in HIV-1 infected adults. Here, we ask the question whether phenotypic variation of R5 viruses could play a role also in mother-to-child transmission (MTCT) of HIV-1 and pediatric disease progression. Viral isolates obtained from a total of 59 HIV-1 seropositive women (24 transmitting and 35 non transmitting) and 28 infected newborn children, were used to infect U87.CD4 cells expressing wild type or six different CCR5/CXCR4 chimeric receptors. HIV-1 isolates obtained from newborn infants had predominantly R5(narrow) phenotype (n = 20), but R5(broad) and R5X4 viruses were also found in seven and one case, respectively. The presence of R5(broad) and R5X4 phenotypes correlated significantly with a severe decline of the CD4+ T cells (CDC stage 3) or death within 2 years of age. Forty-three percent of the maternal R5 isolates displayed an R5(broad) phenotype, however, the presence of the R5(broad) virus was not predictive for MTCT of HIV-1. Of interest, while only 1 of 5 mothers with an R5X4 virus transmitted the dualtropic virus, 5 of 6 mothers carrying R5(broad) viruses transmitted viruses with a similar broad chimeric coreceptor usage. Thus, the maternal R5(broad) phenotype was largely preserved during transmission and could be predictive of the phenotype of the newborn's viral variant. Our results show that R5(broad) viruses are not hampered in transmission. When transmitted, immunological failure occurs earlier than in children infected with HIV-1 of R5(narrow) phenotype. We believe that this finding is of utmost relevance for therapeutic interventions in pediatric HIV-1 infection.

Towards an informative mutant phenotype for every bacterial gene

DOE PAGES

Deutschbauer, Adam; Price, Morgan N.; Wetmore, Kelly M.; ...

2014-08-11

Mutant phenotypes provide strong clues to the functions of the underlying genes and could allow annotation of the millions of sequenced yet uncharacterized bacterial genes. However, it is not known how many genes have a phenotype under laboratory conditions, how many phenotypes are biologically interpretable for predicting gene function, and what experimental conditions are optimal to maximize the number of genes with a phenotype. To address these issues, we measured the mutant fitness of 1,586 genes of the ethanol-producing bacterium Zymomonas mobilis ZM4 across 492 diverse experiments and found statistically significant phenotypes for 89% of all assayed genes. Thus, inmore » Z. mobilis, most genes have a functional consequence under laboratory conditions. We demonstrate that 41% of Z. mobilis genes have both a strong phenotype and a similar fitness pattern (cofitness) to another gene, and are therefore good candidates for functional annotation using mutant fitness. Among 502 poorly characterized Z. mobilis genes, we identified a significant cofitness relationship for 174. For 57 of these genes without a specific functional annotation, we found additional evidence to support the biological significance of these gene-gene associations, and in 33 instances, we were able to predict specific physiological or biochemical roles for the poorly characterized genes. Last, we identified a set of 79 diverse mutant fitness experiments in Z. mobilis that are nearly as biologically informative as the entire set of 492 experiments. Therefore, our work provides a blueprint for the functional annotation of diverse bacteria using mutant fitness.« less

Selection on skewed characters and the paradox of stasis

PubMed Central

Bonamour, Suzanne; Teplitsky, Céline; Charmantier, Anne; Crochet, Pierre-André; Chevin, Luis-Miguel

2018-01-01

Observed phenotypic responses to selection in the wild often differ from predictions based on measurements of selection and genetic variance. An overlooked hypothesis to explain this paradox of stasis is that a skewed phenotypic distribution affects natural selection and evolution. We show through mathematical modelling that, when a trait selected for an optimum phenotype has a skewed distribution, directional selection is detected even at evolutionary equilibrium, where it causes no change in the mean phenotype. When environmental effects are skewed, Lande and Arnold’s (1983) directional gradient is in the direction opposite to the skew. In contrast, skewed breeding values can displace the mean phenotype from the optimum, causing directional selection in the direction of the skew. These effects can be partitioned out using alternative selection estimates based on average derivatives of individual relative fitness, or additive genetic covariances between relative fitness and trait (Robertson-Price identity). We assess the validity of these predictions using simulations of selection estimation under moderate samples size. Ecologically relevant traits may commonly have skewed distributions, as we here exemplify with avian laying date – repeatedly described as more evolutionarily stable than expected –, so this skewness should be accounted for when investigating evolutionary dynamics in the wild. PMID:28921508

Tightly congruent bursts of lineage and phenotypic diversification identified in a continental ant radiation.

PubMed

Price, Shauna L; Etienne, Rampal S; Powell, Scott

2016-04-01

Adaptive diversification is thought to be shaped by ecological opportunity. A prediction of this ecological process of diversification is that it should result in congruent bursts of lineage and phenotypic diversification, but few studies have found this expected association. Here, we study the relationship between rates of lineage diversification and body size evolution in the turtle ants, a diverse Neotropical clade. Using a near complete, time-calibrated phylogeny we investigated lineage diversification dynamics and body size disparity through model fitting analyses and estimation of per-lineage rates of cladogenesis and phenotypic evolution. We identify an exceptionally high degree of congruence between the high rates of lineage and body size diversification in a young clade undergoing renewed diversification in the ecologically distinct Chacoan biogeographical region of South America. It is likely that the region presented turtle ants with novel ecological opportunity, which facilitated a nested burst of diversification and phenotypic evolution within the group. Our results provide a compelling quantitative example of tight congruence between rates of lineage and phenotypic diversification, meeting the key predicted pattern of adaptive diversification shaped by ecological opportunity. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Candidate genes for COPD in two large data sets.

PubMed

Bakke, P S; Zhu, G; Gulsvik, A; Kong, X; Agusti, A G N; Calverley, P M A; Donner, C F; Levy, R D; Make, B J; Paré, P D; Rennard, S I; Vestbo, J; Wouters, E F M; Anderson, W; Lomas, D A; Silverman, E K; Pillai, S G

2011-02-01

Lack of reproducibility of findings has been a criticism of genetic association studies on complex diseases, such as chronic obstructive pulmonary disease (COPD). We selected 257 polymorphisms of 16 genes with reported or potential relationships to COPD and genotyped these variants in a case-control study that included 953 COPD cases and 956 control subjects. We explored the association of these polymorphisms to three COPD phenotypes: a COPD binary phenotype and two quantitative traits (post-bronchodilator forced expiratory volume in 1 s (FEV₁) % predicted and FEV₁/forced vital capacity (FVC)). The polymorphisms significantly associated to these phenotypes in this first study were tested in a second, family-based study that included 635 pedigrees with 1,910 individuals. Significant associations to the binary COPD phenotype in both populations were seen for STAT1 (rs13010343) and NFKBIB/SIRT2 (rs2241704) (p<0.05). Single-nucleotide polymorphisms rs17467825 and rs1155563 of the GC gene were significantly associated with FEV₁ % predicted and FEV₁/FVC, respectively, in both populations (p<0.05). This study has replicated associations to COPD phenotypes in the STAT1, NFKBIB/SIRT2 and GC genes in two independent populations, the associations of the former two genes representing novel findings.

Genomic prediction of the polled and horned phenotypes in Merino sheep.

PubMed

Duijvesteijn, Naomi; Bolormaa, Sunduimijid; Daetwyler, Hans D; van der Werf, Julius H J

2018-05-22

In horned sheep breeds, breeding for polledness has been of interest for decades. The objective of this study was to improve prediction of the horned and polled phenotypes using horn scores classified as polled, scurs, knobs or horns. Derived phenotypes polled/non-polled (P/NP) and horned/non-horned (H/NH) were used to test four different strategies for prediction in 4001 purebred Merino sheep. These strategies include the use of single 'single nucleotide polymorphism' (SNP) genotypes, multiple-SNP haplotypes, genome-wide and chromosome-wide genomic best linear unbiased prediction and information from imputed sequence variants from the region including the RXFP2 gene. Low-density genotypes of these animals were imputed to the Illumina Ovine high-density (600k) chip and the 1.78-kb insertion polymorphism in RXFP2 was included in the imputation process to whole-genome sequence. We evaluated the mode of inheritance and validated models by a fivefold cross-validation and across- and between-family prediction. The most significant SNPs for prediction of P/NP and H/NH were OAR10_29546872.1 and OAR10_29458450, respectively, located on chromosome 10 close to the 1.78-kb insertion at 29.5 Mb. The mode of inheritance included an additive effect and a sex-dependent effect for dominance for P/NP and a sex-dependent additive and dominance effect for H/NH. Models with the highest prediction accuracies for H/NH used either single SNPs or 3-SNP haplotypes and included a polygenic effect estimated based on traditional pedigree relationships. Prediction accuracies for H/NH were 0.323 for females and 0.725 for males. For predicting P/NP, the best models were the same as for H/NH but included a genomic relationship matrix with accuracies of 0.713 for females and 0.620 for males. Our results show that prediction accuracy is high using a single SNP, but does not reach 1 since the causative mutation is not genotyped. Incomplete penetrance or allelic heterogeneity, which can influence expression of the phenotype, may explain why prediction accuracy did not approach 1 with any of the genetic models tested here. Nevertheless, a breeding program to eradicate horns from Merino sheep can be effective by selecting genotypes GG of SNP OAR10_29458450 or TT of SNP OAR10_29546872.1 since all sheep with these genotypes will be non-horned.

[Phenotypic trends and breeding values for canine congenital sensorineural deafness in Dalmatian dogs].

PubMed

Blum, Meike; Distl, Ottmar

2014-01-01

In the present study, breeding values for canine congenital sensorineural deafness, the presence of blue eyes and patches have been predicted using multivariate animal models to test the reliability of the breeding values for planned matings. The dataset consisted of 6669 German Dalmatian dogs born between 1988 and 2009. Data were provided by the Dalmatian kennel clubs which are members of the German Association for Dog Breeding and Husbandry (VDH). The hearing status for all dogs was evaluated using brainstem auditory evoked potentials. The reliability using the prediction error variance of breeding values and the realized reliability of the prediction of the phenotype of future progeny born in each one year between 2006 and 2009 were used as parameters to evaluate the goodness of prediction through breeding values. All animals from the previous birth years were used for prediction of the breeding values of the progeny in each of the up-coming birth years. The breeding values based on pedigree records achieved an average reliability of 0.19 for the future 1951 progeny. The predictive accuracy (R2) for the hearing status of single future progeny was at 1.3%. Combining breeding values for littermates increased the predictive accuracy to 3.5%. Corresponding values for maternal and paternal half-sib groups were at 3.2 and 7.3%. The use of breeding values for planned matings increases the phenotypic selection response over mass selection. The breeding values of sires may be used for planned matings because reliabilities and predictive accuracies for future paternal progeny groups were highest.

The Broad Autism Phenotype Predicts Relationship Outcomes in Newly Formed College Roommates

ERIC Educational Resources Information Center

Faso, Daniel J.; Corretti, Conrad A.; Ackerman, Robert A.; Sasson, Noah J.

2016-01-01

Although previous studies have reported that the broad autism phenotype is associated with reduced relationship quality within established relationships, understanding how this association emerges requires assessment prior to relationship development. In the present longitudinal study, college roommates with minimal familiarity prior to…

Sensitivity analysis of gene ranking methods in phenotype prediction.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan L; Sonis, Stephen T

2016-12-01

It has become clear that noise generated during the assay and analytical processes has the ability to disrupt accurate interpretation of genomic studies. Not only does such noise impact the scientific validity and costs of studies, but when assessed in the context of clinically translatable indications such as phenotype prediction, it can lead to inaccurate conclusions that could ultimately impact patients. We applied a sequence of ranking methods to damp noise associated with microarray outputs, and then tested the utility of the approach in three disease indications using publically available datasets. This study was performed in three phases. We first theoretically analyzed the effect of noise in phenotype prediction problems showing that it can be expressed as a modeling error that partially falsifies the pathways. Secondly, via synthetic modeling, we performed the sensitivity analysis for the main gene ranking methods to different types of noise. Finally, we studied the predictive accuracy of the gene lists provided by these ranking methods in synthetic data and in three different datasets related to cancer, rare and neurodegenerative diseases to better understand the translational aspects of our findings. In the case of synthetic modeling, we showed that Fisher's Ratio (FR) was the most robust gene ranking method in terms of precision for all the types of noise at different levels. Significance Analysis of Microarrays (SAM) provided slightly lower performance and the rest of the methods (fold change, entropy and maximum percentile distance) were much less precise and accurate. The predictive accuracy of the smallest set of high discriminatory probes was similar for all the methods in the case of Gaussian and Log-Gaussian noise. In the case of class assignment noise, the predictive accuracy of SAM and FR is higher. Finally, for real datasets (Chronic Lymphocytic Leukemia, Inclusion Body Myositis and Amyotrophic Lateral Sclerosis) we found that FR and SAM provided the highest predictive accuracies with the smallest number of genes. Biological pathways were found with an expanded list of genes whose discriminatory power has been established via FR. We have shown that noise in expression data and class assignment partially falsifies the sets of discriminatory probes in phenotype prediction problems. FR and SAM better exploit the principle of parsimony and are able to find subsets with less number of high discriminatory genes. The predictive accuracy and the precision are two different metrics to select the important genes, since in the presence of noise the most predictive genes do not completely coincide with those that are related to the phenotype. Based on the synthetic results, FR and SAM are recommended to unravel the biological pathways that are involved in the disease development. Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

PubMed

Yang, Lei; Zhao, Xudong; Tang, Xianglong

2014-01-01

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

High-throughput phenotyping of large wheat breeding nurseries using unmanned aerial system, remote sensing and GIS techniques

NASA Astrophysics Data System (ADS)

Haghighattalab, Atena

Wheat breeders are in a race for genetic gain to secure the future nutritional needs of a growing population. Multiple barriers exist in the acceleration of crop improvement. Emerging technologies are reducing these obstacles. Advances in genotyping technologies have significantly decreased the cost of characterizing the genetic make-up of candidate breeding lines. However, this is just part of the equation. Field-based phenotyping informs a breeder's decision as to which lines move forward in the breeding cycle. This has long been the most expensive and time-consuming, though most critical, aspect of breeding. The grand challenge remains in connecting genetic variants to observed phenotypes followed by predicting phenotypes based on the genetic composition of lines or cultivars. In this context, the current study was undertaken to investigate the utility of UAS in assessment field trials in wheat breeding programs. The major objective was to integrate remotely sensed data with geospatial analysis for high throughput phenotyping of large wheat breeding nurseries. The initial step was to develop and validate a semi-automated high-throughput phenotyping pipeline using a low-cost UAS and NIR camera, image processing, and radiometric calibration to build orthomosaic imagery and 3D models. The relationship between plot-level data (vegetation indices and height) extracted from UAS imagery and manual measurements were examined and found to have a high correlation. Data derived from UAS imagery performed as well as manual measurements while exponentially increasing the amount of data available. The high-resolution, high-temporal HTP data extracted from this pipeline offered the opportunity to develop a within season grain yield prediction model. Due to the variety in genotypes and environmental conditions, breeding trials are inherently spatial in nature and vary non-randomly across the field. This makes geographically weighted regression models a good choice as a geospatial prediction model. Finally, with the addition of georeferenced and spatial data integral in HTP and imagery, we were able to reduce the environmental effect from the data and increase the accuracy of UAS plot-level data. The models developed through this research, when combined with genotyping technologies, increase the volume, accuracy, and reliability of phenotypic data to better inform breeder selections. This increased accuracy with evaluating and predicting grain yield will help breeders to rapidly identify and advance the most promising candidate wheat varieties.

A phenotype of early infancy predicts reactivity of the amygdala in male adults.

PubMed

Schwartz, C E; Kunwar, P S; Greve, D N; Kagan, J; Snidman, N C; Bloch, R B

2012-10-01

One of the central questions that has occupied those disciplines concerned with human development is the nature of continuities and discontinuities from birth to maturity. The amygdala has a central role in the processing of novelty and emotion in the brain. Although there is considerable variability among individuals in the reactivity of the amygdala to novel and emotional stimuli, the origin of these individual differences is not well understood. Four-month old infants called high reactive (HR) demonstrate a distinctive pattern of vigorous motor activity and crying to specific unfamiliar visual, auditory and olfactory stimuli in the laboratory. Low-reactive infants show the complementary pattern. Here, we demonstrate that the HR infant phenotype predicts greater amygdalar reactivity to novel faces almost two decades later in adults. A prediction of individual differences in brain function at maturity can be made on the basis of a single behavioral assessment made in the laboratory at 4 months of age. This is the earliest known human behavioral phenotype that predicts individual differences in patterns of neural activity at maturity. These temperamental differences rooted in infancy may be relevant to understanding individual differences in vulnerability and resilience to clinical psychiatric disorder. Males who were HR infants showed particularly high levels of reactivity to novel faces in the amygdala that distinguished them as adults from all other sex/temperament subgroups, suggesting that their amygdala is particularly prone to engagement by unfamiliar faces. These findings underline the importance of taking gender into account when studying the developmental neurobiology of human temperament and anxiety disorders. The genetic study of behavioral and biologic intermediate phenotypes (or 'endophenotypes') indexing anxiety-proneness offers an important alternative to examining phenotypes based on clinically defined disorder. As the HR phenotype is characterized by specific patterns of reactivity to elemental visual, olfactory and auditory stimuli, well before complex social behaviors such as shyness or fearful interaction with strangers can be observed, it may be closer to underlying neurobiological mechanisms than behavioral profiles observed later in life. This possibility, together with the fact that environmental factors have less time to impact the 4-month phenotype, suggests that this temperamental profile may be a fruitful target for high-risk genetic studies.

Phenotypic plasticity with instantaneous but delayed switches.

PubMed

Utz, Margarete; Jeschke, Jonathan M; Loeschcke, Volker; Gabriel, Wilfried

2014-01-07

Phenotypic plasticity is a widespread phenomenon, allowing organisms to better adapt to changing environments. Most empirical and theoretical studies are restricted to irreversible plasticity where the expression of a specific phenotype is mostly determined during development. However, reversible plasticity is not uncommon; here, organisms are able to switch back and forth between phenotypes. We present two optimization models for the fitness of (i) non-plastic, (ii) irreversibly plastic, and (iii) reversibly plastic genotypes in a fluctuating environment. In one model, the fitness values of an organism during different life phases act together multiplicatively (so as to consider traits that are related to survival). The other model additionally considers additive effects (corresponding to traits related to fecundity). Both models yield qualitatively similar results. If the only costs of reversible plasticity are due to temporal maladaptation while switching between phenotypes, reversibility is virtually always advantageous over irreversibility, especially for slow environmental fluctuations. If reversibility implies an overall decreased fitness, then irreversibility is advantageous if the environment fluctuates quickly or if stress events last relatively short. Our results are supported by observations from different types of organisms and have implications for many basic and applied research questions, e.g., on invasive alien species. © 2013 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

Selection towards different adaptive optima drove the early diversification of locomotor phenotypes in the radiation of Neotropical geophagine cichlids.

PubMed

Astudillo-Clavijo, Viviana; Arbour, Jessica H; López-Fernández, Hernán

2015-05-01

Simpson envisaged a conceptual model of adaptive radiation in which lineages diversify into "adaptive zones" within a macroevolutionary adaptive landscape. However, only a handful of studies have empirically investigated this adaptive landscape and its consequences for our interpretation of the underlying mechanisms of phenotypic evolution. In fish radiations the evolution of locomotor phenotypes may represent an important dimension of ecomorphological diversification given the implications of locomotion for feeding and habitat use. Neotropical geophagine cichlids represent a newly identified adaptive radiation and provide a useful system for studying patterns of locomotor diversification and the implications of selective constraints on phenotypic divergence in general. We use multivariate ordination, models of phenotypic evolution and posterior predictive approaches to investigate the macroevolutionary adaptive landscape and test for evidence of early divergence of locomotor phenotypes in Geophagini. The evolution of locomotor phenotypes was characterized by selection towards at least two distinct adaptive peaks and the early divergence of modern morphological disparity. One adaptive peak included the benthic and epibenthic invertivores and was characterized by fishes with deep, laterally compressed bodies that optimize precise, slow-swimming manoeuvres. The second adaptive peak resulted from a shift in adaptive optima in the species-rich ram-feeding/rheophilic Crenicichla-Teleocichla clade and was characterized by species with streamlined bodies that optimize fast starts and rapid manoeuvres. Evolutionary models and posterior predictive approaches favoured an early shift to a new adaptive peak over decreasing rates of evolution as the underlying process driving the early divergence of locomotor phenotypes. The influence of multiple adaptive peaks on the divergence of locomotor phenotypes in Geophagini is compatible with the expectations of an ecologically driven adaptive radiation. This study confirms that the diversification of locomotor phenotypes represents an important dimension of phenotypic evolution in the geophagine adaptive radiation. It also suggests that the commonly observed early burst of phenotypic evolution during adaptive radiations may be better explained by the concentration of shifts to new adaptive peaks deep in the phylogeny rather than overall decreasing rates of evolution.

Towards precision medicine-based therapies for glioblastoma: interrogating human disease genomics and mouse phenotypes.

PubMed

Chen, Yang; Gao, Zhen; Wang, Bingcheng; Xu, Rong

2016-08-22

Glioblastoma (GBM) is the most common and aggressive brain tumors. It has poor prognosis even with optimal radio- and chemo-therapies. Since GBM is highly heterogeneous, drugs that target on specific molecular profiles of individual tumors may achieve maximized efficacy. Currently, the Cancer Genome Atlas (TCGA) projects have identified hundreds of GBM-associated genes. We develop a drug repositioning approach combining disease genomics and mouse phenotype data towards predicting targeted therapies for GBM. We first identified disease specific mouse phenotypes using the most recently discovered GBM genes. Then we systematically searched all FDA-approved drugs for candidates that share similar mouse phenotype profiles with GBM. We evaluated the ranks for approved and novel GBM drugs, and compared with an existing approach, which also use the mouse phenotype data but not the disease genomics data. We achieved significantly higher ranks for the approved and novel GBM drugs than the earlier approach. For all positive examples of GBM drugs, we achieved a median rank of 9.2 45.6 of the top predictions have been demonstrated effective in inhibiting the growth of human GBM cells. We developed a computational drug repositioning approach based on both genomic and phenotypic data. Our approach prioritized existing GBM drugs and outperformed a recent approach. Overall, our approach shows potential in discovering new targeted therapies for GBM.

IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

PubMed

Hepler, N Lance; Scheffler, Konrad; Weaver, Steven; Murrell, Ben; Richman, Douglas D; Burton, Dennis R; Poignard, Pascal; Smith, Davey M; Kosakovsky Pond, Sergei L

2014-09-01

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

Predicting adaptive phenotypes from multilocus genotypes in Sitka spruce (Picea sitchensis) using random forest.

PubMed

Holliday, Jason A; Wang, Tongli; Aitken, Sally

2012-09-01

Climate is the primary driver of the distribution of tree species worldwide, and the potential for adaptive evolution will be an important factor determining the response of forests to anthropogenic climate change. Although association mapping has the potential to improve our understanding of the genomic underpinnings of climatically relevant traits, the utility of adaptive polymorphisms uncovered by such studies would be greatly enhanced by the development of integrated models that account for the phenotypic effects of multiple single-nucleotide polymorphisms (SNPs) and their interactions simultaneously. We previously reported the results of association mapping in the widespread conifer Sitka spruce (Picea sitchensis). In the current study we used the recursive partitioning algorithm 'Random Forest' to identify optimized combinations of SNPs to predict adaptive phenotypes. After adjusting for population structure, we were able to explain 37% and 30% of the phenotypic variation, respectively, in two locally adaptive traits--autumn budset timing and cold hardiness. For each trait, the leading five SNPs captured much of the phenotypic variation. To determine the role of epistasis in shaping these phenotypes, we also used a novel approach to quantify the strength and direction of pairwise interactions between SNPs and found such interactions to be common. Our results demonstrate the power of Random Forest to identify subsets of markers that are most important to climatic adaptation, and suggest that interactions among these loci may be widespread.

Assessing the Efficiency of Phenotyping Early Traits in a Greenhouse Automated Platform for Predicting Drought Tolerance of Soybean in the Field

PubMed Central

Peirone, Laura S.; Pereyra Irujo, Gustavo A.; Bolton, Alejandro; Erreguerena, Ignacio; Aguirrezábal, Luis A. N.

2018-01-01

Conventional field phenotyping for drought tolerance, the most important factor limiting yield at a global scale, is labor-intensive and time-consuming. Automated greenhouse platforms can increase the precision and throughput of plant phenotyping and contribute to a faster release of drought tolerant varieties. The aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform, for predicting the drought tolerance of field grown soybean genotypes. A group of genotypes was evaluated, which showed variation in their drought susceptibility index (DSI) for final biomass and leaf area. A large number of traits were measured before and after the onset of a water deficit treatment, which were analyzed under several criteria: the significance of the regression with the DSI, phenotyping cost, earliness, and repeatability. The most efficient trait was found to be transpiration efficiency measured at 13 days after emergence. This trait was further tested in a second experiment with different water deficit intensities, and validated using a different set of genotypes against field data from a trial network in a third experiment. The framework applied in this work for assessing traits under different criteria could be helpful for selecting those most efficient for automated phenotyping. PMID:29774042

Association between cytochrome P450 2D6 polymorphisms and body fluid methamphetamine concentrations in Japanese forensic autopsy cases.

PubMed

Matsusue, Aya; Ikeda, Tomoya; Tani, Naoto; Waters, Brian; Hara, Kenji; Kashiwagi, Masayuki; Takayama, Mio; Ikematsu, Natsuki; Kubo, Shin-Ichi; Ishikawa, Takaki

2018-05-18

Methamphetamine (MA) is an illicit stimulant that affects the central nervous system. Cytochrome P450 2D6 (CYP2D6) plays an important role in MA metabolism. Numerous allelic variants confer substantial variation in CYP2D6 activity among individuals. In the present study, we examined the frequencies of CYP2D6 alleles, including CYP2D6*1, *2, *4, *5, *10, *14A, *14B, *18, and *36, and multiplication, in 82 forensic autopsy cases of MA abusers and 567 autopsy cases in which MA was not detected (controls). Ultrarapid metabolizer (UM), extensive metabolizer (EM), intermediate metabolizer (IM), and poor metabolizer (PM) phenotypes were predicted from CYP2D6 genotypes. Of MA abusers, 64 subjects were predicted to be EM, 17 were IM, and 1 was UM. No MA abuser had the predicted PM phenotype. No significant differences in CYP2D6 phenotype frequencies were found between MA abusers and controls. MA and amphetamine (AMP) concentrations were measured in the right heart blood, left heart blood, peripheral external iliac blood, urine, pericardial fluid, and bone marrow of MA abusers. MA concentrations in urine and bone marrow were significantly higher in IM than in EM. AMP concentration was not associated with CYP2D6 phenotype in any body fluid. These results suggest that the MA concentration in body fluids is influenced by CYP2D6 phenotypes in the Japanese population. Copyright © 2018 Elsevier B.V. All rights reserved.

Using whole genome sequencing to identify resistance determinants and predict antimicrobial resistance phenotypes for year 2015 invasive pneumococcal disease isolates recovered in the United States.

PubMed

Metcalf, B J; Chochua, S; Gertz, R E; Li, Z; Walker, H; Tran, T; Hawkins, P A; Glennen, A; Lynfield, R; Li, Y; McGee, L; Beall, B

2016-12-01

Our whole genome sequence (WGS) pipeline was assessed for accurate prediction of antimicrobial phenotypes. For 2316 invasive pneumococcal isolates recovered during 2015 we compared WGS pipeline data to broth dilution testing (BDT) for 18 antimicrobials. For 11 antimicrobials categorical discrepancies were assigned when WGS-predicted MICs and BDT MICs predicted different categorizations for susceptibility, intermediate resistance or resistance, ranging from 0.9% (tetracycline) to 2.9% (amoxicillin). For β-lactam antibiotics, the occurrence of at least four-fold differences in MIC ranged from 0.2% (meropenem) to 1.0% (penicillin), although phenotypic retesting resolved 25%-78% of these discrepancies. Non-susceptibility to penicillin, predicted by penicillin-binding protein types, was 2.7% (non-meningitis criteria) and 23.8% (meningitis criteria). Other common resistance determinants included mef (475 isolates), ermB (191 isolates), ermB + mef (48 isolates), tetM (261 isolates) and cat (51 isolates). Additional accessory resistance genes (tetS, tet32, aphA-3, sat4) were rarely detected (one to three isolates). Rare core genome mutations conferring erythromycin-resistance included a two-codon rplD insertion (rplD69-KG-70) and the 23S rRNA A2061G substitution (six isolates). Intermediate cotrimoxazole-resistance was associated with one or two codon insertions within folP (238 isolates) or the folA I100L substitution (38 isolates), whereas full cotrimoxazole-resistance was attributed to alterations in both genes (172 isolates). The two levofloxacin-resistant isolates contained parC and/or gyrA mutations. Of 11 remaining isolates with moderately elevated MICs to both ciprofloxacin and levofloxacin, seven contained parC or gyrA mutations. The two rifampin-resistant isolates contained rpoB mutations. WGS-based antimicrobial phenotype prediction was an informative alternative to BDT for invasive pneumococci. Published by Elsevier Ltd.

Context-sensitive network-based disease genetics prediction and its implications in drug discovery.

PubMed

Chen, Yang; Xu, Rong

2017-04-01

Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach ( p

Comparative study of contrast-enhanced ultrasound qualitative and quantitative analysis for identifying benign and malignant breast tumor lumps.

PubMed

Liu, Jian; Gao, Yun-Hua; Li, Ding-Dong; Gao, Yan-Chun; Hou, Ling-Mi; Xie, Ting

2014-01-01

To compare the value of contrast-enhanced ultrasound (CEUS) qualitative and quantitative analysis in the identification of breast tumor lumps. Qualitative and quantitative indicators of CEUS for 73 cases of breast tumor lumps were retrospectively analyzed by univariate and multivariate approaches. Logistic regression was applied and ROC curves were drawn for evaluation and comparison. The CEUS qualitative indicator-generated regression equation contained three indicators, namely enhanced homogeneity, diameter line expansion and peak intensity grading, which demonstrated prediction accuracy for benign and malignant breast tumor lumps of 91.8%; the quantitative indicator-generated regression equation only contained one indicator, namely the relative peak intensity, and its prediction accuracy was 61.5%. The corresponding areas under the ROC curve for qualitative and quantitative analyses were 91.3% and 75.7%, respectively, which exhibited a statistically significant difference by the Z test (P<0.05). The ability of CEUS qualitative analysis to identify breast tumor lumps is better than with quantitative analysis.

Behavioural Phenotypes in the Classroom: A Qualitative Study of Parental and Teacher Knowledge of Classroom Guidelines and Teacher Views on Best Practices

ERIC Educational Resources Information Center

Reilly, Colin; Murtagh, Lelia; Senior, Joyce

2016-01-01

Research suggests that genetic syndromes associated with intellectual disability often have specific cognitive and behavioural profiles. It has been suggested that educational approaches need to reflect these profiles. Parents (n = 381) and teachers (n = 204) of children with one of four syndromes, fragile X syndrome, Prader-Willi syndrome,…

Qualitative simulation for process modeling and control

NASA Technical Reports Server (NTRS)

Dalle Molle, D. T.; Edgar, T. F.

1989-01-01

A qualitative model is developed for a first-order system with a proportional-integral controller without precise knowledge of the process or controller parameters. Simulation of the qualitative model yields all of the solutions to the system equations. In developing the qualitative model, a necessary condition for the occurrence of oscillatory behavior is identified. Initializations that cannot exhibit oscillatory behavior produce a finite set of behaviors. When the phase-space behavior of the oscillatory behavior is properly constrained, these initializations produce an infinite but comprehensible set of asymptotically stable behaviors. While the predictions include all possible behaviors of the real system, a class of spurious behaviors has been identified. When limited numerical information is included in the model, the number of predictions is significantly reduced.

Evolutionary history of human disease genes reveals phenotypic connections and comorbidity among genetic diseases

NASA Astrophysics Data System (ADS)

Park, Solip; Yang, Jae-Seong; Kim, Jinho; Shin, Young-Eun; Hwang, Jihye; Park, Juyong; Jang, Sung Key; Kim, Sanguk

2012-10-01

The extent to which evolutionary changes have impacted the phenotypic relationships among human diseases remains unclear. In this work, we report that phenotypically similar diseases are connected by the evolutionary constraints on human disease genes. Human disease groups can be classified into slowly or rapidly evolving classes, where the diseases in the slowly evolving class are enriched with morphological phenotypes and those in the rapidly evolving class are enriched with physiological phenotypes. Our findings establish a clear evolutionary connection between disease classes and disease phenotypes for the first time. Furthermore, the high comorbidity found between diseases connected by similar evolutionary constraints enables us to improve the predictability of the relative risk of human diseases. We find the evolutionary constraints on disease genes are a new layer of molecular connection in the network-based exploration of human diseases.

Evolutionary history of human disease genes reveals phenotypic connections and comorbidity among genetic diseases.

PubMed

Park, Solip; Yang, Jae-Seong; Kim, Jinho; Shin, Young-Eun; Hwang, Jihye; Park, Juyong; Jang, Sung Key; Kim, Sanguk

2012-01-01

The extent to which evolutionary changes have impacted the phenotypic relationships among human diseases remains unclear. In this work, we report that phenotypically similar diseases are connected by the evolutionary constraints on human disease genes. Human disease groups can be classified into slowly or rapidly evolving classes, where the diseases in the slowly evolving class are enriched with morphological phenotypes and those in the rapidly evolving class are enriched with physiological phenotypes. Our findings establish a clear evolutionary connection between disease classes and disease phenotypes for the first time. Furthermore, the high comorbidity found between diseases connected by similar evolutionary constraints enables us to improve the predictability of the relative risk of human diseases. We find the evolutionary constraints on disease genes are a new layer of molecular connection in the network-based exploration of human diseases.

Genomic scan as a tool for assessing the genetic component of phenotypic variance in wild populations.

PubMed

Herrera, Carlos M

2012-01-01

Methods for estimating quantitative trait heritability in wild populations have been developed in recent years which take advantage of the increased availability of genetic markers to reconstruct pedigrees or estimate relatedness between individuals, but their application to real-world data is not exempt from difficulties. This chapter describes a recent marker-based technique which, by adopting a genomic scan approach and focusing on the relationship between phenotypes and genotypes at the individual level, avoids the problems inherent to marker-based estimators of relatedness. This method allows the quantification of the genetic component of phenotypic variance ("degree of genetic determination" or "heritability in the broad sense") in wild populations and is applicable whenever phenotypic trait values and multilocus data for a large number of genetic markers (e.g., amplified fragment length polymorphisms, AFLPs) are simultaneously available for a sample of individuals from the same population. The method proceeds by first identifying those markers whose variation across individuals is significantly correlated with individual phenotypic differences ("adaptive loci"). The proportion of phenotypic variance in the sample that is statistically accounted for by individual differences in adaptive loci is then estimated by fitting a linear model to the data, with trait value as the dependent variable and scores of adaptive loci as independent ones. The method can be easily extended to accommodate quantitative or qualitative information on biologically relevant features of the environment experienced by each sampled individual, in which case estimates of the environmental and genotype × environment components of phenotypic variance can also be obtained.

SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli.

PubMed

Whiteside, Matthew D; Laing, Chad R; Manji, Akiff; Kruczkiewicz, Peter; Taboada, Eduardo N; Gannon, Victor P J

2016-04-12

Predictive genomics is the translation of raw genome sequence data into a phenotypic assessment of the organism. For bacterial pathogens, these phenotypes can range from environmental survivability, to the severity of human disease. Significant progress has been made in the development of generic tools for genomic analyses that are broadly applicable to all microorganisms; however, a fundamental missing component is the ability to analyze genomic data in the context of organism-specific phenotypic knowledge, which has been accumulated from decades of research and can provide a meaningful interpretation of genome sequence data. In this study, we present SuperPhy, an online predictive genomics platform ( http://lfz.corefacility.ca/superphy/ ) for Escherichia coli. The platform integrates the analytical tools and genome sequence data for all publicly available E. coli genomes and facilitates the upload of new genome sequences from users under public or private settings. SuperPhy provides real-time analyses of thousands of genome sequences with results that are understandable and useful to a wide community, including those in the fields of clinical medicine, epidemiology, ecology, and evolution. SuperPhy includes identification of: 1) virulence and antimicrobial resistance determinants 2) statistical associations between genotypes, biomarkers, geospatial distribution, host, source, and phylogenetic clade; 3) the identification of biomarkers for groups of genomes on the based presence/absence of specific genomic regions and single-nucleotide polymorphisms and 4) in silico Shiga-toxin subtype. SuperPhy is a predictive genomics platform that attempts to provide an essential link between the vast amounts of genome information currently being generated and phenotypic knowledge in an organism-specific context.

Edaphic history over seedling characters predicts integration and plasticity of integration across geologically variable populations of Arabidopsis thaliana.

PubMed

Cousins, Elsa A; Murren, Courtney J

2017-12-01

Studies on phenotypic plasticity and plasticity of integration have uncovered functionally linked modules of aboveground traits and seedlings of Arabidopsis thaliana , but we lack details about belowground variation in adult plants. Functional modules can be comprised of additional suites of traits that respond to environmental variation. We assessed whether shoot and root responses to nutrient environments in adult A. thaliana were predictable from seedling traits or population-specific geologic soil characteristics at the site of origin. We compared 17 natural accessions from across the native range of A. thaliana using 14-day-old seedlings grown on agar or sand and plants grown to maturity across nutrient treatments in sand. We measured aboveground size, reproduction, timing traits, root length, and root diameter. Edaphic characteristics were obtained from a global-scale dataset and related to field data. We detected significant among-population variation in root traits of seedlings and adults and in plasticity in aboveground and belowground traits of adult plants. Phenotypic integration of roots and shoots varied by population and environment. Relative integration was greater in roots than in shoots, and integration was predicted by edaphic soil history, particularly organic carbon content, whereas seedling traits did not predict later ontogenetic stages. Soil environment of origin has significant effects on phenotypic plasticity in response to nutrients, and on phenotypic integration of root modules and shoot modules. Root traits varied among populations in reproductively mature individuals, indicating potential for adaptive and integrated functional responses of root systems in annuals. © 2017 Botanical Society of America.

Feared consequences of panic attacks in panic disorder: a qualitative and quantitative analysis.

PubMed

Raffa, Susan D; White, Kamila S; Barlow, David H

2004-01-01

Cognitions are hypothesized to play a central role in panic disorder (PD). Previous studies have used questionnaires to assess cognitive content, focusing on prototypical cognitions associated with PD; however, few studies have qualitatively examined cognitions associated with the feared consequences of panic attacks. The purpose of this study was to conduct a qualitative and quantitative analysis of feared consequences of panic attacks. The initial, qualitative analysis resulted in the development of 32 categories of feared consequences. The categories were derived from participant responses to a standardized, semi-structured question (n = 207). Five expert-derived categories were then utilized to quantitatively examine the relationship between cognitions and indicators of PD severity. Cognitions did not predict PD severity; however, correlational analyses indicated some predictive validity to the expert-derived categories. The qualitative analysis identified additional areas of patient-reported concern not included in previous research that may be important in the assessment and treatment of PD.

Cow genotyping strategies for genomic selection in a small dairy cattle population.

PubMed

Jenko, J; Wiggans, G R; Cooper, T A; Eaglen, S A E; Luff, W G de L; Bichard, M; Pong-Wong, R; Woolliams, J A

2017-01-01

This study compares how different cow genotyping strategies increase the accuracy of genomic estimated breeding values (EBV) in dairy cattle breeds with low numbers. In these breeds, few sires have progeny records, and genotyping cows can improve the accuracy of genomic EBV. The Guernsey breed is a small dairy cattle breed with approximately 14,000 recorded individuals worldwide. Predictions of phenotypes of milk yield, fat yield, protein yield, and calving interval were made for Guernsey cows from England and Guernsey Island using genomic EBV, with training sets including 197 de-regressed proofs of genotyped bulls, with cows selected from among 1,440 genotyped cows using different genotyping strategies. Accuracies of predictions were tested using 10-fold cross-validation among the cows. Genomic EBV were predicted using 4 different methods: (1) pedigree BLUP, (2) genomic BLUP using only bulls, (3) univariate genomic BLUP using bulls and cows, and (4) bivariate genomic BLUP. Genotyping cows with phenotypes and using their data for the prediction of single nucleotide polymorphism effects increased the correlation between genomic EBV and phenotypes compared with using only bulls by 0.163±0.022 for milk yield, 0.111±0.021 for fat yield, and 0.113±0.018 for protein yield; a decrease of 0.014±0.010 for calving interval from a low base was the only exception. Genetic correlation between phenotypes from bulls and cows were approximately 0.6 for all yield traits and significantly different from 1. Only a very small change occurred in correlation between genomic EBV and phenotypes when using the bivariate model. It was always better to genotype all the cows, but when only half of the cows were genotyped, a divergent selection strategy was better compared with the random or directional selection approach. Divergent selection of 30% of the cows remained superior for the yield traits in 8 of 10 folds. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Developmental mechanisms underlying variation in craniofacial disease and evolution.

PubMed

Fish, Jennifer L

2016-07-15

Craniofacial disease phenotypes exhibit significant variation in penetrance and severity. Although many genetic contributions to phenotypic variation have been identified, genotype-phenotype correlations remain imprecise. Recent work in evolutionary developmental biology has exposed intriguing developmental mechanisms that potentially explain incongruities in genotype-phenotype relationships. This review focuses on two observations from work in comparative and experimental animal model systems that highlight how development structures variation. First, multiple genetic inputs converge on relatively few developmental processes. Investigation of when and how variation in developmental processes occurs may therefore help predict potential genetic interactions and phenotypic outcomes. Second, genetic mutation is typically associated with an increase in phenotypic variance. Several models outlining developmental mechanisms underlying mutational increases in phenotypic variance are discussed using Satb2-mediated variation in jaw size as an example. These data highlight development as a critical mediator of genotype-phenotype correlations. Future research in evolutionary developmental biology focusing on tissue-level processes may help elucidate the "black box" between genotype and phenotype, potentially leading to novel treatment, earlier diagnoses, and better clinical consultations for individuals affected by craniofacial anomalies. Copyright © 2015 Elsevier Inc. All rights reserved.

