Stakeholder perspectives on decision-analytic modeling frameworks to assess genetic services policy.

PubMed

Guzauskas, Gregory F; Garrison, Louis P; Stock, Jacquie; Au, Sylvia; Doyle, Debra Lochner; Veenstra, David L

2013-01-01

Genetic services policymakers and insurers often make coverage decisions in the absence of complete evidence of clinical utility and under budget constraints. We evaluated genetic services stakeholder opinions on the potential usefulness of decision-analytic modeling to inform coverage decisions, and asked them to identify genetic tests for decision-analytic modeling studies. We presented an overview of decision-analytic modeling to members of the Western States Genetic Services Collaborative Reimbursement Work Group and state Medicaid representatives and conducted directed content analysis and an anonymous survey to gauge their attitudes toward decision-analytic modeling. Participants also identified and prioritized genetic services for prospective decision-analytic evaluation. Participants expressed dissatisfaction with current processes for evaluating insurance coverage of genetic services. Some participants expressed uncertainty about their comprehension of decision-analytic modeling techniques. All stakeholders reported openness to using decision-analytic modeling for genetic services assessments. Participants were most interested in application of decision-analytic concepts to multiple-disorder testing platforms, such as next-generation sequencing and chromosomal microarray. Decision-analytic modeling approaches may provide a useful decision tool to genetic services stakeholders and Medicaid decision-makers.

The genetic basis of alcoholism: multiple phenotypes, many genes, complex networks.

PubMed

Morozova, Tatiana V; Goldman, David; Mackay, Trudy F C; Anholt, Robert R H

2012-02-20

Alcoholism is a significant public health problem. A picture of the genetic architecture underlying alcohol-related phenotypes is emerging from genome-wide association studies and work on genetically tractable model organisms.

Testing the Structure of Hydrological Models using Genetic Programming

NASA Astrophysics Data System (ADS)

Selle, B.; Muttil, N.

2009-04-01

Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that genetic programming can be used to test the structure hydrological models and to identify dominant processes in hydrological systems. To test this, genetic programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, water table depths and water ponding times during surface irrigation. Using genetic programming, a simple model of deep percolation was consistently evolved in multiple model runs. This simple and interpretable model confirmed the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that genetic programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history.

PubMed

Sparks, Jeffrey A; Chen, Chia-Yen; Jiang, Xia; Askling, Johan; Hiraki, Linda T; Malspeis, Susan; Klareskog, Lars; Alfredsson, Lars; Costenbader, Karen H; Karlson, Elizabeth W

2015-08-01

To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors and known genetic risk factors. We developed and validated models for RA based on known RA risk factors, among women in two cohorts: the Nurses' Health Study (NHS, 381 RA cases and 410 controls) and the Epidemiological Investigation of RA (EIRA, 1244 RA cases and 971 controls). Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC) in logistic regression models for the study population and for those with positive family history. The joint effect of family history with genetics, smoking and body mass index (BMI) was evaluated using logistic regression models to estimate ORs for RA. The complete model including family history, epidemiologic risk factors and genetics demonstrated AUCs of 0.74 for seropositive RA in NHS and 0.77 for anti-citrullinated protein antibody (ACPA)-positive RA in EIRA. Among women with positive family history, discrimination was excellent for complete models for seropositive RA in NHS (AUC 0.82) and ACPA-positive RA in EIRA (AUC 0.83). Positive family history, high genetic susceptibility, smoking and increased BMI had an OR of 21.73 for ACPA-positive RA. We developed models for seropositive and seronegative RA phenotypes based on family history, epidemiological and genetic factors. Among those with positive family history, models using epidemiologic and genetic factors were highly discriminatory for seropositive and seronegative RA. Assessing epidemiological and genetic factors among those with positive family history may identify individuals suitable for RA prevention strategies. 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.

Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model for diploid plants.

PubMed

Han, Lide; Yang, Jian; Zhu, Jun

2007-06-01

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment (GE) interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction (AUP) method. Data on four quantitative traits (boll number, lint percentage, fiber length, and micronaire) in Upland cotton (Gossypium hirsutum L.) were analyzed as a worked example to show the effectiveness of the model.

The genetic basis of alcoholism: multiple phenotypes, many genes, complex networks

PubMed Central

2012-01-01

Alcoholism is a significant public health problem. A picture of the genetic architecture underlying alcohol-related phenotypes is emerging from genome-wide association studies and work on genetically tractable model organisms. PMID:22348705

Effects of complex life cycles on genetic diversity: cyclical parthenogenesis.