The phenotypic equilibrium of cancer cells: From average-level stability to path-wise convergence.

PubMed

Niu, Yuanling; Wang, Yue; Zhou, Da

2015-12-07

The phenotypic equilibrium, i.e. heterogeneous population of cancer cells tending to a fixed equilibrium of phenotypic proportions, has received much attention in cancer biology very recently. In the previous literature, some theoretical models were used to predict the experimental phenomena of the phenotypic equilibrium, which were often explained by different concepts of stabilities of the models. Here we present a stochastic multi-phenotype branching model by integrating conventional cellular hierarchy with phenotypic plasticity mechanisms of cancer cells. Based on our model, it is shown that: (i) our model can serve as a framework to unify the previous models for the phenotypic equilibrium, and then harmonizes the different kinds of average-level stabilities proposed in these models; and (ii) path-wise convergence of our model provides a deeper understanding to the phenotypic equilibrium from stochastic point of view. That is, the emergence of the phenotypic equilibrium is rooted in the stochastic nature of (almost) every sample path, the average-level stability just follows from it by averaging stochastic samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation.

PubMed

Lande, Russell

2009-07-01

Adaptation to a sudden extreme change in environment, beyond the usual range of background environmental fluctuations, is analysed using a quantitative genetic model of phenotypic plasticity. Generations are discrete, with time lag tau between a critical period for environmental influence on individual development and natural selection on adult phenotypes. The optimum phenotype, and genotypic norms of reaction, are linear functions of the environment. Reaction norm elevation and slope (plasticity) vary among genotypes. Initially, in the average background environment, the character is canalized with minimum genetic and phenotypic variance, and no correlation between reaction norm elevation and slope. The optimal plasticity is proportional to the predictability of environmental fluctuations over time lag tau. During the first generation in the new environment the mean fitness suddenly drops and the mean phenotype jumps towards the new optimum phenotype by plasticity. Subsequent adaptation occurs in two phases. Rapid evolution of increased plasticity allows the mean phenotype to closely approach the new optimum. The new phenotype then undergoes slow genetic assimilation, with reduction in plasticity compensated by genetic evolution of reaction norm elevation in the original environment.

Viewing Knowledge Bases as Qualitative Models.

ERIC Educational Resources Information Center

Clancey, William J.

The concept of a qualitative model provides a unifying perspective for understanding how expert systems differ from conventional programs. Knowledge bases contain qualitative models of systems in the world, that is, primarily non-numeric descriptions that provide a basis for explaining and predicting behavior and formulating action plans. The…

Phenotype at diagnosis predicts recurrence rates in Crohn's disease.

PubMed

Wolters, F L; Russel, M G; Sijbrandij, J; Ambergen, T; Odes, S; Riis, L; Langholz, E; Politi, P; Qasim, A; Koutroubakis, I; Tsianos, E; Vermeire, S; Freitas, J; van Zeijl, G; Hoie, O; Bernklev, T; Beltrami, M; Rodriguez, D; Stockbrügger, R W; Moum, B

2006-08-01

In Crohn's disease (CD), studies associating phenotype at diagnosis and subsequent disease activity are important for patient counselling and health care planning. To calculate disease recurrence rates and to correlate these with phenotypic traits at diagnosis. A prospectively assembled uniformly diagnosed European population based inception cohort of CD patients was classified according to the Vienna classification for disease phenotype at diagnosis. Surgical and non-surgical recurrence rates throughout a 10 year follow up period were calculated. Multivariate analysis was performed to classify risk factors present at diagnosis for recurrent disease. A total of 358 were classified for phenotype at diagnosis, of whom 262 (73.2%) had a first recurrence and 113 patients (31.6%) a first surgical recurrence during the first 10 years after diagnosis. Patients with upper gastrointestinal disease at diagnosis had an excess risk of recurrence (hazard ratio 1.54 (95% confidence interval (CI) 1.13-2.10)) whereas age >/=40 years at diagnosis was protective (hazard ratio 0.82 (95% CI 0.70-0.97)). Colonic disease was a protective characteristic for resective surgery (hazard ratio 0.38 (95% CI 0.21-0.69)). More frequent resective surgical recurrences were reported from Copenhagen (hazard ratio 3.23 (95% CI 1.32-7.89)). A mild course of disease in terms of disease recurrence was observed in this European cohort. Phenotype at diagnosis had predictive value for disease recurrence with upper gastrointestinal disease being the most important positive predictor. A phenotypic North-South gradient in CD may be present, illustrated by higher surgery risks in some of the Northern European centres.

Individualized Hydrocodone Therapy Based on Phenotype, Pharmacogenetics, and Pharmacokinetic Dosing.

PubMed

Linares, Oscar A; Fudin, Jeffrey; Daly, Annemarie L; Boston, Raymond C

2015-12-01

(1) To quantify hydrocodone (HC) and hydromorphone (HM) metabolite pharmacokinetics with pharmacogenetics in CYP2D6 ultra-rapid metabolizer (UM), extensive metabolizer (EM), and poor metabolizer (PM) metabolizer phenotypes. (2) To develop an HC phenotype-specific dosing strategy for HC that accounts for HM production using clinical pharmacokinetics integrated with pharmacogenetics for patient safety. In silico clinical trial simulation. Healthy white men and women without comorbidities or history of opioid, or any other drug or nutraceutical use, age 26.3±5.7 years (mean±SD; range, 19 to 36 y) and weight 71.9±16.8 kg (range, 50 to 108 kg). CYP2D6 phenotype-specific HC clinical pharmacokinetic parameter estimates and phenotype-specific percentages of HM formed from HC. PMs had lower indices of HC disposition compared with UMs and EMs. Clearance was reduced by nearly 60% and the t1/2 was increased by about 68% compared with EMs. The canonical order for HC clearance was UM>EM>PM. HC elimination mainly by the liver, represented by ke, was reduced about 70% in PM. However, HC's apparent Vd was not significantly different among UMs, EMs, and PM. The canonical order of predicted plasma HM concentrations was UM>EM>PM. For each of the CYP2D6 phenotypes, the mean predicted HM levels were within HM's therapeutic range, which indicates HC has significant phenotype-dependent pro-drug effects. Our results demonstrate that pharmacogenetics afford clinicians an opportunity to individualize HC dosing, while adding enhanced opportunity to account for its conversion to HM in the body.

Molecular and agronomic analysis of intraspecific variability in Capsicum baccatum var. pendulum accessions.

PubMed

Leite, P S S; Rodrigues, R; Silva, R N O; Pimenta, S; Medeiros, A M; Bento, C S; Gonçalves, L S A

2016-10-05

Capsicum baccatum is one of the most important chili peppers in South America, since this region is considered to be the center of origin and diversity of this species. In Brazil, C. baccatum has been widely explored by family farmers and there are different local names for each fruit phenotype, such as cambuci and dedo-de-moça (lady's finger). Although very popular among farmers and consumers, C. baccatum has been less extensively studied than other Capsicum species. This study describes the phenotypic and genotypic variability in C. baccatum var. pendulum accessions. Twenty-nine accessions from the Universidade Estadual do Norte Fluminense Darcy Ribeiro gene bank, and one commercial genotype ('BRS-Mari') were evaluated for 53 morphoagronomic descriptors (31 qualitative and 22 quantitative traits). In addition, accessions were genotyped using 30 microsatellite primers. Three accessions from the C. annuum complex were included in the molecular characterization. Nine of 31 qualitative descriptors were monomorphic, while all quantitative descriptors were highly significant different between accessions (P < 0.01). Using the unweighted pair group method using arithmetic averages, four groups were obtained based on multicategoric variables and five groups were obtained based on quantitative variables. In the genotyping analysis, 12 polymorphic simple sequence repeat primers amplified in C. baccatum with dissimilarity between accessions ranging from 0.13 to 0.91, permitting the formation of two distinct groups for Bayesian analysis. These results indicate wide variability among the accessions comparing phenotypic and genotypic data and revealed distinct patterns of dissimilarity between matrices, indicating that both steps are valuable for the characterization of C. baccatum var. pendulum accessions.

Prediction and Validation of Disease Genes Using HeteSim Scores.

PubMed

Zeng, Xiangxiang; Liao, Yuanlu; Liu, Yuansheng; Zou, Quan

2017-01-01

Deciphering the gene disease association is an important goal in biomedical research. In this paper, we use a novel relevance measure, called HeteSim, to prioritize candidate disease genes. Two methods based on heterogeneous networks constructed using protein-protein interaction, gene-phenotype associations, and phenotype-phenotype similarity, are presented. In HeteSim_MultiPath (HSMP), HeteSim scores of different paths are combined with a constant that dampens the contributions of longer paths. In HeteSim_SVM (HSSVM), HeteSim scores are combined with a machine learning method. The 3-fold experiments show that our non-machine learning method HSMP performs better than the existing non-machine learning methods, our machine learning method HSSVM obtains similar accuracy with the best existing machine learning method CATAPULT. From the analysis of the top 10 predicted genes for different diseases, we found that HSSVM avoid the disadvantage of the existing machine learning based methods, which always predict similar genes for different diseases. The data sets and Matlab code for the two methods are freely available for download at http://lab.malab.cn/data/HeteSim/index.jsp.

Genetic markers that influence feed efficiency phenotypes also affect cattle temperament as measured by flight speed

USDA-ARS?s Scientific Manuscript database

The measure of flight speed for cattle has been shown to be a predictive indicator of temperament and has also been associated with feed efficiency phenotypes, thus, genetic markers associated with both traits may assist with the selection of animals with calmer disposition and economic value. Chrom...

Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes.

PubMed

Lopes, F B; Wu, X-L; Li, H; Xu, J; Perkins, T; Genho, J; Ferretti, R; Tait, R G; Bauck, S; Rosa, G J M

2018-02-01

Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP-LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP-LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de-regressed EBV was slightly small (i.e. 0.87%-18.75%). The present study also compared the performance of five genomic prediction models and two cross-validation methods. The five genomic models predicted EBV and de-regressed EBV of the ten traits similarly well. Of the two cross-validation methods, leave-one-out cross-validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle. © 2018 Blackwell Verlag GmbH.

Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning.

PubMed

Rajkomar, Alvin; Yim, Joanne Wing Lan; Grumbach, Kevin; Parekh, Ami

2016-10-14

Characterizing patient complexity using granular electronic health record (EHR) data regularly available to health systems is necessary to optimize primary care processes at scale. To characterize the utilization patterns of primary care patients and create weighted panel sizes for providers based on work required to care for patients with different patterns. We used EHR data over a 2-year period from patients empaneled to primary care clinicians in a single academic health system, including their in-person encounter history and virtual encounters such as telephonic visits, electronic messaging, and care coordination with specialists. Using a combination of decision rules and k-means clustering, we identified clusters of patients with similar health care system activity. Phenotypes with basic demographic information were used to predict future health care utilization using log-linear models. Phenotypes were also used to calculate weighted panel sizes. We identified 7 primary care utilization phenotypes, which were characterized by various combinations of primary care and specialty usage and were deemed clinically distinct by primary care physicians. These phenotypes, combined with age-sex and primary payer variables, predicted future primary care utilization with R 2 of .394 and were used to create weighted panel sizes. Individual patients' health care utilization may be useful for classifying patients by primary care work effort and for predicting future primary care usage.

When should we expect microbial phenotypic traits to predict microbial abundances?

PubMed

Fox, Jeremy W

2012-01-01

Species' phenotypic traits may predict their relative abundances. Intuitively, this is because locally abundant species have traits making them well-adapted to local abiotic and biotic conditions, while locally rare species are not as well-adapted. But this intuition may not be valid. If competing species vary in how well-adapted they are to local conditions, why doesn't the best-adapted species simply exclude the others entirely? But conversely, if species exhibit niche differences that allow them to coexist, then by definition there is no single best adapted species. Rather, demographic rates depend on species' relative abundances, so that phenotypic traits conferring high adaptedness do not necessarily confer high abundance. I illustrate these points using a simple theoretical model incorporating adjustable levels of "adaptedness" and "niche differences." Even very small niche differences can weaken or even reverse the expected correlation between adaptive traits and abundance. Conversely, adaptive traits confer high abundance when niche differences are very strong. Future work should be directed toward understanding the link between phenotypic traits and frequency-dependence of demographic rates.

Classification and prediction of toxicity of chemicals using an automated phenotypic profiling of Caenorhabditis elegans.

PubMed

Gao, Shan; Chen, Weiyang; Zeng, Yingxin; Jing, Haiming; Zhang, Nan; Flavel, Matthew; Jois, Markandeya; Han, Jing-Dong J; Xian, Bo; Li, Guojun

2018-04-18

Traditional toxicological studies have relied heavily on various animal models to understand the effect of various compounds in a biological context. Considering the great cost, complexity and time involved in experiments using higher order organisms. Researchers have been exploring alternative models that avoid these disadvantages. One example of such a model is the nematode Caenorhabditis elegans. There are some advantages of C. elegans, such as small size, short life cycle, well defined genome, ease of maintenance and efficient reproduction. As these benefits allow large scale studies to be initiated with relative ease, the problem of how to efficiently capture, organize and analyze the resulting large volumes of data must be addressed. We have developed a new method for quantitative screening of chemicals using C. elegans. 33 features were identified for each chemical treatment. The compounds with different toxicities were shown to alter the phenotypes of C. elegans in distinct and detectable patterns. We found that phenotypic profiling revealed conserved functions to classify and predict the toxicity of different chemicals. Our results demonstrate the power of phenotypic profiling in C. elegans under different chemical environments.

Impedance of the Grape Berry Cuticle as a Novel Phenotypic Trait to Estimate Resistance to Botrytis Cinerea

PubMed Central

Herzog, Katja; Wind, Rolf; Töpfer, Reinhard

2015-01-01

Warm and moist weather conditions during berry ripening provoke Botrytis cinerea (B. cinerea) causing notable bunch rot on susceptible grapevines with the effect of reduced yield and wine quality. Resistance donors of genetic loci to increase B. cinerea resistance are widely unknown. Promising traits of resistance are represented by physical features like the thickness and permeability of the grape berry cuticle. Sensor-based phenotyping methods or genetic markers are rare for such traits. In the present study, the simple-to-handle I-sensor was developed. The sensor enables the fast and reliable measurement of electrical impedance of the grape berry cuticles and its epicuticular waxes (CW). Statistical experiments revealed highly significant correlations between relative impedance of CW and the resistance of grapevines to B. cinerea. Thus, the relative impedance Zrel of CW was identified as the most important phenotypic factor with regard to the prediction of grapevine resistance to B. cinerea. An ordinal logistic regression analysis revealed a R2McFadden of 0.37 and confirmed the application of Zrel of CW for the prediction of bunch infection and in this way as novel phenotyping trait. Applying the I-sensor, a preliminary QTL region was identified indicating that the novel phenotypic trait is as well a valuable tool for genetic analyses. PMID:26024417

Selection on skewed characters and the paradox of stasis.

PubMed

Bonamour, Suzanne; Teplitsky, Céline; Charmantier, Anne; Crochet, Pierre-André; Chevin, Luis-Miguel

2017-11-01

Observed phenotypic responses to selection in the wild often differ from predictions based on measurements of selection and genetic variance. An overlooked hypothesis to explain this paradox of stasis is that a skewed phenotypic distribution affects natural selection and evolution. We show through mathematical modeling that, when a trait selected for an optimum phenotype has a skewed distribution, directional selection is detected even at evolutionary equilibrium, where it causes no change in the mean phenotype. When environmental effects are skewed, Lande and Arnold's (1983) directional gradient is in the direction opposite to the skew. In contrast, skewed breeding values can displace the mean phenotype from the optimum, causing directional selection in the direction of the skew. These effects can be partitioned out using alternative selection estimates based on average derivatives of individual relative fitness, or additive genetic covariances between relative fitness and trait (Robertson-Price identity). We assess the validity of these predictions using simulations of selection estimation under moderate sample sizes. Ecologically relevant traits may commonly have skewed distributions, as we here exemplify with avian laying date - repeatedly described as more evolutionarily stable than expected - so this skewness should be accounted for when investigating evolutionary dynamics in the wild. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Methodology for the inference of gene function from phenotype data.

PubMed

Ascensao, Joao A; Dolan, Mary E; Hill, David P; Blake, Judith A

2014-12-12

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures. We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes. We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

Computational Models for Prediction of Yeast Strain Potential for Winemaking from Phenotypic Profiles

PubMed Central

Umek, Lan; Fonseca, Elza; Drumonde-Neves, João; Dequin, Sylvie; Zupan, Blaz; Schuller, Dorit

2013-01-01

Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40°C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naïve Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 µg/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection procedures. PMID:23874393

Baseline Gray- and White Matter Volume Predict Successful Weight Loss in the Elderly

PubMed Central

Mokhtari, Fatemeh; Paolini, Brielle M.; Burdette, Jonathan H.; Marsh, Anthony P.; Rejeski, W. Jack; Laurienti, Paul J.

2016-01-01

Objective The purpose of this study is to investigate if structural brain phenotypes can be used to predict weight loss success following behavioral interventions in older adults that are overweight or obese and have cardiometabolic dysfunction. Methods A support vector machine (SVM) with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter (GM) and white matter (WM) volume from 52 individuals that completed the intervention and a magnetic resonance imaging session. Results The SVM resulted in an average classification accuracy of 72.62 % based on GM and WM volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. Conclusions Our findings suggest that baseline brain structure is able to predict weight loss success following 18 months of treatment. The identification of brain structure as a predictor of successful weight loss is an innovative approach to identifying phenotypes for responsiveness to intensive lifestyle interventions. This phenotype could prove useful in future research focusing on the tailoring of treatment for weight loss. PMID:27804273

Splice Site Mutations in the ATP7A Gene

PubMed Central

Møller, Lisbeth Birk

2011-01-01

Menkes disease (MD) is caused by mutations in the ATP7A gene. We describe 33 novel splice site mutations detected in patients with MD or the milder phenotypic form, Occipital Horn Syndrome. We review these 33 mutations together with 28 previously published splice site mutations. We investigate 12 mutations for their effect on the mRNA transcript in vivo. Transcriptional data from another 16 mutations were collected from the literature. The theoretical consequences of splice site mutations, predicted with the bioinformatics tool Human Splice Finder, were investigated and evaluated in relation to in vivo results. Ninety-six percent of the mutations identified in 45 patients with classical MD were predicted to have a significant effect on splicing, which concurs with the absence of any detectable wild-type transcript in all 19 patients investigated in vivo. Sixty-seven percent of the mutations identified in 12 patients with milder phenotypes were predicted to have no significant effect on splicing, which concurs with the presence of wild-type transcript in 7 out of 9 patients investigated in vivo. Both the in silico predictions and the in vivo results support the hypothesis previously suggested by us and others, that the presence of some wild-type transcript is correlated to a milder phenotype. PMID:21494555

Prenatal stress effects in a wild, long-lived primate: predictive adaptive responses in an unpredictable environment.

PubMed

Berghänel, Andreas; Heistermann, Michael; Schülke, Oliver; Ostner, Julia

2016-09-28

Prenatal maternal stress affects offspring phenotype in numerous species including humans, but it is debated whether these effects are evolutionarily adaptive. Relating stress to adverse conditions, current explanations invoke either short-term developmental constraints on offspring phenotype resulting in decelerated growth to avoid starvation, or long-term predictive adaptive responses (PARs) resulting in accelerated growth and reproduction in response to reduced life expectancies. Two PAR subtypes were proposed, acting either on predicted internal somatic states or predicted external environmental conditions, but because both affect phenotypes similarly, they are largely indistinguishable. Only external (not internal) PARs rely on high environmental stability particularly in long-lived species. We report on a crucial test case in a wild long-lived mammal, the Assamese macaque (Macaca assamensis), which evolved and lives in an unpredictable environment where external PARs are probably not advantageous. We quantified food availability, growth, motor skills, maternal caretaking style and maternal physiological stress from faecal glucocorticoid measures. Prenatal maternal stress was negatively correlated to prenatal food availability and led to accelerated offspring growth accompanied by decelerated motor skill acquisition and reduced immune function. These results support the 'internal PAR' theory, which stresses the role of stable adverse internal somatic states rather than stable external environments. © 2016 The Author(s).

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories.

PubMed

Bhanot, Gyan; Alexe, Gabriela; Levine, Arnold J; Stolovitzky, Gustavo

2005-01-01

A major challenge in cancer diagnosis from microarray data is the need for robust, accurate, classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose such a classification scheme originally developed for phenotype identification from mass spectrometry data. The method uses a robust multivariate gene selection procedure and combines the results of several machine learning tools trained on raw and pattern data to produce an accurate meta-classifier. We illustrate and validate our method by applying it to gene expression datasets: the oligonucleotide HuGeneFL microarray dataset of Shipp et al. (www.genome.wi.mit.du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our pattern-based meta-classification technique achieves higher predictive accuracies than each of the individual classifiers , is robust against data perturbations and provides subsets of related predictive genes. Our techniques predict that combinations of some genes in the p53 pathway are highly predictive of phenotype. In particular, we find that in 80% of DLBCL cases the mRNA level of at least one of the three genes p53, PLK1 and CDK2 is elevated, while in 80% of FL cases, the mRNA level of at most one of them is elevated.

Spatial and temporal drivers of phenotypic diversity in polymorphic snakes.

PubMed

Cox, Christian L; Davis Rabosky, Alison R

2013-08-01

Color polymorphism in natural populations presents an ideal opportunity to study the evolutionary drivers of phenotypic diversity. Systems with striking spatial, temporal, and qualitative variation in color can be leveraged to study the mechanisms promoting the distribution of different types of variation in nature. We used the highly polymorphic ground snake (Sonora semiannulata), a putative coral snake mimic with both cryptic and conspicuous morphs, to compare patterns of neutral genetic variation and variation over space and time in color polymorphism to investigate the mechanistic drivers of phenotypic variation across scales. We found that strong selection promotes color polymorphism across spatial and temporal scales, with morph frequencies differing markedly between juvenile and adult age classes within a single population, oscillating over time within multiple populations, and varying drastically over the landscape despite minimal population genetic structure. However, we found no evidence that conspicuousness of morphs was related to which color pattern was favored by selection or to any geographic factors, including sympatry with coral snakes. We suggest that complex patterns of phenotypic variation in polymorphic systems may be a fundamental outcome of the conspicuousness of morphs and that explicit tests of temporal and geographic variation are critical to the interpretation of conspicuousness and mimicry.

Semiautomated confocal imaging of fungal pathogenesis on plants: Microscopic analysis of macroscopic specimens.

PubMed

Minker, Katharine R; Biedrzycki, Meredith L; Kolagunda, Abhishek; Rhein, Stephen; Perina, Fabiano J; Jacobs, Samuel S; Moore, Michael; Jamann, Tiffany M; Yang, Qin; Nelson, Rebecca; Balint-Kurti, Peter; Kambhamettu, Chandra; Wisser, Randall J; Caplan, Jeffrey L

2018-02-01

The study of phenotypic variation in plant pathogenesis provides fundamental information about the nature of disease resistance. Cellular mechanisms that alter pathogenesis can be elucidated with confocal microscopy; however, systematic phenotyping platforms-from sample processing to image analysis-to investigate this do not exist. We have developed a platform for 3D phenotyping of cellular features underlying variation in disease development by fluorescence-specific resolution of host and pathogen interactions across time (4D). A confocal microscopy phenotyping platform compatible with different maize-fungal pathosystems (fungi: Setosphaeria turcica, Cochliobolus heterostrophus, and Cercospora zeae-maydis) was developed. Protocols and techniques were standardized for sample fixation, optical clearing, species-specific combinatorial fluorescence staining, multisample imaging, and image processing for investigation at the macroscale. The sample preparation methods presented here overcome challenges to fluorescence imaging such as specimen thickness and topography as well as physiological characteristics of the samples such as tissue autofluorescence and presence of cuticle. The resulting imaging techniques provide interesting qualitative and quantitative information not possible with conventional light or electron 2D imaging. Microsc. Res. Tech., 81:141-152, 2018. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Ultrasonic vocalizations: a tool for behavioural phenotyping of mouse models of neurodevelopmental disorders

PubMed Central

Scattoni, Maria Luisa; Crawley, Jacqueline; Ricceri, Laura

2009-01-01

In neonatal mice ultrasonic vocalizations have been studied both as an early communicative behavior of the pup-mother dyad and as a sign of an aversive affective state. Adult mice of both sexes produce complex ultrasonic vocalization patterns in different experimental/social contexts. All these vocalizations are becoming an increasingly valuable assay for behavioral phenotyping throughout the mouse life-span and alterations of the ultrasound patterns have been reported in several mouse models of neurodevelopmental disorders. Here we also show that the modulation of vocalizations by maternal cues (maternal potentiation paradigm) – originally identified and investigated in rats - can be measured in C57Bl/6 mouse pups with appropriate modifications of the rat protocol and can likely be applied to mouse behavioral phenotyping. In addition we suggest that a detailed qualitative evaluation of neonatal calls together with analysis of adult mouse vocalization patterns in both sexes in social settings, may lead to a greater understanding of the communication value of vocalizations in mice. Importantly, both neonatal and adult USV altered patterns can be determined during the behavioural phenotyping of mouse models of human neurodevelopmental and neuropsychiatric disorders, starting from those in which deficits in communication are a primary symptom. PMID:18771687

The Pleiotropic Phenotype of Apc Mutations in the Mouse: Allele Specificity and Effects of the Genetic Background

PubMed Central

Halberg, Richard B.; Chen, Xiaodi; Amos-Landgraf, James M.; White, Alanna; Rasmussen, Kristin; Clipson, Linda; Pasch, Cheri; Sullivan, Ruth; Pitot, Henry C.; Dove, William F.

2008-01-01

Familial adenomatous polyposis (FAP) is a human cancer syndrome characterized by the development of hundreds to thousands of colonic polyps and extracolonic lesions including desmoid fibromas, osteomas, epidermoid cysts, and congenital hypertrophy of the pigmented retinal epithelium. Afflicted individuals are heterozygous for mutations in the APC gene. Detailed investigations of mice heterozygous for mutations in the ortholog Apc have shown that other genetic factors strongly influence the phenotype. Here we report qualitative and quantitative modifications of the phenotype of Apc mutants as a function of three genetic variables: Apc allele, p53 allele, and genetic background. We have found major differences between the Apc alleles Min and 1638N in multiplicity and regionality of intestinal tumors, as well as in incidence of extracolonic lesions. By contrast, Min mice homozygous for either of two different knockout alleles of p53 show similar phenotypic effects. These studies illustrate the classic principle that functional genetics is enriched by assessing penetrance and expressivity with allelic series. The mouse permits study of an allelic gene series on multiple genetic backgrounds, thereby leading to a better understanding of gene action in a range of biological processes. PMID:18723878

The pleiotropic phenotype of Apc mutations in the mouse: allele specificity and effects of the genetic background.

PubMed

Halberg, Richard B; Chen, Xiaodi; Amos-Landgraf, James M; White, Alanna; Rasmussen, Kristin; Clipson, Linda; Pasch, Cheri; Sullivan, Ruth; Pitot, Henry C; Dove, William F

2008-09-01

Familial adenomatous polyposis (FAP) is a human cancer syndrome characterized by the development of hundreds to thousands of colonic polyps and extracolonic lesions including desmoid fibromas, osteomas, epidermoid cysts, and congenital hypertrophy of the pigmented retinal epithelium. Afflicted individuals are heterozygous for mutations in the APC gene. Detailed investigations of mice heterozygous for mutations in the ortholog Apc have shown that other genetic factors strongly influence the phenotype. Here we report qualitative and quantitative modifications of the phenotype of Apc mutants as a function of three genetic variables: Apc allele, p53 allele, and genetic background. We have found major differences between the Apc alleles Min and 1638N in multiplicity and regionality of intestinal tumors, as well as in incidence of extracolonic lesions. By contrast, Min mice homozygous for either of two different knockout alleles of p53 show similar phenotypic effects. These studies illustrate the classic principle that functional genetics is enriched by assessing penetrance and expressivity with allelic series. The mouse permits study of an allelic gene series on multiple genetic backgrounds, thereby leading to a better understanding of gene action in a range of biological processes.

Serum Immune Responses Predict Rapid Disease Progression among Children with Crohn’s Disease: Immune Responses Predict Disease Progression

PubMed Central

Dubinsky, Marla C.; Lin, Ying-Chao; Dutridge, Debra; Picornell, Yoana; Landers, Carol J.; Farrior, Sharmayne; Wrobel, Iwona; Quiros, Antonio; Vasiliauskas, Eric A.; Grill, Bruce; Israel, David; Bahar, Ron; Christie, Dennis; Wahbeh, Ghassan; Silber, Gary; Dallazadeh, Saied; Shah, Praful; Thomas, Danny; Kelts, Drew; Hershberg, Robert M.; Elson, Charles O.; Targan, Stephan R.; Taylor, Kent D.; Rotter, Jerome I.; Yang, Huiying

2007-01-01

BACKGROUND AND AIM Crohn’s disease (CD) is a heterogeneous disorder characterized by diverse clinical phenotypes. Childhood-onset CD has been described as a more aggressive phenotype. Genetic and immune factors may influence disease phenotype and clinical course. We examined the association of immune responses to microbial antigens with disease behavior and prospectively determined the influence of immune reactivity on disease progression in pediatric CD patients. METHODS Sera were collected from 196 pediatric CD cases and tested for immune responses: anti-I2, anti-outer membrane protein C (anti-OmpC), anti-CBir1 flagellin (anti-CBir1), and anti-Saccharomyces-cerevisiae (ASCA) using ELISA. Associations between immune responses and clinical phenotype were evaluated. RESULTS Fifty-eight patients (28%) developed internal penetrating and/or stricturing (IP/S) disease after a median follow-up of 18 months. Both anti-OmpC (p < 0.0006) and anti-I2 (p < 0.003) were associated with IP/S disease. The frequency of IP/S disease increased with increasing number of immune responses (p trend = 0.002). The odds of developing IP/S disease were highest in patients positive for all four immune responses (OR (95% CI): 11 (1.5–80.4); p = 0.03). Pediatric CD patients positive for ≥1 immune response progressed to IP/S disease sooner after diagnosis as compared to those negative for all immune responses (p < 0.03). CONCLUSIONS The presence and magnitude of immune responses to microbial antigens are significantly associated with more aggressive disease phenotypes among children with CD. This is the first study to prospectively demonstrate that the time to develop a disease complication in children is significantly faster in the presence of immune reactivity, thereby predicting disease progression to more aggressive disease phenotypes among pediatric CD patients. PMID:16454844

Good neighbors make good defenses: associational refuges reduce defense investment in African savanna plants.

PubMed

Coverdale, Tyler C; Goheen, Jacob R; Palmer, Todd M; Pringle, Robert M

2018-06-25

Intraspecific variation in plant defense phenotype is common and has wide-ranging ecological consequences. Yet prevailing theories of plant defense allocation, which primarily account for interspecific differences in defense phenotype, often fail to predict intraspecific patterns. Furthermore, although individual variation in defense phenotype is often attributed to ecological interactions, few general mechanisms have been proposed to explain the ubiquity of variable defense phenotype within species. Here, we show experimentally that associational refuges and induced resistance interact to create predictable intraspecific variation in defense phenotype in African savanna plants. Physically defended species from four families (Acanthaceae, Asparagaceae, Cactaceae, and Solanaceae) growing in close association with spinescent Acacia trees had 39-78% fewer spines and thorns than did isolated conspecifics. For a subset of these species, we used a series of manipulative experiments to show that this variability is maintained primarily by a reduction in induced responses among individuals that seldom experience mammalian herbivory, whether due to association with Acacia trees or to experimental herbivore exclusion. Unassociated plants incurred 4- to 16-fold more browsing damage than did associated individuals and increased spine density by 16-38% within one month following simulated browsing. In contrast, experimental clipping induced no net change in spine density among plants growing beneath Acacia canopies or inside long-term herbivore exclosures. Associated and unassociated individuals produced similar numbers of flowers and seeds, but seedling recruitment and survival were vastly greater in refuge habitats, suggesting a net fitness benefit of association. We conclude that plant-plant associations consistently decrease defense investment in this system by reducing both the frequency of herbivory and the intensity of induced responses, and that inducible responses enable plants to capitalize on such associations in heterogeneous environments. Given the prevalence of associational and induced defenses in plant communities worldwide, our results suggest a potentially general mechanism by which biotic interactions might predictably shape intraspecific variation in plant defense phenotype. © 2018 by the Ecological Society of America.

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases

PubMed Central

Lin, Chen; Mak, Wayne; Hong, Pengyu; Sepp, Katharine; Perrimon, Norbert

2010-01-01

Recently, High-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques. PMID:21278820

Ab initio genotype–phenotype association reveals intrinsic modularity in genetic networks

PubMed Central

Slonim, Noam; Elemento, Olivier; Tavazoie, Saeed

2006-01-01

Microbial species express an astonishing diversity of phenotypic traits, behaviors, and metabolic capacities. However, our molecular understanding of these phenotypes is based almost entirely on studies in a handful of model organisms that together represent only a small fraction of this phenotypic diversity. Furthermore, many microbial species are not amenable to traditional laboratory analysis because of their exotic lifestyles and/or lack of suitable molecular genetic techniques. As an adjunct to experimental analysis, we have developed a computational information-theoretic framework that produces high-confidence gene–phenotype predictions using cross-species distributions of genes and phenotypes across 202 fully sequenced archaea and eubacteria. In addition to identifying the genetic basis of complex traits, our approach reveals the organization of these genes into generic preferentially co-inherited modules, many of which correspond directly to known enzymatic pathways, molecular complexes, signaling pathways, and molecular machines. PMID:16732191

Assessment of the Ability of Contemporary Climate Models to Assess Adequately the Risk of Possible Regional Anomalies and Trends

NASA Astrophysics Data System (ADS)

Mokhov, I. I.

2018-04-01

The results describing the ability of contemporary global and regional climate models not only to assess the risk of general trends of changes but also to predict qualitatively new regional effects are presented. In particular, model simulations predicted spatially inhomogeneous changes in the wind and wave conditions in the Arctic basins, which have been confirmed in recent years. According to satellite and reanalysis data, a qualitative transition to the regime predicted by model simulations occurred about a decade ago.

No Association between Variation in Longevity Candidate Genes and Aging-related Phenotypes in Oldest-old Danes.

PubMed

Soerensen, Mette; Nygaard, Marianne; Debrabant, Birgit; Mengel-From, Jonas; Dato, Serena; Thinggaard, Mikael; Christensen, Kaare; Christiansen, Lene

2016-06-01

In this study we explored the association between aging-related phenotypes previously reported to predict survival in old age and variation in 77 genes from the DNA repair pathway, 32 genes from the growth hormone 1/ insulin-like growth factor 1/insulin (GH/IGF-1/INS) signalling pathway and 16 additional genes repeatedly considered as candidates for human longevity: APOE, APOA4, APOC3, ACE, CETP, HFE, IL6, IL6R, MTHFR, TGFB1, SIRTs 1, 3, 6; and HSPAs 1A, 1L, 14. Altogether, 1,049 single nucleotide polymorphisms (SNPs) were genotyped in 1,088 oldest-old (age 92-93 years) Danes and analysed with phenotype data on physical functioning (hand grip strength), cognitive functioning (mini mental state examination and a cognitive composite score), activity of daily living and self-rated health. Five SNPs showed association to one of the phenotypes; however, none of these SNPs were associated with a change in the relevant phenotype over time (7 years of follow-up) and none of the SNPs could be confirmed in a replication sample of 1,281 oldest-old Danes (age 94-100). Hence, our study does not support association between common variation in the investigated longevity candidate genes and aging-related phenotypes consistently shown to predict survival. It is possible that larger sample sizes are needed to robustly reveal associations with small effect sizes. Copyright © 2016 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deutschbauer, Adam; Price, Morgan N.; Wetmore, Kelly M.

Mutant phenotypes provide strong clues to the functions of the underlying genes and could allow annotation of the millions of sequenced yet uncharacterized bacterial genes. However, it is not known how many genes have a phenotype under laboratory conditions, how many phenotypes are biologically interpretable for predicting gene function, and what experimental conditions are optimal to maximize the number of genes with a phenotype. To address these issues, we measured the mutant fitness of 1,586 genes of the ethanol-producing bacterium Zymomonas mobilis ZM4 across 492 diverse experiments and found statistically significant phenotypes for 89% of all assayed genes. Thus, inmore » Z. mobilis, most genes have a functional consequence under laboratory conditions. We demonstrate that 41% of Z. mobilis genes have both a strong phenotype and a similar fitness pattern (cofitness) to another gene, and are therefore good candidates for functional annotation using mutant fitness. Among 502 poorly characterized Z. mobilis genes, we identified a significant cofitness relationship for 174. For 57 of these genes without a specific functional annotation, we found additional evidence to support the biological significance of these gene-gene associations, and in 33 instances, we were able to predict specific physiological or biochemical roles for the poorly characterized genes. Last, we identified a set of 79 diverse mutant fitness experiments in Z. mobilis that are nearly as biologically informative as the entire set of 492 experiments. Therefore, our work provides a blueprint for the functional annotation of diverse bacteria using mutant fitness.« less

Using a Stem Cell-Based Signature to Guide Therapeutic Selection in Cancer

PubMed Central

Shats, Igor; Gatza, Michael L.; Chang, Jeffrey T.; Mori, Seiichi; Wang, Jialiang; Rich, Jeremy; Nevins, Joseph R.

2010-01-01

Given the very substantial heterogeneity of most human cancers, it is likely that most cancer therapeutics will be active in only a small fraction of any population of patients. As such, the development of new therapeutics, coupled with methods to match a therapy with the individual patient, will be critical to achieving significant gains in disease outcome. One such opportunity is the use of expression signatures to identify key oncogenic phenotypes that can serve not only as biomarkers but also as a means of identifying therapeutic compounds that might specifically target these phenotypes. Given the potential importance of targeting tumors exhibiting a stem-like phenotype, we have developed an expression signature that reflects common biological aspects of various stem-like characteristics. The Consensus Stemness Ranking (CSR) signature is upregulated in cancer stem cell enriched samples, at advanced tumor stages and is associated with poor prognosis in multiple cancer types. Using two independent computational approaches we utilized the CSR signature to identify clinically useful compounds that could target the CSR phenotype. In vitro assays confirmed selectivity of several predicted compounds including topoisomerase inhibitors and resveratrol towards breast cancer cell lines that exhibit a high-CSR phenotype. Importantly, the CSR signature could predict clinical response of breast cancer patients to a neoadjuvant regimen that included a CSR-specific agent. Collectively, these results suggest therapeutic opportunities to target the CSR phenotype in a relevant cohort of cancer patients. PMID:21169407

Local adaptation in transgenerational responses to predators

PubMed Central

Walsh, Matthew R.; Castoe, Todd; Holmes, Julian; Packer, Michelle; Biles, Kelsey; Walsh, Melissa; Munch, Stephan B.; Post, David M.

2016-01-01

Environmental signals can induce phenotypic changes that span multiple generations. Along with phenotypic responses that occur during development (i.e. ‘within-generation’ plasticity), such ‘transgenerational plasticity’ (TGP) has been documented in a diverse array of taxa spanning many environmental perturbations. New theory predicts that temporal stability is a key driver of the evolution of TGP. We tested this prediction using natural populations of zooplankton from lakes in Connecticut that span a large gradient in the temporal dynamics of predator-induced mortality. We reared more than 120 clones of Daphnia ambigua from nine lakes for multiple generations in the presence/absence of predator cues. We found that temporal variation in mortality selects for within-generation plasticity while consistently strong (or weak) mortality selects for increased TGP. Such results provide us the first evidence for local adaptation in TGP and argue that divergent ecological conditions select for phenotypic responses within and across generations. PMID:26817775

Sign epistasis caused by hierarchy within signalling cascades.

PubMed

Nghe, Philippe; Kogenaru, Manjunatha; Tans, Sander J

2018-04-13

Sign epistasis is a central evolutionary constraint, but its causal factors remain difficult to predict. Here we use the notion of parameterised optima to explain epistasis within a signalling cascade, and test these predictions in Escherichia coli. We show that sign epistasis arises from the benefit of tuning phenotypic parameters of cascade genes with respect to each other, rather than from their complex and incompletely known genetic bases. Specifically, sign epistasis requires only that the optimal phenotypic parameters of one gene depend on the phenotypic parameters of another, independent of other details, such as activating or repressing nature, position within the cascade, intra-genic pleiotropy or genotype. Mutational effects change sign more readily in downstream genes, indicating that optimising downstream genes is more constrained. The findings show that sign epistasis results from the inherent upstream-downstream hierarchy between signalling cascade genes, and can be addressed without exhaustive genotypic mapping.

Using convolutional neural networks to explore the microbiome.

PubMed

Reiman, Derek; Metwally, Ahmed; Yang Dai

2017-07-01

The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.

Spectrum of rhodopsin mutations in Korean patients with retinitis pigmentosa

PubMed Central

Kim, Kwang Joong; Kim, Cinoo; Bok, Jeong; Kim, Kyung-Seon; Lee, Eun-Ju; Park, Sung Pyo; Chung, Hum; Han, Bok-Ghee; Kim, Hyung-Lae; Kimm, Kuchan; Yu, Hyeong Gon

2011-01-01

Purpose To determine the spectrum and frequency of rhodopsin gene (RHO) mutations in Korean patients with retinitis pigmentosa (RP) and to characterize genotype–phenotype correlations in patients with mutations. Methods The RHO mutations were screened by direct sequencing, and mutation prevalence was measured in patients and controls. The impact of missense mutations to RP was predicted by segregation analysis, peptide sequence alignment, and in silico analysis. The severity of disease in patients with the missense mutations was compared by visual acuity, electroretinography, optical coherence tomography, and kinetic visual field testing. Results Five heterozygous mutations were identified in six of 302 probands with RP, including a novel mutation (c.893C>A, p.A298D) and four known mutations (c.50C>T, p.T17M; c.533A>G, p.Y178C; c.888G>T, p.K296N; and c.1040C>T, p.P347L). The allele frequency of missense mutations was measured in 114 ethnically matched controls. p.A298D, newly identified in a sporadic patient, had never been found in controls and was predicted to be pathogenic. Among the patients with the missense mutations, we observed the most severe phenotype in patients with p.P347L, less severe phenotypes in patients with p.Y178C or p.A298D, and a relatively moderate phenotype in a patient with p.T17M. Conclusions The results reveal the spectrum of RHO mutations in Korean RP patients and clinical features that vary according to mutations. Our findings will be useful for understanding these genetic spectra and the genotype–phenotype correlations and will therefore help with predicting disease prognosis and facilitating the development of gene therapy. PMID:21677794

Network Analysis of Sequence-Function Relationships and Exploration of Sequence Space of TEM β-Lactamases.

PubMed

Zeil, Catharina; Widmann, Michael; Fademrecht, Silvia; Vogel, Constantin; Pleiss, Jürgen

2016-05-01

The Lactamase Engineering Database (www.LacED.uni-stuttgart.de) was developed to facilitate the classification and analysis of TEM β-lactamases. The current version contains 474 TEM variants. Two hundred fifty-nine variants form a large scale-free network of highly connected point mutants. The network was divided into three subnetworks which were enriched by single phenotypes: one network with predominantly 2be and two networks with 2br phenotypes. Fifteen positions were found to be highly variable, contributing to the majority of the observed variants. Since it is expected that a considerable fraction of the theoretical sequence space is functional, the currently sequenced 474 variants represent only the tip of the iceberg of functional TEM β-lactamase variants which form a huge natural reservoir of highly interconnected variants. Almost 50% of the variants are part of a quartet. Thus, two single mutations that result in functional enzymes can be combined into a functional protein. Most of these quartets consist of the same phenotype, or the mutations are additive with respect to the phenotype. By predicting quartets from triplets, 3,916 unknown variants were constructed. Eighty-seven variants complement multiple quartets and therefore have a high probability of being functional. The construction of a TEM β-lactamase network and subsequent analyses by clustering and quartet prediction are valuable tools to gain new insights into the viable sequence space of TEM β-lactamases and to predict their phenotype. The highly connected sequence space of TEM β-lactamases is ideally suited to network analysis and demonstrates the strengths of network analysis over tree reconstruction methods. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy.

PubMed

Montero, Joan; Sarosiek, Kristopher A; DeAngelo, Joseph D; Maertens, Ophélia; Ryan, Jeremy; Ercan, Dalia; Piao, Huiying; Horowitz, Neil S; Berkowitz, Ross S; Matulonis, Ursula; Jänne, Pasi A; Amrein, Philip C; Cichowski, Karen; Drapkin, Ronny; Letai, Anthony

2015-02-26

There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient's living cancer cell with the drug(s) in question. To satisfy this unmet need, we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion ("priming") induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling

PubMed Central

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne‐Marie; Bouvier, Michel

2017-01-01

Abstract Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship. PMID:28230923

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.

PubMed

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne-Marie; Bouvier, Michel; Lichtarge, Olivier

2017-05-01

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship. © 2017 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

Distribution of genotype network sizes in sequence-to-structure genotype-phenotype maps.

PubMed

Manrubia, Susanna; Cuesta, José A

2017-04-01

An essential quantity to ensure evolvability of populations is the navigability of the genotype space. Navigability, understood as the ease with which alternative phenotypes are reached, relies on the existence of sufficiently large and mutually attainable genotype networks. The size of genotype networks (e.g. the number of RNA sequences folding into a particular secondary structure or the number of DNA sequences coding for the same protein structure) is astronomically large in all functional molecules investigated: an exhaustive experimental or computational study of all RNA folds or all protein structures becomes impossible even for moderately long sequences. Here, we analytically derive the distribution of genotype network sizes for a hierarchy of models which successively incorporate features of increasingly realistic sequence-to-structure genotype-phenotype maps. The main feature of these models relies on the characterization of each phenotype through a prototypical sequence whose sites admit a variable fraction of letters of the alphabet. Our models interpolate between two limit distributions: a power-law distribution, when the ordering of sites in the prototypical sequence is strongly constrained, and a lognormal distribution, as suggested for RNA, when different orderings of the same set of sites yield different phenotypes. Our main result is the qualitative and quantitative identification of those features of sequence-to-structure maps that lead to different distributions of genotype network sizes. © 2017 The Author(s).

Genome-wide association analysis of bacterial cold water disease resistance in rainbow trout reveals the potential of a hybrid approach between genomic selection and marker assisted selection

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) simultaneously incorporates dense SNP marker genotypes with phenotypic data from related animals to predict animal-specific genomic breeding value (GEBV), which circumvents the need to measure the disease phenotype in potential breeders. Marker assisted selection (MAS) involv...

Genetic and Diagnostic Biomarker Development in ASD Toddlers Using Resting State Functional MRI

DTIC Science & Technology

2017-11-01

Integration Theory of intelligence (Jung and Haier, Behave Brain Sci, 2007...predicting a number of age-related phenotypes. Measures of white matter integrity in the brain are heritable and highly sensitive to both normal and...pathological aging processes. We consider the phenotypic and genetic interrelationships between epigenetic age acceleration and white matter integrity

Subtypes in 22q11.2 Deletion Syndrome Associated with Behaviour and Neurofacial Morphology

ERIC Educational Resources Information Center

Sinderberry, Brooke; Brown, Scott; Hammond, Peter; Stevens, Angela F.; Schall, Ulrich; Murphy, Declan G. M.; Murphy, Kieran C.; Campbell, Linda E.

2013-01-01

22q11.2 deletion syndrome (22q11DS) has a complex phenotype with more than 180 characteristics, including cardiac anomalies, cleft palate, intellectual disabilities, a typical facial morphology, and mental health problems. However, the variable phenotype makes it difficult to predict clinical outcome, such as the high prevalence of psychosis among…

Spatial variation in effects of temperature on Phenotypic characteristics of Phytophthora ramorum isolates from eastern Sonoma county

Treesearch

Valerie Sherron; Nathan E. Rank; Michael Cohen; Brian L. Anacker; Ross K. Meentemeyer

2008-01-01

Quantifying the growth rates of plant pathogens in the laboratory can be useful for predicting rates of disease spread and impact in nature. The purpose of this study was to examine phenotypic variation among isolates of Phytophthora ramorum collected from a foliar host plant species, Umbellularia californica (California bay laurel...

Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

PubMed

Lee, Jia-Ying Joey; Miller, James Alastair; Basu, Sreetama; Kee, Ting-Zhen Vanessa; Loo, Lit-Hsin

2018-06-01

Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide variety of chemicals is not currently available. Here, we report a study that uses high-throughput imaging and artificial intelligence to build an in vitro pulmonotoxicity assay by automatically comparing and selecting human lung-cell lines and their associated quantitative phenotypic features most predictive of in vivo pulmonotoxicity. This approach is called "High-throughput In vitro Phenotypic Profiling for Toxicity Prediction" (HIPPTox). We found that the resulting assay based on two phenotypic features of a human bronchial epithelial cell line, BEAS-2B, can accurately classify 33 reference chemicals with human pulmonotoxicity information (88.8% balance accuracy, 84.6% sensitivity, and 93.0% specificity). In comparison, the predictivity of a standard cell-viability assay on the same set of chemicals is much lower (77.1% balanced accuracy, 84.6% sensitivity, and 69.5% specificity). We also used the assay to evaluate 17 additional test chemicals with unknown/unclear human pulmonotoxicity, and experimentally confirmed that many of the pulmonotoxic reference and predicted-positive test chemicals induce DNA strand breaks and/or activation of the DNA-damage response (DDR) pathway. Therefore, HIPPTox helps us to uncover these common modes-of-action of pulmonotoxic chemicals. HIPPTox may also be applied to other cell types or models, and accelerate the development of predictive in vitro assays for other cell-type- or organ-specific toxicities.

GESPA: classifying nsSNPs to predict disease association.

PubMed

Khurana, Jay K; Reeder, Jay E; Shrimpton, Antony E; Thakar, Juilee

2015-07-25

Non-synonymous single nucleotide polymorphisms (nsSNPs) are the most common DNA sequence variation associated with disease in humans. Thus determining the clinical significance of each nsSNP is of great importance. Potential detrimental nsSNPs may be identified by genetic association studies or by functional analysis in the laboratory, both of which are expensive and time consuming. Existing computational methods lack accuracy and features to facilitate nsSNP classification for clinical use. We developed the GESPA (GEnomic Single nucleotide Polymorphism Analyzer) program to predict the pathogenicity and disease phenotype of nsSNPs. GESPA is a user-friendly software package for classifying disease association of nsSNPs. It allows flexibility in acceptable input formats and predicts the pathogenicity of a given nsSNP by assessing the conservation of amino acids in orthologs and paralogs and supplementing this information with data from medical literature. The development and testing of GESPA was performed using the humsavar, ClinVar and humvar datasets. Additionally, GESPA also predicts the disease phenotype associated with a nsSNP with high accuracy, a feature unavailable in existing software. GESPA's overall accuracy exceeds existing computational methods for predicting nsSNP pathogenicity. The usability of GESPA is enhanced by fast SQL-based cloud storage and retrieval of data. GESPA is a novel bioinformatics tool to determine the pathogenicity and phenotypes of nsSNPs. We anticipate that GESPA will become a useful clinical framework for predicting the disease association of nsSNPs. The program, executable jar file, source code, GPL 3.0 license, user guide, and test data with instructions are available at http://sourceforge.net/projects/gespa.

Multi-pathway Kinase Signatures of Multipotent Stromal Cells are Predictive for Osteogenic Differentiation

PubMed Central

Platt, Manu O.; Wilder, Catera L.; Wells, Alan; Griffith, Linda G.; Lauffenburger, Douglas A.

2010-01-01

Bone marrow-derived multi-potent stromal cells (MSCs) offer great promise for regenerating tissue. While certain transcription factors have been identified in association with tendency toward particular MSC differentiation phenotypes, the regulatory network of key receptor-mediated signaling pathways activated by extracellular ligands that induce various differentiation responses remain poorly understood. Attempts to predict differentiation fate tendencies from individual pathways in isolation are problematic due to the complex pathway interactions inherent in signaling networks. Accordingly, we have undertaken a multi-variate systems approach integrating experimental measurement of multiple kinase pathway activities and osteogenic differentiation in MSCs, together with computational analysis to elucidate quantitative combinations of kinase signals predictive of cell behavior across diverse contexts. In particular, for culture on polymeric biomaterials surfaces presenting tethered epidermal growth factor (tEGF), type-I collagen, neither, or both, we have found that a partial least-squares regression model yields successful prediction of phenotypic behavior on the basis of two principal components comprising the weighted sums of 8 intracellular phosphoproteins: p-EGFR, p-Akt, p-ERK1/2, p-Hsp27, p-c-jun, p-GSK3α/β, p-p38, and p-STAT3. This combination provides strongest predictive capability for 21-day differentiated phenotype status when calculated from day-7 signal measurements (99%); day-4 (88%) and day-14 (89%) signal measurements are also significantly predictive, indicating a broad time-frame during MSC osteogenesis wherein multiple pathways and states of the kinase signaling network are quantitatively integrated to regulate gene expression, cell processes, and ultimately, cell fate. PMID:19750537

Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions

PubMed Central

Snitkin, Evan S; Dudley, Aimée M; Janse, Daniel M; Wong, Kaisheen; Church, George M; Segrè, Daniel

2008-01-01

Background Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored. Results In this study, we measured growth phenotypes of 465 Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes. Conclusions Our study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources. PMID:18808699

Computational discovery and in vivo validation of hnf4 as a regulatory gene in planarian regeneration.

PubMed

Lobo, Daniel; Morokuma, Junji; Levin, Michael

2016-09-01

Automated computational methods can infer dynamic regulatory network models directly from temporal and spatial experimental data, such as genetic perturbations and their resultant morphologies. Recently, a computational method was able to reverse-engineer the first mechanistic model of planarian regeneration that can recapitulate the main anterior-posterior patterning experiments published in the literature. Validating this comprehensive regulatory model via novel experiments that had not yet been performed would add in our understanding of the remarkable regeneration capacity of planarian worms and demonstrate the power of this automated methodology. Using the Michigan Molecular Interactions and STRING databases and the MoCha software tool, we characterized as hnf4 an unknown regulatory gene predicted to exist by the reverse-engineered dynamic model of planarian regeneration. Then, we used the dynamic model to predict the morphological outcomes under different single and multiple knock-downs (RNA interference) of hnf4 and its predicted gene pathway interactors β-catenin and hh Interestingly, the model predicted that RNAi of hnf4 would rescue the abnormal regenerated phenotype (tailless) of RNAi of hh in amputated trunk fragments. Finally, we validated these predictions in vivo by performing the same surgical and genetic experiments with planarian worms, obtaining the same phenotypic outcomes predicted by the reverse-engineered model. These results suggest that hnf4 is a regulatory gene in planarian regeneration, validate the computational predictions of the reverse-engineered dynamic model, and demonstrate the automated methodology for the discovery of novel genes, pathways and experimental phenotypes. michael.levin@tufts.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Evaluation of the genotypic prediction of HIV-1 coreceptor use versus a phenotypic assay and correlation with the virological response to maraviroc: the ANRS GenoTropism study.

PubMed

Recordon-Pinson, Patricia; Soulié, Cathia; Flandre, Philippe; Descamps, Diane; Lazrek, Mouna; Charpentier, Charlotte; Montes, Brigitte; Trabaud, Mary-Anne; Cottalorda, Jacqueline; Schneider, Véronique; Morand-Joubert, Laurence; Tamalet, Catherine; Desbois, Delphine; Macé, Muriel; Ferré, Virginie; Vabret, Astrid; Ruffault, Annick; Pallier, Coralie; Raymond, Stéphanie; Izopet, Jacques; Reynes, Jacques; Marcelin, Anne-Geneviève; Masquelier, Bernard

2010-08-01

Genotypic algorithms for prediction of HIV-1 coreceptor usage need to be evaluated in a clinical setting. We aimed at studying (i) the correlation of genotypic prediction of coreceptor use in comparison with a phenotypic assay and (ii) the relationship between genotypic prediction of coreceptor use at baseline and the virological response (VR) to a therapy including maraviroc (MVC). Antiretroviral-experienced patients were included in the MVC Expanded Access Program if they had an R5 screening result with Trofile (Monogram Biosciences). V3 loop sequences were determined at screening, and coreceptor use was predicted using 13 genotypic algorithms or combinations of algorithms. Genotypic predictions were compared to Trofile; dual or mixed (D/M) variants were considered as X4 variants. Both genotypic and phenotypic results were obtained for 189 patients at screening, with 54 isolates scored as X4 or D/M and 135 scored as R5 with Trofile. The highest sensitivity (59.3%) for detection of X4 was obtained with the Geno2pheno algorithm, with a false-positive rate set up at 10% (Geno2pheno10). In the 112 patients receiving MVC, a plasma viral RNA load of <50 copies/ml was obtained in 68% of cases at month 6. In multivariate analysis, the prediction of the X4 genotype at baseline with the Geno2pheno10 algorithm including baseline viral load and CD4 nadir was independently associated with a worse VR at months 1 and 3. The baseline weighted genotypic sensitivity score was associated with VR at month 6. There were strong arguments in favor of using genotypic coreceptor use assays for determining which patients would respond to CCR5 antagonist.

Determination of nonlinear genetic architecture using compressed sensing.

PubMed

Ho, Chiu Man; Hsu, Stephen D H

2015-01-01

One of the fundamental problems of modern genomics is to extract the genetic architecture of a complex trait from a data set of individual genotypes and trait values. Establishing this important connection between genotype and phenotype is complicated by the large number of candidate genes, the potentially large number of causal loci, and the likely presence of some nonlinear interactions between different genes. Compressed Sensing methods obtain solutions to under-constrained systems of linear equations. These methods can be applied to the problem of determining the best model relating genotype to phenotype, and generally deliver better performance than simply regressing the phenotype against each genetic variant, one at a time. We introduce a Compressed Sensing method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. Our method uses L1-penalized regression applied to nonlinear functions of the sensing matrix. The computational and data resource requirements for our method are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using simulated human genomes and the small amount of currently available real data. A phase transition (i.e., dramatic and qualitative change) in the behavior of the algorithm indicates when sufficient data is available for its successful application. Our results indicate that predictive models for many complex traits, including a variety of human disease susceptibilities (e.g., with additive heritability h (2)∼0.5), can be extracted from data sets comprised of n ⋆∼100s individuals, where s is the number of distinct causal variants influencing the trait. For example, given a trait controlled by ∼10 k loci, roughly a million individuals would be sufficient for application of the method.

Comparison of Marker-Based Genomic Estimated Breeding Values and Phenotypic Evaluation for Selection of Bacterial Spot Resistance in Tomato.

PubMed

Liabeuf, Debora; Sim, Sung-Chur; Francis, David M

2018-03-01

Bacterial spot affects tomato crops (Solanum lycopersicum) grown under humid conditions. Major genes and quantitative trait loci (QTL) for resistance have been described, and multiple loci from diverse sources need to be combined to improve disease control. We investigated genomic selection (GS) prediction models for resistance to Xanthomonas euvesicatoria and experimentally evaluated the accuracy of these models. The training population consisted of 109 families combining resistance from four sources and directionally selected from a population of 1,100 individuals. The families were evaluated on a plot basis in replicated inoculated trials and genotyped with single nucleotide polymorphisms (SNP). We compared the prediction ability of models developed with 14 to 387 SNP. Genomic estimated breeding values (GEBV) were derived using Bayesian least absolute shrinkage and selection operator regression (BL) and ridge regression (RR). Evaluations were based on leave-one-out cross validation and on empirical observations in replicated field trials using the next generation of inbred progeny and a hybrid population resulting from selections in the training population. Prediction ability was evaluated based on correlations between GEBV and phenotypes (r g ), percentage of coselection between genomic and phenotypic selection, and relative efficiency of selection (r g /r p ). Results were similar with BL and RR models. Models using only markers previously identified as significantly associated with resistance but weighted based on GEBV and mixed models with markers associated with resistance treated as fixed effects and markers distributed in the genome treated as random effects offered greater accuracy and a high percentage of coselection. The accuracy of these models to predict the performance of progeny and hybrids exceeded the accuracy of phenotypic selection.

Evolutionary perspectives on the links between mitochondrial genotype and disease phenotype.

PubMed

Dowling, Damian K

2014-04-01

Disorders of the mitochondrial respiratory chain are heterogeneous in their symptoms and underlying genetics. Simple links between candidate mutations and expression of disease phenotype typically do not exist. It thus remains unclear how the genetic variation in the mitochondrial genome contributes to the phenotypic expression of complex traits and disease phenotypes. I summarize the basic genetic processes known to underpin mitochondrial disease. I highlight other plausible processes, drawn from the evolutionary biological literature, whose contribution to mitochondrial disease expression remains largely empirically unexplored. I highlight recent advances to the field, and discuss common-ground and -goals shared by researchers across medical and evolutionary domains. Mitochondrial genetic variance is linked to phenotypic variance across a variety of traits (e.g. reproductive function, life expectancy) fundamental to the upkeep of good health. Evolutionary theory predicts that mitochondrial genomes are destined to accumulate male-harming (but female-friendly) mutations, and this prediction has received proof-of-principle support. Furthermore, mitochondrial effects on the phenotype are typically manifested via interactions between mitochondrial and nuclear genes. Thus, whether a mitochondrial mutation is pathogenic in effect can depend on the nuclear genotype in which is it expressed. Many disease phenotypes associated with OXPHOS malfunction might be determined by the outcomes of mitochondrial-nuclear interactions, and by the evolutionary forces that historically shaped mitochondrial DNA (mtDNA) sequences. Concepts and results drawn from the evolutionary sciences can have broad, but currently under-utilized, applicability to the medical sciences and provide new insights into understanding the complex genetics of mitochondrial disease. This article is part of a Special Issue entitled Frontiers of Mitochondrial Research. Copyright © 2013. Published by Elsevier B.V.

In silico molecular comparisons of C. elegans and mammalian pharmacology identify distinct targets that regulate feeding.

PubMed

Lemieux, George A; Keiser, Michael J; Sassano, Maria F; Laggner, Christian; Mayer, Fahima; Bainton, Roland J; Werb, Zena; Roth, Bryan L; Shoichet, Brian K; Ashrafi, Kaveh

2013-11-01

Phenotypic screens can identify molecules that are at once penetrant and active on the integrated circuitry of a whole cell or organism. These advantages are offset by the need to identify the targets underlying the phenotypes. Additionally, logistical considerations limit screening for certain physiological and behavioral phenotypes to organisms such as zebrafish and C. elegans. This further raises the challenge of elucidating whether compound-target relationships found in model organisms are preserved in humans. To address these challenges we searched for compounds that affect feeding behavior in C. elegans and sought to identify their molecular mechanisms of action. Here, we applied predictive chemoinformatics to small molecules previously identified in a C. elegans phenotypic screen likely to be enriched for feeding regulatory compounds. Based on the predictions, 16 of these compounds were tested in vitro against 20 mammalian targets. Of these, nine were active, with affinities ranging from 9 nM to 10 µM. Four of these nine compounds were found to alter feeding. We then verified the in vitro findings in vivo through genetic knockdowns, the use of previously characterized compounds with high affinity for the four targets, and chemical genetic epistasis, which is the effect of combined chemical and genetic perturbations on a phenotype relative to that of each perturbation in isolation. Our findings reveal four previously unrecognized pathways that regulate feeding in C. elegans with strong parallels in mammals. Together, our study addresses three inherent challenges in phenotypic screening: the identification of the molecular targets from a phenotypic screen, the confirmation of the in vivo relevance of these targets, and the evolutionary conservation and relevance of these targets to their human orthologs.

PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes

PubMed Central

2014-01-01

Background We propose a phenotype-driven analysis of encrypted exome data to facilitate the widespread implementation of exome sequencing as a clinical genetic screening test. Twenty test-patients with varied syndromes were selected from the literature. For each patient, the mutation, phenotypic data, and genetic diagnosis were available. Next, control exome-files, each modified to include one of these twenty mutations, were assigned to the corresponding test-patients. These data were used by a geneticist blinded to the diagnoses to test the efficiency of our software, PhenoVar. The score assigned by PhenoVar to any genetic diagnosis listed in OMIM (Online Mendelian Inheritance in Man) took into consideration both the patient’s phenotype and all variations present in the corresponding exome. The physician did not have access to the individual mutations. PhenoVar filtered the search using a cut-off phenotypic match threshold to prevent undesired discovery of incidental findings and ranked the OMIM entries according to diagnostic score. Results When assigning the same weight to all variants in the exome, PhenoVar predicted the correct diagnosis in 10/20 patients, while in 15/20 the correct diagnosis was among the 4 highest ranked diagnoses. When assigning a higher weight to variants known, or bioinformatically predicted, to cause disease, PhenoVar’s yield increased to 14/20 (18/20 in top 4). No incidental findings were identified using our cut-off phenotypic threshold. Conclusion The phenotype-driven approach described could render widespread use of ES more practical, ethical and clinically useful. The implications about novel disease identification, advancement of complex diseases and personalized medicine are discussed. PMID:24884844

Relationship between endophenotype and phenotype in ADHD

PubMed Central

Rommelse, Nanda NJ; Altink, Marieke E; Martin, Neilson C; Buschgens, Cathelijne JM; Faraone, Stephen V; Buitelaar, Jan K; Sergeant, Joseph A; Oosterlaan, Jaap

2008-01-01

Background It has been hypothesized that genetic and environmental factors relate to psychiatric disorders through the effect of intermediating, vulnerability traits called endophenotypes. The study had a threefold aim: to examine the predictive validity of an endophenotypic construct for the ADHD diagnosis, to test whether the magnitude of group differences at the endophenotypic and phenotypic level is comparable, and to investigate whether four factors (gender, age, IQ, rater bias) have an effect (moderation or mediation) on the relation between endophenotype and phenotype. Methods Ten neurocognitive tasks were administered to 143 children with ADHD, 68 non-affected siblings, and 120 control children (first-borns) and 132 children with ADHD, 78 non-affected siblings, and 113 controls (second-borns) (5 – 19 years). The task measures have been investigated previously for their endophenotypic viability and were combined to one component which was labeled 'the endophenotypic construct': one measure representative of endophenotypic functioning across several domains of functioning. Results The endophenotypic construct classified children with moderate accuracy (about 50% for each of the three groups). Non-affected children differed as much from controls at the endophenotypic as at the phenotypic level, but affected children displayed a more severe phenotype than endophenotype. Although a potentially moderating effect (age) and several mediating effects (gender, age, IQ) were found affecting the relation between endophenotypic construct and phenotype, none of the effects studied could account for the finding that affected children had a more severe phenotype than endophenotype. Conclusion Endophenotypic functioning is moderately predictive of the ADHD diagnosis, though findings suggest substantial overlap exists between endophenotypic functioning in the groups of affected children, non-affected siblings, and controls. Results suggest other factors may be crucial and aggravate the ADHD symptoms in affected children. PMID:18234079

A novel test of the phenotype-linked fertility hypothesis reveals independent components of fertility.

PubMed Central

Pizzari, Tommaso; Jensen, Per; Cornwallis, Charles K.

2004-01-01

The phenotype-linked fertility hypothesis predicts that male sexual ornaments signal fertilizing efficiency and that the coevolution of male ornaments and female preference for such ornaments is driven by female pursuit of fertility benefits. In addition, directional testicular asymmetry frequently observed in birds has been suggested to reflect fertilizing efficiency and to covary with ornament expression. However, the idea of a phenotypic relationship between male ornaments and fertilizing efficiency is often tested in populations where environmental effects mask the underlying genetic associations between ornaments and fertilizing efficiency implied by this idea. Here, we adopt a novel design, which increases genetic diversity through the crossing of two divergent populations while controlling for environmental effects, to test: (i) the phenotypic relationship between male ornaments and both, gonadal (testicular mass) and gametic (sperm quality) components of fertilizing efficiency; and (ii) the extent to which these components are phenotypically integrated in the fowl, Gallus gallus. We show that consistent with theory, the testosterone-dependent expression of a male ornament, the comb, predicted testicular mass. However, despite their functional inter-dependence, testicular mass and sperm quality were not phenotypically integrated. Consistent with this result, males of one parental population invested more in testicular and comb mass, whereas males of the other parental population had higher sperm quality. We found no evidence that directional testicular asymmetry covaried with ornament expression. These results shed new light on the evolutionary relationship between male fertilizing efficiency and ornaments. Although testosterone-dependent ornaments may covary with testicular mass and thus reflect sperm production rate, the lack of phenotypic integration between gonadal and gametic traits reveals that the expression of an ornament is unlikely to reflect the overall fertilizing efficiency of a male. PMID:15002771

Phenotype at diagnosis predicts recurrence rates in Crohn's disease

PubMed Central

Wolters, F L; Russel, M G; Sijbrandij, J; Ambergen, T; Odes, S; Riis, L; Langholz, E; Politi, P; Qasim, A; Koutroubakis, I; Tsianos, E; Vermeire, S; Freitas, J; van Zeijl, G; Hoie, O; Bernklev, T; Beltrami, M; Rodriguez, D; Stockbrügger, R W; Moum, B

2006-01-01

Background In Crohn's disease (CD), studies associating phenotype at diagnosis and subsequent disease activity are important for patient counselling and health care planning. Aims To calculate disease recurrence rates and to correlate these with phenotypic traits at diagnosis. Methods A prospectively assembled uniformly diagnosed European population based inception cohort of CD patients was classified according to the Vienna classification for disease phenotype at diagnosis. Surgical and non‐surgical recurrence rates throughout a 10 year follow up period were calculated. Multivariate analysis was performed to classify risk factors present at diagnosis for recurrent disease. Results A total of 358 were classified for phenotype at diagnosis, of whom 262 (73.2%) had a first recurrence and 113 patients (31.6%) a first surgical recurrence during the first 10 years after diagnosis. Patients with upper gastrointestinal disease at diagnosis had an excess risk of recurrence (hazard ratio 1.54 (95% confidence interval (CI) 1.13–2.10)) whereas age ⩾40 years at diagnosis was protective (hazard ratio 0.82 (95% CI 0.70–0.97)). Colonic disease was a protective characteristic for resective surgery (hazard ratio 0.38 (95% CI 0.21–0.69)). More frequent resective surgical recurrences were reported from Copenhagen (hazard ratio 3.23 (95% CI 1.32–7.89)). Conclusions A mild course of disease in terms of disease recurrence was observed in this European cohort. Phenotype at diagnosis had predictive value for disease recurrence with upper gastrointestinal disease being the most important positive predictor. A phenotypic North‐South gradient in CD may be present, illustrated by higher surgery risks in some of the Northern European centres. PMID:16361306

Network Hubs Buffer Environmental Variation in Saccharomyces cerevisiae

PubMed Central

Levy, Sasha F; Siegal, Mark L

2008-01-01

Regulatory and developmental systems produce phenotypes that are robust to environmental and genetic variation. A gene product that normally contributes to this robustness is termed a phenotypic capacitor. When a phenotypic capacitor fails, for example when challenged by a harsh environment or mutation, the system becomes less robust and thus produces greater phenotypic variation. A functional phenotypic capacitor provides a mechanism by which hidden polymorphism can accumulate, whereas its failure provides a mechanism by which evolutionary change might be promoted. The primary example to date of a phenotypic capacitor is Hsp90, a molecular chaperone that targets a large set of signal transduction proteins. In both Drosophila and Arabidopsis, compromised Hsp90 function results in pleiotropic phenotypic effects dependent on the underlying genotype. For some traits, Hsp90 also appears to buffer stochastic variation, yet the relationship between environmental and genetic buffering remains an important unresolved question. We previously used simulations of knockout mutations in transcriptional networks to predict that many gene products would act as phenotypic capacitors. To test this prediction, we use high-throughput morphological phenotyping of individual yeast cells from single-gene deletion strains to identify gene products that buffer environmental variation in Saccharomyces cerevisiae. We find more than 300 gene products that, when absent, increase morphological variation. Overrepresented among these capacitors are gene products that control chromosome organization and DNA integrity, RNA elongation, protein modification, cell cycle, and response to stimuli such as stress. Capacitors have a high number of synthetic-lethal interactions but knockouts of these genes do not tend to cause severe decreases in growth rate. Each capacitor can be classified based on whether or not it is encoded by a gene with a paralog in the genome. Capacitors with a duplicate are highly connected in the protein–protein interaction network and show considerable divergence in expression from their paralogs. In contrast, capacitors encoded by singleton genes are part of highly interconnected protein clusters whose other members also tend to affect phenotypic variability or fitness. These results suggest that buffering and release of variation is a widespread phenomenon that is caused by incomplete functional redundancy at multiple levels in the genetic architecture. PMID:18986213

Applications of Genomic Selection in Breeding Wheat for Rust Resistance.

PubMed

Ornella, Leonardo; González-Camacho, Juan Manuel; Dreisigacker, Susanne; Crossa, Jose

2017-01-01

There are a lot of methods developed to predict untested phenotypes in schemes commonly used in genomic selection (GS) breeding. The use of GS for predicting disease resistance has its own particularities: (a) most populations shows additivity in quantitative adult plant resistance (APR); (b) resistance needs effective combinations of major and minor genes; and (c) phenotype is commonly expressed in ordinal categorical traits, whereas most parametric applications assume that the response variable is continuous and normally distributed. Machine learning methods (MLM) can take advantage of examples (data) that capture characteristics of interest from an unknown underlying probability distribution (i.e., data-driven). We introduce some state-of-the-art MLM capable to predict rust resistance in wheat. We also present two parametric R packages for the reader to be able to compare.

Applications of chemogenomic library screening in drug discovery.

PubMed

Jones, Lyn H; Bunnage, Mark E

2017-04-01

The allure of phenotypic screening, combined with the industry preference for target-based approaches, has prompted the development of innovative chemical biology technologies that facilitate the identification of new therapeutic targets for accelerated drug discovery. A chemogenomic library is a collection of selective small-molecule pharmacological agents, and a hit from such a set in a phenotypic screen suggests that the annotated target or targets of that pharmacological agent may be involved in perturbing the observable phenotype. In this Review, we describe opportunities for chemogenomic screening to considerably expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Other applications are explored, including drug repositioning, predictive toxicology and the discovery of novel pharmacological modalities.

Apparent Genetic Complexity Generated by Developmental Thresholds: The Apterous Locus in DROSOPHILA MELANOGASTER

PubMed Central

Stevens, Mary E.; Bryant, Peter J.

1985-01-01

Mutations at the apterous (ap) locus in Drosophila melanogaster give rise to three distinct phenotypes: aberrant wings, female sterility and precocious adult death. The wing phenotype includes five types of abnormality: blistering, deficiencies, duplications, high-order repetitions and transformation of structures. The mildest phenotype is seen with homozygous apblt animals which have either normal or slightly blistered wings. Most alleles produce, in the homozygote, a deficient wing in which part or all of the wing margin and wing blade is missing, but wing hinge and notum regions are normal. Animals hemizygous for each of 20 ap alleles, as well as apID/apXa heterozygotes, show duplication of parts of the notum associated with complete wing deficiency. Animals heterozygous for apc and the other tested ap alleles show repetitions of parts of the anterior wing margin, an engrailed-like transformation of posterior wing margin into anterior margin or both. Both apblt and apc show similar phenotypes in homozygotes and hemizygotes, yet both produce a less extreme phenotype than that of the other hemizygotes, suggesting that neither mutation causes loss of the entire ap+ function. The 15 alleles that cause precocious death and female sterility occur in six complementation groups based on complementation for these phenotypes. This supports the previous conclusion that the effects of apterous mutations on the wing do not correlate with their effects on viability and fertility. We propose an explanation for the effects of apterous mutations on the wing in which quantitative reductions in the activity of gene product give rise to qualitatively different phenotypes because of different threshold requirements of the ap+ function for critical events in wing disc development. PMID:3924726

Phenotype detection in morphological mutant mice using deformation features.

PubMed

Roy, Sharmili; Liang, Xi; Kitamoto, Asanobu; Tamura, Masaru; Shiroishi, Toshihiko; Brown, Michael S

2013-01-01

Large-scale global efforts are underway to knockout each of the approximately 25,000 mouse genes and interpret their roles in shaping the mammalian embryo. Given the tremendous amount of data generated by imaging mutated prenatal mice, high-throughput image analysis systems are inevitable to characterize mammalian development and diseases. Current state-of-the-art computational systems offer only differential volumetric analysis of pre-defined anatomical structures between various gene-knockout mice strains. For subtle anatomical phenotypes, embryo phenotyping still relies on the laborious histological techniques that are clearly unsuitable in such big data environment. This paper presents a system that automatically detects known phenotypes and assists in discovering novel phenotypes in muCT images of mutant mice. Deformation features obtained from non-linear registration of mutant embryo to a normal consensus average image are extracted and analyzed to compute phenotypic and candidate phenotypic areas. The presented system is evaluated using C57BL/10 embryo images. All cases of ventricular septum defect and polydactyly, well-known to be present in this strain, are successfully detected. The system predicts potential phenotypic areas in the liver that are under active histological evaluation for possible phenotype of this mouse line.

Long-term phenotypic evolution of bacteria.

PubMed

Plata, Germán; Henry, Christopher S; Vitkup, Dennis

2015-01-15

For many decades comparative analyses of protein sequences and structures have been used to investigate fundamental principles of molecular evolution. In contrast, relatively little is known about the long-term evolution of species' phenotypic and genetic properties. This represents an important gap in our understanding of evolution, as exactly these proprieties play key roles in natural selection and adaptation to diverse environments. Here we perform a comparative analysis of bacterial growth and gene deletion phenotypes using hundreds of genome-scale metabolic models. Overall, bacterial phenotypic evolution can be described by a two-stage process with a rapid initial phenotypic diversification followed by a slow long-term exponential divergence. The observed average divergence trend, with approximately similar fractions of phenotypic properties changing per unit time, continues for billions of years. We experimentally confirm the predicted divergence trend using the phenotypic profiles of 40 diverse bacterial species across more than 60 growth conditions. Our analysis suggests that, at long evolutionary distances, gene essentiality is significantly more conserved than the ability to utilize different nutrients, while synthetic lethality is significantly less conserved. We also find that although a rapid phenotypic evolution is sometimes observed within the same species, a transition from high to low phenotypic similarity occurs primarily at the genus level.

Clinical Phenotype Predicts Early Staged Bilateral Deep Brain Stimulation in Parkinson’s Disease

PubMed Central

Sung, Victor W.; Watts, Ray L.; Schrandt, Christian J.; Guthrie, Stephanie; Wang, Deli; Amara, Amy W.; Guthrie, Barton L.; Walker, Harrison C.

2014-01-01

Object While many centers place bilateral DBS systems simultaneously, unilateral STN DBS followed by a staged contralateral procedure has emerged as a treatment option for many patients. However little is known about whether the preoperative phenotype predicts when staged placement of a DBS electrode in the opposite subthalamic nucleus will be required. We aimed to determine whether preoperative clinical phenotype predicts early staged placement of a second subthalamic deep brain stimulation (DBS) electrode in patients who undergo unilateral subthalamic DBS for Parkinson's disease (PD). Methods Eighty-two consecutive patients with advanced PD underwent unilateral subthalamic DBS contralateral to the most affected hemibody and had at least 2 years of follow-up. Multivariate logistic regression determined preoperative characteristics that predicted staged placement of a second electrode in the opposite subthalamic nucleus. Preoperative measurements included aspects of the Unified Parkinson Disease Rating Scale (UPDRS), motor asymmetry index, and body weight. Results At 2 years follow-up, 28 of the 82 patients (34%) had undergone staged placement of a contralateral electrode while the remainder chose to continue with unilateral stimulation. Statistically significant improvements in UPDRS total and part 3 scores were retained at the end of the 2 year follow-up period in both subsets of patients. Multivariate logistic regression showed that the most important predictors for early staged placement of a second subthalamic stimulator were low asymmetry index (odds ratio 13.4; 95% confidence interval 2.8, 64.9), high tremor subscore (OR 7.2; CI 1.5, 35.0), and low body weight (OR 5.5; CI 1.4, 22.3). Conclusions This single center study provides evidence that elements of the preoperative PD phenotype predict whether patients will require early staged bilateral subthalamic DBS. These data may aid in the management of patients with advanced PD who undergo subthalamic DBS. PMID:24074493

Effect of phenotype on health care costs in Crohn's disease: A European study using the Montreal classification.

PubMed

Odes, Selwyn; Vardi, Hillel; Friger, Michael; Wolters, Frank; Hoie, Ole; Moum, Bjørn; Bernklev, Tomm; Yona, Hagit; Russel, Maurice; Munkholm, Pia; Langholz, Ebbe; Riis, Lene; Politi, Patrizia; Bondini, Paolo; Tsianos, Epameinondas; Katsanos, Kostas; Clofent, Juan; Vermeire, Severine; Freitas, João; Mouzas, Iannis; Limonard, Charles; O'Morain, Colm; Monteiro, Estela; Fornaciari, Giovanni; Vatn, Morten; Stockbrugger, Reinhold

2007-12-01

Crohn's disease (CD) is a chronic inflammation of the gastrointestinal tract associated with life-long high health care costs. We aimed to determine the effect of disease phenotype on cost. Clinical and economic data of a community-based CD cohort with 10-year follow-up were analyzed retrospectively in relation to Montreal classification phenotypes. In 418 patients, mean total costs of health care for the behavior phenotypes were: nonstricturing-nonpenetrating 1690, stricturing 2081, penetrating 3133 and penetrating-with-perianal-fistula 3356 €/patient-phenotype-year (P<0.001), and mean costs of surgical hospitalization 215, 751, 1293 and 1275 €/patient-phenotype-year respectively (P<0.001). Penetrating-with-perianal-fistula patients incurred significantly greater expenses than penetrating patients for total care, diagnosis and drugs, but not surgical hospitalization. Total costs were similar in the location phenotypes: ileum 1893, colon 1748, ileo-colonic 2010 and upper gastrointestinal tract 1758 €/patient-phenotype-year, but surgical hospitalization costs differed significantly, 558, 209, 492 and 542 €/patient-phenotype-year respectively (P<0.001). By multivariate analysis, the behavior phenotype significantly impacted total, medical and surgical hospitalization costs, whereas the location phenotype affected only surgical costs. Younger age at diagnosis predicted greater surgical expenses. Behavior is the dominant phenotype driving health care cost. Use of the Montreal classification permits detection of cost differences caused by perianal fistula.

Covariance and decoupling of floral and vegetative traits in nine Neotropical plants: a re-evaluation of Berg's correlation-pleiades concept.