PubMed

Rouger, R; Reichel, K; Malrieu, F; Masson, J P; Stoeckel, S

2016-11-01

Neutral patterns of population genetic diversity in species with complex life cycles are difficult to anticipate. Cyclical parthenogenesis (CP), in which organisms undergo several rounds of clonal reproduction followed by a sexual event, is one such life cycle. Many species, including crop pests (aphids), human parasites (trematodes) or models used in evolutionary science (Daphnia), are cyclical parthenogens. It is therefore crucial to understand the impact of such a life cycle on neutral genetic diversity. In this paper, we describe distributions of genetic diversity under conditions of CP with various clonal phase lengths. Using a Markov chain model of CP for a single locus and individual-based simulations for two loci, our analysis first demonstrates that strong departures from full sexuality are observed after only a few generations of clonality. The convergence towards predictions made under conditions of full clonality during the clonal phase depends on the balance between mutations and genetic drift. Second, the sexual event of CP usually resets the genetic diversity at a single locus towards predictions made under full sexuality. However, this single recombination event is insufficient to reshuffle gametic phases towards full-sexuality predictions. Finally, for similar levels of clonality, CP and acyclic partial clonality (wherein a fixed proportion of individuals are clonally produced within each generation) differentially affect the distribution of genetic diversity. Overall, this work provides solid predictions of neutral genetic diversity that may serve as a null model in detecting the action of common evolutionary or demographic processes in cyclical parthenogens (for example, selection or bottlenecks).

Genetic characterization of Kenai brown bears (Ursus arctos): Microsatellite and mitochondrial DNA control region variation in brown bears of the Kenai Peninsula, south central Alaska

USGS Publications Warehouse

Jackson, J.V.; Talbot, S.L.; Farley, S.

2008-01-01

We collected data from 20 biparentally inherited microsatellite loci, and nucleotide sequence from the maternally inherited mitochondrial DNA (mtDNA) control region, to determine levels of genetic variation of the brown bears (Ursus arctos L., 1758) of the Kenai Peninsula, south central Alaska. Nuclear genetic variation was similar to that observed in other Alaskan peninsular populations. We detected no significant inbreeding and found no evidence of population substructuring on the Kenai Peninsula. We observed a genetic signature of a bottleneck under the infinite alleles model (IAM), but not under the stepwise mutation model (SMM) or the two-phase model (TPM) of microsatellite mutation. Kenai brown bears have lower levels of mtDNA haplotypic diversity relative to most other brown bear populations in Alaska. ?? 2008 NRC.

Genetic architecture underlying convergent evolution of egg-laying behavior in a seed-feeding beetle.

PubMed

Fox, Charles W; Wagner, James D; Cline, Sara; Thomas, Frances Ann; Messina, Frank J

2009-05-01

Independent populations subjected to similar environments often exhibit convergent evolution. An unresolved question is the frequency with which such convergence reflects parallel genetic mechanisms. We examined the convergent evolution of egg-laying behavior in the seed-feeding beetle Callosobruchus maculatus. Females avoid ovipositing on seeds bearing conspecific eggs, but the degree of host discrimination varies among geographic populations. In a previous experiment, replicate lines switched from a small host to a large one evolved reduced discrimination after 40 generations. We used line crosses to determine the genetic architecture underlying this rapid response. The most parsimonious genetic models included dominance and/or epistasis for all crosses. The genetic architecture underlying reduced discrimination in two lines was not significantly different from the architecture underlying differences between geographic populations, but the architecture underlying the divergence of a third line differed from all others. We conclude that convergence of this complex trait may in some cases involve parallel genetic mechanisms.

Genetic Diversity and Ecological Niche Modelling of Wild Barley: Refugia, Large-Scale Post-LGM Range Expansion and Limited Mid-Future Climate Threats?

PubMed Central

Russell, Joanne; van Zonneveld, Maarten; Dawson, Ian K.; Booth, Allan; Waugh, Robbie; Steffenson, Brian

2014-01-01

Describing genetic diversity in wild barley (Hordeum vulgare ssp. spontaneum) in geographic and environmental space in the context of current, past and potential future climates is important for conservation and for breeding the domesticated crop (Hordeum vulgare ssp. vulgare). Spatial genetic diversity in wild barley was revealed by both nuclear- (2,505 SNP, 24 nSSR) and chloroplast-derived (5 cpSSR) markers in 256 widely-sampled geo-referenced accessions. Results were compared with MaxEnt-modelled geographic distributions under current, past (Last Glacial Maximum, LGM) and mid-term future (anthropogenic scenario A2, the 2080s) climates. Comparisons suggest large-scale post-LGM range expansion in Central Asia and relatively small, but statistically significant, reductions in range-wide genetic diversity under future climate. Our analyses support the utility of ecological niche modelling for locating genetic diversity hotspots and determine priority geographic areas for wild barley conservation under anthropogenic climate change. Similar research on other cereal crop progenitors could play an important role in tailoring conservation and crop improvement strategies to support future human food security. PMID:24505252

Predicting the genetic consequences of future climate change: The power of coupling spatial demography, the coalescent, and historical landscape changes.

PubMed

Brown, Jason L; Weber, Jennifer J; Alvarado-Serrano, Diego F; Hickerson, Michael J; Franks, Steven J; Carnaval, Ana C

2016-01-01

Climate change is a widely accepted threat to biodiversity. Species distribution models (SDMs) are used to forecast whether and how species distributions may track these changes. Yet, SDMs generally fail to account for genetic and demographic processes, limiting population-level inferences. We still do not understand how predicted environmental shifts will impact the spatial distribution of genetic diversity within taxa. We propose a novel method that predicts spatially explicit genetic and demographic landscapes of populations under future climatic conditions. We use carefully parameterized SDMs as estimates of the spatial distribution of suitable habitats and landscape dispersal permeability under present-day, past, and future conditions. We use empirical genetic data and approximate Bayesian computation to estimate unknown demographic parameters. Finally, we employ these parameters to simulate realistic and complex models of responses to future environmental shifts. We contrast parameterized models under current and future landscapes to quantify the expected magnitude of change. We implement this framework on neutral genetic data available from Penstemon deustus. Our results predict that future climate change will result in geographically widespread declines in genetic diversity in this species. The extent of reduction will heavily depend on the continuity of population networks and deme sizes. To our knowledge, this is the first study to provide spatially explicit predictions of within-species genetic diversity using climatic, demographic, and genetic data. Our approach accounts for climatic, geographic, and biological complexity. This framework is promising for understanding evolutionary consequences of climate change, and guiding conservation planning. © 2016 Botanical Society of America.

Efficient simulation and likelihood methods for non-neutral multi-allele models.

PubMed

Joyce, Paul; Genz, Alan; Buzbas, Erkan Ozge

2012-06-01

Throughout the 1980s, Simon Tavaré made numerous significant contributions to population genetics theory. As genetic data, in particular DNA sequence, became more readily available, a need to connect population-genetic models to data became the central issue. The seminal work of Griffiths and Tavaré (1994a , 1994b , 1994c) was among the first to develop a likelihood method to estimate the population-genetic parameters using full DNA sequences. Now, we are in the genomics era where methods need to scale-up to handle massive data sets, and Tavaré has led the way to new approaches. However, performing statistical inference under non-neutral models has proved elusive. In tribute to Simon Tavaré, we present an article in spirit of his work that provides a computationally tractable method for simulating and analyzing data under a class of non-neutral population-genetic models. Computational methods for approximating likelihood functions and generating samples under a class of allele-frequency based non-neutral parent-independent mutation models were proposed by Donnelly, Nordborg, and Joyce (DNJ) (Donnelly et al., 2001). DNJ (2001) simulated samples of allele frequencies from non-neutral models using neutral models as auxiliary distribution in a rejection algorithm. However, patterns of allele frequencies produced by neutral models are dissimilar to patterns of allele frequencies produced by non-neutral models, making the rejection method inefficient. For example, in some cases the methods in DNJ (2001) require 10(9) rejections before a sample from the non-neutral model is accepted. Our method simulates samples directly from the distribution of non-neutral models, making simulation methods a practical tool to study the behavior of the likelihood and to perform inference on the strength of selection.

Testing the structure of a hydrological model using Genetic Programming

NASA Astrophysics Data System (ADS)

Selle, Benny; Muttil, Nitin

2011-01-01

SummaryGenetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, watertable depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

Lods, wrods, and mods: the interpretation of lod scores calculated under different models.

PubMed

Hodge, S E; Elston, R C

1994-01-01

In this paper we examine the relationships among classical lod scores, "wrod" scores (lod scores calculated under the wrong genetic model), and "mod" scores (lod scores maximized over genetic model parameters). We compare the behavior of these scores when the state of nature is linkage to their behavior when the state of nature is no linkage. We describe sufficient conditions for mod scores to be valid and discuss their use to determine the correct genetic model. We show that lod scores represent a likelihood-ratio test for independence. We explain the "ascertainment-assumption-free" aspect of using mod scores to determine mode of inheritance and we set this aspect into a well-established statistical framework. Finally, we summarize practical guidelines for the use of mod scores.

Signatures of positive selection: from selective sweeps at individual loci to subtle allele frequency changes in polygenic adaptation.

PubMed

Stephan, Wolfgang

2016-01-01

In the past 15 years, numerous methods have been developed to detect selective sweeps underlying adaptations. These methods are based on relatively simple population genetic models, including one or two loci at which positive directional selection occurs, and one or two marker loci at which the impact of selection on linked neutral variation is quantified. Information about the phenotype under selection is not included in these models (except for fitness). In contrast, in the quantitative genetic models of adaptation, selection acts on one or more phenotypic traits, such that a genotype-phenotype map is required to bridge the gap to population genetics theory. Here I describe the range of population genetic models from selective sweeps in a panmictic population of constant size to evolutionary traffic when simultaneous sweeps at multiple loci interfere, and I also consider the case of polygenic selection characterized by subtle allele frequency shifts at many loci. Furthermore, I present an overview of the statistical tests that have been proposed based on these population genetics models to detect evidence for positive selection in the genome. © 2015 John Wiley & Sons Ltd.