PubMed

Armbruster, W S; Di Stilio, V S; Tuxill, J D; Flores, T C; Velásquez Runk, J L

1999-01-01

Nearly forty years ago R. L. Berg proposed that plants with specialized pollination ecology evolve genetic and developmental systems that decouple floral morphology from phenotypic variation in vegetative traits. These species evolve separate floral and vegetative trait clusters, or as she termed them, "correlation pleiades." The predictions of this hypothesis have been generally supported, but only a small sample of temperate-zone herb and grass species has been tested. To further evaluate this hypothesis, especially its applicability to plants of other growth forms, we examined the patterns of phenotypic variation and covariation of floral and vegetative traits in nine species of Neotropical plants. We recognized seven specific predictions of Berg's hypothesis. Our results supported some predictions but not others. Species with specialized pollination systems usually had floral traits decoupled (weak correlation; Canna and Eichornia) or buffered (relationship with shallow proportional slope; Calathea and Canna) from variation in vegetative traits. However, the same trend was also observed in three species with unspecialized pollination systems (Echinodorus, Muntingia, and Wedelia). One species with unspecialized pollination (Croton) and one wind-pollinated species (Cyperus) showed no decoupling or buffering, as predicted. While species with specialized pollination usually showed lower coefficients of variation for floral traits than vegetative traits (as predicted), the same was also true of species with unspecialized or wind pollination (unlike our prediction). Species with specialized pollination showed less variation in floral traits than did species with unspecialized or wind pollination, as predicted. However, the same was true of the corresponding vegetative traits, which was unexpected. Also in contrast to our prediction, plants with specialized pollination systems did not exhibit tighter phenotypic integration of floral characters than did species with generalized pollination systems. We conclude that the patterns of morphological integration among floral traits and between floral and vegetative traits tend to be species specific, not easily predicted from pollination ecology, and generally more complicated than R. L. Berg envisaged.

Phenotypic spectrum associated with mutations of the mitochondrial polymerase gamma gene.

PubMed

Horvath, Rita; Hudson, Gavin; Ferrari, Gianfrancesco; Fütterer, Nancy; Ahola, Sofia; Lamantea, Eleonora; Prokisch, Holger; Lochmüller, Hanns; McFarland, Robert; Ramesh, V; Klopstock, Thomas; Freisinger, Peter; Salvi, Fabrizio; Mayr, Johannes A; Santer, Rene; Tesarova, Marketa; Zeman, Jiri; Udd, Bjarne; Taylor, Robert W; Turnbull, Douglass; Hanna, Michael; Fialho, Doreen; Suomalainen, Anu; Zeviani, Massimo; Chinnery, Patrick F

2006-07-01

Mutations in the gene coding for the catalytic subunit of the mitochondrial DNA (mtDNA) polymerase gamma (POLG1) have recently been described in patients with diverse clinical presentations, revealing a complex relationship between genotype and phenotype in patients and their families. POLG1 was sequenced in patients from different European diagnostic and research centres to define the phenotypic spectrum and advance understanding of the recurrence risks. Mutations were identified in 38 cases, with the majority being sporadic compound heterozygotes. Eighty-nine DNA sequence changes were identified, including 2 predicted to alter a splice site, 1 predicted to cause a premature stop codon and 13 predicted to cause novel amino acid substitutions. The majority of children had a mutation in the linker region, often 1399G-->A (A467T), and a mutation affecting the polymerase domain. Others had mutations throughout the gene, and 11 had 3 or more substitutions. The clinical presentation ranged from the neonatal period to late adult life, with an overlapping phenotypic spectrum from severe encephalopathy and liver failure to late-onset external ophthalmoplegia, ataxia, myopathy and isolated muscle pain or epilepsy. There was a strong gender bias in children, with evidence of an environmental interaction with sodium valproate. POLG1 mutations cause an overlapping clinical spectrum of disease with both dominant and recessive modes of inheritance. 1399G-->A (A467T) is common in children, but complete POLG1 sequencing is required to identify multiple mutations that can have complex implications for genetic counselling.

Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles.

PubMed

Zhu, Jie; Qin, Yufang; Liu, Taigang; Wang, Jun; Zheng, Xiaoqi

2013-01-01

Identification of gene-phenotype relationships is a fundamental challenge in human health clinic. Based on the observation that genes causing the same or similar phenotypes tend to correlate with each other in the protein-protein interaction network, a lot of network-based approaches were proposed based on different underlying models. A recent comparative study showed that diffusion-based methods achieve the state-of-the-art predictive performance. In this paper, a new diffusion-based method was proposed to prioritize candidate disease genes. Diffusion profile of a disease was defined as the stationary distribution of candidate genes given a random walk with restart where similarities between phenotypes are incorporated. Then, candidate disease genes are prioritized by comparing their diffusion profiles with that of the disease. Finally, the effectiveness of our method was demonstrated through the leave-one-out cross-validation against control genes from artificial linkage intervals and randomly chosen genes. Comparative study showed that our method achieves improved performance compared to some classical diffusion-based methods. To further illustrate our method, we used our algorithm to predict new causing genes of 16 multifactorial diseases including Prostate cancer and Alzheimer's disease, and the top predictions were in good consistent with literature reports. Our study indicates that integration of multiple information sources, especially the phenotype similarity profile data, and introduction of global similarity measure between disease and gene diffusion profiles are helpful for prioritizing candidate disease genes. Programs and data are available upon request.

Modeling thrombin generation: plasma composition based approach.

PubMed

Brummel-Ziedins, Kathleen E; Everse, Stephen J; Mann, Kenneth G; Orfeo, Thomas

2014-01-01

Thrombin has multiple functions in blood coagulation and its regulation is central to maintaining the balance between hemorrhage and thrombosis. Empirical and computational methods that capture thrombin generation can provide advancements to current clinical screening of the hemostatic balance at the level of the individual. In any individual, procoagulant and anticoagulant factor levels together act to generate a unique coagulation phenotype (net balance) that is reflective of the sum of its developmental, environmental, genetic, nutritional and pharmacological influences. Defining such thrombin phenotypes may provide a means to track disease progression pre-crisis. In this review we briefly describe thrombin function, methods for assessing thrombin dynamics as a phenotypic marker, computationally derived thrombin phenotypes versus determined clinical phenotypes, the boundaries of normal range thrombin generation using plasma composition based approaches and the feasibility of these approaches for predicting risk.

Prediction of reduction in aggressive behaviour of growing pigs using skin lesion traits as selection criteria.

PubMed

Desire, S; Turner, S P; D'Eath, R B; Doeschl-Wilson, A B; Lewis, C R G; Roehe, R

2016-08-01

Aggression at regrouping is a common issue in pig farming. Skin lesions are genetically and phenotypically correlated with aggression and have been shown to have a significant heritable component. This study predicts the magnitude of reduction in complex aggressive behavioural traits when using lesion numbers on different body regions at two different time points as selection criteria, to identify the optimum skin lesion trait for selection purposes. In total, 1146 pigs were mixed into new social groups, and skin lesions were counted 24 h (SL24h) and 3 weeks (SL3wk) post-mixing, on the anterior, centre and posterior regions of the body. An animal model was used to estimate genetic parameters for skin lesion traits and 14 aggressive behavioural traits. Estimated breeding values (EBVs) and phenotypic values were scaled and standardised to allow direct comparison across multiple traits. Individuals with SL24h and SL3wk EBVs in the least aggressive 10% of the population were compared with the population mean to predict the expected genetic and phenotypic response in aggressive behaviour to selection. At mixing, selection for low anterior lesions was predicted to affect substantially more behavioural traits of aggressiveness than lesions obtained on other body parts, with EBVs between -0.21 and -1.17 SD below the population mean. Individuals with low central SL24h EBVs also had low EBVs for aggressive traits (-0.33 to -0.55). Individuals with high SL3wk EBVs had low EBVs for aggression at mixing (between -0.24 and -0.53 SD below the population mean), although this was predicted to affect fewer traits than selection against SL24h. These results suggest that selection against anterior SL24h would result in the greatest genetic and phenotypic reduction in aggressive behaviour recorded at mixing. Selection for increased SL3wk was predicted to reduce aggression at mixing; however, current understanding about aggressive behaviour under stable social conditions is insufficient to recommend using this trait for selection purposes.

A comparison of accuracy validation methods for genomic and pedigree-based predictions of swine litter size traits using Large White and simulated data.

PubMed

Putz, A M; Tiezzi, F; Maltecca, C; Gray, K A; Knauer, M T

2018-02-01

The objective of this study was to compare and determine the optimal validation method when comparing accuracy from single-step GBLUP (ssGBLUP) to traditional pedigree-based BLUP. Field data included six litter size traits. Simulated data included ten replicates designed to mimic the field data in order to determine the method that was closest to the true accuracy. Data were split into training and validation sets. The methods used were as follows: (i) theoretical accuracy derived from the prediction error variance (PEV) of the direct inverse (iLHS), (ii) approximated accuracies from the accf90(GS) program in the BLUPF90 family of programs (Approx), (iii) correlation between predictions and the single-step GEBVs from the full data set (GEBV Full ), (iv) correlation between predictions and the corrected phenotypes of females from the full data set (Y c ), (v) correlation from method iv divided by the square root of the heritability (Y ch ) and (vi) correlation between sire predictions and the average of their daughters' corrected phenotypes (Y cs ). Accuracies from iLHS increased from 0.27 to 0.37 (37%) in the Large White. Approximation accuracies were very consistent and close in absolute value (0.41 to 0.43). Both iLHS and Approx were much less variable than the corrected phenotype methods (ranging from 0.04 to 0.27). On average, simulated data showed an increase in accuracy from 0.34 to 0.44 (29%) using ssGBLUP. Both iLHS and Y ch approximated the increase well, 0.30 to 0.46 and 0.36 to 0.45, respectively. GEBV Full performed poorly in both data sets and is not recommended. Results suggest that for within-breed selection, theoretical accuracy using PEV was consistent and accurate. When direct inversion is infeasible to get the PEV, correlating predictions to the corrected phenotypes divided by the square root of heritability is adequate given a large enough validation data set. © 2017 Blackwell Verlag GmbH.

A Qualitative Simulation Framework in Smalltalk Based on Fuzzy Arithmetic

Treesearch

Richard L. Olson; Daniel L. Schmoldt; David L. Peterson

1996-01-01

For many systems, it is not practical to collect and correlate empirical data necessary to formulate a mathematical model. However, it is often sufficient to predict qualitative dynamics effects (as opposed to system quantities), especially for research purposes. In this effort, an object-oriented application framework (AF) was developed for the qualitative modeling of...

Investigating the impact of age-depended hair colour darkening during childhood on DNA-based hair colour prediction with the HIrisPlex system.

PubMed

Kukla-Bartoszek, Magdalena; Pośpiech, Ewelina; Spólnicka, Magdalena; Karłowska-Pik, Joanna; Strapagiel, Dominik; Żądzińska, Elżbieta; Rosset, Iwona; Sobalska-Kwapis, Marta; Słomka, Marcin; Walsh, Susan; Kayser, Manfred; Sitek, Aneta; Branicki, Wojciech

2018-06-06

Predictive DNA analysis of externally visible characteristics exerts an increasing influence on contemporary forensic and anthropological investigations, with pigmentation traits currently being the most advanced for predictive modelling. Since pigmentation prediction error in some cases may be due to the result of age-related hair colour darkening, and sex influence in eye colour, this study aims to investigate these less explored phenomena on a group of juvenile individuals. Pigmentation phenotypes of children between the age of 6-13 years old were evaluated, in addition to data about their hair colour during early childhood from a select number of these individuals. The HIrisPlex models for DNA-based eye and hair colour prediction were used with input from SNP genotyping using massive parallel sequencing. Analysis of the total group of 476 children showed high accuracy in blue (AUC = 0.89) and brown (AUC = 0.91) eye colour prediction, while hair colour was predicted with AUC = 0.64 for blond, AUC = 0.64 for brown and AUC = 0.97 for red. 70.8% (n = 143) of the total number of children phenotypically blond for hair colour during early childhood progressed to brown during advanced childhood. In 70.6% (n = 101) of those cases, an incorrect blond hair prediction was made during the time of analysis. A noticeable decline in AUC values for blond (from 0.76 to 0.65) and brown (from 0.72 to 0.64) were observed when comparing hair colour prediction outcomes for the phenotypes recorded for the two different time points (at the age of 2-3 and 6-13). The number of incorrect blond hair colour predictions was significantly higher in children with brown hair at age 6-13 who were blond at early childhood (n = 47, 32.9%), relative to children who had brown hair at both time points (n = 6, 9.4%). However, in 28.0% (n = 40) of children who did experience hair colour darkening, HIrisPlex provided the correct prediction for the darkened hair colour phenotype, despite them being blond in early childhood. Our study implies that HIrisPlex can correctly predict adult hair colour in some individuals who experience age-related hair colour darkening during adolescence. However, in most instances prediction seems to default to the pre-adolescent hair colour for individuals with this phenomenon. In the future, the full adolescent age range in which hair colour darkening can occur should be considered in the study samples used for training hair colour prediction models to obtain a more complete picture of the phenomenon and its impact on DNA-based hair colour prediction in adults. Copyright © 2018 Elsevier B.V. All rights reserved.

Analysis of conditional genetic effects and variance components in developmental genetics.

PubMed

Zhu, J

1995-12-01

A genetic model with additive-dominance effects and genotype x environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t-1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.

Analysis of Conditional Genetic Effects and Variance Components in Developmental Genetics

PubMed Central

Zhu, J.

1995-01-01

A genetic model with additive-dominance effects and genotype X environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t - 1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects. PMID:8601500

Phenotypic diversity, major genes and production potential of local chickens and guinea fowl in Tamale, northern Ghana

PubMed Central

Brown, Michael Mensah; Alenyorege, Benjamin; Teye, Gabriel Ayum; Roessler, Regina

2017-01-01

Objective Our study provides information on phenotypes of local chickens and guinea fowl and their body measures as well as on major genes in local chickens in northern Ghana. Methods Qualitative and morphometric traits were recorded on 788 local chickens and 394 guinea fowl in urban households in Tamale, Ghana. Results The results showed considerable variation of color traits and numerous major genes in local chickens, while color variations and related genotypes in guinea fowl were limited. In local chickens, white was preferred for plumage, whereas dark colors were preferred for beak and shanks. More than half of the chickens carried at least one major gene, but the contributions of single gene carriers were low. All calculated allele frequencies were significantly lower than their expected Mendelian allele frequencies. We observed higher mean body weight and larger linear body measures in male as compared to female chickens. In female chickens, we detected a small effect of major genes on body weight and chest circumference. In addition, we found some association between feather type and plumage color. In guinea fowl, seven distinct plumage colors were observed, of which pearl grey pied and pearl grey were the most prevalent. Male pearl grey pied guinea fowl were inferior to pearl grey and white guinea fowl in terms of body weight, body length and chest circumference; their shank length was lower than that of pearl grey fowl. Conclusion Considerable variation in qualitative traits of local chickens may be indicative of genetic diversity within local chicken populations, but major genes were rare. In contrast, phenotypic and genetic diversity in local guinea fowl is limited. Broader genetic diversity studies and evaluation of trait preferences of local poultry producers are required for the design of appropriate breeding programs. PMID:28728378

Phenotypic diversity, major genes and production potential of local chickens and guinea fowl in Tamale, northern Ghana.

PubMed

Brown, Michael Mensah; Alenyorege, Benjamin; Teye, Gabriel Ayum; Roessler, Regina

2017-10-01

Our study provides information on phenotypes of local chickens and guinea fowl and their body measures as well as on major genes in local chickens in northern Ghana. Qualitative and morphometric traits were recorded on 788 local chickens and 394 guinea fowl in urban households in Tamale, Ghana. The results showed considerable variation of color traits and numerous major genes in local chickens, while color variations and related genotypes in guinea fowl were limited. In local chickens, white was preferred for plumage, whereas dark colors were preferred for beak and shanks. More than half of the chickens carried at least one major gene, but the contributions of single gene carriers were low. All calculated allele frequencies were significantly lower than their expected Mendelian allele frequencies. We observed higher mean body weight and larger linear body measures in male as compared to female chickens. In female chickens, we detected a small effect of major genes on body weight and chest circumference. In addition, we found some association between feather type and plumage color. In guinea fowl, seven distinct plumage colors were observed, of which pearl grey pied and pearl grey were the most prevalent. Male pearl grey pied guinea fowl were inferior to pearl grey and white guinea fowl in terms of body weight, body length and chest circumference; their shank length was lower than that of pearl grey fowl. Considerable variation in qualitative traits of local chickens may be indicative of genetic diversity within local chicken populations, but major genes were rare. In contrast, phenotypic and genetic diversity in local guinea fowl is limited. Broader genetic diversity studies and evaluation of trait preferences of local poultry producers are required for the design of appropriate breeding programs.

Sex differences in genetic and environmental influences on educational attainment and income.

PubMed

Orstavik, Ragnhild E; Czajkowski, Nikolai; Røysamb, Espen; Knudsen, Gun Peggy; Tambs, Kristian; Reichborn-Kjennerud, Ted

2014-12-01

In many Western countries, women now reach educational levels comparable to men, although their income remains considerably lower. For the past decades, it has become increasingly clear that these measures of socio-economic status are influenced by genetic as well as environmental factors. Less is known about the relationship between education and income, and sex differences. The aim of this study was to explore genetic and environmental factors influencing education and income in a large cohort of young Norwegian twins, with special emphasis on gender differences. National register data on educational level and income were obtained for 7,710 twins (aged 29-41 years). Bivariate Cholesky models were applied to estimate qualitative and quantitative gender differences in genetic and environmental influences, the relative contribution of genetic and environmental factors to the correlation between education and income, and genetic correlations within and between sexes and phenotypes. The phenotypic correlation between educational level and income was 0.34 (0.32-0.39) for men and 0.45 (0.43-0.48) for women. An ACE model with both qualitative and quantitative sex differences fitted the data best. The genetic correlation between men and women (rg) was 0.66 (0.22-1.00) for educational attainment and 0.38 (0.01-0.75) for income, and between the two phenotypes 0.31 (0.08-0.52) for men and 0.72 (0.64-0.85) for women. Our results imply that, in relatively egalitarian societies with state-supported access to higher education and political awareness of gender equality, genetic factors may play an important role in explaining sex differences in the relationship between education and income.

A Qualitative Study to Explore the Views and Attitudes towards Prenatal Testing in Adults Who Have Muenke Syndrome and their Partners.

PubMed

Phipps, Julie; Skirton, Heather

2017-10-01

Muenke syndrome constitutes the most common syndromic form of craniosynostosis, occurring in 1 in 30,000 live births. The phenotype is variable, ranging from no clinical findings to complex presentation. Facilitating reproductive decision making for couples at genetic risk of having a child with Muenke syndrome is an important aspect of genetic counselling. Prenatal genetic testing for Muenke syndrome is accurate; however the value of testing is uncertain with a variable phenotype. The purpose of this study was to explore attitudes towards prenatal testing in couples where one partner had tested positive for the Muenke mutation. We used a qualitative approach based on thematic analysis and collected data using individual semi-structured interviews with eight parents. Five key themes were: The Muenke journey; Impact and knowledge of diagnosis; Knowledge and attitude to prenatal testing; Stigma and sharing of information; and Information retention. Knowledge of Muenke syndrome and prenatal testing was poor. Genetic information was provided when treatment of their affected child was their paramount concern. Couples reported not sharing genetic information with family due to fear of stigmatisation. Couples cannot make reproductive decisions if lacking appropriate understanding of the choices: timely genetic counselling regarding prenatal testing is needed when relevant to them.

Exercises in Anatomy, Connectivity, and Morphology using Neuromorpho.org and the Allen Brain Atlas.

PubMed

Chu, Philip; Peck, Joshua; Brumberg, Joshua C

2015-01-01

Laboratory instruction of neuroscience is often limited by the lack of physical resources and supplies (e.g., brains specimens, dissection kits, physiological equipment). Online databases can serve as supplements to material labs by providing professionally collected images of brain specimens and their underlying cellular populations with resolution and quality that is extremely difficult to access for strictly pedagogical purposes. We describe a method using two online databases, the Neuromorpho.org and the Allen Brain Atlas (ABA), that freely provide access to data from working brain scientists that can be modified for laboratory instruction/exercises. Neuromorpho.org is the first neuronal morphology database that provides qualitative and quantitative data from reconstructed cells analyzed in published scientific reports. The Neuromorpho.org database contains cross species and multiple neuronal phenotype datasets which allows for comparative examinations. The ABA provides modules that allow students to study the anatomy of the rodent brain, as well as observe the different cellular phenotypes that exist using histochemical labeling. Using these tools in conjunction, advanced students can ask questions about qualitative and quantitative neuronal morphology, then examine the distribution of the same cell types across the entire brain to gain a full appreciation of the magnitude of the brain's complexity.

Dimensional Structure of the Autism Phenotype: Relations between Early Development and Current Presentation

ERIC Educational Resources Information Center

Kamp-Becker, Inge; Ghahreman, Mardjan; Smidt, Judith; Remschmidt, Helmut

2009-01-01

The dimensional structure of higher functioning autism phenotype was investigated by factor analysis. The goal of this study was to identify the degree to which early symptoms of autism (measured using the ADI-R) could be predictive of the current symptoms of autism as identified using the ADOS, the adaptive behavior scales, IQ scores and theory…

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.

PubMed

Tiwari, Pradeep; Kutum, Rintu; Sethi, Tavpritesh; Shrivastava, Ankita; Girase, Bhushan; Aggarwal, Shilpi; Patil, Rutuja; Agarwal, Dhiraj; Gautam, Pramod; Agrawal, Anurag; Dash, Debasis; Ghosh, Saurabh; Juvekar, Sanjay; Mukerji, Mitali; Prasher, Bhavana

2017-01-01

In Ayurveda system of medicine individuals are classified into seven constitution types, "Prakriti", for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

PubMed

Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

2017-10-01

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

Exploring Genetic Attributions Underlying Radiotherapy-Induced Fatigue in Prostate Cancer Patients.

PubMed

Hashemi, Sepehr; Fernandez Martinez, Juan Luis; Saligan, Leorey; Sonis, Stephen

2017-09-01

Despite numerous proposed mechanisms, no definitive pathophysiology underlying radiotherapy-induced fatigue (RIF) has been established. However, the dysregulation of a set of 35 genes was recently validated to predict development of fatigue in prostate cancer patients receiving radiotherapy. To hypothesize novel pathways, and provide genetic targets for currently proposed pathways implicated in RIF development through analysis of the previously validated gene set. The gene set was analyzed for all phenotypic attributions implicated in the phenotype of fatigue. Initially, a "directed" approach was used by querying specific fatigue-related sub-phenotypes against all known phenotypic attributions of the gene set. Then, an "undirected" approach, reviewing the entirety of the literature referencing the 35 genes, was used to increase analysis sensitivity. The dysregulated genes attribute to neural, immunological, mitochondrial, muscular, and metabolic pathways. In addition, certain genes suggest phenotypes not previously emphasized in the context of RIF, such as ionizing radiation sensitivity, DNA damage, and altered DNA repair frequency. Several genes also associated with prostate cancer depression, possibly emphasizing variable radiosensitivity by RIF-prone patients, which may have palliative care implications. Despite the relevant findings, many of the 35 RIF-predictive genes are poorly characterized, warranting their investigation. The implications of herein presented RIF pathways are purely theoretical until specific end-point driven experiments are conducted in more congruent contexts. Nevertheless, the presented attributions are informative, directing future investigation to definitively elucidate RIF's pathoetiology. This study demonstrates an arguably comprehensive method of approaching known differential expression underlying a complex phenotype, to correlate feasible pathophysiology. Copyright © 2017 American Academy of Hospice and Palliative Medicine. All rights reserved.

Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops.

PubMed

Hammer, Graeme L; van Oosterom, Erik; McLean, Greg; Chapman, Scott C; Broad, Ian; Harland, Peter; Muchow, Russell C

2010-05-01

Progress in molecular plant breeding is limited by the ability to predict plant phenotype based on its genotype, especially for complex adaptive traits. Suitably constructed crop growth and development models have the potential to bridge this predictability gap. A generic cereal crop growth and development model is outlined here. It is designed to exhibit reliable predictive skill at the crop level while also introducing sufficient physiological rigour for complex phenotypic responses to become emergent properties of the model dynamics. The approach quantifies capture and use of radiation, water, and nitrogen within a framework that predicts the realized growth of major organs based on their potential and whether the supply of carbohydrate and nitrogen can satisfy that potential. The model builds on existing approaches within the APSIM software platform. Experiments on diverse genotypes of sorghum that underpin the development and testing of the adapted crop model are detailed. Genotypes differing in height were found to differ in biomass partitioning among organs and a tall hybrid had significantly increased radiation use efficiency: a novel finding in sorghum. Introducing these genetic effects associated with plant height into the model generated emergent simulated phenotypic differences in green leaf area retention during grain filling via effects associated with nitrogen dynamics. The relevance to plant breeding of this capability in complex trait dissection and simulation is discussed.

Convergent evolution at the pathway level: predictable regulatory changes during flower color transitions.

PubMed

Larter, Maximilian; Dunbar-Wallis, Amy; Berardi, Andrea E; Smith, Stacey D

2018-06-07

The predictability of evolution, or whether lineages repeatedly follow the same evolutionary trajectories during phenotypic convergence remains an open question of evolutionary biology. In this study, we investigate evolutionary convergence at the biochemical pathway level and test the predictability of evolution using floral anthocyanin pigmentation, a trait with a well-understood genetic and regulatory basis. We reconstructed the evolution of floral anthocyanin content across 28 species of the Andean clade Iochrominae (Solanaceae) and investigated how shifts in pigmentation are related to changes in expression of 7 key anthocyanin pathway genes. We used phylogenetic multivariate analysis of gene expression to test for phenotypic and developmental convergence at a macroevolutionary scale. Our results show that the four independent losses of the ancestral pigment delphinidin involved convergent losses of expression of the three late pathway genes (F3'5'h, Dfr and Ans). Transitions between pigment types affecting floral hue (e.g. blue to red) involve changes to the expression of branching genes F3'h and F3'5'h, while the expression levels of early steps of the pathway are strongly conserved in all species. These patterns support the idea that the macroevolution of floral pigmentation follows predictable evolutionary trajectories to reach convergent phenotype space, repeatedly involving regulatory changes. This is likely driven by constraints at the pathway level, such as pleiotropy and regulatory structure.

Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis.

PubMed

Kooke, Rik; Kruijer, Willem; Bours, Ralph; Becker, Frank; Kuhn, André; van de Geest, Henri; Buntjer, Jaap; Doeswijk, Timo; Guerra, José; Bouwmeester, Harro; Vreugdenhil, Dick; Keurentjes, Joost J B

2016-04-01

Quantitative traits in plants are controlled by a large number of genes and their interaction with the environment. To disentangle the genetic architecture of such traits, natural variation within species can be explored by studying genotype-phenotype relationships. Genome-wide association studies that link phenotypes to thousands of single nucleotide polymorphism markers are nowadays common practice for such analyses. In many cases, however, the identified individual loci cannot fully explain the heritability estimates, suggesting missing heritability. We analyzed 349 Arabidopsis accessions and found extensive variation and high heritabilities for different morphological traits. The number of significant genome-wide associations was, however, very low. The application of genomic prediction models that take into account the effects of all individual loci may greatly enhance the elucidation of the genetic architecture of quantitative traits in plants. Here, genomic prediction models revealed different genetic architectures for the morphological traits. Integrating genomic prediction and association mapping enabled the assignment of many plausible candidate genes explaining the observed variation. These genes were analyzed for functional and sequence diversity, and good indications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained. For ACS11, an ethylene biosynthesis gene, haplotype differences explaining variation in the ratio of petiole and leaf length could be identified. © 2016 American Society of Plant Biologists. All Rights Reserved.

System monitoring and diagnosis with qualitative models

NASA Technical Reports Server (NTRS)

Kuipers, Benjamin

1991-01-01

A substantial foundation of tools for model-based reasoning with incomplete knowledge was developed: QSIM (a qualitative simulation program) and its extensions for qualitative simulation; Q2, Q3 and their successors for quantitative reasoning on a qualitative framework; and the CC (component-connection) and QPC (Qualitative Process Theory) model compilers for building QSIM QDE (qualitative differential equation) models starting from different ontological assumptions. Other model-compilers for QDE's, e.g., using bond graphs or compartmental models, have been developed elsewhere. These model-building tools will support automatic construction of qualitative models from physical specifications, and further research into selection of appropriate modeling viewpoints. For monitoring and diagnosis, plausible hypotheses are unified against observations to strengthen or refute the predicted behaviors. In MIMIC (Model Integration via Mesh Interpolation Coefficients), multiple hypothesized models of the system are tracked in parallel in order to reduce the 'missing model' problem. Each model begins as a qualitative model, and is unified with a priori quantitative knowledge and with the stream of incoming observational data. When the model/data unification yields a contradiction, the model is refuted. When there is no contradiction, the predictions of the model are progressively strengthened, for use in procedure planning and differential diagnosis. Only under a qualitative level of description can a finite set of models guarantee the complete coverage necessary for this performance. The results of this research are presented in several publications. Abstracts of these published papers are presented along with abtracts of papers representing work that was synergistic with the NASA grant but funded otherwise. These 28 papers include but are not limited to: 'Combined qualitative and numerical simulation with Q3'; 'Comparative analysis and qualitative integral representations'; 'Model-based monitoring of dynamic systems'; 'Numerical behavior envelopes for qualitative models'; 'Higher-order derivative constraints in qualitative simulation'; and 'Non-intersection of trajectories in qualitative phase space: a global constraint for qualitative simulation.'

Vesicular stomatitis forecasting based on Google Trends

PubMed Central

Lu, Yi; Zhou, GuangYa; Chen, Qin

2018-01-01

Background Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends. Methods American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression. Results For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively. Conclusion This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast. PMID:29385198

Phosphodiesterase 4 Inhibitors Attenuate the Asthma Phenotype Produced by β2-Adrenoceptor Agonists in Phenylethanolamine N-Methyltransferase-Knockout Mice.

PubMed

Forkuo, Gloria S; Kim, Hosu; Thanawala, Vaidehi J; Al-Sawalha, Nour; Valdez, Daniel; Joshi, Radhika; Parra, Sergio; Pera, Tonio; Gonnella, Patricia A; Knoll, Brian J; Walker, Julia K L; Penn, Raymond B; Bond, Richard A

2016-08-01

Mice lacking the endogenous β2-adrenoceptor (β2AR) agonist epinephrine (phenylethanolamine N-methyltransferase [PNMT]-knockout mice) are resistant to developing an "asthma-like" phenotype in an ovalbumin sensitization and challenge (Ova S/C) model, and chronic administration of β2AR agonists to PNMT-KO mice restores the phenotype. Based on these and other studies showing differential effects of various β2AR ligands on the asthma phenotype, we have speculated that the permissive effect of endogenous epinephrine and exogenous β2AR agonists on allergic lung inflammation can be explained by qualitative β2AR signaling. The β2AR can signal through at least two pathways: the canonical Gαs-cAMP pathway and a β-arrestin-dependent pathway. Previous studies suggest that β-arrestin-2 is required for allergic lung inflammation. On the other hand, cell-based assays suggest antiinflammatory effects of Gαs-cAMP signaling. This study was designed to test whether the in vitro antiinflammatory effects of phosphodiesterase 4 inhibitors, known to increase intracellular cAMP in multiple airway cell types, attenuate the asthma-like phenotype produced by the β2AR agonists formoterol and salmeterol in vivo in PNMT-KO mice, based on the hypothesis that skewing β2AR signaling toward Gαs-cAMP pathway is beneficial. Airway inflammatory cells, epithelial mucus production, and airway hyperresponsiveness were quantified. In Ova S/C PNMT-KO mice, formoterol and salmeterol restored the asthma-like phenotype comparable to Ova S/C wild-type mice. However, coadministration of either roflumilast or rolipram attenuated this formoterol- or salmeterol-driven phenotype in Ova S/C PNMT-KO. These findings suggest that amplification of β2AR-mediated cAMP by phosphodiesterase 4 inhibitors attenuates the asthma-like phenotype promoted by β-agonists.

Inference of Genotype–Phenotype Relationships in the Antigenic Evolution of Human Influenza A (H3N2) Viruses

PubMed Central

Steinbrück, Lars; McHardy, Alice Carolyn

2012-01-01

Distinguishing mutations that determine an organism's phenotype from (near-) neutral ‘hitchhikers’ is a fundamental challenge in genome research, and is relevant for numerous medical and biotechnological applications. For human influenza viruses, recognizing changes in the antigenic phenotype and a strains' capability to evade pre-existing host immunity is important for the production of efficient vaccines. We have developed a method for inferring ‘antigenic trees’ for the major viral surface protein hemagglutinin. In the antigenic tree, antigenic weights are assigned to all tree branches, which allows us to resolve the antigenic impact of the associated amino acid changes. Our technique predicted antigenic distances with comparable accuracy to antigenic cartography. Additionally, it identified both known and novel sites, and amino acid changes with antigenic impact in the evolution of influenza A (H3N2) viruses from 1968 to 2003. The technique can also be applied for inference of ‘phenotype trees’ and genotype–phenotype relationships from other types of pairwise phenotype distances. PMID:22532796

Directional selection effects on patterns of phenotypic (co)variation in wild populations

PubMed Central

Patton, J. L.; Hubbe, A.; Marroig, G.

2016-01-01

Phenotypic (co)variation is a prerequisite for evolutionary change, and understanding how (co)variation evolves is of crucial importance to the biological sciences. Theoretical models predict that under directional selection, phenotypic (co)variation should evolve in step with the underlying adaptive landscape, increasing the degree of correlation among co-selected traits as well as the amount of genetic variance in the direction of selection. Whether either of these outcomes occurs in natural populations is an open question and thus an important gap in evolutionary theory. Here, we documented changes in the phenotypic (co)variation structure in two separate natural populations in each of two chipmunk species (Tamias alpinus and T. speciosus) undergoing directional selection. In populations where selection was strongest (those of T. alpinus), we observed changes, at least for one population, in phenotypic (co)variation that matched theoretical expectations, namely an increase of both phenotypic integration and (co)variance in the direction of selection and a re-alignment of the major axis of variation with the selection gradient. PMID:27881744

Preemptive spatial competition under a reproduction-mortality constraint.

PubMed

Allstadt, Andrew; Caraco, Thomas; Korniss, G

2009-06-21

Spatially structured ecological interactions can shape selection pressures experienced by a population's different phenotypes. We study spatial competition between phenotypes subject to antagonistic pleiotropy between reproductive effort and mortality rate. The constraint we invoke reflects a previous life-history analysis; the implied dependence indicates that although propagation and mortality rates both vary, their ratio is fixed. We develop a stochastic invasion approximation predicting that phenotypes with higher propagation rates will invade an empty environment (no biotic resistance) faster, despite their higher mortality rate. However, once population density approaches demographic equilibrium, phenotypes with lower mortality are favored, despite their lower propagation rate. We conducted a set of pairwise invasion analyses by simulating an individual-based model of preemptive competition. In each case, the phenotype with the lowest mortality rate and (via antagonistic pleiotropy) the lowest propagation rate qualified as evolutionarily stable among strategies simulated. This result, for a fixed propagation to mortality ratio, suggests that a selective response to spatial competition can extend the time scale of the population's dynamics, which in turn decelerates phenotypic evolution.

Eco-genetic modeling of contemporary life-history evolution.

PubMed

Dunlop, Erin S; Heino, Mikko; Dieckmann, Ulf

2009-10-01

We present eco-genetic modeling as a flexible tool for exploring the course and rates of multi-trait life-history evolution in natural populations. We build on existing modeling approaches by combining features that facilitate studying the ecological and evolutionary dynamics of realistically structured populations. In particular, the joint consideration of age and size structure enables the analysis of phenotypically plastic populations with more than a single growth trajectory, and ecological feedback is readily included in the form of density dependence and frequency dependence. Stochasticity and life-history trade-offs can also be implemented. Critically, eco-genetic models permit the incorporation of salient genetic detail such as a population's genetic variances and covariances and the corresponding heritabilities, as well as the probabilistic inheritance and phenotypic expression of quantitative traits. These inclusions are crucial for predicting rates of evolutionary change on both contemporary and longer timescales. An eco-genetic model can be tightly coupled with empirical data and therefore may have considerable practical relevance, in terms of generating testable predictions and evaluating alternative management measures. To illustrate the utility of these models, we present as an example an eco-genetic model used to study harvest-induced evolution of multiple traits in Atlantic cod. The predictions of our model (most notably that harvesting induces a genetic reduction in age and size at maturation, an increase or decrease in growth capacity depending on the minimum-length limit, and an increase in reproductive investment) are corroborated by patterns observed in wild populations. The predicted genetic changes occur together with plastic changes that could phenotypically mask the former. Importantly, our analysis predicts that evolutionary changes show little signs of reversal following a harvest moratorium. This illustrates how predictions offered by eco-genetic models can enable and guide evolutionarily sustainable resource management.

GLADIATOR: a global approach for elucidating disease modules.

PubMed

Silberberg, Yael; Kupiec, Martin; Sharan, Roded

2017-05-26

Understanding the genetic basis of disease is an important challenge in biology and medicine. The observation that disease-related proteins often interact with one another has motivated numerous network-based approaches for deciphering disease mechanisms. In particular, protein-protein interaction networks were successfully used to illuminate disease modules, i.e., interacting proteins working in concert to drive a disease. The identification of these modules can further our understanding of disease mechanisms. We devised a global method for the prediction of multiple disease modules simultaneously named GLADIATOR (GLobal Approach for DIsease AssociaTed mOdule Reconstruction). GLADIATOR relies on a gold-standard disease phenotypic similarity to obtain a pan-disease view of the underlying modules. To traverse the search space of potential disease modules, we applied a simulated annealing algorithm aimed at maximizing the correlation between module similarity and the gold-standard phenotypic similarity. Importantly, this optimization is employed over hundreds of diseases simultaneously. GLADIATOR's predicted modules highly agree with current knowledge about disease-related proteins. Furthermore, the modules exhibit high coherence with respect to functional annotations and are highly enriched with known curated pathways, outperforming previous methods. Examination of the predicted proteins shared by similar diseases demonstrates the diverse role of these proteins in mediating related processes across similar diseases. Last, we provide a detailed analysis of the suggested molecular mechanism predicted by GLADIATOR for hyperinsulinism, suggesting novel proteins involved in its pathology. GLADIATOR predicts disease modules by integrating knowledge of disease-related proteins and phenotypes across multiple diseases. The predicted modules are functionally coherent and are more in line with current biological knowledge compared to modules obtained using previous disease-centric methods. The source code for GLADIATOR can be downloaded from http://www.cs.tau.ac.il/~roded/GLADIATOR.zip .

Forming groups of aggressive sows based on a predictive test of aggression does not affect overall sow aggression or welfare.

PubMed

Verdon, Megan; Morrison, R S; Hemsworth, P H

2018-05-01

This experiment examined the effects of group composition on sow aggressive behaviour and welfare. Over 6 time replicates, 360 sows (parity 1-6) were mixed into groups (10 sows per pen, 1.8 m 2 /sow) composed of animals that were predicted to be aggressive (n = 18 pens) or groups composed of animals that were randomly selected (n = 18 pens). Predicted aggressive sows were selected based on a model-pig test that has been shown to be related to the aggressive behaviour of parity 2 sows when subsequently mixed in groups. Measurements were taken on aggression delivered post-mixing, and aggression delivered around feeding, fresh skin injuries and plasma cortisol concentrations at days 2 and 24 post-mixing. Live weight gain, litter size (born alive, total born, stillborn piglets), and farrowing rate were also recorded. Manipulating the group composition based on predicted sow aggressiveness had no effect (P > 0.05) on sow aggression delivered at mixing or around feeding, fresh injuries, cortisol, weight gain from day 2 to day 24, farrowing rate, or litter size. The lack of treatment effects in the present experiment could be attributed to (1) a failure of the model-pig test to predict aggression in older sows in groups, or (2) the dependence of the expression of the aggressive phenotype on factors such as social experience and characteristics (e.g., physical size and aggressive phenotype) of pen mates. This research draws attention to the intrinsic difficulties associated with predicting behaviour across contexts, particularly when the behaviour is highly dependent on interactions with conspecifics, and highlights the social complexities involved in the presentation of a behavioural phenotype. Copyright © 2018 Elsevier B.V. All rights reserved.

Renin-angiotensin system phenotyping as a guidance toward personalized medicine for ACE inhibitors: can the response to ACE inhibition be predicted on the basis of plasma renin or ACE?

PubMed

Schilders, Joyce E M; Wu, Haiyan; Boomsma, Frans; van den Meiracker, Anton H; Danser, A H Jan

2014-08-01

Not all hypertensive patients respond well to ACE inhibition. Here we determined whether renin-angiotensin system (RAS) phenotyping, i.e., the measurement of renin or ACE, can predict the individual response to RAS blockade, either chronically (enalapril vs. enalapril + candesartan) or acutely (enalapril ± hydrochlorothiazide, HCT). Chronic enalapril + candesartan induced larger renin rises, but did not lower blood pressure (BP) more than enalapril. Similar observations were made for enalapril + HCT vs. enalapril when given acutely. Baseline renin predicted the peak changes in BP chronically, but not acutely. Baseline ACE levels had no predictive value. Yet, after acute drug intake, the degree of ACE inhibition, like Δrenin, did correlate with ΔBP. Only the relationship with Δrenin remained significant after chronic RAS blockade. Thus, a high degree of ACE inhibition and a steep renin rise associate with larger acute responses to enalapril. However, variation was large, ranging >50 mm Hg for a given degree of ACE inhibition or Δrenin. The same was true for the relationships between Δrenin and ΔBP, and between baseline renin and the maximum reduction in BP in the chronic study. Our data do not support that RAS phenotyping will help to predict the individual BP response to RAS blockade. Notably, these conclusions were reached in a carefully characterized, homogenous population, and when taking into account the known fluctuations in renin that relate to gender, age, ethnicity, salt intake and diuretic treatment, it seems unlikely that a cut-off renin level can be defined that has predictive value.

A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions.

PubMed

Mian, Shahid; Ball, Graham; Hornbuckle, Jo; Holding, Finn; Carmichael, James; Ellis, Ian; Ali, Selman; Li, Geng; McArdle, Stephanie; Creaser, Colin; Rees, Robert

2003-09-01

An ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI-TOF matrix-assisted laser desorption/ionization-time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic 'fingerprints' indicative of treatment regimen for chemosensitive (MCF-7, T47D) and chemoresistant (MCF-7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign 'class' for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot-strap statistical validation techniques and that biomarker ion patterns indicative of response/non-response phenotypes are associated with MCF-7 and MCF-7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF-7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF-7/ADR were compared against those derived from MCF-7 and T47D as a model system of drug resistant and drug sensitive phenotypes. This approach might offer a potential methodology for predicting the biological behaviour of cancer cells towards particular chemotherapeutics and through protein isolation and sequence identification could result in the identification of biological molecules associated with chemosensitive/chemoresistance tumour phenotypes.

Capitalizing on fine milk composition for breeding and management of dairy cows.

PubMed

Gengler, N; Soyeurt, H; Dehareng, F; Bastin, C; Colinet, F; Hammami, H; Vanrobays, M-L; Lainé, A; Vanderick, S; Grelet, C; Vanlierde, A; Froidmont, E; Dardenne, P

2016-05-01

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

[Behavioral phenotypes: cognitive and emotional explanation].

PubMed

Pérez-Alvarez, F; Timoneda-Gallart, C

We present a series of Behavioural phenotypes treated with neurocognitive and neuroemotional procedure. A sample of 26 cases were selected according to qualitative methodology from neuropediatric patients. The method was based on using the PASS theory of intelligence to approach the cognitive problem and the theory of masquerade behaviour as self-defence to solve the emotional problem. Both theories have neurological bases. DN:CAS battery was utilized for assessment of cognitive processes. On the other hand, analysis of cases was carried out doing data analysis with video recorder device. All cases were considered responder cases although in different degree. The responder was defined as the patient which reached better intellectual achievement with respect to cognitive function and which gave up, at least partially, masquerade Behaviour with respect to emotional function. The Behaviour of the Behavioural phenotypes has neurological rationale. The PASS theory and the planning, in particular, supported by prefrontal cortex justifies consistently some behaviours. The masquerade Behaviour theory is explained by the fear emotional response mechanism which means emotion is a cerebral processing with neurological rationale. The Behavioural phenotypes are Behaviours and every Behaviour can be explained by neurological reasons both cognitive and emotional reasons. So, they can be treated by a cognitive and emotional procedure understood in the light of the neurology.

Chloroplast 2010: A Database for Large-Scale Phenotypic Screening of Arabidopsis Mutants1[W][OA

PubMed Central

Lu, Yan; Savage, Linda J.; Larson, Matthew D.; Wilkerson, Curtis G.; Last, Robert L.

2011-01-01

Large-scale phenotypic screening presents challenges and opportunities not encountered in typical forward or reverse genetics projects. We describe a modular database and laboratory information management system that was implemented in support of the Chloroplast 2010 Project, an Arabidopsis (Arabidopsis thaliana) reverse genetics phenotypic screen of more than 5,000 mutants (http://bioinfo.bch.msu.edu/2010_LIMS; www.plastid.msu.edu). The software and laboratory work environment were designed to minimize operator error and detect systematic process errors. The database uses Ruby on Rails and Flash technologies to present complex quantitative and qualitative data and pedigree information in a flexible user interface. Examples are presented where the database was used to find opportunities for process changes that improved data quality. We also describe the use of the data-analysis tools to discover mutants defective in enzymes of leucine catabolism (heteromeric mitochondrial 3-methylcrotonyl-coenzyme A carboxylase [At1g03090 and At4g34030] and putative hydroxymethylglutaryl-coenzyme A lyase [At2g26800]) based upon a syndrome of pleiotropic seed amino acid phenotypes that resembles previously described isovaleryl coenzyme A dehydrogenase (At3g45300) mutants. In vitro assay results support the computational annotation of At2g26800 as hydroxymethylglutaryl-coenzyme A lyase. PMID:21224340

Using simulation models to predict feed intake: phenotypic and genetic relationships between observed and predicted values in cattle.

PubMed

Williams, C B; Bennett, G L; Jenkins, T G; Cundiff, L V; Ferrell, C L

2006-06-01

The objectives of this study were to evaluate the accuracy of the Decision Evaluator for the Cattle Industry (DECI) and the Cornell Value Discovery System (CVDS) in predicting individual DMI and to assess the feasibility of using predicted DMI data in genetic evaluations of cattle. Observed individual animal data on the average daily DMI (OFI), ADG, and carcass measurements were obtained from postweaning records of 504 steers from 52 sires (502 with complete data). The experimental data and daily temperature and wind speed data were used as inputs to predict average daily feed DMI (kg) required (feed required; FR) for maintenance, cold stress, and ADG; maintenance and cold stress; ADG; maintenance and ADG; and maintenance alone, with CVDS (CFRmcg, CFRmc, CFRg, CFRmg, and CFRm, respectively) and DECI (DFRmcg, DFRmc, DFRg, DFRmg, and DFRm, respectively). Genetic parameters were estimated by REML using an animal model with age on test as a covariate and with genotype, age of dam, and year as fixed effects. Regression equations for observed on predicted DMI were OFI = 1.27 (SE = 0.27) + 0.83 (SE = 0.04) x CFRmcg [R2 = 0.44, residual SD (s(y.x)) = 0.669 kg/d] and OFI = 1.32 (SE = 0.22) + 0.8 (SE = 0.03) x DFRmcg (R2 = 0.53, s(y.x) = 0.612 kg/d). Heritability of OFI was 0.27 +/- 0.12, and heritabilities ranged from 0.33 +/- 0.12 to 0.41 +/- 0.13 for predicted measures of DMI. Phenotypic and genetic correlations between OFI and CFRmcg, CFRmc, CFRg, CFRmg, CFRm, DFRmcg, DFRmc, DFRg, DFRmg, and DFRm were 0.67, 0.73, 0.41, 0.63, 0.78, 0.73, 0.82, 0.45, 0.77, and 0.86 (P < 0.001 for all phenotypic correlations); and 0.95 +/- 0.07, 0.82 +/- 0.13, 0.89 +/- 0.09, 0.95 +/- 0.07, 0.91 +/- 0.09, 0.96 +/- 0.07, 0.89 +/- 0.09, 0.88 +/- 0.09, 0.96 +/- 0.06, and 0.96 +/- 0.07, respectively. Phenotypic and genetic correlations between CFRmcg and DFRmcg, CFRmc and DFRmc, CFRg and DFRg, CFRmg and DFRmg, and CFRm and DFRm were 0.98, 0.94, 0.99, 0.98, and 0.95 (P < 0.001 for all phenotypic correlations), and 0.99 +/- 0.004, 0.98 +/- 0.017, 0.99 +/- 0.004, 0.99 +/- 0.005, and 0.97 +/- 0.021, respectively. The strong genetic relationships between OFI and CFRmcg, CFRmg, DFRmcg, and DFRmg indicate that these predicted measures of DMI may be used in genetic evaluations and that DM requirements for cold stress may not be needed, thus reducing model complexity. However, high genetic correlations for final weight with OFI, CFRmcg, and DFRmcg suggest that the technology needs to be further evaluated in populations with genetic variance in feed efficiency.

Phenotypic characterization of glioblastoma identified through shape descriptors

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2016-03-01

This paper proposes quantitatively describing the shape of glioblastoma (GBM) tissue phenotypes as a set of shape features derived from segmentations, for the purposes of discriminating between GBM phenotypes and monitoring tumor progression. GBM patients were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Three GBM tissue phenotypes are considered including necrosis, active tumor and edema/invasion. Volumetric tissue segmentations are obtained from registered T1˗weighted (T1˗WI) postcontrast and fluid-attenuated inversion recovery (FLAIR) MRI modalities. Shape features are computed from respective tissue phenotype segmentations, and a Kruskal-Wallis test was employed to select features capable of classification with a significance level of p < 0.05. Several classifier models are employed to distinguish phenotypes, where a leave-one-out cross-validation was performed. Eight features were found statistically significant for classifying GBM phenotypes with p <0.05, orientation is uninformative. Quantitative evaluations show the SVM results in the highest classification accuracy of 87.50%, sensitivity of 94.59% and specificity of 92.77%. In summary, the shape descriptors proposed in this work show high performance in predicting GBM tissue phenotypes. They are thus closely linked to morphological characteristics of GBM phenotypes and could potentially be used in a computer assisted labeling system.

Smoothness within ruggedness: the role of neutrality in adaptation.

PubMed Central

Huynen, M A; Stadler, P F; Fontana, W

1996-01-01

RNA secondary structure folding algorithms predict the existence of connected networks of RNA sequences with identical structure. On such networks, evolving populations split into subpopulations, which diffuse independently in sequence space. This demands a distinction between two mutation thresholds: one at which genotypic information is lost and one at which phenotypic information is lost. In between, diffusion enables the search of vast areas in genotype space while still preserving the dominant phenotype. By this dynamic the success of phenotypic adaptation becomes much less sensitive to the initial conditions in genotype space. Images Fig. 2 PMID:8552647

Introduction to Focus Issue: Genetic Interactions

NASA Astrophysics Data System (ADS)

Segrè, Daniel; Marx, Christopher J.

2010-06-01

The perturbation of a gene in an organism's genome often causes changes in the organism's observable properties or phenotypes. It is not obvious a priori whether the simultaneous perturbation of two genes produces a phenotypic change that is easily predictable from the changes caused by individual perturbations. In fact, this is often not the case: the nonlinearity and interdependence between genetic variants in determining phenotypes, also known as epistasis, is a prevalent phenomenon in biological systems. This focus issue presents recent developments in the study of epistasis and genetic interactions, emphasizing the broad implications of this phenomenon in evolutionary biology, functional genomics, and human diseases.

Warped linear mixed models for the genetic analysis of transformed phenotypes

PubMed Central

Fusi, Nicolo; Lippert, Christoph; Lawrence, Neil D.; Stegle, Oliver

2014-01-01

Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic values to make them as Gaussian as possible, for instance by applying logarithmic or other nonlinear transformations. Unfortunately, different phenotypes require different transformations, and choosing an appropriate transformation is challenging and subjective. Here we present an extension of the LMM that estimates an optimal transformation from the observed data. In simulations and applications to real data from human, mouse and yeast, we show that using transformations inferred by our model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction. PMID:25234577

Warped linear mixed models for the genetic analysis of transformed phenotypes.

PubMed

Fusi, Nicolo; Lippert, Christoph; Lawrence, Neil D; Stegle, Oliver

2014-09-19

Linear mixed models (LMMs) are a powerful and established tool for studying genotype-phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic values to make them as Gaussian as possible, for instance by applying logarithmic or other nonlinear transformations. Unfortunately, different phenotypes require different transformations, and choosing an appropriate transformation is challenging and subjective. Here we present an extension of the LMM that estimates an optimal transformation from the observed data. In simulations and applications to real data from human, mouse and yeast, we show that using transformations inferred by our model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction.

Assessing genomic selection prediction accuracy in a dynamic barley breeding

USDA-ARS?s Scientific Manuscript database

Genomic selection is a method to improve quantitative traits in crops and livestock by estimating breeding values of selection candidates using phenotype and genome-wide marker data sets. Prediction accuracy has been evaluated through simulation and cross-validation, however validation based on prog...

Testing the adaptive radiation hypothesis for the lemurs of Madagascar.

PubMed

Herrera, James P

2017-01-01

Lemurs, the diverse, endemic primates of Madagascar, are thought to represent a classic example of adaptive radiation. Based on the most complete phylogeny of living and extinct lemurs yet assembled, I tested predictions of adaptive radiation theory by estimating rates of speciation, extinction and adaptive phenotypic evolution. As predicted, lemur speciation rate exceeded that of their sister clade by nearly twofold, indicating the diversification dynamics of lemurs and mainland relatives may have been decoupled. Lemur diversification rates did not decline over time, however, as predicted by adaptive radiation theory. Optimal body masses diverged among dietary and activity pattern niches as lineages diversified into unique multidimensional ecospace. Based on these results, lemurs only partially fulfil the predictions of adaptive radiation theory, with phenotypic evolution corresponding to an 'early burst' of adaptive differentiation. The results must be interpreted with caution, however, because over the long evolutionary history of lemurs (approx. 50 million years), the 'early burst' signal of adaptive radiation may have been eroded by extinction.

Predicting rates of interspecific interaction from phylogenetic trees.

PubMed

Nuismer, Scott L; Harmon, Luke J

2015-01-01

Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction. © 2014 John Wiley & Sons Ltd/CNRS.

Testing the adaptive radiation hypothesis for the lemurs of Madagascar

PubMed Central

2017-01-01

Lemurs, the diverse, endemic primates of Madagascar, are thought to represent a classic example of adaptive radiation. Based on the most complete phylogeny of living and extinct lemurs yet assembled, I tested predictions of adaptive radiation theory by estimating rates of speciation, extinction and adaptive phenotypic evolution. As predicted, lemur speciation rate exceeded that of their sister clade by nearly twofold, indicating the diversification dynamics of lemurs and mainland relatives may have been decoupled. Lemur diversification rates did not decline over time, however, as predicted by adaptive radiation theory. Optimal body masses diverged among dietary and activity pattern niches as lineages diversified into unique multidimensional ecospace. Based on these results, lemurs only partially fulfil the predictions of adaptive radiation theory, with phenotypic evolution corresponding to an ‘early burst’ of adaptive differentiation. The results must be interpreted with caution, however, because over the long evolutionary history of lemurs (approx. 50 million years), the ‘early burst’ signal of adaptive radiation may have been eroded by extinction. PMID:28280597

Metabolic Host Responses to Malarial Infection during the Intraerythrocytic Developmental Cycle

DTIC Science & Technology

2016-08-08

by reproducing the experimentally determined 1) stage-specific production of biomass components and their precursors in the parasite and 2) metabolite...uptake, allow for the prediction of cellular growth ( biomass accumulation) and other phenotypic functions related to metabolism [9]. For example...our group to capture stage-specific growth phenotypes and biomass metabolite production [15]. Among these metabolic descriptions, only the network

Information Uncertainty to Compare Qualitative Reasoning Security Risk Assessment Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chavez, Gregory M; Key, Brian P; Zerkle, David K

2009-01-01

The security risk associated with malevolent acts such as those of terrorism are often void of the historical data required for a traditional PRA. Most information available to conduct security risk assessments for these malevolent acts is obtained from subject matter experts as subjective judgements. Qualitative reasoning approaches such as approximate reasoning and evidential reasoning are useful for modeling the predicted risk from information provided by subject matter experts. Absent from these approaches is a consistent means to compare the security risk assessment results. Associated with each predicted risk reasoning result is a quantifiable amount of information uncertainty which canmore » be measured and used to compare the results. This paper explores using entropy measures to quantify the information uncertainty associated with conflict and non-specificity in the predicted reasoning results. The measured quantities of conflict and non-specificity can ultimately be used to compare qualitative reasoning results which are important in triage studies and ultimately resource allocation. Straight forward extensions of previous entropy measures are presented here to quantify the non-specificity and conflict associated with security risk assessment results obtained from qualitative reasoning models.« less

Virtual tissues in toxicology.

PubMed

Shah, Imran; Wambaugh, John

2010-02-01

New approaches are vital for efficiently evaluating human health risk of thousands of chemicals in commerce. In vitro models offer a high-throughput approach for assaying chemical-induced molecular and cellular changes; however, bridging these perturbations to in vivo effects across chemicals, dose, time, and species remains challenging. Technological advances in multiresolution imaging and multiscale simulation are making it feasible to reconstruct tissues in silico. In toxicology, these "virtual" tissues (VT) aim to predict histopathological outcomes from alterations of cellular phenotypes that are controlled by chemical-induced perturbations in molecular pathways. The behaviors of thousands of heterogeneous cells in tissues are simulated discretely using agent-based modeling (ABM), in which computational "agents" mimic cell interactions and cellular responses to the microenvironment. The behavior of agents is constrained by physical laws and biological rules derived from experimental evidence. VT extend compartmental physiologic models to simulate both acute insults as well as the chronic effects of low-dose exposure. Furthermore, agent behavior can encode the logic of signaling and genetic regulatory networks to evaluate the role of different pathways in chemical-induced injury. To extrapolate toxicity across species, chemicals, and doses, VT require four main components: (a) organization of prior knowledge on physiologic events to define the mechanistic rules for agent behavior, (b) knowledge on key chemical-induced molecular effects, including activation of stress sensors and changes in molecular pathways that alter the cellular phenotype, (c) multiresolution quantitative and qualitative analysis of histologic data to characterize and measure chemical-, dose-, and time-dependent physiologic events, and (d) multiscale, spatiotemporal simulation frameworks to effectively calibrate and evaluate VT using experimental data. This investigation presents the motivation, implementation, and application of VT with examples from hepatotoxicity and carcinogenesis.

Non-destructive and fast identification of cotton-polyester blend fabrics by the portable near-infrared spectrometer.

PubMed

Li, Wen-xia; Li, Feng; Zhao, Guo-liang; Tang, Shi-jun; Liu, Xiao-ying

2014-12-01

A series of 376 cotton-polyester (PET) blend fabrics were studied by a portable near-infrared (NIR) spectrometer. A NIR semi-quantitative-qualitative calibration model was established by Partial Least Squares (PLS) method combined with qualitative identification coefficient. In this process, PLS method in a quantitative analysis was used as a correction method, and the qualitative identification coefficient was set by the content of cotton and polyester in blend fabrics. Cotton-polyester blend fabrics were identified qualitatively by the model and their relative contents were obtained quantitatively, the model can be used for semi-quantitative identification analysis. In the course of establishing the model, the noise and baseline drift of the spectra were eliminated by Savitzky-Golay(S-G) derivative. The influence of waveband selection and different pre-processing method was also studied in the qualitative calibration model. The major absorption bands of 100% cotton samples were in the 1400~1600 nm region, and the one for 100% polyester were around 1600~1800 nm, the absorption intensity was enhancing with the content increasing of cotton or polyester. Therefore, the cotton-polyester's major absorption region was selected as the base waveband, the optimal waveband (1100~2500 nm) was found by expanding the waveband in two directions (the correlation coefficient was 0.6, and wave-point number was 934). The validation samples were predicted by the calibration model, the results showed that the model evaluation parameters was optimum in the 1100~2500 nm region, and the combination of S-G derivative, multiplicative scatter correction (MSC) and mean centering was used as the pre-processing method. RC (relational coefficient of calibration) value was 0.978, RP (relational coefficient of prediction) value was 0.940, SEC (standard error of calibration) value was 1.264, SEP (standard error of prediction) value was 1.590, and the sample's recognition accuracy was up to 93.4%. It showed that the cotton-polyester blend fabrics could be predicted by the semi-quantitative-qualitative calibration model.

Nasal lavage, blood or sputum: Which is best for phenotyping asthma?

PubMed

de Farias, Camyla F; Amorim, Maria M F; Dracoulakis, Michel; Caetano, Lilian B; Santoro, Ilka L; Fernandes, Ana L G

2017-05-01

Determination of asthma phenotypes, particularly inflammatory phenotypes, helps guide treatment and management of this heterogeneous disease. Induced sputum cytology has been the gold standard for determination of inflammatory phenotypes, but sputum induction is fairly invasive and technically challenging. Blood and nasal lavage cytology have been suggested as substitutes, but have not been fully verified. The aim of this study is to determine the accuracy of blood and nasal lavage cytometry as indicators of inflammatory phenotypes in asthma. Clinical evaluation, Asthma Control Questionnaire (ACQ) and spirometry were performed for 121 adult asthma patients, and blood, nasal lavage and induced sputum samples were taken. Eosinophils and neutrophils were counted in three samples from each subject. Inflammatory phenotypes (eosinophilic, neutrophilic, mixed and paucicellular) and cells counts were analysed using Venn diagram and receiver operating characteristic (ROC) curve, respectively. ACQ score, spirometry and bronchodilator response did not differ among subjects with different inflammatory phenotypes. Inflammatory phenotypes defined by nasal lavage cytometry were in better concordance than those defined by blood cell counts with phenotypes determined by sputum cytology, and were significantly correlated with sputum phenotypes. For eosinophilia, nasal lavage cytology showed better accuracy than blood cytology (area under the curve (AUC): 0.89 vs 0.65). For all phenotypes, sensitivity and positive and negative predictive power were higher for nasal lavage cytometry than for blood. Blood cell counts gave a high level of false positives for all inflammatory phenotypes. We recommend nasal lavage cytology over blood cell count as a substitute for sputum cytology to identify inflammatory phenotypes in asthma. © 2016 Asian Pacific Society of Respirology.

Genotype-phenotype association study via new multi-task learning model

PubMed Central

Huo, Zhouyuan; Shen, Dinggang

2018-01-01

Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2,1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2,1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs. PMID:29218896

The Face of Noonan Syndrome: Does Phenotype Predict Genotype

PubMed Central

Allanson, Judith E.; Bohring, Axel; Dorr, Helmuth-Guenther; Dufke, Andreas; Gillessen-Kaesbach, Gabrielle; Horn, Denise; König, Rainer; Kratz, Christian P.; Kutsche, Kerstin; Pauli, Silke; Raskin, Salmo; Rauch, Anita; Turner, Anne; Wieczorek, Dagmar; Zenker, Martin

2011-01-01

The facial photographs of 81 individuals with Noonan syndrome, from infancy to adulthood, have been evaluated by two dysmorphologists (JA and MZ), each of whom has considerable experience with disorders of the Ras/MAPK pathway. Thirty-two of this cohort have PTPN11 mutations, 21 SOS1 mutations, 11 RAF1 mutations, and 17 KRAS mutations. The facial appearance of each person was judged to be typical of Noonan syndrome or atypical. In each gene category both typical and unusual faces were found. We determined that some individuals with mutations in the most commonly affected gene, PTPN11, which is correlated with the cardinal physical features, may have a quite atypical face. Conversely, some individuals with KRAS mutations, which may be associated with a less characteristic intellectual phenotype and a resemblance to Costello and cardio-facio-cutaneous syndromes, can have a very typical face. Thus, the facial phenotype, alone, is insufficient to predict the genotype, but certain facial features may facilitate an educated guess in some cases. PMID:20602484

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth.

PubMed

Zhang, Xuehai; Huang, Chenglong; Wu, Di; Qiao, Feng; Li, Wenqiang; Duan, Lingfeng; Wang, Ke; Xiao, Yingjie; Chen, Guoxing; Liu, Qian; Xiong, Lizhong; Yang, Wanneng; Yan, Jianbing

2017-03-01

With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize ( Zea mays ) recombinant inbred line population ( n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction. © 2017 American Society of Plant Biologists. All Rights Reserved.

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth1[OPEN

PubMed Central

Huang, Chenglong; Wu, Di; Qiao, Feng; Li, Wenqiang; Duan, Lingfeng; Wang, Ke; Xiao, Yingjie; Chen, Guoxing; Liu, Qian; Yang, Wanneng

2017-01-01

With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize (Zea mays) recombinant inbred line population (n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction. PMID:28153923

Polarity, cell division, and out-of-equilibrium dynamics control the growth of epithelial structures

PubMed Central

Cerruti, Benedetta; Puliafito, Alberto; Shewan, Annette M.; Yu, Wei; Combes, Alexander N.; Little, Melissa H.; Chianale, Federica; Primo, Luca; Serini, Guido; Mostov, Keith E.; Celani, Antonio

2013-01-01

The growth of a well-formed epithelial structure is governed by mechanical constraints, cellular apico-basal polarity, and spatially controlled cell division. Here we compared the predictions of a mathematical model of epithelial growth with the morphological analysis of 3D epithelial structures. In both in vitro cyst models and in developing epithelial structures in vivo, epithelial growth could take place close to or far from mechanical equilibrium, and was determined by the hierarchy of time-scales of cell division, cell–cell rearrangements, and lumen dynamics. Equilibrium properties could be inferred by the analysis of cell–cell contact topologies, and the nonequilibrium phenotype was altered by inhibiting ROCK activity. The occurrence of an aberrant multilumen phenotype was linked to fast nonequilibrium growth, even when geometric control of cell division was correctly enforced. We predicted and verified experimentally that slowing down cell division partially rescued a multilumen phenotype induced by altered polarity. These results improve our understanding of the development of epithelial organs and, ultimately, of carcinogenesis. PMID:24145168

Beyond mean allelic effects: A locus at the major color gene MC1R associates also with differing levels of phenotypic and genetic (co)variance for coloration in barn owls.

PubMed

San-Jose, Luis M; Ducret, Valérie; Ducrest, Anne-Lyse; Simon, Céline; Roulin, Alexandre

2017-10-01

The mean phenotypic effects of a discovered variant help to predict major aspects of the evolution and inheritance of a phenotype. However, differences in the phenotypic variance associated to distinct genotypes are often overlooked despite being suggestive of processes that largely influence phenotypic evolution, such as interactions between the genotypes with the environment or the genetic background. We present empirical evidence for a mutation at the melanocortin-1-receptor gene, a major vertebrate coloration gene, affecting phenotypic variance in the barn owl, Tyto alba. The white MC1R allele, which associates with whiter plumage coloration, also associates with a pronounced phenotypic and additive genetic variance for distinct color traits. Contrarily, the rufous allele, associated with a rufous coloration, relates to a lower phenotypic and additive genetic variance, suggesting that this allele may be epistatic over other color loci. Variance differences between genotypes entailed differences in the strength of phenotypic and genetic associations between color traits, suggesting that differences in variance also alter the level of integration between traits. This study highlights that addressing variance differences of genotypes in wild populations provides interesting new insights into the evolutionary mechanisms and the genetic architecture underlying the phenotype. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Histopathology reveals correlative and unique phenotypes in a high-throughput mouse phenotyping screen

PubMed Central

Adissu, Hibret A.; Estabel, Jeanne; Sunter, David; Tuck, Elizabeth; Hooks, Yvette; Carragher, Damian M.; Clarke, Kay; Karp, Natasha A.; Project, Sanger Mouse Genetics; Newbigging, Susan; Jones, Nora; Morikawa, Lily; White, Jacqueline K.; McKerlie, Colin

2014-01-01

The Mouse Genetics Project (MGP) at the Wellcome Trust Sanger Institute aims to generate and phenotype over 800 genetically modified mouse lines over the next 5 years to gain a better understanding of mammalian gene function and provide an invaluable resource to the scientific community for follow-up studies. Phenotyping includes the generation of a standardized biobank of paraffin-embedded tissues for each mouse line, but histopathology is not routinely performed. In collaboration with the Pathology Core of the Centre for Modeling Human Disease (CMHD) we report the utility of histopathology in a high-throughput primary phenotyping screen. Histopathology was assessed in an unbiased selection of 50 mouse lines with (n=30) or without (n=20) clinical phenotypes detected by the standard MGP primary phenotyping screen. Our findings revealed that histopathology added correlating morphological data in 19 of 30 lines (63.3%) in which the primary screen detected a phenotype. In addition, seven of the 50 lines (14%) presented significant histopathology findings that were not associated with or predicted by the standard primary screen. Three of these seven lines had no clinical phenotype detected by the standard primary screen. Incidental and strain-associated background lesions were present in all mutant lines with good concordance to wild-type controls. These findings demonstrate the complementary and unique contribution of histopathology to high-throughput primary phenotyping of mutant mice. PMID:24652767

Identification of signaling components required for the prediction of cytokine release in RAW 264.7 macrophages

PubMed Central

Pradervand, Sylvain; Maurya, Mano R; Subramaniam, Shankar

2006-01-01

Background Release of immuno-regulatory cytokines and chemokines during inflammatory response is mediated by a complex signaling network. Multiple stimuli produce different signals that generate different cytokine responses. Current knowledge does not provide a complete picture of these signaling pathways. However, using specific markers of signaling pathways, such as signaling proteins, it is possible to develop a 'coarse-grained network' map that can help understand common regulatory modules for various cytokine responses and help differentiate between the causes of their release. Results Using a systematic profiling of signaling responses and cytokine release in RAW 264.7 macrophages made available by the Alliance for Cellular Signaling, an analysis strategy is presented that integrates principal component regression and exhaustive search-based model reduction to identify required signaling factors necessary and sufficient to predict the release of seven cytokines (G-CSF, IL-1α, IL-6, IL-10, MIP-1α, RANTES, and TNFα) in response to selected ligands. This study provides a model-based quantitative estimate of cytokine release and identifies ten signaling components involved in cytokine production. The models identified capture many of the known signaling pathways involved in cytokine release and predict potentially important novel signaling components, like p38 MAPK for G-CSF release, IFNγ- and IL-4-specific pathways for IL-1a release, and an M-CSF-specific pathway for TNFα release. Conclusion Using an integrative approach, we have identified the pathways responsible for the differential regulation of cytokine release in RAW 264.7 macrophages. Our results demonstrate the power of using heterogeneous cellular data to qualitatively and quantitatively map intermediate cellular phenotypes. PMID:16507166

In Vivo Cardiotoxicity Induced by Sodium Aescinate in Zebrafish Larvae.

PubMed

Liang, Jinfeng; Jin, Wangdong; Li, Hongwen; Liu, Hongcui; Huang, Yanfeng; Shan, Xiaowen; Li, Chunqi; Shan, Letian; Efferth, Thomas

2016-02-23

Sodium aescinate (SA) is a widely-applied triterpene saponin product derived from horse chestnut seeds, possessing vasoactive and organ-protective activities with oral or injection administration in the clinic. To date, no toxicity or adverse events in SA have been reported, by using routine models (in vivo or in vitro), which are insufficient to predict all aspects of its pharmacological and toxicological actions. In this study, taking advantage of transparent zebrafish larvae (Danio rerio), we evaluated cardiovascular toxicity of SA at doses of 1/10 MNLC, 1/3 MNLC, MNLC and LC10 by yolk sac microinjection. The qualitative and quantitative cardiotoxicity in zebrafish was assessed at 48 h post-SA treatment, using specific phenotypic endpoints: heart rate, heart rhythm, heart malformation, pericardial edema, circulation abnormalities, thrombosis and hemorrhage. The results showed that SA at 1/10 MNLC and above doses could induce obvious cardiac and pericardial malformations, whilst 1/3 MNLC and above doses could induce significant cardiac malfunctions (heart rate and circulation decrease/absence), as compared to untreated or vehicle-treated control groups. Such cardiotoxic manifestations occurred in more than 50% to 100% of all zebrafish treated with SA at MNLC and LC10. Our findings have uncovered the potential cardiotoxicity of SA for the first time, suggesting more attention to the risk of its clinical application. Such a time- and cost-saving zebrafish cardiotoxicity assay is very valid and reliable for rapid prediction of compound toxicity during drug research and development.

A Spatio-Temporal Model of Phenotypic Evolution in the Atlantic Silverside (Menidia menidia) and Its Implications for Size-Selective Fishing in a Warmer World

NASA Astrophysics Data System (ADS)

Sbrocco, E. J.

2016-02-01

A pervasive phenotypic pattern observed across marine fishes is that vertebral number increases with latitude. Jordan's Rule, as it is known, holds true both within and across species, and like other ecogeographic principles (e.g., Bergmann's Rule), it is presumed to be an adaptive response to latitudinal gradients in temperature. As such, future ocean warming is expected to impact not only the geographic range limits of marine fishes that conform to Jordan's Rule, but also their phenotype, with warmer waters selecting for fish with fewer vertebrae at any given latitude. Here I present a model of phenotypic evolution over space and time for the Atlantic silverside (Menidia menidia), a common marine fish found in coastal waters along the western North Atlantic. This species has long served as a model organism for the study of fisheries-induced selection and exhibits numerous latitudinal clines in phenotypic and life-history traits, including vertebral number. Common garden experiments have shown that vertebral number is genetically determined in this species, but correlative models of observed vertebral counts and climate reveal that SST is the single strongest predictor of phenotype, even after accounting for gene flow. This result indicates that natural selection is responsible for maintaining vertebral clines in the silverside, and allows for the prediction of phenotypic responses to ocean warming. By integrating genetic estimates of population connectivity, species distribution models, and statistical models, I find that by the end of the 21st century, ocean warming will select for silversides with up to 8% fewer vertebrae. Mid-Atlantic populations are the most mal-adapted for future conditions, but may be rescued by migration from small-phenotype southern neighbors or by directional selection. Despite smaller temperature anomalies, the strongest impacts of warming will be felt at both northern and southern edges of the distribution, where genetic rescue from neighboring populations is not predicted to occur and in situ directional selection is less likely due to low phenotypic variation. This study has important implications for marine fisheries, since climate-induced phenotypic evolution may compound issues that already exist as a result of size-selective harvest of large, fast-growing fish.

Genetic dissection of ethanol tolerance in the budding yeast Saccharomyces cerevisiae.

PubMed

Hu, X H; Wang, M H; Tan, T; Li, J R; Yang, H; Leach, L; Zhang, R M; Luo, Z W

2007-03-01

Uncovering genetic control of variation in ethanol tolerance in natural populations of yeast Saccharomyces cerevisiae is essential for understanding the evolution of fermentation, the dominant lifestyle of the species, and for improving efficiency of selection for strains with high ethanol tolerance, a character of great economic value for the brewing and biofuel industries. To date, as many as 251 genes have been predicted to be involved in influencing this character. Candidacy of these genes was determined from a tested phenotypic effect following gene knockout, from an induced change in gene function under an ethanol stress condition, or by mutagenesis. This article represents the first genomics approach for dissecting genetic variation in ethanol tolerance between two yeast strains with a highly divergent trait phenotype. We developed a simple but reliable experimental protocol for scoring the phenotype and a set of STR/SNP markers evenly covering the whole genome. We created a mapping population comprising 319 segregants from crossing the parental strains. On the basis of the data sets, we find that the tolerance trait has a high heritability and that additive genetic variance dominates genetic variation of the trait. Segregation at five QTL detected has explained approximately 50% of phenotypic variation; in particular, the major QTL mapped on yeast chromosome 9 has accounted for a quarter of the phenotypic variation. We integrated the QTL analysis with the predicted candidacy of ethanol resistance genes and found that only a few of these candidates fall in the QTL regions.

Beyond Trisomy 21: Phenotypic Variability in People with Down Syndrome Explained by Further Chromosome Mis-segregation and Mosaic Aneuploidy

PubMed Central

Potter, Huntington

2017-01-01

Phenotypic variability is a fundamental feature of the human population and is particularly evident among people with Down syndrome and/or Alzheimer’s disease. Herein, we review current theories of the potential origins of this phenotypic variability and propose a novel mechanism based on our finding that the Alzheimer’s disease-associated Aβ peptide, encoded on chromosome 21, disrupts the mitotic spindle, induces abnormal chromosome segregation, and produces mosaic populations of aneuploid cells in all tissues of people with Alzheimer’s disease and in mouse and cell models thereof. Thus, individuals exposed to increased levels of the Aβ peptide should accumulate mosaic populations of aneuploid cells, with different chromosomes affected in different tissues and in different individuals. Specifically, people with Down syndrome, who express elevated levels of Aβ peptide throughout their lifetimes, would be predicted to accumulate additional types of aneuploidy, beyond trisomy 21 and including changes in their trisomy 21 status, in mosaic cell populations. Such mosaic aneuploidy would introduce a novel form of genetic variability that could potentially underlie much of the observed phenotypic variability among people with Down syndrome, and possibly also among people with Alzheimer’s disease. This mosaic aneuploidy theory of phenotypic variability in Down syndrome is supported by several observations, makes several testable predictions, and identifies a potential approach to reducing the frequency of some of the most debilitating features of Down syndrome, including Alzheimer’s disease. PMID:29516054

Simulating boundary layer transition with low-Reynolds-number k-epsilon turbulence models. I - An evaluation of prediction characteristics. II - An approach to improving the predictions

NASA Technical Reports Server (NTRS)

Schmidt, R. C.; Patankar, S. V.

1991-01-01

The capability of two k-epsilon low-Reynolds number (LRN) turbulence models, those of Jones and Launder (1972) and Lam and Bremhorst (1981), to predict transition in external boundary-layer flows subject to free-stream turbulence is analyzed. Both models correctly predict the basic qualitative aspects of boundary-layer transition with free stream turbulence, but for calculations started at low values of certain defined Reynolds numbers, the transition is generally predicted at unrealistically early locations. Also, the methods predict transition lengths significantly shorter than those found experimentally. An approach to overcoming these deficiencies without abandoning the basic LRN k-epsilon framework is developed. This approach limits the production term in the turbulent kinetic energy equation and is based on a simple stability criterion. It is correlated to the free-stream turbulence value. The modification is shown to improve the qualitative and quantitative characteristics of the transition predictions.

The Discriminatory Value of CYP2D6 Genotyping in Predicting the Dextromethorphan/Dextrorphan Phenotype in Women with Breast Cancer

PubMed Central

Trojan, Andreas; Vergopoulos, Athanasios; Breitenstein, Urs; Seifert, Burkhardt; Rageth, Christoph; Joechle, Wolfgang

2012-01-01

Background The growth inhibitory effect of tamoxifen is used for the treatment of breast cancer. Tamoxifen efficacy is mediated by its biotransformation, predominantly via the cytochrome P450 2D6 (CYP2D6) isoenzyme, to the active metabolite endoxifen. We investigated the relationship of CYP2D6 genotypes to the metabolism of dextromethorphan (DM), which is frequently used as a surrogate marker for the formation of endoxifen. Methods The CYP2D6 genotype was determined by polymerase chain reaction (PCR) in previously untreated patients with hormone receptor-positive invasive breast cancer considered to receive antihormonal therapy. The DM/dextrorphan (DX) urinary excretion ratios were obtained in a subset of patients by high-pressure liquid chromatography (HPLC)-mediated urine analysis after intake of 25 mg DM. The relationships of genotype and corresponding phenotype were statistically analyzed for association. Results From 151 patients predicted based on their genotype data for the ‘traditional’ CYP2D6 phenotype classes poor, intermediate, extensive and ultrarapid, 83 patients were examined for their DM/DX urinary ratios. The genotype-based poor metabolizer status correlated with the DM/DX ratios, whereas the intermediate, extensive and ultrarapid genotypes could not be distinguished based on their phenotype. Citalopram intake did not significantly influence the phenotype. Conclusions The DM metabolism can be reliably used to assess the CYP2D6 enzyme activity. The correlation with the genotype can be incomplete and the metabolic ratios do not seem to be compromised by citalopram. DM phenotyping may provide a standardized tool to better assess the CYP2D6 metabolic capacity. PMID:22553469

Predictive oncology: multidisciplinary, multi-scale in-silico modeling linking phenotype, morphology and growth

PubMed Central

Sanga, Sandeep; Frieboes, Hermann B.; Zheng, Xiaoming; Gatenby, Robert; Bearer, Elaine L.; Cristini, Vittorio

2007-01-01

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically review advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we propose and discuss a multi-scale, i.e., from the molecular to the gross tumor scale, mathematical and computational “first-principle” approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We demonstrate that this methodology, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as phenotype-diagnostic tool and thus to predict collective and individual tumor cell invasion of surrounding host. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior. PMID:17629503

Self-calibrating models for dynamic monitoring and diagnosis

NASA Technical Reports Server (NTRS)

Kuipers, Benjamin

1994-01-01

The present goal in qualitative reasoning is to develop methods for automatically building qualitative and semiquantitative models of dynamic systems and to use them for monitoring and fault diagnosis. The qualitative approach to modeling provides a guarantee of coverage while our semiquantitative methods support convergence toward a numerical model as observations are accumulated. We have developed and applied methods for automatic creation of qualitative models, developed two methods for obtaining tractable results on problems that were previously intractable for qualitative simulation, and developed more powerful methods for learning semiquantitative models from observations and deriving semiquantitative predictions from them. With these advances, qualitative reasoning comes significantly closer to realizing its aims as a practical engineering method.

Mapping the four-horned locus and testing the polled locus in three Chinese sheep breeds.

PubMed

He, Xiaohong; Zhou, Zhengkui; Pu, Yabin; Chen, Xiaofei; Ma, Yuehui; Jiang, Lin

2016-10-01

Four-horned sheep are an ideal animal model for illuminating the genetic basis of horn development. The objective of this study was to locate the genetic region responsible for the four-horned phenotype and to verify a previously reported polled locus in three Chinese breeds. A genome-wide association study (GWAS) was performed using 34 two-horned and 32 four-horned sheep from three Chinese indigenous breeds: Altay, Mongolian and Sishui Fur sheep. The top two significant single nucleotide polymorphisms (SNPs) associated with the four-horned phenotype were both located in a region spanning positions 132.6 to 132.7 Mb on sheep chromosome 2. Similar locations for the four-horned trait were previously identified in Jacob, Navajo-Churro, Damara and Sishui Fur sheep, suggesting a common genetic component underlying the four-horned phenotype. The two identified SNPs were both downstream of the metaxin 2 (MTX2) gene and the HOXD gene cluster. For the top SNP-OAR2:g.132619300G>A-the strong associations of the AA and AG genotypes with the four-horned phenotype and the GG genotype with the two-horned phenotype indicated the dominant inheritance of the four-horned trait. No significant SNPs for the polled phenotype were identified in the GWAS analysis, and a PCR analysis for the detection of the 1.8-kb insertion associated with polled sheep in other breeds failed to verify the association with polledness in the three Chinese breeds. This study supports the hypothesis that two different loci are responsible for horn existence and number. This study contributes to the understanding of the molecular regulation of horn development and enriches the knowledge of qualitative traits in domestic animals. © 2016 Stichting International Foundation for Animal Genetics.