A simulations approach for meta-analysis of genetic association studies based on additive genetic model.

PubMed

John, Majnu; Lencz, Todd; Malhotra, Anil K; Correll, Christoph U; Zhang, Jian-Ping

2018-06-01

Meta-analysis of genetic association studies is being increasingly used to assess phenotypic differences between genotype groups. When the underlying genetic model is assumed to be dominant or recessive, assessing the phenotype differences based on summary statistics, reported for individual studies in a meta-analysis, is a valid strategy. However, when the genetic model is additive, a similar strategy based on summary statistics will lead to biased results. This fact about the additive model is one of the things that we establish in this paper, using simulations. The main goal of this paper is to present an alternate strategy for the additive model based on simulating data for the individual studies. We show that the alternate strategy is far superior to the strategy based on summary statistics.

Effects of complex life cycles on genetic diversity: cyclical parthenogenesis

PubMed Central

Rouger, R; Reichel, K; Malrieu, F; Masson, J P; Stoeckel, S

2016-01-01

Neutral patterns of population genetic diversity in species with complex life cycles are difficult to anticipate. Cyclical parthenogenesis (CP), in which organisms undergo several rounds of clonal reproduction followed by a sexual event, is one such life cycle. Many species, including crop pests (aphids), human parasites (trematodes) or models used in evolutionary science (Daphnia), are cyclical parthenogens. It is therefore crucial to understand the impact of such a life cycle on neutral genetic diversity. In this paper, we describe distributions of genetic diversity under conditions of CP with various clonal phase lengths. Using a Markov chain model of CP for a single locus and individual-based simulations for two loci, our analysis first demonstrates that strong departures from full sexuality are observed after only a few generations of clonality. The convergence towards predictions made under conditions of full clonality during the clonal phase depends on the balance between mutations and genetic drift. Second, the sexual event of CP usually resets the genetic diversity at a single locus towards predictions made under full sexuality. However, this single recombination event is insufficient to reshuffle gametic phases towards full-sexuality predictions. Finally, for similar levels of clonality, CP and acyclic partial clonality (wherein a fixed proportion of individuals are clonally produced within each generation) differentially affect the distribution of genetic diversity. Overall, this work provides solid predictions of neutral genetic diversity that may serve as a null model in detecting the action of common evolutionary or demographic processes in cyclical parthenogens (for example, selection or bottlenecks). PMID:27436524

Dissection of Host Susceptibility to Bacterial Infections and Its Toxins.

PubMed

Nashef, Aysar; Agbaria, Mahmoud; Shusterman, Ariel; Lorè, Nicola Ivan; Bragonzi, Alessandra; Wiess, Ervin; Houri-Haddad, Yael; Iraqi, Fuad A

2017-01-01

Infection is one of the leading causes of human mortality and morbidity. Exposure to microbial agents is obviously required. However, also non-microbial environmental and host factors play a key role in the onset, development and outcome of infectious disease, resulting in large of clinical variability between individuals in a population infected with the same microbe. Controlled and standardized investigations of the genetics of susceptibility to infectious disease are almost impossible to perform in humans whereas mouse models allow application of powerful genomic techniques to identify and validate causative genes underlying human diseases with complex etiologies. Most of current animal models used in complex traits diseases genetic mapping have limited genetic diversity. This limitation impedes the ability to create incorporated network using genetic interactions, epigenetics, environmental factors, microbiota, and other phenotypes. A novel mouse genetic reference population for high-resolution mapping and subsequently identifying genes underlying the QTL, namely the Collaborative Cross (CC) mouse genetic reference population (GRP) was recently developed. In this chapter, we discuss a variety of approaches using CC mice for mapping genes underlying quantitative trait loci (QTL) to dissect the host response to polygenic traits, including infectious disease caused by bacterial agents and its toxins.

Mitochondrial genetics in Bakers' yeast: a molecular mechanism for recombinational polarity and suppressiveness.

PubMed

Perlman, P S; Birky, C W

1974-11-01

Recombinational polarity and suppressiveness are two well-known but puzzling cytoplasmic genetic phenomena in bakers' yeast, Saccharomyces cerevisiae. Little progress has been made in characterizing the underlying molecular mechanisms of these phenomena. In this paper we describe a molecular model for recombinational polarity that is compatible with the available genetic evidence. The model stresses the role of small deletions and excision/repair processes in otherwise canonical recombinational events. According to the model, both phenomena require recombination and may share mechanistic elements.

Integrative Analysis of Genetic, Genomic, and Phenotypic Data for Ethanol Behaviors: A Network-Based Pipeline for Identifying Mechanisms and Potential Drug Targets.

PubMed

Bogenpohl, James W; Mignogna, Kristin M; Smith, Maren L; Miles, Michael F

2017-01-01

Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce nonbiased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA, and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x environmental interactions affecting brain functioning in health and disease.