Variable Bone Fragility Associated With an Amish COL1A2 Variant and a Knock-in Mouse Model

PubMed Central

Daley, Ethan; Streeten, Elizabeth A; Sorkin, John D; Kuznetsova, Natalia; Shapses, Sue A; Carleton, Stephanie M; Shuldiner, Alan R; Marini, Joan C; Phillips, Charlotte L; Goldstein, Steven A; Leikin, Sergey; McBride, Daniel J

2010-01-01

Osteogenesis imperfecta (OI) is a heritable form of bone fragility typically associated with a dominant COL1A1 or COL1A2 mutation. Variable phenotype for OI patients with identical collagen mutations is well established, but phenotype variability is described using the qualitative Sillence classification. Patterning a new OI mouse model on a specific collagen mutation therefore has been hindered by the absence of an appropriate kindred with extensive quantitative phenotype data. We benefited from the large sibships of the Old Order Amish (OOA) to define a wide range of OI phenotypes in 64 individuals with the identical COL1A2 mutation. Stratification of carrier spine (L1–4) areal bone mineral density (aBMD) Z-scores demonstrated that 73% had moderate to severe disease (less than −2), 23% had mild disease (−1 to −2), and 4% were in the unaffected range (greater than −1). A line of knock-in mice was patterned on the OOA mutation. Bone phenotype was evaluated in four F1 lines of knock-in mice that each shared approximately 50% of their genetic background. Consistent with the human pedigree, these mice had reduced body mass, aBMD, and bone strength. Whole-bone fracture susceptibility was influenced by individual genomic factors that were reflected in size, shape, and possibly bone metabolic regulation. The results indicate that the G610C OI (Amish) knock-in mouse is a novel translational model to identify modifying genes that influence phenotype and for testing potential therapies for OI. © 2010 American Society for Bone and Mineral Research PMID:19594296

Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa.

PubMed

Khaledi, Ariane; Schniederjans, Monika; Pohl, Sarah; Rainer, Roman; Bodenhofer, Ulrich; Xia, Boyang; Klawonn, Frank; Bruchmann, Sebastian; Preusse, Matthias; Eckweiler, Denitsa; Dötsch, Andreas; Häussler, Susanne

2016-08-01

Emerging resistance to antimicrobials and the lack of new antibiotic drug candidates underscore the need for optimization of current diagnostics and therapies to diminish the evolution and spread of multidrug resistance. As the antibiotic resistance status of a bacterial pathogen is defined by its genome, resistance profiling by applying next-generation sequencing (NGS) technologies may in the future accomplish pathogen identification, prompt initiation of targeted individualized treatment, and the implementation of optimized infection control measures. In this study, qualitative RNA sequencing was used to identify key genetic determinants of antibiotic resistance in 135 clinical Pseudomonas aeruginosa isolates from diverse geographic and infection site origins. By applying transcriptome-wide association studies, adaptive variations associated with resistance to the antibiotic classes fluoroquinolones, aminoglycosides, and β-lactams were identified. Besides potential novel biomarkers with a direct correlation to resistance, global patterns of phenotype-associated gene expression and sequence variations were identified by predictive machine learning approaches. Our research serves to establish genotype-based molecular diagnostic tools for the identification of the current resistance profiles of bacterial pathogens and paves the way for faster diagnostics for more efficient, targeted treatment strategies to also mitigate the future potential for resistance evolution. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa

PubMed Central

Khaledi, Ariane; Schniederjans, Monika; Pohl, Sarah; Rainer, Roman; Bodenhofer, Ulrich; Xia, Boyang; Klawonn, Frank; Bruchmann, Sebastian; Preusse, Matthias; Eckweiler, Denitsa; Dötsch, Andreas

2016-01-01

Emerging resistance to antimicrobials and the lack of new antibiotic drug candidates underscore the need for optimization of current diagnostics and therapies to diminish the evolution and spread of multidrug resistance. As the antibiotic resistance status of a bacterial pathogen is defined by its genome, resistance profiling by applying next-generation sequencing (NGS) technologies may in the future accomplish pathogen identification, prompt initiation of targeted individualized treatment, and the implementation of optimized infection control measures. In this study, qualitative RNA sequencing was used to identify key genetic determinants of antibiotic resistance in 135 clinical Pseudomonas aeruginosa isolates from diverse geographic and infection site origins. By applying transcriptome-wide association studies, adaptive variations associated with resistance to the antibiotic classes fluoroquinolones, aminoglycosides, and β-lactams were identified. Besides potential novel biomarkers with a direct correlation to resistance, global patterns of phenotype-associated gene expression and sequence variations were identified by predictive machine learning approaches. Our research serves to establish genotype-based molecular diagnostic tools for the identification of the current resistance profiles of bacterial pathogens and paves the way for faster diagnostics for more efficient, targeted treatment strategies to also mitigate the future potential for resistance evolution. PMID:27216077

Autism as a disorder of prediction

PubMed Central

Sinha, Pawan; Kjelgaard, Margaret M.; Gandhi, Tapan K.; Tsourides, Kleovoulos; Cardinaux, Annie L.; Pantazis, Dimitrios; Diamond, Sidney P.; Held, Richard M.

2014-01-01

A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly “magical” world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one’s ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy. PMID:25288765

Associations among multiple markers and complex disease: models, algorithms, and applications.

PubMed

Assimes, Themistocles L; Olshen, Adam B; Narasimhan, Balasubramanian; Olshen, Richard A

2008-01-01

This chapter is a report on collaborations among its authors and others over many years. It devolves from our goal of understanding genes, their main and epistatic effects combined with interactions involving demographic and environmental features also, as together they predict genetically complex diseases. Thus, our goal is "association." Particular phenotypes of interest to us are hypertension, insulin resistance, angina, and myocardial infarction. Prediction of complex disease is notoriously difficult, though it would be made easier were we given strand-specific information on genotype. Unfortunately, with current technology, genotypic information comes to us "unphased." While obviously we have strand-specific information when genotype is homozygous, we do not have such information when genotype is heterozygous. To summarize, the ultimate goals of approaches we provide is to predict phenotype, typically untoward or not, within a specific window of time. Our approach is neither through linkage nor from finding haplotype frequencies per se.

Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data.

PubMed

Huang, Yi-Fei; Gulko, Brad; Siepel, Adam

2017-04-01

Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the 'big data' available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.

Hypogonadotropic Hypogonadism due to Novel FGFR1 Mutations.

PubMed

Akkuş, Gamze; Kotan, Leman Damla; Durmaz, Erdem; Mengen, Eda; Turan, İhsan; Ulubay, Ayça; Gürbüz, Fatih; Yüksel, Bilgin; Tetiker, Tamer; Topaloğlu, A Kemal

2017-06-01

The underlying genetic etiology of hypogonadotropic hypogonadism (HH) is heterogeneous. Fibroblast growth factor signaling is pivotal in the ontogeny of gonadotropin-releasing hormone neurons. Loss-of-function mutations in FGFR1 gene cause variable HH phenotypes encompassing pubertal delay to idiopathic HH (IHH) or Kallmann syndrome (KS). As FGFR1 mutations are common, recognizing mutations and associated phenotypes may enhance clinical management. Using a candidate gene approach, we screened 52 IHH/KS patients. We identified three novel (IVS3-1G>C and p.W2X, p.R209C) FGFR1 gene mutations. Despite predictive null protein function, patients from the novel mutation families had normosmic IHH without non-reproductive phenotype. These findings further emphasize the great variability of FGFR1 mutation phenotypes in IHH/KS.

Lipid accumulation product as a marker of cardiometabolic susceptibility in women with different phenotypes of polycystic ovary syndrome.

PubMed

Božić-Antić, Ivana; Ilić, Dušan; Bjekić-Macut, Jelica; Bogavac, Tamara; Vojnović-Milutinović, Danijela; Kastratovic-Kotlica, Biljana; Milić, Nataša; Stanojlović, Olivera; Andrić, Zoran; Macut, Djuro

2016-12-01

There are limited data on cardiometabolic risk factors and the prevalence of metabolic syndrome (MetS) across the different PCOS phenotypes in Caucasian population. Lipid accumulation product (LAP) is a clinical surrogate marker that could be used for evaluation of MetS in clinical practice. The aim of the study was to analyze metabolic characteristics and the ability of LAP to predict MetS in different PCOS phenotypes. Cross-sectional clinical study analyzing 365 women with PCOS divided into four phenotypes according to the ESHRE/ASRM criteria, and 125 healthy BMI-matched controls. In all subjects, LAP was determined and MetS was diagnosed according to the National Cholesterol Education Program/Adult Treatment Panel III (NCEP-ATP III), the International Diabetes Federation (IDF) and the Joint Interim Statement (JIS) criteria. Logistic regression and ROC curve analyses were used to determine predictors of MetS in each PCOS phenotype. All analyses were performed with age and BMI adjustment. All PCOS phenotypes in comparison to controls had higher prevalence of MetS assessed by NCEP-ATP III criteria, and only classic phenotypes when IDF and JIS criteria were used. All phenotypes had the same prevalence of MetS irrespective of used definition. LAP and exhibited the highest diagnostic accuracy and was an independent predictor of MetS in all phenotypes. LAP is an independent and accurate clinical determinant of MetS in all PCOS phenotypes in our Caucasian population. All PCOS phenotypes, including non-classic ones, are metabolically challenged and with cardiovascular risk, particularly phenotype B. © 2016 European Society of Endocrinology.

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.

PubMed

Kagawa, Rina; Kawazoe, Yoshimasa; Ida, Yusuke; Shinohara, Emiko; Tanaka, Katsuya; Imai, Takeshi; Ohe, Kazuhiko

2017-07-01

Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects. We employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value. The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects. We develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users' objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.

Monogenic diabetes syndromes: Locus‐specific databases for Alström, Wolfram, and Thiamine‐responsive megaloblastic anemia

PubMed Central

Astuti, Dewi; Sabir, Ataf; Fulton, Piers; Zatyka, Malgorzata; Williams, Denise; Hardy, Carol; Milan, Gabriella; Favaretto, Francesca; Yu‐Wai‐Man, Patrick; Rohayem, Julia; López de Heredia, Miguel; Hershey, Tamara; Tranebjaerg, Lisbeth; Chen, Jian‐Hua; Chaussenot, Annabel; Nunes, Virginia; Marshall, Bess; McAfferty, Susan; Tillmann, Vallo; Maffei, Pietro; Paquis‐Flucklinger, Veronique; Geberhiwot, Tarekign; Mlynarski, Wojciech; Parkinson, Kay; Picard, Virginie; Bueno, Gema Esteban; Dias, Renuka; Arnold, Amy; Richens, Caitlin; Paisey, Richard; Urano, Fumihiko; Semple, Robert; Sinnott, Richard

2017-01-01

Abstract We developed a variant database for diabetes syndrome genes, using the Leiden Open Variation Database platform, containing observed phenotypes matched to the genetic variations. We populated it with 628 published disease‐associated variants (December 2016) for: WFS1 (n = 309), CISD2 (n = 3), ALMS1 (n = 268), and SLC19A2 (n = 48) for Wolfram type 1, Wolfram type 2, Alström, and Thiamine‐responsive megaloblastic anemia syndromes, respectively; and included 23 previously unpublished novel germline variants in WFS1 and 17 variants in ALMS1. We then investigated genotype–phenotype relations for the WFS1 gene. The presence of biallelic loss‐of‐function variants predicted Wolfram syndrome defined by insulin‐dependent diabetes and optic atrophy, with a sensitivity of 79% (95% CI 75%–83%) and specificity of 92% (83%–97%). The presence of minor loss‐of‐function variants in WFS1 predicted isolated diabetes, isolated deafness, or isolated congenital cataracts without development of the full syndrome (sensitivity 100% [93%–100%]; specificity 78% [73%–82%]). The ability to provide a prognostic prediction based on genotype will lead to improvements in patient care and counseling. The development of the database as a repository for monogenic diabetes gene variants will allow prognostic predictions for other diabetes syndromes as next‐generation sequencing expands the repertoire of genotypes and phenotypes. The database is publicly available online at https://lovd.euro-wabb.org. PMID:28432734

Evaluation of antimicrobial resistance phenotypes for predicting multidrug-resistant Salmonella recovered from retail meats and humans in the United States.

PubMed

Whichard, Jean M; Medalla, Felicita; Hoekstra, Robert M; McDermott, Patrick F; Joyce, Kevin; Chiller, Tom; Barrett, Timothy J; White, David G

2010-03-01

Although multidrug-resistant (MDR) non-Typhi Salmonella (NTS) strains are a concern in food production, determining resistance to multiple antimicrobial agents at slaughter or processing may be impractical. Single antimicrobial resistance results for predicting multidrug resistance are desirable. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value were used to determine each antimicrobial agent's ability to predict MDR phenotypes of human health significance: ACSSuT (resistance to at least ampicillin, chloramphenicol, streptomycin, sulfamethoxazole, tetracycline) in NTS isolates, and MDR-AmpC-SN (resistance to ACSSuT, additional resistance to amoxicillin-clavulanate and to ceftiofur, and decreased susceptibility [MIC >= 2 microg/ml] to ceftriaxone) in NTS serotype Newport. The U.S. National Antimicrobial Resistance Monitoring System determined MICs to 15 or more antimicrobial agents for 9,955 NTS isolates from humans from 1999 to 2004 and 689 NTS isolates from retail meat from 2002 to 2004. A total of 847 (8.5%) human and 26 (3.8%) retail NTS isolates were ACSSuT; 995 (10.0%) human and 16 (2.3%) retail isolates were serotype Newport. Among Salmonella Newport, 204 (20.5%) human and 9 (56.3%) retail isolates were MDR-AmpC-SN. Chloramphenicol resistance provided the highest PPVs for ACSSuT among human (90.5%; 95% confidence interval, 88.4 to 92.3) and retail NTS isolates (96.3%; 95% confidence interval, 81.0 to 99.9). Resistance to ceftiofur and to amoxicillin-clavulanate and decreased susceptibility to ceftriaxone provided the highest PPVs (97.1, 98.1, and 98.6%, respectively) for MDR-AmpC-SN from humans. High PPVs for these agents applied to retail meat MDR-AmpC-SN, but isolate numbers were lower. Variations in MIC results may complicate ceftriaxone's predictive utility. Selecting specific antimicrobial resistance offers practical alternatives for predicting MDR phenotypes. Chloramphenicol resistance works best for ACSSuT-NTS, and resistance to ceftiofur, amoxicillin-clavulanate, or chloramphenicol works best for MDR-AmpC-SN.

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers.

PubMed

Heidaritabar, M; Wolc, A; Arango, J; Zeng, J; Settar, P; Fulton, J E; O'Sullivan, N P; Bastiaansen, J W M; Fernando, R L; Garrick, D J; Dekkers, J C M

2016-10-01

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01-0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population. © 2016 Blackwell Verlag GmbH.

Brief Report: Impact of Child Problem Behaviors and Parental Broad Autism Phenotype Traits on Substance Use among Parents of Children with ASD

ERIC Educational Resources Information Center

Wade, Jordan L.; Cox, Neill Broderick; Reeve, Ronald E.; Hull, Michael

2014-01-01

Using data from the Simons Simplex Collection, the present study examined the impact of child externalizing behavior and parental broad autism phenotype traits on substance use among parents of children with autism spectrum disorder (n = 2,388). For both fathers and mothers, child externalizing behaviors predicted tobacco use (OR = 1.01 and OR =…

RHO Mutations (p.W126L and p.A346P) in Two Japanese Families with Autosomal Dominant Retinitis Pigmentosa

PubMed Central

Akahori, Masakazu; Itabashi, Takeshi; Nishino, Jo; Yoshitake, Kazutoshi; Ikeo, Kazuho; Tsuneoka, Hiroshi

2014-01-01

Purpose. To investigate genetic and clinical features of patients with rhodopsin (RHO) mutations in two Japanese families with autosomal dominant retinitis pigmentosa (adRP). Methods. Whole-exome sequence analysis was performed in ten adRP families. Identified RHO mutations for the cosegregation analysis were confirmed by Sanger sequencing. Ophthalmic examinations were performed to evaluate the RP phenotypes. The impact of the RHO mutation on the rhodopsin conformation was examined by molecular modeling analysis. Results. In two adRP families, we identified two RHO mutations (c.377G>T (p.W126L) and c.1036G>C (p.A346P)), one of which was novel. Complete cosegregation was confirmed for each mutation exhibiting the RP phenotype in both families. Molecular modeling predicted that the novel mutation (p.W126L) might impair rhodopsin function by affecting its conformational transition in the light-adapted form. Clinical phenotypes showed that patients with p.W126L exhibited sector RP, whereas patients with p.A346P exhibited classic RP. Conclusions. Our findings demonstrated that the novel mutation (p.W126L) may be associated with the phenotype of sector RP. Identification of RHO mutations is a very useful tool for predicting disease severity and providing precise genetic counseling. PMID:25485142

Median regression spline modeling of longitudinal FEV1 measurements in cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients.

PubMed

Conrad, Douglas J; Bailey, Barbara A; Hardie, Jon A; Bakke, Per S; Eagan, Tomas M L; Aarli, Bernt B

2017-01-01

Clinical phenotyping, therapeutic investigations as well as genomic, airway secretion metabolomic and metagenomic investigations can benefit from robust, nonlinear modeling of FEV1 in individual subjects. We demonstrate the utility of measuring FEV1 dynamics in representative cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) populations. Individual FEV1 data from CF and COPD subjects were modeled by estimating median regression splines and their predicted first and second derivatives. Classes were created from variables that capture the dynamics of these curves in both cohorts. Nine FEV1 dynamic variables were identified from the splines and their predicted derivatives in individuals with CF (n = 177) and COPD (n = 374). Three FEV1 dynamic classes (i.e. stable, intermediate and hypervariable) were generated and described using these variables from both cohorts. In the CF cohort, the FEV1 hypervariable class (HV) was associated with a clinically unstable, female-dominated phenotypes while stable FEV1 class (S) individuals were highly associated with the male-dominated milder clinical phenotype. In the COPD cohort, associations were found between the FEV1 dynamic classes, the COPD GOLD grades, with exacerbation frequency and symptoms. Nonlinear modeling of FEV1 with splines provides new insights and is useful in characterizing CF and COPD clinical phenotypes.

Shifts and disruptions in resource-use trait syndromes during the evolution of herbaceous crops.

PubMed

Milla, Rubén; Morente-López, Javier; Alonso-Rodrigo, J Miguel; Martín-Robles, Nieves; Chapin, F Stuart

2014-10-22

Trait-based ecology predicts that evolution in high-resource agricultural environments should select for suites of traits that enable fast resource acquisition and rapid canopy closure. However, crop breeding targets specific agronomic attributes rather than broad trait syndromes. Breeding for specific traits, together with evolution in high-resource environments, might lead to reduced phenotypic integration, according to predictions from the ecological literature. We provide the first comprehensive test of these hypotheses, based on a trait-screening programme of 30 herbaceous crops and their wild progenitors. During crop evolution plants became larger, which enabled them to compete more effectively for light, but they had poorly integrated phenotypes. In a subset of six herbaceous crop species investigated in greater depth, competitiveness for light increased during early plant domestication, whereas diminished phenotypic integration occurred later during crop improvement. Mass-specific leaf and root traits relevant to resource-use strategies (e.g. specific leaf area or tissue density of fine roots) changed during crop evolution, but in diverse and contrasting directions and magnitudes, depending on the crop species. Reductions in phenotypic integration and overinvestment in traits involved in competition for light may affect the chances of upgrading modern herbaceous crops to face current climatic and food security challenges. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

A Human Thrifty Phenotype Associated With Less Weight Loss During Caloric Restriction

PubMed Central

Thearle, Marie S.; Ibrahim, Mostafa; Hohenadel, Maximilian G.; Bogardus, Clifton; Krakoff, Jonathan; Votruba, Susanne B.

2015-01-01

Successful weight loss is variable for reasons not fully elucidated. Whether effective weight loss results from smaller reductions in energy expenditure during caloric restriction is not known. We analyzed whether obese individuals with a “thrifty” phenotype, that is, greater reductions in 24-h energy expenditure during fasting and smaller increases with overfeeding, lose less weight during caloric restriction than those with a “spendthrift” phenotype. During a weight-maintaining period, 24-h energy expenditure responses to fasting and 200% overfeeding were measured in a whole-room indirect calorimeter. Volunteers then underwent 6 weeks of 50% caloric restriction. We calculated the daily energy deficit (kilocalories per day) during caloric restriction, incorporating energy intake and waste, energy expenditure, and daily activity. We found that a smaller reduction in 24-h energy expenditure during fasting and a larger response to overfeeding predicted more weight loss over 6 weeks, even after accounting for age, sex, race, and baseline weight, as well as a greater rate of energy deficit accumulation. The success of dietary weight loss efforts is influenced by the energy expenditure response to caloric restriction. Greater decreases in energy expenditure during caloric restriction predict less weight loss, indicating the presence of thrifty and spendthrift phenotypes in obese humans. PMID:25964395

[Predictive value of qualitative assessment of general movements for adverse outcomes at 24 months of age in infants with asphyxia].

PubMed

Chen, Nan; Wen, Xiao-Hong; Huang, Jin-Hua; Wang, Shui-Yun; Zhu, Yue-E

2015-12-01

To investigate the predictive value of the qualitative assessment of general movements (GMs) for adverse outcomes at 24 months of age in full-term infants with asphyxia. A total of 114 full-term asphyxiated infants, who were admitted to the neonatal intensive care unit between 2009 and 2012 and took part in follow-ups after discharge were included in the study. All of them received the qualitative assessment of GMs within 3 months after birth. The development quotient was determined with the Bayley Scales of Infant Development at 24 months of age. The results of the qualitative assessment of GMs within 3 months after birth showed that among 114 infants, 20 (17.5%) had poor repertoire movements and 7 (6.1%) had cramped-synchronized movements during the writhing movements period; 8 infants (7.0%) had the absence of fidgety movements during the fidgety movements period. The results of development quotient at 24 months of age showed that 7 infants (6.1%) had adverse developmental outcomes: 6 cases of cerebral palsy and mental retardation and 1 case of mental retardation. There was a poor consistency between poor repertoire movements during the writhing movements period and the developmental outcomes at 24 months of age (Kappa=-0.019; P>0.05). There was a high consistency between cramped-synchronized movements during the writhing movements period and the developmental outcomes at 24 months of age (Kappa=0.848; P<0.05), and the results of predictive values of cramped-synchronized movements were shown as follows: predictive validity 98.2%, sensitivity 85.7%, specificity 99.1%, positive predictive value 85.7%, and negative predictive value 99.1%. There was a high consistency between the absence of fidgety movements during the fidgety movements period and the developmental outcomes at 24 months of age (Kappa=0.786; P<0.05), and its predictive values were expressed as follows: predictive validity 97.4%, sensitivity 85.7%, specificity 98.1%, positive predictive value 75.0%, and negative predictive value 99.1%. Cramped-synchronized movements and absence of fidgety movements can predict adverse developmental outcomes at 24 months of age in full-term infants with asphyxia.

Starvation stress during larval development reveals predictive adaptive response in adult worker honey bees (Apis mellifera)

USDA-ARS?s Scientific Manuscript database

A variety of organisms exhibit developmental plasticity that results in differences in adult morphology, physiology or behavior. This variation in the phenotype, called “Predictive Adaptive Response (PAR),” gives a selective advantage in an adult's environment if the adult experiences environments s...

The ToxCast Pathway Database for Identifying Toxicity Signatures and Potential Modes of Action from Chemical Screening Data

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA), through its ToxCast program, is developing predictive toxicity approaches that will use in vitro high-throughput screening (HTS), high-content screening (HCS) and toxicogenomic data to predict in vivo toxicity phenotypes. There are ...

PhenoTips: patient phenotyping software for clinical and research use.

PubMed

Girdea, Marta; Dumitriu, Sergiu; Fiume, Marc; Bowdin, Sarah; Boycott, Kym M; Chénier, Sébastien; Chitayat, David; Faghfoury, Hanna; Meyn, M Stephen; Ray, Peter N; So, Joyce; Stavropoulos, Dimitri J; Brudno, Michael

2013-08-01

We have developed PhenoTips: open source software for collecting and analyzing phenotypic information for patients with genetic disorders. Our software combines an easy-to-use interface, compatible with any device that runs a Web browser, with a standardized database back end. The PhenoTips' user interface closely mirrors clinician workflows so as to facilitate the recording of observations made during the patient encounter. Collected data include demographics, medical history, family history, physical and laboratory measurements, physical findings, and additional notes. Phenotypic information is represented using the Human Phenotype Ontology; however, the complexity of the ontology is hidden behind a user interface, which combines simple selection of common phenotypes with error-tolerant, predictive search of the entire ontology. PhenoTips supports accurate diagnosis by analyzing the entered data, then suggesting additional clinical investigations and providing Online Mendelian Inheritance in Man (OMIM) links to likely disorders. By collecting, classifying, and analyzing phenotypic information during the patient encounter, PhenoTips allows for streamlining of clinic workflow, efficient data entry, improved diagnosis, standardization of collected patient phenotypes, and sharing of anonymized patient phenotype data for the study of rare disorders. Our source code and a demo version of PhenoTips are available at http://phenotips.org. © 2013 WILEY PERIODICALS, INC.

SigniSite: Identification of residue-level genotype-phenotype correlations in protein multiple sequence alignments.

PubMed

Jessen, Leon Eyrich; Hoof, Ilka; Lund, Ole; Nielsen, Morten

2013-07-01

Identifying which mutation(s) within a given genotype is responsible for an observable phenotype is important in many aspects of molecular biology. Here, we present SigniSite, an online application for subgroup-free residue-level genotype-phenotype correlation. In contrast to similar methods, SigniSite does not require any pre-definition of subgroups or binary classification. Input is a set of protein sequences where each sequence has an associated real number, quantifying a given phenotype. SigniSite will then identify which amino acid residues are significantly associated with the data set phenotype. As output, SigniSite displays a sequence logo, depicting the strength of the phenotype association of each residue and a heat-map identifying 'hot' or 'cold' regions. SigniSite was benchmarked against SPEER, a state-of-the-art method for the prediction of specificity determining positions (SDP) using a set of human immunodeficiency virus protease-inhibitor genotype-phenotype data and corresponding resistance mutation scores from the Stanford University HIV Drug Resistance Database, and a data set of protein families with experimentally annotated SDPs. For both data sets, SigniSite was found to outperform SPEER. SigniSite is available at: http://www.cbs.dtu.dk/services/SigniSite/.

Qualitative Differences in Real-Time Solution of Standardized Figural Analogies.

ERIC Educational Resources Information Center

Schiano, Diane J.; And Others

Performance on standardized figural analogy tests is considered highly predictive of academic success. While information-processing models of analogy solution attribute performance differences to quantitative differences in processing parameters, the problem-solving literature suggests that qualitative differences in problem representation and…

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle.

PubMed

Belay, T K; Dagnachew, B S; Kowalski, Z M; Ådnøy, T

2017-08-01

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.

PubMed

Masuda, Y; Misztal, I; Tsuruta, S; Legarra, A; Aguilar, I; Lourenco, D A L; Fragomeni, B O; Lawlor, T J

2016-03-01

The objectives of this study were to develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP. We validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins. Phenotypic data included 11,626,576 final scores on 7,093,380 US Holstein cows, and genotypes were available for 569,404 animals. Daughter deviations for young bulls with no classified daughters in 2009, but at least 30 classified daughters in 2014 were computed using all the phenotypic data. Genomic predictions for the same bulls were calculated with single-step genomic BLUP using phenotypes up to 2009. We calculated the inverse of the genomic relationship matrix GAPY(-1) based on a direct inversion of genomic relationship matrix on a small subset of genotyped animals (core animals) and extended that information to noncore animals by recursion. We tested several sets of core animals including 9,406 bulls with at least 1 classified daughter, 9,406 bulls and 1,052 classified dams of bulls, 9,406 bulls and 7,422 classified cows, and random samples of 5,000 to 30,000 animals. Validation reliability was assessed by the coefficient of determination from regression of daughter deviation on genomic predictions for the predicted young bulls. The reliabilities were 0.39 with 5,000 randomly chosen core animals, 0.45 with the 9,406 bulls, and 7,422 cows as core animals, and 0.44 with the remaining sets. With phenotypes truncated in 2009 and the preconditioned conjugate gradient to solve mixed model equations, the number of rounds to convergence for core animals defined by bulls was 1,343; defined by bulls and cows, 2,066; and defined by 10,000 random animals, at most 1,629. With complete phenotype data, the number of rounds decreased to 858, 1,299, and at most 1,092, respectively. Setting up GAPY(-1) for 569,404 genotyped animals with 10,000 core animals took 1.3h and 57 GB of memory. The validation reliability with APY reaches a plateau when the number of core animals is at least 10,000. Predictions with APY have little differences in reliability among definitions of core animals. Single-step genomic BLUP with APY is applicable to millions of genotyped animals. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Human Intellectual Disability Genes Form Conserved Functional Modules in Drosophila

PubMed Central

Oortveld, Merel A. W.; Keerthikumar, Shivakumar; Oti, Martin; Nijhof, Bonnie; Fernandes, Ana Clara; Kochinke, Korinna; Castells-Nobau, Anna; van Engelen, Eva; Ellenkamp, Thijs; Eshuis, Lilian; Galy, Anne; van Bokhoven, Hans; Habermann, Bianca; Brunner, Han G.; Zweier, Christiane; Verstreken, Patrik; Huynen, Martijn A.; Schenck, Annette

2013-01-01

Intellectual Disability (ID) disorders, defined by an IQ below 70, are genetically and phenotypically highly heterogeneous. Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies. To systematically establish their functional connectivity, we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye. Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs. Most of these genotype-phenotype associations were novel. For example, we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism. ID gene orthologs with morphological eye phenotypes, in contrast to genes without phenotypes, are relatively highly expressed in the human nervous system and are enriched for neuronal functions, suggesting that eye phenotyping can distinguish different classes of ID genes. Indeed, grouping genes by Drosophila phenotype uncovered 26 connected functional modules. Novel links between ID genes successfully predicted that MYCN, PIGV and UPF3B regulate synapse development. Drosophila phenotype groups show, in addition to ID, significant phenotypic similarity also in humans, indicating that functional modules are conserved. The combined data indicate that ID disorders, despite their extreme genetic diversity, are caused by disruption of a limited number of highly connected functional modules. PMID:24204314

Human intellectual disability genes form conserved functional modules in Drosophila.

PubMed

Oortveld, Merel A W; Keerthikumar, Shivakumar; Oti, Martin; Nijhof, Bonnie; Fernandes, Ana Clara; Kochinke, Korinna; Castells-Nobau, Anna; van Engelen, Eva; Ellenkamp, Thijs; Eshuis, Lilian; Galy, Anne; van Bokhoven, Hans; Habermann, Bianca; Brunner, Han G; Zweier, Christiane; Verstreken, Patrik; Huynen, Martijn A; Schenck, Annette

2013-10-01

Intellectual Disability (ID) disorders, defined by an IQ below 70, are genetically and phenotypically highly heterogeneous. Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies. To systematically establish their functional connectivity, we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye. Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs. Most of these genotype-phenotype associations were novel. For example, we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism. ID gene orthologs with morphological eye phenotypes, in contrast to genes without phenotypes, are relatively highly expressed in the human nervous system and are enriched for neuronal functions, suggesting that eye phenotyping can distinguish different classes of ID genes. Indeed, grouping genes by Drosophila phenotype uncovered 26 connected functional modules. Novel links between ID genes successfully predicted that MYCN, PIGV and UPF3B regulate synapse development. Drosophila phenotype groups show, in addition to ID, significant phenotypic similarity also in humans, indicating that functional modules are conserved. The combined data indicate that ID disorders, despite their extreme genetic diversity, are caused by disruption of a limited number of highly connected functional modules.

Optimizing complex phenotypes through model-guided multiplex genome engineering

DOE PAGES

Kuznetsov, Gleb; Goodman, Daniel B.; Filsinger, Gabriel T.; ...

2017-05-25

Here, we present a method for identifying genomic modifications that optimize a complex phenotype through multiplex genome engineering and predictive modeling. We apply our method to identify six single nucleotide mutations that recover 59% of the fitness defect exhibited by the 63-codon E. coli strain C321.ΔA. By introducing targeted combinations of changes in multiplex we generate rich genotypic and phenotypic diversity and characterize clones using whole-genome sequencing and doubling time measurements. Regularized multivariate linear regression accurately quantifies individual allelic effects and overcomes bias from hitchhiking mutations and context-dependence of genome editing efficiency that would confound other strategies.

Optimizing complex phenotypes through model-guided multiplex genome engineering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuznetsov, Gleb; Goodman, Daniel B.; Filsinger, Gabriel T.

Here, we present a method for identifying genomic modifications that optimize a complex phenotype through multiplex genome engineering and predictive modeling. We apply our method to identify six single nucleotide mutations that recover 59% of the fitness defect exhibited by the 63-codon E. coli strain C321.ΔA. By introducing targeted combinations of changes in multiplex we generate rich genotypic and phenotypic diversity and characterize clones using whole-genome sequencing and doubling time measurements. Regularized multivariate linear regression accurately quantifies individual allelic effects and overcomes bias from hitchhiking mutations and context-dependence of genome editing efficiency that would confound other strategies.

Qualitative assessment of awake nasopharyngoscopy for prediction of oral appliance treatment response in obstructive sleep apnoea.

PubMed

Sutherland, Kate; Chan, Andrew S L; Ngiam, Joachim; Darendeliler, M Ali; Cistulli, Peter A

2018-01-23

Clinical methods to identify responders to oral appliance (OA) therapy for obstructive sleep apnoea (OSA) are needed. Awake nasopharyngoscopy during mandibular advancement, with image capture and subsequent processing and analysis, may predict treatment response. A qualitative assessment of awake nasopharyngoscopy would be simpler for clinical practice. We aimed to determine if a qualitative classification system of nasopharyngoscopic observations reflects treatment response. OSA patients were recruited for treatment with a customised two-piece OA. A custom scoring sheet was used to record observations of the pharyngeal airway (velopharynx, oropharynx, hypopharynx) during supine nasopharyngoscopy in response to mandibular advancement and performance of the Müller manoeuvre. Qualitative scores for degree (< 25%, 25-50%, 50-75%, > 75%), collapse pattern (concentric, anteroposterior, lateral) and diameter change (uniform, anteroposterior, lateral) were recorded. Treatment outcome was confirmed by polysomnography after a titration period of 14.6 ± 9.8 weeks. Treatment response was defined as (1) Treatment AHI < 5, (2) Treatment AHI < 10 plus > 50% AHI reduction and (3) > 50% AHI reduction. Eighty OSA patients (53.8% male) underwent nasopharyngoscopy. The most common naspharyngoscopic observation with mandibular advancement was a small (< 50%) increase in velopharyngeal lateral diameter (37.5%). The majority of subjects (72.5%) were recorded as having > 75% velopharyngeal collapse on performance of the Müller manoeuvre. Mandibular advancement reduced the observed level of pharyngeal collapse at all three pharyngeal regions (p < 0.001). None of the nasopharyngoscopic qualitative scores differed between responder and non-responder groups. Qualitative assessment of awake nasopharyngoscopy appears useful for assessing the effect of mandibular advancement on upper airway collapsibility. However, it is not sensitive enough to predict oral appliance treatment outcome.

Evaluation of Genome-Enabled Selection for Bacterial Cold Water Disease Resistance Using Progeny Performance Data in Rainbow Trout: Insights on Genotyping Methods and Genomic Prediction Models.

PubMed

Vallejo, Roger L; Leeds, Timothy D; Fragomeni, Breno O; Gao, Guangtu; Hernandez, Alvaro G; Misztal, Ignacy; Welch, Timothy J; Wiens, Gregory D; Palti, Yniv

2016-01-01

Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic breeding values (GEBVs) for BCWD resistance in 10 families from the first generation of the NCCCWA BCWD resistance breeding line, compared the predictive ability (PA) of GEBVs to pedigree-based estimated breeding values (EBVs), and compared the impact of two SNP genotyping methods on the accuracy of GEBV predictions. The BCWD phenotypes survival days (DAYS) and survival status (STATUS) had been recorded in training fish (n = 583) subjected to experimental BCWD challenge. Training fish, and their full sibs without phenotypic data that were used as parents of the subsequent generation, were genotyped using two methods: restriction-site associated DNA (RAD) sequencing and the Rainbow Trout Axiom® 57 K SNP array (Chip). Animal-specific GEBVs were estimated using four GS models: BayesB, BayesC, single-step GBLUP (ssGBLUP), and weighted ssGBLUP (wssGBLUP). Family-specific EBVs were estimated using pedigree and phenotype data in the training fish only. The PA of EBVs and GEBVs was assessed by correlating mean progeny phenotype (MPP) with mid-parent EBV (family-specific) or GEBV (animal-specific). The best GEBV predictions were similar to EBV with PA values of 0.49 and 0.46 vs. 0.50 and 0.41 for DAYS and STATUS, respectively. Among the GEBV prediction methods, ssGBLUP consistently had the highest PA. The RAD genotyping platform had GEBVs with similar PA to those of GEBVs from the Chip platform. The PA of ssGBLUP and wssGBLUP methods was higher with the Chip, but for BayesB and BayesC methods it was higher with the RAD platform. The overall GEBV accuracy in this study was low to moderate, likely due to the small training sample used. This study explored the potential of GS for improving resistance to BCWD in rainbow trout using, for the first time, progeny testing data to assess the accuracy of GEBVs, and it provides the basis for further investigation on the implementation of GS in commercial rainbow trout populations.

Evaluation of Genome-Enabled Selection for Bacterial Cold Water Disease Resistance Using Progeny Performance Data in Rainbow Trout: Insights on Genotyping Methods and Genomic Prediction Models

PubMed Central

Vallejo, Roger L.; Leeds, Timothy D.; Fragomeni, Breno O.; Gao, Guangtu; Hernandez, Alvaro G.; Misztal, Ignacy; Welch, Timothy J.; Wiens, Gregory D.; Palti, Yniv

2016-01-01

Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic breeding values (GEBVs) for BCWD resistance in 10 families from the first generation of the NCCCWA BCWD resistance breeding line, compared the predictive ability (PA) of GEBVs to pedigree-based estimated breeding values (EBVs), and compared the impact of two SNP genotyping methods on the accuracy of GEBV predictions. The BCWD phenotypes survival days (DAYS) and survival status (STATUS) had been recorded in training fish (n = 583) subjected to experimental BCWD challenge. Training fish, and their full sibs without phenotypic data that were used as parents of the subsequent generation, were genotyped using two methods: restriction-site associated DNA (RAD) sequencing and the Rainbow Trout Axiom® 57 K SNP array (Chip). Animal-specific GEBVs were estimated using four GS models: BayesB, BayesC, single-step GBLUP (ssGBLUP), and weighted ssGBLUP (wssGBLUP). Family-specific EBVs were estimated using pedigree and phenotype data in the training fish only. The PA of EBVs and GEBVs was assessed by correlating mean progeny phenotype (MPP) with mid-parent EBV (family-specific) or GEBV (animal-specific). The best GEBV predictions were similar to EBV with PA values of 0.49 and 0.46 vs. 0.50 and 0.41 for DAYS and STATUS, respectively. Among the GEBV prediction methods, ssGBLUP consistently had the highest PA. The RAD genotyping platform had GEBVs with similar PA to those of GEBVs from the Chip platform. The PA of ssGBLUP and wssGBLUP methods was higher with the Chip, but for BayesB and BayesC methods it was higher with the RAD platform. The overall GEBV accuracy in this study was low to moderate, likely due to the small training sample used. This study explored the potential of GS for improving resistance to BCWD in rainbow trout using, for the first time, progeny testing data to assess the accuracy of GEBVs, and it provides the basis for further investigation on the implementation of GS in commercial rainbow trout populations. PMID:27303436

Directional selection effects on patterns of phenotypic (co)variation in wild populations.

PubMed

Assis, A P A; Patton, J L; Hubbe, A; Marroig, G

2016-11-30

Phenotypic (co)variation is a prerequisite for evolutionary change, and understanding how (co)variation evolves is of crucial importance to the biological sciences. Theoretical models predict that under directional selection, phenotypic (co)variation should evolve in step with the underlying adaptive landscape, increasing the degree of correlation among co-selected traits as well as the amount of genetic variance in the direction of selection. Whether either of these outcomes occurs in natural populations is an open question and thus an important gap in evolutionary theory. Here, we documented changes in the phenotypic (co)variation structure in two separate natural populations in each of two chipmunk species (Tamias alpinus and T. speciosus) undergoing directional selection. In populations where selection was strongest (those of T. alpinus), we observed changes, at least for one population, in phenotypic (co)variation that matched theoretical expectations, namely an increase of both phenotypic integration and (co)variance in the direction of selection and a re-alignment of the major axis of variation with the selection gradient. © 2016 The Author(s).

Wireless Network Simulation in Aircraft Cabins

NASA Technical Reports Server (NTRS)

Beggs, John H.; Youssef, Mennatoallah; Vahala, Linda

2004-01-01

An electromagnetic propagation prediction tool was used to predict electromagnetic field strength inside airplane cabins. A commercial software package, Wireless Insite, was used to predict power levels inside aircraft cabins and the data was compared with previously collected experimental data. It was concluded that the software could qualitatively predict electromagnetic propagation inside the aircraft cabin environment.

Whole exome sequencing identifies genetic variants in inherited thrombocytopenia with secondary qualitative function defects

PubMed Central

Johnson, Ben; Lowe, Gillian C.; Futterer, Jane; Lordkipanidzé, Marie; MacDonald, David; Simpson, Michael A.; Sanchez-Guiú, Isabel; Drake, Sian; Bem, Danai; Leo, Vincenzo; Fletcher, Sarah J.; Dawood, Ban; Rivera, José; Allsup, David; Biss, Tina; Bolton-Maggs, Paula HB; Collins, Peter; Curry, Nicola; Grimley, Charlotte; James, Beki; Makris, Mike; Motwani, Jayashree; Pavord, Sue; Talks, Katherine; Thachil, Jecko; Wilde, Jonathan; Williams, Mike; Harrison, Paul; Gissen, Paul; Mundell, Stuart; Mumford, Andrew; Daly, Martina E.; Watson, Steve P.; Morgan, Neil V.