INTEGRATIVE ANALYSIS OF GENETIC, GENOMIC AND PHENOTYPIC DATA FOR ETHANOL BEHAVIORS: A NETWORK-BASED PIPELINE FOR IDENTIFYING MECHANISMS AND POTENTIAL DRUG TARGETS

PubMed Central

Bogenpohl, James W.; Mignogna, Kristin M.; Smith, Maren L.; Miles, Michael F.

2016-01-01

Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce non-biased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x environmental interactions affecting brain functioning in health and disease. PMID:27933543

Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study.

PubMed

Talmud, Philippa J; Hingorani, Aroon D; Cooper, Jackie A; Marmot, Michael G; Brunner, Eric J; Kumari, Meena; Kivimäki, Mika; Humphries, Steve E

2010-01-14

To assess the performance of a panel of common single nucleotide polymorphisms (genotypes) associated with type 2 diabetes in distinguishing incident cases of future type 2 diabetes (discrimination), and to examine the effect of adding genetic information to previously validated non-genetic (phenotype based) models developed to estimate the absolute risk of type 2 diabetes. Workplace based prospective cohort study with three 5 yearly medical screenings. 5535 initially healthy people (mean age 49 years; 33% women), of whom 302 developed new onset type 2 diabetes over 10 years. Non-genetic variables included in two established risk models-the Cambridge type 2 diabetes risk score (age, sex, drug treatment, family history of type 2 diabetes, body mass index, smoking status) and the Framingham offspring study type 2 diabetes risk score (age, sex, parental history of type 2 diabetes, body mass index, high density lipoprotein cholesterol, triglycerides, fasting glucose)-and 20 single nucleotide polymorphisms associated with susceptibility to type 2 diabetes. Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor's diagnosis, or the use of anti-diabetic drugs. A genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes. The Cambridge risk score (area under curve 0.72, 0.69 to 0.76) and the Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82) led to better discrimination of cases than did genotype based tests. Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score. The phenotype based risk models provided greater discrimination for type 2 diabetes than did models based on 20 common independently inherited diabetes risk alleles. The addition of genotypes to phenotype based risk models produced only minimal improvement in accuracy of risk estimation assessed by recalibration and, at best, a minor net reclassification improvement. The major translational application of the currently known common, small effect genetic variants influencing susceptibility to type 2 diabetes is likely to come from the insight they provide on causes of disease and potential therapeutic targets.

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.

Consequences of the genetic threshold model for observing partial migration under climate change scenarios.

PubMed

Cobben, Marleen M P; van Noordwijk, Arie J

2017-10-01

Migration is a widespread phenomenon across the animal kingdom as a response to seasonality in environmental conditions. Partially migratory populations are populations that consist of both migratory and residential individuals. Such populations are very common, yet their stability has long been debated. The inheritance of migratory activity is currently best described by the threshold model of quantitative genetics. The inclusion of such a genetic threshold model for migratory behavior leads to a stable zone in time and space of partially migratory populations under a wide range of demographic parameter values, when assuming stable environmental conditions and unlimited genetic diversity. Migratory species are expected to be particularly sensitive to global warming, as arrival at the breeding grounds might be increasingly mistimed as a result of the uncoupling of long-used cues and actual environmental conditions, with decreasing reproduction as a consequence. Here, we investigate the consequences for migratory behavior and the stability of partially migratory populations under five climate change scenarios and the assumption of a genetic threshold value for migratory behavior in an individual-based model. The results show a spatially and temporally stable zone of partially migratory populations after different lengths of time in all scenarios. In the scenarios in which the species expands its range from a particular set of starting populations, the genetic diversity and location at initialization determine the species' colonization speed across the zone of partial migration and therefore across the entire landscape. Abruptly changing environmental conditions after model initialization never caused a qualitative change in phenotype distributions, or complete extinction. This suggests that climate change-induced shifts in species' ranges as well as changes in survival probabilities and reproductive success can be met with flexibility in migratory behavior at the species level, which will reduce the risk of extinction.

Model - SEO - serious ovarian cancer | Center for Cancer Research

Cancer.gov

Genetically engineered mouse model Developed in house Genetic aberrations: Inactivation of Rb tumor suppression (via K18-T121 transgene) Tp53 loss or mutation (R172H) Brca1 or Brca2 loss Induction by injection of adenovirus expressing Cre recombinase under the ovrian bursa Pathology:

Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution.

PubMed

Ling, Shaoping; Hu, Zheng; Yang, Zuyu; Yang, Fang; Li, Yawei; Lin, Pei; Chen, Ke; Dong, Lili; Cao, Lihua; Tao, Yong; Hao, Lingtong; Chen, Qingjian; Gong, Qiang; Wu, Dafei; Li, Wenjie; Zhao, Wenming; Tian, Xiuyun; Hao, Chunyi; Hungate, Eric A; Catenacci, Daniel V T; Hudson, Richard R; Li, Wen-Hsiung; Lu, Xuemei; Wu, Chung-I

2015-11-24

The prevailing view that the evolution of cells in a tumor is driven by Darwinian selection has never been rigorously tested. Because selection greatly affects the level of intratumor genetic diversity, it is important to assess whether intratumor evolution follows the Darwinian or the non-Darwinian mode of evolution. To provide the statistical power, many regions in a single tumor need to be sampled and analyzed much more extensively than has been attempted in previous intratumor studies. Here, from a hepatocellular carcinoma (HCC) tumor, we evaluated multiregional samples from the tumor, using either whole-exome sequencing (WES) (n = 23 samples) or genotyping (n = 286) under both the infinite-site and infinite-allele models of population genetics. In addition to the many single-nucleotide variations (SNVs) present in all samples, there were 35 "polymorphic" SNVs among samples. High genetic diversity was evident as the 23 WES samples defined 20 unique cell clones. With all 286 samples genotyped, clonal diversity agreed well with the non-Darwinian model with no evidence of positive Darwinian selection. Under the non-Darwinian model, MALL (the number of coding region mutations in the entire tumor) was estimated to be greater than 100 million in this tumor. DNA sequences reveal local diversities in small patches of cells and validate the estimation. In contrast, the genetic diversity under a Darwinian model would generally be orders of magnitude smaller. Because the level of genetic diversity will have implications on therapeutic resistance, non-Darwinian evolution should be heeded in cancer treatments even for microscopic tumors.

Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution

PubMed Central

Ling, Shaoping; Hu, Zheng; Yang, Zuyu; Yang, Fang; Li, Yawei; Lin, Pei; Chen, Ke; Dong, Lili; Cao, Lihua; Tao, Yong; Hao, Lingtong; Chen, Qingjian; Gong, Qiang; Wu, Dafei; Li, Wenjie; Zhao, Wenming; Tian, Xiuyun; Hao, Chunyi; Hungate, Eric A.; Catenacci, Daniel V. T.; Hudson, Richard R.; Li, Wen-Hsiung; Lu, Xuemei; Wu, Chung-I

2015-01-01

The prevailing view that the evolution of cells in a tumor is driven by Darwinian selection has never been rigorously tested. Because selection greatly affects the level of intratumor genetic diversity, it is important to assess whether intratumor evolution follows the Darwinian or the non-Darwinian mode of evolution. To provide the statistical power, many regions in a single tumor need to be sampled and analyzed much more extensively than has been attempted in previous intratumor studies. Here, from a hepatocellular carcinoma (HCC) tumor, we evaluated multiregional samples from the tumor, using either whole-exome sequencing (WES) (n = 23 samples) or genotyping (n = 286) under both the infinite-site and infinite-allele models of population genetics. In addition to the many single-nucleotide variations (SNVs) present in all samples, there were 35 “polymorphic” SNVs among samples. High genetic diversity was evident as the 23 WES samples defined 20 unique cell clones. With all 286 samples genotyped, clonal diversity agreed well with the non-Darwinian model with no evidence of positive Darwinian selection. Under the non-Darwinian model, MALL (the number of coding region mutations in the entire tumor) was estimated to be greater than 100 million in this tumor. DNA sequences reveal local diversities in small patches of cells and validate the estimation. In contrast, the genetic diversity under a Darwinian model would generally be orders of magnitude smaller. Because the level of genetic diversity will have implications on therapeutic resistance, non-Darwinian evolution should be heeded in cancer treatments even for microscopic tumors. PMID:26561581

Estimation and interpretation of genetic effects with epistasis using the NOIA model.

PubMed

Alvarez-Castro, José M; Carlborg, Orjan; Rönnegård, Lars

2012-01-01

We introduce this communication with a brief outline of the historical landmarks in genetic modeling, especially concerning epistasis. Then, we present methods for the use of genetic modeling in QTL analyses. In particular, we summarize the essential expressions of the natural and orthogonal interactions (NOIA) model of genetic effects. Our motivation for reviewing that theory here is twofold. First, this review presents a digest of the expressions for the application of the NOIA model, which are often mixed with intermediate and additional formulae in the original articles. Second, we make the required theory handy for the reader to relate the genetic concepts to the particular mathematical expressions underlying them. We illustrate those relations by providing graphical interpretations and a diagram summarizing the key features for applying genetic modeling with epistasis in comprehensive QTL analyses. Finally, we briefly review some examples of the application of NOIA to real data and the way it improves the interpretability of the results.

Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix.