2016-01-01

Inherited thrombocytopenias are a heterogeneous group of disorders characterized by abnormally low platelet counts which can be associated with abnormal bleeding. Next-generation sequencing has previously been employed in these disorders for the confirmation of suspected genetic abnormalities, and more recently in the discovery of novel disease-causing genes. However its full potential has not yet been exploited. Over the past 6 years we have sequenced the exomes from 55 patients, including 37 index cases and 18 additional family members, all of whom were recruited to the UK Genotyping and Phenotyping of Platelets study. All patients had inherited or sustained thrombocytopenia of unknown etiology with platelet counts varying from 11×109/L to 186×109/L. Of the 51 patients phenotypically tested, 37 (73%), had an additional secondary qualitative platelet defect. Using whole exome sequencing analysis we have identified “pathogenic” or “likely pathogenic” variants in 46% (17/37) of our index patients with thrombocytopenia. In addition, we report variants of uncertain significance in 12 index cases, including novel candidate genetic variants in previously unreported genes in four index cases. These results demonstrate that whole exome sequencing is an efficient method for elucidating potential pathogenic genetic variants in inherited thrombocytopenia. Whole exome sequencing also has the added benefit of discovering potentially pathogenic genetic variants for further study in novel genes not previously implicated in inherited thrombocytopenia. PMID:27479822

The potential of Fourier transform infrared spectroscopy of milk samples to predict energy intake and efficiency in dairy cows.

PubMed

McParland, S; Berry, D P

2016-05-01

Knowledge of animal-level and herd-level energy intake, energy balance, and feed efficiency affect day-to-day herd management strategies; information on these traits at an individual animal level is also useful in animal breeding programs. A paucity of data (especially at the individual cow level), of feed intake in particular, hinders the inclusion of such attributes in herd management decision-support tools and breeding programs. Dairy producers have access to an individual cow milk sample at least once daily during lactation, and consequently any low-cost phenotyping strategy should consider exploiting measureable properties in this biological sample, reflecting the physiological status and performance of the cow. Infrared spectroscopy is the study of the interaction of an electromagnetic wave with matter and it is used globally to predict milk quality parameters on routinely acquired individual cow milk samples and bulk tank samples. Thus, exploiting infrared spectroscopy in next-generation phenotyping will ensure potentially rapid application globally with a negligible additional implementation cost as the infrastructure already exists. Fourier-transform infrared spectroscopy (FTIRS) analysis is already used to predict milk fat and protein concentrations, the ratio of which has been proposed as an indicator of energy balance. Milk FTIRS is also able to predict the concentration of various fatty acids in milk, the composition of which is known to change when body tissue is mobilized; that is, when the cow is in negative energy balance. Energy balance is mathematically very similar to residual energy intake (REI), a suggested measure of feed efficiency. Therefore, the prediction of energy intake, energy balance, and feed efficiency (i.e., REI) from milk FTIRS seems logical. In fact, the accuracy of predicting (i.e., correlation between predicted and actual values; root mean square error in parentheses) energy intake, energy balance, and REI from milk FTIRS in dairy cows was 0.88 (20.0MJ), 0.78 (18.6MJ), and 0.63 (22.0MJ), respectively, based on cross-validation. These studies, however, are limited to results from one research group based on data from 2 contrasting production systems in the United Kingdom and Ireland and would need to be replicated, especially in a range of production systems because the prediction equations are not accurate when the variability used in validation is not represented in the calibration data set. Heritable genetic variation exists for all predicted traits. Phenotypic differences in energy intake also exists among animals stratified based on genetic merit for energy intake predicted from milk FTIRS, substantiating the usefulness of such FTIR-predicted phenotypes not only for day-to-day herd management, but also as part of a breeding strategy to improve cow performance. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The predictive value of quantitative fibronectin testing in combination with cervical length measurement in symptomatic women.

PubMed

Bruijn, Merel M C; Kamphuis, Esme I; Hoesli, Irene M; Martinez de Tejada, Begoña; Loccufier, Anne R; Kühnert, Maritta; Helmer, Hanns; Franz, Marie; Porath, Martina M; Oudijk, Martijn A; Jacquemyn, Yves; Schulzke, Sven M; Vetter, Grit; Hoste, Griet; Vis, Jolande Y; Kok, Marjolein; Mol, Ben W J; van Baaren, Gert-Jan

2016-12-01

The combination of the qualitative fetal fibronectin test and cervical length measurement has a high negative predictive value for preterm birth within 7 days; however, positive prediction is poor. A new bedside quantitative fetal fibronectin test showed potential additional value over the conventional qualitative test, but there is limited evidence on the combination with cervical length measurement. The purpose of this study was to compare quantitative fetal fibronectin and qualitative fetal fibronectin testing in the prediction of spontaneous preterm birth within 7 days in symptomatic women who undergo cervical length measurement. We performed a European multicenter cohort study in 10 perinatal centers in 5 countries. Women between 24 and 34 weeks of gestation with signs of active labor and intact membranes underwent quantitative fibronectin testing and cervical length measurement. We assessed the risk of preterm birth within 7 days in predefined strata based on fibronectin concentration and cervical length. Of 455 women who were included in the study, 48 women (11%) delivered within 7 days. A combination of cervical length and qualitative fibronectin resulted in the identification of 246 women who were at low risk: 164 women with a cervix between 15 and 30 mm and a negative fibronectin test (<50 ng/mL; preterm birth rate, 2%) and 82 women with a cervix at >30 mm (preterm birth rate, 2%). Use of quantitative fibronectin alone resulted in a predicted risk of preterm birth within 7 days that ranged from 2% in the group with the lowest fibronectin level (<10 ng/mL) to 38% in the group with the highest fibronectin level (>500 ng/mL), with similar accuracy as that of the combination of cervical length and qualitative fibronectin. Combining cervical length and quantitative fibronectin resulted in the identification of an additional 19 women at low risk (preterm birth rate, 5%), using a threshold of 10 ng/mL in women with a cervix at <15 mm, and 6 women at high risk (preterm birth rate, 33%) using a threshold of >500 ng/mL in women with a cervix at >30 mm. In women with threatened preterm birth, quantitative fibronectin testing alone performs equal to the combination of cervical length and qualitative fibronectin. Possibly, the combination of quantitative fibronectin testing and cervical length increases this predictive capacity. Cost-effectiveness analysis and the availability of these tests in a local setting should determine the final choice. Copyright © 2016 Elsevier Inc. All rights reserved.

Current cytochrome P450 phenotyping methods applied to metabolic drug-drug interaction prediction in dogs.

PubMed

Mills, Beth Miskimins; Zaya, Matthew J; Walters, Rodney R; Feenstra, Kenneth L; White, Julie A; Gagne, Jason; Locuson, Charles W

2010-03-01

Recombinant cytochrome P450 (P450) phenotyping, different approaches for estimating fraction metabolized (f(m)), and multiple measures of in vivo inhibitor exposure were tested for their ability to predict drug interaction magnitude in dogs. In previous reports, midazolam-ketoconazole interaction studies in dogs have been attributed to inhibition of CYP3A pathways. However, in vitro phenotyping studies demonstrated higher apparent intrinsic clearances (CL(int,app)) of midazolam with canine CYP2B11 and CYP2C21. Application of activity correction factors and isoform hepatic abundance to liver microsome CL(int,app) values further implicated CYP2B11 (f(m) >or= 0.89) as the dog enzyme responsible for midazolam- and temazepam-ketoconazole interactions in vivo. Mean area under the curve (AUC) in the presence of the inhibitor/AUC ratios from intravenous and oral midazolam interaction studies were predicted well with unbound K(i) and estimates of unbound hepatic inlet inhibitor concentrations and intestinal metabolism using the AUC-competitive inhibitor relationship. No interactions were observed in vivo with bufuralol, although significant interactions with bufuralol were predicted with fluoxetine via CYP2D and CYP2C pathways (>2.45-fold) but not with clomipramine (<2-fold). The minor caffeine-fluvoxamine interaction (1.78-fold) was slightly higher than predicted values based on determination of a moderate f(m) value for CYP1A1, although CYP1A2 may also be involved in caffeine metabolism. The findings suggest promise for in vitro approaches to drug interaction assessment in dogs, but they also highlight the need to identify improved substrate and inhibitor probes for canine P450s.

Development of a Melanoma Risk Prediction Model Incorporating MC1R Genotype and Indoor Tanning Exposure: Impact of Mole Phenotype on Model Performance

PubMed Central

Penn, Lauren A.; Qian, Meng; Zhang, Enhan; Ng, Elise; Shao, Yongzhao; Berwick, Marianne; Lazovich, DeAnn; Polsky, David

2014-01-01

Background Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. Methods Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as “none”, “few”, “some” or “many” moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant (“none” and “few” moles) and nevus-prone (“some” and “many” moles)). Results Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71–0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. Conclusions These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals. PMID:25003831

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

PubMed

Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs.

PubMed

Le, Duc-Hau; Dao, Lan T M

2018-05-23

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence. Copyright © 2018 Elsevier Ltd. All rights reserved.

Valorization of genetic variability for the qualitative improvement of autochthonous grape cultivars of Cirò's terroir through the self-fertilization.

PubMed

Meneghetti, Stefano; Gaiotti, Federica; Giust, Mirella; Belfiore, Nicola; Tomasi, Diego

2015-03-01

This study uses PCR-derived marker systems to investigate the genetic differences of 22 grapevine accessions obtained through a self-fertilization program using Gaglioppo and Magliocco dolce. The aim of the study was to improve some qualitative parameters, while preserving the adaptive characteristics of these two cultivars to the adverse environmental conditions of the Calabria region (southern Italy). These two Calabrian grapevines have been cultivated within a restricted area and have been placed under a strong anthropic pressure which has limited their phenotypical variability with no selection of higher performant biotypes. Therefore, to have accessions with improved qualitative traits, a program of genetic improvement based on the self-fertilization of Gaglioppo and Magliocco dolce cultivars was performed in 1998, producing 3,122 accessions. Selection cycles were performed in 14 years. A first selection cycle (1998-2000), based on visual inspection of vegetative traits, selected 1,320 accessions, planted in an experimental vineyard in 2000. A second selection cycle (2000-2008), based on phenotypic traits, sanitary aspects, and chemical composition of the grapes, selected 42 accessions, planted in a new experimental vineyard in 2008. A final selection cycle (2008-2012), produced 22 accessions (virus free), with the best agronomic, sanitary, and qualitative aspects: two accessions obtained from Gaglioppo have been selected by color characteristics (i.e., anthocyanin total content and stability); 20 genotypes obtained from Magliocco dolce had a better macro-composition of the grape (i.e., good sugar content with a balanced acidity). SSR analyses were performed to check the self-fertilization process. The study of genetic differences between accessions was performed by AFLPs, SAMPLs, and M-AFLPs. The application of the above-mentioned techniques allowed both to discriminate molecularly the 22 accessions grouped these accessions according to their genetic similarity. The self-fertilization approach has enabled improvement in the quality of the grapes, while preserving the high degree of adaptation to the environment of these two native Calabrian cultivars in southern Italy.

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement.

PubMed

Cobb, Joshua N; Declerck, Genevieve; Greenberg, Anthony; Clark, Randy; McCouch, Susan

2013-04-01

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

Experimental verification of a progressive damage model for composite laminates based on continuum damage mechanics. M.S. Thesis Final Report

NASA Technical Reports Server (NTRS)

Coats, Timothy William

1994-01-01

Progressive failure is a crucial concern when using laminated composites in structural design. Therefore the ability to model damage and predict the life of laminated composites is vital. The purpose of this research was to experimentally verify the application of the continuum damage model, a progressive failure theory utilizing continuum damage mechanics, to a toughened material system. Damage due to tension-tension fatigue was documented for the IM7/5260 composite laminates. Crack density and delamination surface area were used to calculate matrix cracking and delamination internal state variables, respectively, to predict stiffness loss. A damage dependent finite element code qualitatively predicted trends in transverse matrix cracking, axial splits and local stress-strain distributions for notched quasi-isotropic laminates. The predictions were similar to the experimental data and it was concluded that the continuum damage model provided a good prediction of stiffness loss while qualitatively predicting damage growth in notched laminates.

Predicting evolutionary rescue via evolving plasticity in stochastic environments

PubMed Central

Baskett, Marissa L.

2016-01-01

Phenotypic plasticity and its evolution may help evolutionary rescue in a novel and stressful environment, especially if environmental novelty reveals cryptic genetic variation that enables the evolution of increased plasticity. However, the environmental stochasticity ubiquitous in natural systems may alter these predictions, because high plasticity may amplify phenotype–environment mismatches. Although previous studies have highlighted this potential detrimental effect of plasticity in stochastic environments, they have not investigated how it affects extinction risk in the context of evolutionary rescue and with evolving plasticity. We investigate this question here by integrating stochastic demography with quantitative genetic theory in a model with simultaneous change in the mean and predictability (temporal autocorrelation) of the environment. We develop an approximate prediction of long-term persistence under the new pattern of environmental fluctuations, and compare it with numerical simulations for short- and long-term extinction risk. We find that reduced predictability increases extinction risk and reduces persistence because it increases stochastic load during rescue. This understanding of how stochastic demography, phenotypic plasticity, and evolution interact when evolution acts on cryptic genetic variation revealed in a novel environment can inform expectations for invasions, extinctions, or the emergence of chemical resistance in pests. PMID:27655762

Behavior change is not one size fits all: psychosocial phenotypes of childhood obesity prevention intervention participants.

PubMed

Burgermaster, Marissa; Contento, Isobel; Koch, Pamela; Mamykina, Lena

2018-01-17

Variability in individuals' responses to interventions may contribute to small average treatment effects of childhood obesity prevention interventions. But, neither the causes of this individual variability nor the mechanism by which it influences behavior are clear. We used qualitative methods to characterize variability in students' responses to participating in a childhood obesity prevention intervention and psychosocial characteristics related to the behavior change process. We interviewed 18 students participating in a school-based curriculum and policy behavior change intervention. Descriptive coding, summary, and case-ordered descriptive meta-matrices were used to group participants by their psychosocial responses to the intervention and associated behavior changes. Four psychosocial phenotypes of responses emerged: (a) Activated-successful behavior-changers with strong internal supports; (b) Inspired-motivated, but not fully successful behavior-changers with some internal supports, whose taste preferences and food environment overwhelmed their motivation; (c) Reinforced-already practiced target behaviors, were motivated, and had strong family support; and (d) Indifferent-uninterested in behavior change and only did target behaviors if family insisted. Our findings contribute to the field of behavioral medicine by suggesting the presence of specific subgroups of participants who respond differently to behavior change interventions and salient psychosocial characteristics that differentiate among these phenotypes. Future research should examine the utility of prospectively identifying psychosocial phenotypes for improving the tailoring of nutrition behavior change interventions. © Society of Behavioral Medicine 2018.

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity.

PubMed

Almendro, Vanessa; Cheng, Yu-Kang; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; Gonzalez-Farre, Xavier; Muñoz, Montse; Russnes, Hege G; Helland, Aslaug; Rye, Inga H; Borresen-Dale, Anne-Lise; Maruyama, Reo; van Oudenaarden, Alexander; Dowsett, Mitchell; Jones, Robin L; Reis-Filho, Jorge; Gascon, Pere; Gönen, Mithat; Michor, Franziska; Polyak, Kornelia

2014-02-13

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and posttreatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of cellular diversity for genetic and phenotypic features

PubMed Central

Almendro, Vanessa; Cheng, Yu-Kang; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; Gonzalez-Farre, Xavier; Muñoz, Montse; Russnes, Hege G.; Helland, Åslaug; Rye, Inga H.; Borresen-Dale, Anne-Lise; Maruyama, Reo; van Oudenaarden, Alexander; Dowsett, Mitchell; Jones, Robin L.; Reis-Filho, Jorge; Gascon, Pere; Gönen, Mithat; Michor, Franziska; Polyak, Kornelia

2014-01-01

SUMMARY Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor subtype-specific and it did not change during treatment in tumors with partial or no response. However, lower pre-treatment genetic diversity was significantly associated with complete pathologic response. In contrast, phenotypic diversity was different between pre- and post-treatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution. PMID:24462293

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity

DOE PAGES

Almendro, Vanessa; Cheng, Yu -Kang; Randles, Amanda; ...

2014-02-01

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and post-treatment samples. We also observed significant changes in the spatialmore » distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution.« less

Environmental stress, inbreeding, and the nature of phenotypic and genetic variance in Drosophila melanogaster.

PubMed Central

Fowler, Kevin; Whitlock, Michael C

2002-01-01

Fifty-two lines of Drosophila melanogaster founded by single-pair population bottlenecks were used to study the effects of inbreeding and environmental stress on phenotypic variance, genetic variance and survivorship. Cold temperature and high density cause reduced survivorship, but these stresses do not cause repeatable changes in the phenotypic variance of most wing morphological traits. Wing area, however, does show increased phenotypic variance under both types of environmental stress. This increase is no greater in inbred than in outbred lines, showing that inbreeding does not increase the developmental effects of stress. Conversely, environmental stress does not increase the extent of inbreeding depression. Genetic variance is not correlated with environmental stress, although the amount of genetic variation varies significantly among environments and lines vary significantly in their response to environmental change. Drastic changes in the environment can cause changes in phenotypic and genetic variance, but not in a way reliably predicted by the notion of 'stress'. PMID:11934358

Multilevel biological characterization of exomic variants at the protein level significantly improves the identification of their deleterious effects.

PubMed

Raimondi, Daniele; Gazzo, Andrea M; Rooman, Marianne; Lenaerts, Tom; Vranken, Wim F

2016-06-15

There are now many predictors capable of identifying the likely phenotypic effects of single nucleotide variants (SNVs) or short in-frame Insertions or Deletions (INDELs) on the increasing amount of genome sequence data. Most of these predictors focus on SNVs and use a combination of features related to sequence conservation, biophysical, and/or structural properties to link the observed variant to either neutral or disease phenotype. Despite notable successes, the mapping between genetic variants and their phenotypic effects is riddled with levels of complexity that are not yet fully understood and that are often not taken into account in the predictions, despite their promise of significantly improving the prediction of deleterious mutants. We present DEOGEN, a novel variant effect predictor that can handle both missense SNVs and in-frame INDELs. By integrating information from different biological scales and mimicking the complex mixture of effects that lead from the variant to the phenotype, we obtain significant improvements in the variant-effect prediction results. Next to the typical variant-oriented features based on the evolutionary conservation of the mutated positions, we added a collection of protein-oriented features that are based on functional aspects of the gene affected. We cross-validated DEOGEN on 36 825 polymorphisms, 20 821 deleterious SNVs, and 1038 INDELs from SwissProt. The multilevel contextualization of each (variant, protein) pair in DEOGEN provides a 10% improvement of MCC with respect to current state-of-the-art tools. The software and the data presented here is publicly available at http://ibsquare.be/deogen : wvranken@vub.ac.be Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A mesenchymal-like phenotype and expression of CD44 predict lack of apoptotic response to sorafenib in liver tumor cells.

PubMed

Fernando, Joan; Malfettone, Andrea; Cepeda, Edgar B; Vilarrasa-Blasi, Roser; Bertran, Esther; Raimondi, Giulia; Fabra, Àngels; Alvarez-Barrientos, Alberto; Fernández-Salguero, Pedro; Fernández-Rodríguez, Conrado M; Giannelli, Gianluigi; Sancho, Patricia; Fabregat, Isabel

2015-02-15

The multikinase inhibitor sorafenib is the only effective drug in advanced cases of hepatocellular carcinoma (HCC). However, response differs among patients and effectiveness only implies a delay. We have recently described that sorafenib sensitizes HCC cells to apoptosis. In this work, we have explored the response to this drug of six different liver tumor cell lines to define a phenotypic signature that may predict lack of response in HCC patients. Results have indicated that liver tumor cells that show a mesenchymal-like phenotype, resistance to the suppressor effects of transforming growth factor beta (TGF-β) and high expression of the stem cell marker CD44 were refractory to sorafenib-induced cell death in in vitro studies, which correlated with lack of response to sorafenib in nude mice xenograft models of human HCC. In contrast, epithelial-like cells expressing the stem-related proteins EpCAM or CD133 were sensitive to sorafenib-induced apoptosis both in vitro and in vivo. A cross-talk between the TGF-β pathway and the acquisition of a mesenchymal-like phenotype with up-regulation of CD44 expression was found in the HCC cell lines. Targeted CD44 knock-down in the mesenchymal-like cells indicated that CD44 plays an active role in protecting HCC cells from sorafenib-induced apoptosis. However, CD44 effect requires a TGF-β-induced mesenchymal background, since the only overexpression of CD44 in epithelial-like HCC cells is not sufficient to impair sorafenib-induced cell death. In conclusion, a mesenchymal profile and expression of CD44, linked to activation of the TGF-β pathway, may predict lack of response to sorafenib in HCC patients. © 2014 UICC.

Whole-Genome Sequencing and Concordance Between Antimicrobial Susceptibility Genotypes and Phenotypes of Bacterial Isolates Associated with Bovine Respiratory Disease

PubMed Central

Owen, Joseph R.; Noyes, Noelle; Young, Amy E.; Prince, Daniel J.; Blanchard, Patricia C.; Lehenbauer, Terry W.; Aly, Sharif S.; Davis, Jessica H.; O’Rourke, Sean M.; Abdo, Zaid; Belk, Keith; Miller, Michael R.; Morley, Paul; Van Eenennaam, Alison L.

2017-01-01

Extended laboratory culture and antimicrobial susceptibility testing timelines hinder rapid species identification and susceptibility profiling of bacterial pathogens associated with bovine respiratory disease, the most prevalent cause of cattle mortality in the United States. Whole-genome sequencing offers a culture-independent alternative to current bacterial identification methods, but requires a library of bacterial reference genomes for comparison. To contribute new bacterial genome assemblies and evaluate genetic diversity and variation in antimicrobial resistance genotypes, whole-genome sequencing was performed on bovine respiratory disease–associated bacterial isolates (Histophilus somni, Mycoplasma bovis, Mannheimia haemolytica, and Pasteurella multocida) from dairy and beef cattle. One hundred genomically distinct assemblies were added to the NCBI database, doubling the available genomic sequences for these four species. Computer-based methods identified 11 predicted antimicrobial resistance genes in three species, with none being detected in M. bovis. While computer-based analysis can identify antibiotic resistance genes within whole-genome sequences (genotype), it may not predict the actual antimicrobial resistance observed in a living organism (phenotype). Antimicrobial susceptibility testing on 64 H. somni, M. haemolytica, and P. multocida isolates had an overall concordance rate between genotype and phenotypic resistance to the associated class of antimicrobials of 72.7% (P < 0.001), showing substantial discordance. Concordance rates varied greatly among different antimicrobial, antibiotic resistance gene, and bacterial species combinations. This suggests that antimicrobial susceptibility phenotypes are needed to complement genomically predicted antibiotic resistance gene genotypes to better understand how the presence of antibiotic resistance genes within a given bacterial species could potentially impact optimal bovine respiratory disease treatment and morbidity/mortality outcomes. PMID:28739600

Improving the baking quality of bread wheat by genomic selection in early generations.

PubMed

Michel, Sebastian; Kummer, Christian; Gallee, Martin; Hellinger, Jakob; Ametz, Christian; Akgöl, Batuhan; Epure, Doru; Güngör, Huseyin; Löschenberger, Franziska; Buerstmayr, Hermann

2018-02-01

Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis. The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009-2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38-0.63). Genomic selection can furthermore be applied 2-3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.

Investigation of NS3 Protease Resistance-Associated Variants and Phenotypes for the Prediction of Treatment Response to HCV Triple Therapy.

PubMed

Dietz, Julia; Rupp, Daniel; Susser, Simone; Vermehren, Johannes; Peiffer, Kai-Henrik; Filmann, Natalie; Bon, Dimitra; Kuntzen, Thomas; Mauss, Stefan; Grammatikos, Georgios; Perner, Dany; Berkowski, Caterina; Herrmann, Eva; Zeuzem, Stefan; Bartenschlager, Ralf; Sarrazin, Christoph

2016-01-01

Triple therapy of chronic hepatitis C virus (HCV) infection with boceprevir (BOC) or telaprevir (TVR) leads to virologic failure in many patients which is often associated with the selection of resistance-associated variants (RAVs). These resistance profiles are of importance for the selection of potential rescue treatment options. In this study, we sequenced baseline NS3 RAVs population-based and investigated the sensitivity of NS3 phenotypes in an HCV replicon assay together with clinical factors for a prediction of treatment response in a cohort of 165 German and Swiss patients treated with a BOC or TVR-based triple therapy. Overall, the prevalence of baseline RAVs was low, although the frequency of RAVs was higher in patients with virologic failure compared to those who achieved a sustained virologic response (SVR) (7% versus 1%, P = 0.06). The occurrence of RAVs was associated with a resistant NS3 quasispecies phenotype (P<0.001), but the sensitivity of phenotypes was not associated with treatment outcome (P = 0.2). The majority of single viral and host predictors of SVR was only weakly associated with treatment response. In multivariate analyses, low AST levels, female sex and an IFNL4 CC genotype were independently associated with SVR. However, a combined analysis of negative predictors revealed a significantly lower overall number of negative predictors in patients with SVR in comparison to individuals with virologic failure (P<0.0001) and the presence of 2 or less negative predictors was indicative for SVR. These results demonstrate that most single baseline viral and host parameters have a weak influence on the response to triple therapy, whereas the overall number of negative predictors has a high predictive value for SVR.

Whole-Genome Sequencing and Concordance Between Antimicrobial Susceptibility Genotypes and Phenotypes of Bacterial Isolates Associated with Bovine Respiratory Disease.

PubMed

Owen, Joseph R; Noyes, Noelle; Young, Amy E; Prince, Daniel J; Blanchard, Patricia C; Lehenbauer, Terry W; Aly, Sharif S; Davis, Jessica H; O'Rourke, Sean M; Abdo, Zaid; Belk, Keith; Miller, Michael R; Morley, Paul; Van Eenennaam, Alison L

2017-09-07

Extended laboratory culture and antimicrobial susceptibility testing timelines hinder rapid species identification and susceptibility profiling of bacterial pathogens associated with bovine respiratory disease, the most prevalent cause of cattle mortality in the United States. Whole-genome sequencing offers a culture-independent alternative to current bacterial identification methods, but requires a library of bacterial reference genomes for comparison. To contribute new bacterial genome assemblies and evaluate genetic diversity and variation in antimicrobial resistance genotypes, whole-genome sequencing was performed on bovine respiratory disease-associated bacterial isolates ( Histophilus somni , Mycoplasma bovis , Mannheimia haemolytica , and Pasteurella multocida ) from dairy and beef cattle. One hundred genomically distinct assemblies were added to the NCBI database, doubling the available genomic sequences for these four species. Computer-based methods identified 11 predicted antimicrobial resistance genes in three species, with none being detected in M. bovis While computer-based analysis can identify antibiotic resistance genes within whole-genome sequences (genotype), it may not predict the actual antimicrobial resistance observed in a living organism (phenotype). Antimicrobial susceptibility testing on 64 H. somni , M. haemolytica , and P. multocida isolates had an overall concordance rate between genotype and phenotypic resistance to the associated class of antimicrobials of 72.7% ( P < 0.001), showing substantial discordance. Concordance rates varied greatly among different antimicrobial, antibiotic resistance gene, and bacterial species combinations. This suggests that antimicrobial susceptibility phenotypes are needed to complement genomically predicted antibiotic resistance gene genotypes to better understand how the presence of antibiotic resistance genes within a given bacterial species could potentially impact optimal bovine respiratory disease treatment and morbidity/mortality outcomes. Copyright © 2017 Owen et al.

Mapping disease-related missense mutations in the immunoglobulin-like fold domain of lamin A/C reveals novel genotype-phenotype associations for laminopathies.

PubMed

Scharner, Juergen; Lu, Hui-Chun; Fraternali, Franca; Ellis, Juliet A; Zammit, Peter S

2014-06-01

Mutations in A-type nuclear lamins cause laminopathies. However, genotype-phenotype correlations using the 340 missense mutations within the LMNA gene are unclear: partially due to the limited availability of three-dimensional structure. The immunoglobulin (Ig)-like fold domain has been solved, and using bioinformatics tools (including Polyphen-2, Fold X, Parameter OPtimized Surfaces, and PocketPicker) we characterized 56 missense mutations for position, surface exposure, change in charge and effect on Ig-like fold stability. We find that 21 of the 27 mutations associated with a skeletal muscle phenotype are distributed throughout the Ig-like fold, are nonsurface exposed and predicted to disrupt overall stability of the Ig-like fold domain. Intriguingly, the remaining 6 mutations clustered, had higher surface exposure, and did not affect stability. The majority of 9 lipodystrophy or 10 premature aging syndrome mutations also did not disrupt Ig-like fold domain stability and were surface exposed and clustered in distinct regions that overlap predicted binding pockets. Although buried, the 10 cardiac mutations had no other consistent properties. Finally, most lipodystrophy and premature aging mutations resulted in a -1 net charge change, whereas skeletal muscle mutations caused no consistent net charge changes. Since premature aging, lipodystrophy and the subset of 6 skeletal muscle mutations cluster tightly in distinct, charged regions, they likely affect lamin A/C -protein/DNA/RNA interactions: providing a consistent genotype-phenotype relationship for mutations in this domain. Thus, this subgroup of skeletal muscle laminopathies that we term the 'Skeletal muscle cluster', may have a distinct pathological mechanism. These novel associations refine the ability to predict clinical features caused by certain LMNA missense mutations. © 2013 Wiley Periodicals, Inc.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Efficient use of historical data for genomic selection: a case study of rust resistance in wheat

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) is a new methodology that can improve wheat breeding efficiency. To implement GS, a training population (TP) with both phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs). Several factors impact prediction...

Predicting age-age genetic correlations in tree-breeding programs: a case study of Pinus taeda L.

Treesearch

D.P. Gwaze; F.E. Bridgwater; T.D. Byram; J.A. Woolliams; C.G. Williams

2000-01-01

A meta-analysis of 520 parents and 51,439 individuals was used to develop two equations for predicting age-age genetic correlations in Pinus taeda L. Genetic and phenotypic family mean correlations and heritabilities were estimated for ages ranging from 2 to 25 years on 31...

Genomic predictability of single-step GBLUP for production traits in US Holstein

USDA-ARS?s Scientific Manuscript database

The objective of this study was to validate genomic predictability of single-step genomic BLUP for 305-day protein yield for US Holsteins. The genomic relationship matrix was created with the Algorithm of Proven and Young (APY) with 18,359 core animals. The full data set consisted of phenotypes coll...

Bayesian whole-genome prediction and genome-wide association analysis with missing genotypes using variable selection

USDA-ARS?s Scientific Manuscript database

Single-step Genomic Best Linear Unbiased Predictor (ssGBLUP) has become increasingly popular for whole-genome prediction (WGP) modeling as it utilizes any available pedigree and phenotypes on both genotyped and non-genotyped individuals. The WGP accuracy of ssGBLUP has been demonstrated to be greate...

The Hardy-Weinberg Equilibrium--Some Helpful Suggestions.

ERIC Educational Resources Information Center

Ortiz, Mary T.; Taras, Loretta; Stavroulakis, Anthea M.

2000-01-01

Describes an approach that provides mathematical tips and helpful suggestions for presenting the Hardy-Weinberg equilibrium to predict allele frequencies, phenotypes, and genotypes in populations. (ASK)

Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data.

PubMed

Iwata, Hiroaki; Sawada, Ryusuke; Mizutani, Sayaka; Yamanishi, Yoshihiro

2015-02-23

Drug repositioning, or the application of known drugs to new indications, is a challenging issue in pharmaceutical science. In this study, we developed a new computational method to predict unknown drug indications for systematic drug repositioning in a framework of supervised network inference. We defined a descriptor for each drug-disease pair based on the phenotypic features of drugs (e.g., medicinal effects and side effects) and various molecular features of diseases (e.g., disease-causing genes, diagnostic markers, disease-related pathways, and environmental factors) and constructed a statistical model to predict new drug-disease associations for a wide range of diseases in the International Classification of Diseases. Our results show that the proposed method outperforms previous methods in terms of accuracy and applicability, and its performance does not depend on drug chemical structure similarity. Finally, we performed a comprehensive prediction of a drug-disease association network consisting of 2349 drugs and 858 diseases and described biologically meaningful examples of newly predicted drug indications for several types of cancers and nonhereditary diseases.

Systematic identification of proteins that elicit drug side effects

PubMed Central

Kuhn, Michael; Al Banchaabouchi, Mumna; Campillos, Monica; Jensen, Lars Juhl; Gross, Cornelius; Gavin, Anne-Claude; Bork, Peer

2013-01-01

Side effect similarities of drugs have recently been employed to predict new drug targets, and networks of side effects and targets have been used to better understand the mechanism of action of drugs. Here, we report a large-scale analysis to systematically predict and characterize proteins that cause drug side effects. We integrated phenotypic data obtained during clinical trials with known drug–target relations to identify overrepresented protein–side effect combinations. Using independent data, we confirm that most of these overrepresentations point to proteins which, when perturbed, cause side effects. Of 1428 side effects studied, 732 were predicted to be predominantly caused by individual proteins, at least 137 of them backed by existing pharmacological or phenotypic data. We prove this concept in vivo by confirming our prediction that activation of the serotonin 7 receptor (HTR7) is responsible for hyperesthesia in mice, which, in turn, can be prevented by a drug that selectively inhibits HTR7. Taken together, we show that a large fraction of complex drug side effects are mediated by individual proteins and create a reference for such relations. PMID:23632385

Elucidation of the Metabolic Network of Helicobacter pylori J99 and Malaysian Clinical Strains by Phenotype Microarray.

PubMed

Lee, Woon Ching; Goh, Khean Lee; Loke, Mun Fai; Vadivelu, Jamuna

2017-02-01

Helicobacter pylori colonizes almost half of the human population worldwide. H. pylori strains are genetically diverse, and the specific genotypes are associated with various clinical manifestations including gastric adenocarcinoma, peptic ulcer disease (PUD), and nonulcer dyspepsia (NUD). However, our current knowledge of the H. pylori metabolism is limited. To understand the metabolic differences among H. pylori strains, we investigated four Malaysian H. pylori clinical strains, which had been previously sequenced, and a standard strain, H. pylori J99, at the phenotypic level. The phenotypes of the H. pylori strains were profiled using the Biolog Phenotype Microarray system to corroborate genomic data. We initiated the analyses by predicting carbon and nitrogen metabolic pathways from the H. pylori genomic data from the KEGG database. Biolog PM aided the validation of the prediction and provided a more intensive analysis of the H. pylori phenomes. We have identified a core set of metabolic nutrient sources that was utilized by all strains tested and another set that was differentially utilized by only the local strains. Pentose sugars are the preferred carbon nutrients utilized by H. pylori. The amino acids l-aspartic acid, d-alanine, and l-asparagine serve as both carbon and nitrogen sources in the metabolism of the bacterium. The phenotypic profile based on this study provides a better understanding on the survival of H. pylori in its natural host. Our data serve as a foundation for future challenges in correlating interstrain metabolic differences in H. pylori. © 2016 The Authors. Helicobacter Published by John Wiley & Sons Ltd.

Intraspecific variation in flight metabolic rate in the bumblebee Bombus impatiens: repeatability and functional determinants in workers and drones.

PubMed

Darveau, Charles-A; Billardon, Fannie; Bélanger, Kasandra

2014-02-15

The evolution of flight energetics requires that phenotypes be variable, repeatable and heritable. We studied intraspecific variation in flight energetics in order to assess the repeatability of flight metabolic rate and wingbeat frequency, as well as the functional basis of phenotypic variation in workers and drones of the bumblebee species Bombus impatiens. We showed that flight metabolic rate and wingbeat frequency were highly repeatable in workers, even when controlling for body mass variation using residual analysis. We did not detect significant repeatability in drones, but a smaller range of variation might have prevented us from finding significant values in our sample. Based on our results and previous findings, we associated the high repeatability of flight phenotypes in workers to the functional links between body mass, thorax mass, wing size, wingbeat frequency and metabolic rate. Moreover, differences between workers and drones were as predicted from these functional associations, where drones had larger wings for their size, lower wingbeat frequency and lower flight metabolic rate. We also investigated thoracic muscle metabolic phenotypes by measuring the activity of carbohydrate metabolism enzymes, and we found positive correlations between mass-independent metabolic rate and the activity of all enzymes measured, but in workers only. When comparing workers and drones that differ in flight metabolic rate, only the activity of the enzymes hexokinase and trehalase showed the predicted differences. Overall, our study indicates that there should be correlated evolution among physiological phenotypes at multiple levels of organization and morphological traits associated with flight.

DNM1 encephalopathy

PubMed Central

von Spiczak, Sarah; Helbig, Katherine L.; Shinde, Deepali N.; Huether, Robert; Pendziwiat, Manuela; Lourenço, Charles; Nunes, Mark E.; Sarco, Dean P.; Kaplan, Richard A.; Dlugos, Dennis J.; Kirsch, Heidi; Slavotinek, Anne; Cilio, Maria R.; Cervenka, Mackenzie C.; Cohen, Julie S.; McClellan, Rebecca; Fatemi, Ali; Yuen, Amy; Sagawa, Yoshimi; Littlejohn, Rebecca; McLean, Scott D.; Hernandez-Hernandez, Laura; Maher, Bridget; Møller, Rikke S.; Palmer, Elizabeth; Lawson, John A.; Campbell, Colleen A.; Joshi, Charuta N.; Kolbe, Diana L.; Hollingsworth, Georgie; Neubauer, Bernd A.; Muhle, Hiltrud; Stephani, Ulrich; Scheffer, Ingrid E.; Pena, Sérgio D.J.; Sisodiya, Sanjay M.

2017-01-01

Objective: To evaluate the phenotypic spectrum caused by mutations in dynamin 1 (DNM1), encoding the presynaptic protein DNM1, and to investigate possible genotype-phenotype correlations and predicted functional consequences based on structural modeling. Methods: We reviewed phenotypic data of 21 patients (7 previously published) with DNM1 mutations. We compared mutation data to known functional data and undertook biomolecular modeling to assess the effect of the mutations on protein function. Results: We identified 19 patients with de novo mutations in DNM1 and a sibling pair who had an inherited mutation from a mosaic parent. Seven patients (33.3%) carried the recurrent p.Arg237Trp mutation. A common phenotype emerged that included severe to profound intellectual disability and muscular hypotonia in all patients and an epilepsy characterized by infantile spasms in 16 of 21 patients, frequently evolving into Lennox-Gastaut syndrome. Two patients had profound global developmental delay without seizures. In addition, we describe a single patient with normal development before the onset of a catastrophic epilepsy, consistent with febrile infection-related epilepsy syndrome at 4 years. All mutations cluster within the GTPase or middle domains, and structural modeling and existing functional data suggest a dominant-negative effect on DMN1 function. Conclusions: The phenotypic spectrum of DNM1-related encephalopathy is relatively homogeneous, in contrast to many other genetic epilepsies. Up to one-third of patients carry the recurrent p.Arg237Trp variant, which is now one of the most common recurrent variants in epileptic encephalopathies identified to date. Given the predicted dominant-negative mechanism of this mutation, this variant presents a prime target for therapeutic intervention. PMID:28667181

Mapping the functional landscape of frequent phenylalanine hydroxylase (PAH) genotypes promotes personalised medicine in phenylketonuria.

PubMed

Danecka, Marta K; Woidy, Mathias; Zschocke, Johannes; Feillet, François; Muntau, Ania C; Gersting, Søren W

2015-03-01

In phenylketonuria, genetic heterogeneity, frequent compound heterozygosity, and the lack of functional data for phenylalanine hydroxylase genotypes hamper reliable phenotype prediction and individualised treatment. A literature search revealed 690 different phenylalanine hydroxylase genotypes in 3066 phenylketonuria patients from Europe and the Middle East. We determined phenylalanine hydroxylase function of 30 frequent homozygous and compound heterozygous genotypes covering 55% of the study population, generated activity landscapes, and assessed the phenylalanine hydroxylase working range in the metabolic (phenylalanine) and therapeutic (tetrahydrobiopterin) space. Shared patterns in genotype-specific functional landscapes were linked to biochemical and pharmacological phenotypes, where (1) residual activity below 3.5% was associated with classical phenylketonuria unresponsive to pharmacological treatment; (2) lack of defined peak activity induced loss of response to tetrahydrobiopterin; (3) a higher cofactor need was linked to inconsistent clinical phenotypes and low rates of tetrahydrobiopterin response; and (4) residual activity above 5%, a defined peak of activity, and a normal cofactor need were associated with pharmacologically treatable mild phenotypes. In addition, we provide a web application for retrieving country-specific information on genotypes and genotype-specific phenylalanine hydroxylase function that warrants continuous extension, updates, and research on demand. The combination of genotype-specific functional analyses with biochemical, clinical, and therapeutic data of individual patients may serve as a powerful tool to enable phenotype prediction and to establish personalised medicine strategies for dietary regimens and pharmacological treatment in phenylketonuria. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Comparative genomics of 28 Salmonella enterica isolates: evidence for CRISPR-mediated adaptive sublineage evolution.

PubMed

Fricke, W Florian; Mammel, Mark K; McDermott, Patrick F; Tartera, Carmen; White, David G; Leclerc, J Eugene; Ravel, Jacques; Cebula, Thomas A

2011-07-01

Despite extensive surveillance, food-borne Salmonella enterica infections continue to be a significant burden on public health systems worldwide. As the S. enterica species comprises sublineages that differ greatly in antigenic representation, virulence, and antimicrobial resistance phenotypes, a better understanding of the species' evolution is critical for the prediction and prevention of future outbreaks. The roles that virulence and resistance phenotype acquisition, exchange, and loss play in the evolution of S. enterica sublineages, which to a certain extent are represented by serotypes, remains mostly uncharacterized. Here, we compare 17 newly sequenced and phenotypically characterized nontyphoidal S. enterica strains to 11 previously sequenced S. enterica genomes to carry out the most comprehensive comparative analysis of this species so far. These phenotypic and genotypic data comparisons in the phylogenetic species context suggest that the evolution of known S. enterica sublineages is mediated mostly by two mechanisms, (i) the loss of coding sequences with known metabolic functions, which leads to functional reduction, and (ii) the acquisition of horizontally transferred phage and plasmid DNA, which provides virulence and resistance functions and leads to increasing specialization. Matches between S. enterica clustered regularly interspaced short palindromic repeats (CRISPR), part of a defense mechanism against invading plasmid and phage DNA, and plasmid and prophage regions suggest that CRISPR-mediated immunity could control short-term phenotype changes and mediate long-term sublineage evolution. CRISPR analysis could therefore be critical in assessing the evolutionary potential of S. enterica sublineages and aid in the prediction and prevention of future S. enterica outbreaks.