PubMed

Zhang, Zhe; Erbe, Malena; He, Jinlong; Ober, Ulrike; Gao, Ning; Zhang, Hao; Simianer, Henner; Li, Jiaqi

2015-02-09

Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|GA (BLUP-given genetic architecture), which can use genetic architecture information within the dataset at hand rather than from public sources. This is achieved by using a trait-specific covariance matrix ( T: ), which is a weighted sum of a genetic architecture part ( S: matrix) and the realized relationship matrix ( G: ). The algorithm of BLUP|GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP|GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches. Results show that BLUP|GA outperformed GBLUP in 20 of 21 scenarios in the dairy cattle dataset and outperformed GBLUP, BayesA, and BayesB in 12 of 13 traits in the analyzed public datasets. Further analyses showed that the difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T: and G: matrices. The new strategy applied in BLUP|GA is a favorable and flexible alternative to the standard GBLUP model, allowing to account for the genetic architecture of the quantitative trait under consideration when necessary. This feature is mainly due to the increased similarity between the trait-specific relationship matrix ( T: matrix) and the genetic relationship matrix at unobserved causal loci. Applying BLUP|GA in WGP would ease the burden of model selection. Copyright © 2015 Zhang et al.

Genetics of common forms of heart failure: challenges and potential solutions.

PubMed

Rau, Christoph D; Lusis, Aldons J; Wang, Yibin

2015-05-01

In contrast to many other human diseases, the use of genome-wide association studies (GWAS) to identify genes for heart failure (HF) has had limited success. We will discuss the underlying challenges as well as potential new approaches to understanding the genetics of common forms of HF. Recent research using intermediate phenotypes, more detailed and quantitative stratification of HF symptoms, founder populations and novel animal models has begun to allow researchers to make headway toward explaining the genetics underlying HF using GWAS techniques. By expanding analyses of HF to improved clinical traits, additional HF classifications and innovative model systems, the intractability of human HF GWAS should be ameliorated significantly.

Testing the limits of the 'joint account' model of genetic information: a legal thought experiment.

PubMed

Foster, Charles; Herring, Jonathan; Boyd, Magnus

2015-05-01

We examine the likely reception in the courtroom of the 'joint account' model of genetic confidentiality. We conclude that the model, as modified by Gilbar and others, is workable and reflects, better than more conventional legal approaches, both the biological and psychological realities and the obligations owed under Articles 8 and 10 of the European Convention on Human Rights (ECHR). 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.

Genetic pleiotropy explains associations between musical auditory discrimination and intelligence.

PubMed

Mosing, Miriam A; Pedersen, Nancy L; Madison, Guy; Ullén, Fredrik

2014-01-01

Musical aptitude is commonly measured using tasks that involve discrimination of different types of musical auditory stimuli. Performance on such different discrimination tasks correlates positively with each other and with intelligence. However, no study to date has explored these associations using a genetically informative sample to estimate underlying genetic and environmental influences. In the present study, a large sample of Swedish twins (N = 10,500) was used to investigate the genetic architecture of the associations between intelligence and performance on three musical auditory discrimination tasks (rhythm, melody and pitch). Phenotypic correlations between the tasks ranged between 0.23 and 0.42 (Pearson r values). Genetic modelling showed that the covariation between the variables could be explained by shared genetic influences. Neither shared, nor non-shared environment had a significant effect on the associations. Good fit was obtained with a two-factor model where one underlying shared genetic factor explained all the covariation between the musical discrimination tasks and IQ, and a second genetic factor explained variance exclusively shared among the discrimination tasks. The results suggest that positive correlations among musical aptitudes result from both genes with broad effects on cognition, and genes with potentially more specific influences on auditory functions.

Genetic Pleiotropy Explains Associations between Musical Auditory Discrimination and Intelligence

PubMed Central

Mosing, Miriam A.; Pedersen, Nancy L.; Madison, Guy; Ullén, Fredrik

2014-01-01

Musical aptitude is commonly measured using tasks that involve discrimination of different types of musical auditory stimuli. Performance on such different discrimination tasks correlates positively with each other and with intelligence. However, no study to date has explored these associations using a genetically informative sample to estimate underlying genetic and environmental influences. In the present study, a large sample of Swedish twins (N = 10,500) was used to investigate the genetic architecture of the associations between intelligence and performance on three musical auditory discrimination tasks (rhythm, melody and pitch). Phenotypic correlations between the tasks ranged between 0.23 and 0.42 (Pearson r values). Genetic modelling showed that the covariation between the variables could be explained by shared genetic influences. Neither shared, nor non-shared environment had a significant effect on the associations. Good fit was obtained with a two-factor model where one underlying shared genetic factor explained all the covariation between the musical discrimination tasks and IQ, and a second genetic factor explained variance exclusively shared among the discrimination tasks. The results suggest that positive correlations among musical aptitudes result from both genes with broad effects on cognition, and genes with potentially more specific influences on auditory functions. PMID:25419664

Expansion Under Climate Change: The Genetic Consequences.