Comparative Genomics of 28 Salmonella enterica Isolates: Evidence for CRISPR-Mediated Adaptive Sublineage Evolution ▿†

PubMed Central

Fricke, W. Florian; Mammel, Mark K.; McDermott, Patrick F.; Tartera, Carmen; White, David G.; LeClerc, J. Eugene; Ravel, Jacques; Cebula, Thomas A.

2011-01-01

Despite extensive surveillance, food-borne Salmonella enterica infections continue to be a significant burden on public health systems worldwide. As the S. enterica species comprises sublineages that differ greatly in antigenic representation, virulence, and antimicrobial resistance phenotypes, a better understanding of the species' evolution is critical for the prediction and prevention of future outbreaks. The roles that virulence and resistance phenotype acquisition, exchange, and loss play in the evolution of S. enterica sublineages, which to a certain extent are represented by serotypes, remains mostly uncharacterized. Here, we compare 17 newly sequenced and phenotypically characterized nontyphoidal S. enterica strains to 11 previously sequenced S. enterica genomes to carry out the most comprehensive comparative analysis of this species so far. These phenotypic and genotypic data comparisons in the phylogenetic species context suggest that the evolution of known S. enterica sublineages is mediated mostly by two mechanisms, (i) the loss of coding sequences with known metabolic functions, which leads to functional reduction, and (ii) the acquisition of horizontally transferred phage and plasmid DNA, which provides virulence and resistance functions and leads to increasing specialization. Matches between S. enterica clustered regularly interspaced short palindromic repeats (CRISPR), part of a defense mechanism against invading plasmid and phage DNA, and plasmid and prophage regions suggest that CRISPR-mediated immunity could control short-term phenotype changes and mediate long-term sublineage evolution. CRISPR analysis could therefore be critical in assessing the evolutionary potential of S. enterica sublineages and aid in the prediction and prevention of future S. enterica outbreaks. PMID:21602358

Expansion of phenotype and genotypic data in CRB2-related syndrome.

PubMed

Lamont, Ryan E; Tan, Wen-Hann; Innes, A Micheil; Parboosingh, Jillian S; Schneidman-Duhovny, Dina; Rajkovic, Aleksandar; Pappas, John; Altschwager, Pablo; DeWard, Stephanie; Fulton, Anne; Gray, Kathryn J; Krall, Max; Mehta, Lakshmi; Rodan, Lance H; Saller, Devereux N; Steele, Deanna; Stein, Deborah; Yatsenko, Svetlana A; Bernier, François P; Slavotinek, Anne M

2016-10-01

Sequence variants in CRB2 cause a syndrome with greatly elevated maternal serum alpha-fetoprotein and amniotic fluid alpha-fetoprotein levels, cerebral ventriculomegaly and renal findings similar to Finnish congenital nephrosis. All reported patients have been homozygotes or compound heterozygotes for sequence variants in the Crumbs, Drosophila, Homolog of, 2 (CRB2) genes. Variants affecting CRB2 function have also been identified in four families with steroid resistant nephrotic syndrome, but without any other known systemic findings. We ascertained five, previously unreported individuals with biallelic variants in CRB2 that were predicted to affect function. We compiled the clinical features of reported cases and reviewed available literature for cases with features suggestive of CRB2-related syndrome in order to better understand the phenotypic and genotypic manifestations. Phenotypic analyses showed that ventriculomegaly was a common clinical manifestation (9/11 confirmed cases), in contrast to the original reports, in which patients were ascertained due to renal disease. Two children had minor eye findings and one was diagnosed with a B-cell lymphoma. Further genetic analysis identified one family with two affected siblings who were both heterozygous for a variant in NPHS2 predicted to affect function and separate families with sequence variants in NPHS4 and BBS7 in addition to the CRB2 variants. Our report expands the clinical phenotype of CRB2-related syndrome and establishes ventriculomegaly and hydrocephalus as frequent manifestations. We found additional sequence variants in genes involved in kidney development and ciliopathies in patients with CRB2-related syndrome, suggesting that these variants may modify the phenotype.

Folate network genetic variation, plasma homocysteine, and global genomic methylation content: a genetic association study

PubMed Central

2011-01-01

Background Sequence variants in genes functioning in folate-mediated one-carbon metabolism are hypothesized to lead to changes in levels of homocysteine and DNA methylation, which, in turn, are associated with risk of cardiovascular disease. Methods 330 SNPs in 52 genes were studied in relation to plasma homocysteine and global genomic DNA methylation. SNPs were selected based on functional effects and gene coverage, and assays were completed on the Illumina Goldengate platform. Age-, smoking-, and nutrient-adjusted genotype--phenotype associations were estimated in regression models. Results Using a nominal P ≤ 0.005 threshold for statistical significance, 20 SNPs were associated with plasma homocysteine, 8 with Alu methylation, and 1 with LINE-1 methylation. Using a more stringent false discovery rate threshold, SNPs in FTCD, SLC19A1, and SLC19A3 genes remained associated with plasma homocysteine. Gene by vitamin B-6 interactions were identified for both Alu and LINE-1 methylation, and epistatic interactions with the MTHFR rs1801133 SNP were identified for the plasma homocysteine phenotype. Pleiotropy involving the MTHFD1L and SARDH genes for both plasma homocysteine and Alu methylation phenotypes was identified. Conclusions No single gene was associated with all three phenotypes, and the set of the most statistically significant SNPs predictive of homocysteine or Alu or LINE-1 methylation was unique to each phenotype. Genetic variation in folate-mediated one-carbon metabolism, other than the well-known effects of the MTHFR c.665C>T (known as c.677 C>T, rs1801133, p.Ala222Val), is predictive of cardiovascular disease biomarkers. PMID:22103680

Enhancement of plant metabolite fingerprinting by machine learning.

PubMed

Scott, Ian M; Vermeer, Cornelia P; Liakata, Maria; Corol, Delia I; Ward, Jane L; Lin, Wanchang; Johnson, Helen E; Whitehead, Lynne; Kular, Baldeep; Baker, John M; Walsh, Sean; Dave, Anuja; Larson, Tony R; Graham, Ian A; Wang, Trevor L; King, Ross D; Draper, John; Beale, Michael H

2010-08-01

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Evaluation of limited sampling models for prediction of oral midazolam AUC for CYP3A phenotyping and drug interaction studies.

PubMed

Mueller, Silke C; Drewelow, Bernd

2013-05-01

The area under the concentration-time curve (AUC) after oral midazolam administration is commonly used for cytochrome P450 (CYP) 3A phenotyping studies. The aim of this investigation was to evaluate a limited sampling strategy for the prediction of AUC with oral midazolam. A total of 288 concentration-time profiles from 123 healthy volunteers who participated in four previously performed drug interaction studies with intense sampling after a single oral dose of 7.5 mg midazolam were available for evaluation. Of these, 45 profiles served for model building, which was performed by stepwise multiple linear regression, and the remaining 243 datasets served for validation. Mean prediction error (MPE), mean absolute error (MAE) and root mean squared error (RMSE) were calculated to determine bias and precision The one- to four-sampling point models with the best coefficient of correlation were the one-sampling point model (8 h; r (2) = 0.84), the two-sampling point model (0.5 and 8 h; r (2) = 0.93), the three-sampling point model (0.5, 2, and 8 h; r (2) = 0.96), and the four-sampling point model (0.5,1, 2, and 8 h; r (2) = 0.97). However, the one- and two-sampling point models were unable to predict the midazolam AUC due to unacceptable bias and precision. Only the four-sampling point model predicted the very low and very high midazolam AUC of the validation dataset with acceptable precision and bias. The four-sampling point model was also able to predict the geometric mean ratio of the treatment phase over the baseline (with 90 % confidence interval) results of three drug interaction studies in the categories of strong, moderate, and mild induction, as well as no interaction. A four-sampling point limited sampling strategy to predict the oral midazolam AUC for CYP3A phenotyping is proposed. The one-, two- and three-sampling point models were not able to predict midazolam AUC accurately.

De novo PHIP-predicted deleterious variants are associated with developmental delay, intellectual disability, obesity, and dysmorphic features.

PubMed

Webster, Emily; Cho, Megan T; Alexander, Nora; Desai, Sonal; Naidu, Sakkubai; Bekheirnia, Mir Reza; Lewis, Andrea; Retterer, Kyle; Juusola, Jane; Chung, Wendy K

2016-11-01

Using whole-exome sequencing, we have identified novel de novo heterozygous pleckstrin homology domain-interacting protein ( PHIP ) variants that are predicted to be deleterious, including a frameshift deletion, in two unrelated patients with common clinical features of developmental delay, intellectual disability, anxiety, hypotonia, poor balance, obesity, and dysmorphic features. A nonsense mutation in PHIP has previously been associated with similar clinical features. Patients with microdeletions of 6q14.1, including PHIP , have a similar phenotype of developmental delay, intellectual disability, hypotonia, and obesity, suggesting that the phenotype of our patients is a result of loss-of-function mutations. PHIP produces multiple protein products, such as PHIP1 (also known as DCAF14), PHIP, and NDRP. PHIP1 is one of the multiple substrate receptors of the proteolytic CUL4-DDB1 ubiquitin ligase complex. CUL4B deficiency has been associated with intellectual disability, central obesity, muscle wasting, and dysmorphic features. The overlapping phenotype associated with CUL4B deficiency suggests that PHIP mutations cause disease through disruption of the ubiquitin ligase pathway.

Identification of Nascent Memory CD8 T Cells and Modeling of Their Ontogeny.

PubMed

Crauste, Fabien; Mafille, Julien; Boucinha, Lilia; Djebali, Sophia; Gandrillon, Olivier; Marvel, Jacqueline; Arpin, Christophe

2017-03-22

Primary immune responses generate short-term effectors and long-term protective memory cells. The delineation of the genealogy linking naive, effector, and memory cells has been complicated by the lack of phenotypes discriminating effector from memory differentiation stages. Using transcriptomics and phenotypic analyses, we identify Bcl2 and Mki67 as a marker combination that enables the tracking of nascent memory cells within the effector phase. We then use a formal approach based on mathematical models describing the dynamics of population size evolution to test potential progeny links and demonstrate that most cells follow a linear naive→early effector→late effector→memory pathway. Moreover, our mathematical model allows long-term prediction of memory cell numbers from a few early experimental measurements. Our work thus provides a phenotypic means to identify effector and memory cells, as well as a mathematical framework to investigate their genealogy and to predict the outcome of immunization regimens in terms of memory cell numbers generated. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Ethical, legal and social implications of forensic molecular phenotyping in South Africa.

PubMed

Slabbert, Nandi; Heathfield, Laura Jane

2018-06-01

Conventional forensic DNA analysis involves a matching principle, which compares DNA profiles from evidential samples to those from reference samples of known origin. In casework, however, the accessibility to a reference sample is not guaranteed which limits the use of DNA as an investigative tool. This has led to the development of phenotype prediction, which uses SNP analysis to estimate the physical appearance of the sample donor. Physical traits, such as eye, hair and skin colour, have been associated with certain alleles within specific genes involved in the melanogenesis pathways. These genetic markers are also associated with ancestry and their trait prediction ability has mainly been assessed in European and North American populations. This has prompted research investigating the discriminatory power of these markers in other populations, especially those exhibiting admixture. South Africa is well known for its diversity, and the viability of these particular SNPs still needs to be assessed within this population. South African law currently restricts the use of DNA for molecular phenotyping, and there are also numerous ethical and social considerations, all of which are discussed. © 2018 John Wiley & Sons Ltd.

Comparative analyses of the influence of developmental mode on phenotypic diversification rates in shorebirds

PubMed Central

Thomas, Gavin H; Freckleton, Robert P; Székely, Tamás

2006-01-01

Phenotypic diversity is not evenly distributed across lineages. Here, we describe and apply a maximum-likelihood phylogenetic comparative method to test for different rates of phenotypic evolution between groups of the avian order Charadriiformes (shorebirds, gulls and alcids) to test the influence of a binary trait (offspring demand; semi-precocial or precocial) on rates of evolution of parental care, mating systems and secondary sexual traits. In semi-precocial species, chicks are reliant on the parents for feeding, but in precocial species the chicks feed themselves. Thus, where the parents are emancipated from feeding the young, we predict that there is an increased potential for brood desertion, and consequently for the divergence of mating systems. In addition, secondary sexual traits are predicted to evolve faster in groups with less demanding young. We found that precocial development not only allows rapid divergence of parental care and mating behaviours, but also promotes the rapid diversification of secondary sexual characters, most notably sexual size dimorphism (SSD) in body mass. Thus, less demanding offspring appear to facilitate rapid evolution of breeding systems and some sexually selected traits. PMID:16769632

Comparative analyses of the influence of developmental mode on phenotypic diversification rates in shorebirds.

PubMed

Thomas, Gavin H; Freckleton, Robert P; Székely, Tamás

2006-07-07

Phenotypic diversity is not evenly distributed across lineages. Here, we describe and apply a maximum-likelihood phylogenetic comparative method to test for different rates of phenotypic evolution between groups of the avian order Charadriiformes (shorebirds, gulls and alcids) to test the influence of a binary trait (offspring demand; semi-precocial or precocial) on rates of evolution of parental care, mating systems and secondary sexual traits. In semi-precocial species, chicks are reliant on the parents for feeding, but in precocial species the chicks feed themselves. Thus, where the parents are emancipated from feeding the young, we predict that there is an increased potential for brood desertion, and consequently for the divergence of mating systems. In addition, secondary sexual traits are predicted to evolve faster in groups with less demanding young. We found that precocial development not only allows rapid divergence of parental care and mating behaviours, but also promotes the rapid diversification of secondary sexual characters, most notably sexual size dimorphism (SSD) in body mass. Thus, less demanding offspring appear to facilitate rapid evolution of breeding systems and some sexually selected traits.

Brainstem response patterns in deeply-sedated critically-ill patients predict 28-day mortality.

PubMed

Rohaut, Benjamin; Porcher, Raphael; Hissem, Tarik; Heming, Nicholas; Chillet, Patrick; Djedaini, Kamel; Moneger, Guy; Kandelman, Stanislas; Allary, Jeremy; Cariou, Alain; Sonneville, Romain; Polito, Andréa; Antona, Marion; Azabou, Eric; Annane, Djillali; Siami, Shidasp; Chrétien, Fabrice; Mantz, Jean; Sharshar, Tarek

2017-01-01

Deep sedation is associated with acute brain dysfunction and increased mortality. We had previously shown that early-assessed brainstem reflexes may predict outcome in deeply sedated patients. The primary objective was to determine whether patterns of brainstem reflexes might predict mortality in deeply sedated patients. The secondary objective was to generate a score predicting mortality in these patients. Observational prospective multicenter cohort study of 148 non-brain injured deeply sedated patients, defined by a Richmond Assessment sedation Scale (RASS) <-3. Brainstem reflexes and Glasgow Coma Scale were assessed within 24 hours of sedation and categorized using latent class analysis. The Full Outline Of Unresponsiveness score (FOUR) was also assessed. Primary outcome measure was 28-day mortality. A "Brainstem Responses Assessment Sedation Score" (BRASS) was generated. Two distinct sub-phenotypes referred as homogeneous and heterogeneous brainstem reactivity were identified (accounting for respectively 54.6% and 45.4% of patients). Homogeneous brainstem reactivity was characterized by preserved reactivity to nociceptive stimuli and a partial and topographically homogenous depression of brainstem reflexes. Heterogeneous brainstem reactivity was characterized by a loss of reactivity to nociceptive stimuli associated with heterogeneous brainstem reflexes depression. Heterogeneous sub-phenotype was a predictor of increased risk of 28-day mortality after adjustment to Simplified Acute Physiology Score-II (SAPS-II) and RASS (Odds Ratio [95% confidence interval] = 6.44 [2.63-15.8]; p<0.0001) or Sequential Organ Failure Assessment (SOFA) and RASS (OR [95%CI] = 5.02 [2.01-12.5]; p = 0.0005). The BRASS (and marginally the FOUR) predicted 28-day mortality (c-index [95%CI] = 0.69 [0.54-0.84] and 0.65 [0.49-0.80] respectively). In this prospective cohort study, around half of all deeply sedated critically ill patients displayed an early particular neurological sub-phenotype predicting 28-day mortality, which may reflect a dysfunction of the brainstem.

Predict drug permeability to blood–brain-barrier from clinical phenotypes: drug side effects and drug indications

PubMed Central

Gao, Zhen; Chen, Yang; Cai, Xiaoshu; Xu, Rong

2017-01-01

Abstract Motivation: Blood–Brain-Barrier (BBB) is a rigorous permeability barrier for maintaining homeostasis of Central Nervous System (CNS). Determination of compound’s permeability to BBB is prerequisite in CNS drug discovery. Existing computational methods usually predict drug BBB permeability from chemical structure and they generally apply to small compounds passing BBB through passive diffusion. As abundant information on drug side effects and indications has been recorded over time through extensive clinical usage, we aim to explore BBB permeability prediction from a new angle and introduce a novel approach to predict BBB permeability from drug clinical phenotypes (drug side effects and drug indications). This method can apply to both small compounds and macro-molecules penetrating BBB through various mechanisms besides passive diffusion. Results: We composed a training dataset of 213 drugs with known brain and blood steady-state concentrations ratio and extracted their side effects and indications as features. Next, we trained SVM models with polynomial kernel and obtained accuracy of 76.0%, AUC 0.739, and F1 score (macro weighted) 0.760 with Monte Carlo cross validation. The independent test accuracy was 68.3%, AUC 0.692, F1 score 0.676. When both chemical features and clinical phenotypes were available, combining the two types of features achieved significantly better performance than chemical feature based approach (accuracy 85.5% versus 72.9%, AUC 0.854 versus 0.733, F1 score 0.854 versus 0.725; P < e−90). We also conducted de novo prediction and identified 110 drugs in SIDER database having the potential to penetrate BBB, which could serve as start point for CNS drug repositioning research. Availability and Implementation: https://github.com/bioinformatics-gao/CASE-BBB-prediction-Data Contact: rxx@case.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27993785

Design of Biomedical Robots for Phenotype Prediction Problems

PubMed Central

deAndrés-Galiana, Enrique J.; Sonis, Stephen T.

2016-01-01

Abstract Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem. PMID:27347715

Leukotriene A4 Hydrolase Genotype and HIV Infection Influence Intracerebral Inflammation and Survival From Tuberculous Meningitis.

PubMed

Thuong, Nguyen T T; Heemskerk, Dorothee; Tram, Trinh T B; Thao, Le T P; Ramakrishnan, Lalita; Ha, Vu T N; Bang, Nguyen D; Chau, Tran T H; Lan, Nguyen H; Caws, Maxine; Dunstan, Sarah J; Chau, Nguyen V V; Wolbers, Marcel; Mai, Nguyen T H; Thwaites, Guy E

2017-04-01

Tuberculous meningitis (TBM) is the most devastating form of tuberculosis, yet very little is known about the pathophysiology. We hypothesized that the genotype of leukotriene A4 hydrolase (encoded by LTA4H), which determines inflammatory eicosanoid expression, influences intracerebral inflammation, and predicts survival from TBM. We characterized the pretreatment clinical and intracerebral inflammatory phenotype and 9-month survival of 764 adults with TBM. All were genotyped for single-nucleotide polymorphism rs17525495, and inflammatory phenotype was defined by cerebrospinal fluid (CSF) leukocyte and cytokine concentrations. LTA4H genotype predicted survival of human immunodeficiency virus (HIV)-uninfected patients, with TT-genotype patients significantly more likely to survive TBM than CC-genotype patients, according to Cox regression analysis (univariate P = .040 and multivariable P = .037). HIV-uninfected, TT-genotype patients had high CSF proinflammatory cytokine concentrations, with intermediate and lower concentrations in those with CT and CC genotypes. Increased CSF cytokine concentrations correlated with more-severe disease, but patients with low CSF leukocytes and cytokine concentrations were more likely to die from TBM. HIV infection independently predicted death due to TBM (hazard ratio, 3.94; 95% confidence interval, 2.79-5.56) and was associated with globally increased CSF cytokine concentrations, independent of LTA4H genotype. LTA4H genotype and HIV infection influence pretreatment inflammatory phenotype and survival from TBM. LTA4H genotype may predict adjunctive corticosteroid responsiveness in HIV-uninfected individuals. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

Design of Biomedical Robots for Phenotype Prediction Problems.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan Luis; Sonis, Stephen T

2016-08-01

Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem.

Reinventing the ames test as a quantitative lab that connects classical and molecular genetics.

PubMed

Goodson-Gregg, Nathan; De Stasio, Elizabeth A

2009-01-01

While many institutions use a version of the Ames test in the undergraduate genetics laboratory, students typically are not exposed to techniques or procedures beyond qualitative analysis of phenotypic reversion, thereby seriously limiting the scope of learning. We have extended the Ames test to include both quantitative analysis of reversion frequency and molecular analysis of revertant gene sequences. By giving students a role in designing their quantitative methods and analyses, students practice and apply quantitative skills. To help students connect classical and molecular genetic concepts and techniques, we report here procedures for characterizing the molecular lesions that confer a revertant phenotype. We suggest undertaking reversion of both missense and frameshift mutants to allow a more sophisticated molecular genetic analysis. These modifications and additions broaden the educational content of the traditional Ames test teaching laboratory, while simultaneously enhancing students' skills in experimental design, quantitative analysis, and data interpretation.

Experimental studies of adaptation in Clarkia xantiana. III. Phenotypic selection across a subspecies border.

PubMed

Anderson, Jill T; Eckhart, Vincent M; Geber, Monica A

2015-09-01

Sister taxa with distinct phenotypes often occupy contrasting environments in parapatric ranges, yet we generally do not know whether trait divergence reflects spatially varying selection. We conducted a reciprocal transplant experiment to test whether selection favors "native phenotypes" in two subspecies of Clarkia xantiana (Onagraceae), an annual plant in California. For four quantitative traits that differ between subspecies, we estimated phenotypic selection in subspecies' exclusive ranges and their contact zone in two consecutive years. We predicted that in the arid, pollinator-scarce eastern region, selection favors phenotypes of the native subspecies parviflora: small leaves, slow leaf growth, early flowering, and diminutive flowers. In the wetter, pollinator-rich, western range of subspecies xantiana, we expected selection for opposite phenotypes. We investigated pollinator contributions to selection by comparing naturally pollinated and pollen-supplemented individuals. For reproductive traits and for subspecies xantiana, selection generally matched expectations. The contact zone sometimes showed distinctive selection, and in ssp. parviflora selection sometimes favored nonnative phenotypes. Pollinators influenced selection on flowering time but not on flower size. Little temporal variation in selection occurred, possibly because of plastic trait responses across years. Though there were exceptions and some causes of selection remain obscure, phenotypic differentiation between subspecies appears to reflect spatially variable selection. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

The Use of Modelling for Theory Building in Qualitative Analysis

ERIC Educational Resources Information Center

Briggs, Ann R. J.

2007-01-01

The purpose of this article is to exemplify and enhance the place of modelling as a qualitative process in educational research. Modelling is widely used in quantitative research as a tool for analysis, theory building and prediction. Statistical data lend themselves to graphical representation of values, interrelationships and operational…

Dissociation of the Ethyl Radical: An Exercise in Computational Chemistry

ERIC Educational Resources Information Center

Nassabeh, Nahal; Tran, Mark; Fleming, Patrick E.

2014-01-01

A set of exercises for use in a typical physical chemistry laboratory course are described, modeling the unimolecular dissociation of the ethyl radical to form ethylene and atomic hydrogen. Students analyze the computational results both qualitatively and quantitatively. Qualitative structural changes are compared to approximate predicted values…

Factor VII Deficiency: Clinical Phenotype, Genotype and Therapy.

PubMed

Napolitano, Mariasanta; Siragusa, Sergio; Mariani, Guglielmo

2017-03-28

Factor VII deficiency is the most common among rare inherited autosomal recessive bleeding disorders, and is a chameleon disease due to the lack of a direct correlation between plasma levels of coagulation Factor VII and bleeding manifestations. Clinical phenotypes range from asymptomatic condition-even in homozygous subjects-to severe life-threatening bleedings (central nervous system, gastrointestinal bleeding). Prediction of bleeding risk is thus based on multiple parameters that challenge disease management. Spontaneous or surgical bleedings require accurate treatment schedules, and patients at high risk of severe hemorrhages may need prophylaxis from childhood onwards. The aim of the current review is to depict an updated summary of clinical phenotype, laboratory diagnosis, and treatment of inherited Factor VII deficiency.

Factor VII Deficiency: Clinical Phenotype, Genotype and Therapy

PubMed Central

Napolitano, Mariasanta; Siragusa, Sergio; Mariani, Guglielmo

2017-01-01

Factor VII deficiency is the most common among rare inherited autosomal recessive bleeding disorders, and is a chameleon disease due to the lack of a direct correlation between plasma levels of coagulation Factor VII and bleeding manifestations. Clinical phenotypes range from asymptomatic condition—even in homozygous subjects—to severe life-threatening bleedings (central nervous system, gastrointestinal bleeding). Prediction of bleeding risk is thus based on multiple parameters that challenge disease management. Spontaneous or surgical bleedings require accurate treatment schedules, and patients at high risk of severe hemorrhages may need prophylaxis from childhood onwards. The aim of the current review is to depict an updated summary of clinical phenotype, laboratory diagnosis, and treatment of inherited Factor VII deficiency. PMID:28350321

Changing practice: red blood cell typing by molecular methods for patients with sickle cell disease.

PubMed

Casas, Jessica; Friedman, David F; Jackson, Tannoa; Vege, Sunitha; Westhoff, Connie M; Chou, Stella T

2015-06-01

Extended red blood cell (RBC) antigen matching is recommended to limit alloimmunization in patients with sickle cell disease (SCD). DNA-based testing to predict blood group phenotypes has enhanced availability of antigen-negative donor units and improved typing of transfused patients, but replacement of routine serologic typing for non-ABO antigens with molecular typing for patients has not been reported. This study compared the historical RBC antigen phenotypes obtained by hemagglutination methods with genotype predictions in 494 patients with SCD. For discrepant results, repeat serologic testing was performed and/or investigated by gene sequencing for silent or variant alleles. Seventy-one typing discrepancies were identified among 6360 antigen comparisons (1.1%). New specimens for repeat serologic testing were obtained for 66 discrepancies and retyping agreed with the genotype in 64 cases. One repeat Jk(b-) serologic phenotype, predicted Jk(b+) by genotype, was found by direct sequencing of JK to be a silenced allele, and one N typing discrepancy remains under investigation. Fifteen false-negative serologic results were associated with alleles encoding weak antigens or single-dose Fy(b) expression. DNA-based RBC typing provided improved accuracy and expanded information on RBC antigens compared to hemagglutination methods, leading to its implementation as the primary method for extended RBC typing for patients with SCD at our institution. © 2015 AABB.

Risk factors of treatment failure and 30-day mortality in patients with bacteremia due to MRSA with reduced vancomycin susceptibility.

PubMed

Yang, Chien-Chang; Sy, Cheng-Len; Huang, Yhu-Chering; Shie, Shian-Sen; Shu, Jwu-Ching; Hsieh, Pang-Hsin; Hsiao, Ching-Hsi; Chen, Chih-Jung

2018-05-18

Bacteremia caused by MRSA with reduced vancomycin susceptibility (MRSA-RVS) frequently resulted in treatment failure and mortality. The relation of bacterial factors and unfavorable outcomes remains controversial. We retrospectively reviewed clinical data of patients with bacteremia caused by MRSA with vancomycin MIC = 2 mg/L from 2009 to 2012. The significance of bacterial genotypes, agr function and heterogeneous vancomycin-intermediate S. aureus (hIVSA) phenotype in predicting outcomes were determined after clinical covariates adjustment with multivariate analysis. A total of 147 patients with mean age of 63.5 (±18.1) years were included. Seventy-nine (53.7%) patients failed treatment. Forty-seven (31.9%) patients died within 30 days of onset of MRSA bacteremia. The Charlson index, Pitt bacteremia score and definitive antibiotic regimen were independent factors significantly associated with either treatment failure or mortality. The hVISA phenotype was a potential risk factor predicting treatment failure (adjusted odds ratio 2.420, 95% confidence interval 0.946-6.191, P = 0.0652). No bacterial factors were significantly associated with 30-day mortality. In conclusion, the comorbidities, disease severity and antibiotic regimen remained the most relevant factors predicting treatment failure and 30-day mortality in patients with MRSA-RVS bacteremia. hIVSA phenotype was the only bacterial factor potentially associated with unfavorable outcome in this cohort.

In situ genetic association for serotiny, a fire-related trait, in Mediterranean maritime pine (Pinus pinaster).

PubMed

Budde, Katharina B; Heuertz, Myriam; Hernández-Serrano, Ana; Pausas, Juli G; Vendramin, Giovanni G; Verdú, Miguel; González-Martínez, Santiago C

2014-01-01

Wildfire is a major ecological driver of plant evolution. Understanding the genetic basis of plant adaptation to wildfire is crucial, because impending climate change will involve fire regime changes worldwide. We studied the molecular genetic basis of serotiny, a fire-related trait, in Mediterranean maritime pine using association genetics. A single nucleotide polymorphism (SNP) set was used to identify genotype : phenotype associations in situ in an unstructured natural population of maritime pine (eastern Iberian Peninsula) under a mixed-effects model framework. RR-BLUP was used to build predictive models for serotiny in this region. Model prediction power outside the focal region was tested using independent range-wide serotiny data. Seventeen SNPs were potentially associated with serotiny, explaining approximately 29% of the trait phenotypic variation in the eastern Iberian Peninsula. Similar prediction power was found for nearby geographical regions from the same maternal lineage, but not for other genetic lineages. Association genetics for ecologically relevant traits evaluated in situ is an attractive approach for forest trees provided that traits are under strong genetic control and populations are unstructured, with large phenotypic variability. This will help to extend the research focus to ecological keystone non-model species in their natural environments, where polymorphisms acquired their adaptive value. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

Adaptation to a latitudinal thermal gradient within a widespread copepod species: the contributions of genetic divergence and phenotypic plasticity

PubMed Central

2017-01-01

Understanding how populations adapt to heterogeneous thermal regimes is essential for comprehending how latitudinal gradients in species diversification are formed, and how taxa will respond to ongoing climate change. Adaptation can occur by innate genetic factors, by phenotypic plasticity, or by a combination of both mechanisms. Yet, the relative contribution of such mechanisms to large-scale latitudinal gradients of thermal tolerance across conspecific populations remains unclear. We examine thermal performance in 11 populations of the intertidal copepod Tigriopus californicus, ranging from Baja California Sur (Mexico) to British Columbia (Canada). Common garden experiments show that survivorship to acute heat-stress differs between populations (by up to 3.8°C in LD50 values), reflecting a strong genetic thermal adaptation. Using a split-brood experiment with two rearing temperatures, we also show that developmental phenotypic plasticity is beneficial to thermal tolerance (by up to 1.3°C), and that this effect differs across populations. Although genetic divergence in heat tolerance strongly correlates with latitude and temperature, differences in the plastic response do not. In the context of climate warming, our results confirm the general prediction that low-latitude populations are most susceptible to local extinction because genetic adaptation has placed physiological limits closer to current environmental maxima, but our results also contradict the prediction that phenotypic plasticity is constrained at lower latitudes. PMID:28446698

Mitochondrial “power” drives tamoxifen resistance: NQO1 and GCLC are new therapeutic targets in breast cancer

PubMed Central

Fiorillo, Marco; Sotgia, Federica; Sisci, Diego; Cappello, Anna Rita; Lisanti, Michael P.

2017-01-01

Here, we identified two new molecular targets, which are functionally sufficient to metabolically confer the tamoxifen-resistance phenotype in human breast cancer cells. Briefly, ~20 proteins were first selected as potential candidates, based on unbiased proteomics analysis, using tamoxifen-resistant cell lines. Then, the cDNAs of the most promising candidates were systematically transduced into MCF-7 cells. Remarkably, NQO1 and GCLC were both functionally sufficient to autonomously confer a tamoxifen-resistant metabolic phenotype, characterized by i) increased mitochondrial biogenesis, ii) increased ATP production and iii) reduced glutathione levels. Thus, we speculate that pharmacological inhibition of NQO1 and GCLC may be new therapeutic strategies for overcoming tamoxifen-resistance in breast cancer patients. In direct support of this notion, we demonstrate that treatment with a known NQO1 inhibitor (dicoumarol) is indeed sufficient to revert the tamoxifen-resistance phenotype. As such, these findings could have important translational significance for the prevention of tumor recurrence in ER(+) breast cancers, which is due to an endocrine resistance phenotype. Importantly, we also show here that NQO1 has significant prognostic value as a biomarker for the prediction of tumor recurrence. More specifically, higher levels of NQO1 mRNA strongly predict patient relapse in high-risk ER(+) breast cancer patients receiving endocrine therapy (mostly tamoxifen; H.R. > 2.15; p = 0.007). PMID:28411284

Initial CGE Model Results Summary Exogenous and Endogenous Variables Tests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edwards, Brian Keith; Boero, Riccardo; Rivera, Michael Kelly

The following discussion presents initial results of tests of the most recent version of the National Infrastructure Simulation and Analysis Center Dynamic Computable General Equilibrium (CGE) model developed by Los Alamos National Laboratory (LANL). The intent of this is to test and assess the model’s behavioral properties. The test evaluated whether the predicted impacts are reasonable from a qualitative perspective. This issue is whether the predicted change, be it an increase or decrease in other model variables, is consistent with prior economic intuition and expectations about the predicted change. One of the purposes of this effort is to determine whethermore » model changes are needed in order to improve its behavior qualitatively and quantitatively.« less

Local durian (Durio zibethinus murr.) exploration for potentially superior tree as parents in Ngrambe District, Ngawi

NASA Astrophysics Data System (ADS)

Yuniastuti, E.; Anggita, A.; Nandariyah; Sukaya

2018-03-01

The characteristics durian based on specific area gives a wide diversity of phenotype. This research objective was to build an inventory of the local durian of Ngrambe as well as to obtain potentially superior local durian as prospective parent trees. The research was conducted in Ngrambe sub-district, on October 2015 until April 2016 using the explorative descriptive method. The determination of sample point used the non-probability method of snowball sampling type. Primary data include the morphology of plant characters, trunks, leaves, flower, fruits and seeds and their superiority. The data of the research were analyzed using SIMQUAL (Similarity for Qualitative) function based on the DICE coefficient on NTSYS v.2.02. The data cluster and dendrogram analyses were determined by Unweighted Pair-Group Arithmetic Average (UPGMA) method. The result of DICE coefficient analyses of 58 local durian accession based on the phenotypic character of vegetative organs ranged from 0.84-1.0. The phenotypic character of the vegetative and generative organ from 3 local durian accession superior potential ranged from 0.7 to 0.8. In conclusion, the accession of local durian which were Miyem and Rusmiyati have advantage and potential as prospective parent trees.

Anthropogenic ecosystem fragmentation drives shared and unique patterns of sexual signal divergence among three species of Bahamian mosquitofish.

PubMed

Giery, Sean T; Layman, Craig A; Langerhans, R Brian

2015-08-01

When confronted with similar environmental challenges, different organisms can exhibit dissimilar phenotypic responses. Therefore, understanding patterns of phenotypic divergence for closely related species requires considering distinct evolutionary histories. Here, we investigated how a common form of human-induced environmental alteration, habitat fragmentation, may drive phenotypic divergence among three closely related species of Bahamian mosquitofish (Gambusia spp.). Focusing on one phenotypic trait (male coloration), having a priori predictions of divergence, we tested whether populations persisting in fragmented habitats differed from those inhabiting unfragmented habitats and examined the consistency of the pattern across species. Species exhibited both shared and unique patterns of phenotypic divergence between the two types of habitats, with shared patterns representing the stronger effect. For all species, populations in fragmented habitats had fewer dorsal-fin spots. In contrast, the magnitude and trajectory of divergence in dorsal-fin color, a sexually selected trait, differed among species. We identified fragmentation-mediated increased turbidity as a possible driver of these trait shifts. These results suggest that even closely related species can exhibit diverse phenotypic responses when encountering similar human-mediated selection regimes. This element of unpredictability complicates forecasting the phenotypic responses of wild organisms faced with anthropogenic change - an important component of biological conservation and ecosystem management.

Anthropogenic ecosystem fragmentation drives shared and unique patterns of sexual signal divergence among three species of Bahamian mosquitofish

PubMed Central

Giery, Sean T; Layman, Craig A; Langerhans, R Brian

2015-01-01

When confronted with similar environmental challenges, different organisms can exhibit dissimilar phenotypic responses. Therefore, understanding patterns of phenotypic divergence for closely related species requires considering distinct evolutionary histories. Here, we investigated how a common form of human-induced environmental alteration, habitat fragmentation, may drive phenotypic divergence among three closely related species of Bahamian mosquitofish (Gambusia spp.). Focusing on one phenotypic trait (male coloration), having a priori predictions of divergence, we tested whether populations persisting in fragmented habitats differed from those inhabiting unfragmented habitats and examined the consistency of the pattern across species. Species exhibited both shared and unique patterns of phenotypic divergence between the two types of habitats, with shared patterns representing the stronger effect. For all species, populations in fragmented habitats had fewer dorsal-fin spots. In contrast, the magnitude and trajectory of divergence in dorsal-fin color, a sexually selected trait, differed among species. We identified fragmentation-mediated increased turbidity as a possible driver of these trait shifts. These results suggest that even closely related species can exhibit diverse phenotypic responses when encountering similar human-mediated selection regimes. This element of unpredictability complicates forecasting the phenotypic responses of wild organisms faced with anthropogenic change – an important component of biological conservation and ecosystem management. PMID:26240605

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis

PubMed Central

Twarog, Nathaniel R.; Low, Jonathan A.; Currier, Duane G.; Miller, Greg; Chen, Taosheng; Shelat, Anang A.

2016-01-01

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2’-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used. PMID:26886014

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis.

PubMed

Twarog, Nathaniel R; Low, Jonathan A; Currier, Duane G; Miller, Greg; Chen, Taosheng; Shelat, Anang A

2016-01-01

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2'-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used.

[Waardenburg syndrome. A heterogenic disorder with variable penetrance].

PubMed

Apaydin, F; Bereketoglu, M; Turan, O; Hribar, K; Maassen, M M; Günhan, O; Zenner, H-P; Pfister, M

2004-06-01

Waardenburg syndrome (WS) is an autosomal dominant disorder characterised by pigmentary anomalies of the skin, hairs, eyes and various defects of other neural crest derived tissues. It accounts for over 2% of congenital hearing impairment. At least four types are recognized on the basis of clinical and genetic criteria. Based on a screening of congenitally hearing impaired children, 12 families with WS type II were detected. Of special interest was the phenotype of these families, in particular the reduced penetrance of hearing impairment within the families. In all cases a high variability of the disease phenotype was detected and the penetrance of the clinical traits varied accordingly. Therefore, it is not possible to predict the clinical phenotype even in a single family. Based on these studies, we plan to identify the pathogenetic cause of the disease in order to perform a detailed genotype/phenotype analysis.

Huntington's Disease: Relationship Between Phenotype and Genotype.

PubMed

Sun, Yi-Min; Zhang, Yan-Bin; Wu, Zhi-Ying

2017-01-01

Huntington's disease (HD) is an autosomal dominant inherited neurodegenerative disease with the typical manifestations of involuntary movements, psychiatric and behavior disorders, and cognitive impairment. It is caused by the dynamic mutation in CAG triplet repeat number in exon 1 of huntingtin (HTT) gene. The symptoms of HD especially the age at onset are related to the genetic characteristics, both the CAG triplet repeat and the modified factors. Here, we reviewed the recent advancement on the genotype-phenotype relationship of HD, mainly focus on the characteristics of different expanded CAG repeat number, genetic modifiers, and CCG repeat number in the 3' end of CAG triplet repeat and their effects on the phenotype. We also reviewed the special forms of HD (juvenile HD, atypical onset HD, and homozygous HD) and their phenotype-genotype correlations. The review will aid clinicians to predict the onset age and disease course of HD, give the genetic counseling, and accelerate research into the HD mechanism.

Target identification by image analysis.

PubMed

Fetz, V; Prochnow, H; Brönstrup, M; Sasse, F

2016-05-04

Covering: 1997 to the end of 2015Each biologically active compound induces phenotypic changes in target cells that are characteristic for its mode of action. These phenotypic alterations can be directly observed under the microscope or made visible by labelling structural elements or selected proteins of the cells with dyes. A comparison of the cellular phenotype induced by a compound of interest with the phenotypes of reference compounds with known cellular targets allows predicting its mode of action. While this approach has been successfully applied to the characterization of natural products based on a visual inspection of images, recent studies used automated microscopy and analysis software to increase speed and to reduce subjective interpretation. In this review, we give a general outline of the workflow for manual and automated image analysis, and we highlight natural products whose bacterial and eucaryotic targets could be identified through such approaches.

PhenoPredict: A disease phenome-wide drug repositioning approach towards schizophrenia drug discovery.

PubMed

Xu, Rong; Wang, QuanQiu

2015-08-01

Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs. Copyright © 2015 Elsevier Inc. All rights reserved.