PubMed

Garnier, Jimmy; Lewis, Mark A

2016-11-01

Range expansion and range shifts are crucial population responses to climate change. Genetic consequences are not well understood but are clearly coupled to ecological dynamics that, in turn, are driven by shifting climate conditions. We model a population with a deterministic reaction-diffusion model coupled to a heterogeneous environment that develops in time due to climate change. We decompose the resulting travelling wave solution into neutral genetic components to analyse the spatio-temporal dynamics of its genetic structure. Our analysis shows that range expansions and range shifts under slow climate change preserve genetic diversity. This is because slow climate change creates range boundaries that promote spatial mixing of genetic components. Mathematically, the mixing leads to so-called pushed travelling wave solutions. This mixing phenomenon is not seen in spatially homogeneous environments, where range expansion reduces genetic diversity through gene surfing arising from pulled travelling wave solutions. However, the preservation of diversity is diminished when climate change occurs too quickly. Using diversity indices, we show that fast expansions and range shifts erode genetic diversity more than slow range expansions and range shifts. Our study provides analytical insight into the dynamics of travelling wave solutions in heterogeneous environments.

Genetics of dispersal.

PubMed

Saastamoinen, Marjo; Bocedi, Greta; Cote, Julien; Legrand, Delphine; Guillaume, Frédéric; Wheat, Christopher W; Fronhofer, Emanuel A; Garcia, Cristina; Henry, Roslyn; Husby, Arild; Baguette, Michel; Bonte, Dries; Coulon, Aurélie; Kokko, Hanna; Matthysen, Erik; Niitepõld, Kristjan; Nonaka, Etsuko; Stevens, Virginie M; Travis, Justin M J; Donohue, Kathleen; Bullock, James M; Del Mar Delgado, Maria

2018-02-01

Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences. Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal-related phenotypes or evidence for the micro-evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment-dependent. By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non-additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non-equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context-dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits. © 2017 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.

Genetics of dispersal

PubMed Central

Bocedi, Greta; Cote, Julien; Legrand, Delphine; Guillaume, Frédéric; Wheat, Christopher W.; Fronhofer, Emanuel A.; Garcia, Cristina; Henry, Roslyn; Husby, Arild; Baguette, Michel; Bonte, Dries; Coulon, Aurélie; Kokko, Hanna; Matthysen, Erik; Niitepõld, Kristjan; Nonaka, Etsuko; Stevens, Virginie M.; Travis, Justin M. J.; Donohue, Kathleen; Bullock, James M.; del Mar Delgado, Maria

2017-01-01

ABSTRACT Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences. Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal‐related phenotypes or evidence for the micro‐evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment‐dependent. By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non‐additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non‐equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context‐dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits. PMID:28776950

Lessons learned from the dog genome.

PubMed

Wayne, Robert K; Ostrander, Elaine A

2007-11-01

Extensive genetic resources and a high-quality genome sequence position the dog as an important model species for understanding genome evolution, population genetics and genes underlying complex phenotypic traits. Newly developed genomic resources have expanded our understanding of canine evolutionary history and dog origins. Domestication involved genetic contributions from multiple populations of gray wolves probably through backcrossing. More recently, the advent of controlled breeding practices has segregated genetic variability into distinct dog breeds that possess specific phenotypic traits. Consequently, genome-wide association and selective sweep scans now allow the discovery of genes underlying breed-specific characteristics. The dog is finally emerging as a novel resource for studying the genetic basis of complex traits, including behavior.

An Underlying Common Factor, Influenced by Genetics and Unique Environment, Explains the Covariation Between Major Depressive Disorder, Generalized Anxiety Disorder, and Burnout: A Swedish Twin Study.

PubMed

Mather, Lisa; Blom, Victoria; Bergström, Gunnar; Svedberg, Pia

2016-12-01

Depression and anxiety are highly comorbid due to shared genetic risk factors, but less is known about whether burnout shares these risk factors. We aimed to examine whether the covariation between major depressive disorder (MDD), generalized anxiety disorder (GAD), and burnout is explained by common genetic and/or environmental factors. This cross-sectional study included 25,378 Swedish twins responding to a survey in 2005-2006. Structural equation models were used to analyze whether the trait variances and covariances were due to additive genetics, non-additive genetics, shared environment, and unique environment. Univariate analyses tested sex limitation models and multivariate analysis tested Cholesky, independent pathway, and common pathway models. The phenotypic correlations were 0.71 (0.69-0.74) between MDD and GAD, 0.58 (0.56-0.60) between MDD and burnout, and 0.53 (0.50-0.56) between GAD and burnout. Heritabilities were 45% for MDD, 49% for GAD, and 38% for burnout; no statistically significant sex differences were found. A common pathway model was chosen as the final model. The common factor was influenced by genetics (58%) and unique environment (42%), and explained 77% of the variation in MDD, 69% in GAD, and 44% in burnout. GAD and burnout had additive genetic factors unique to the phenotypes (11% each), while MDD did not. Unique environment explained 23% of the variability in MDD, 20% in GAD, and 45% in burnout. In conclusion, the covariation was explained by an underlying common factor, largely influenced by genetics. Burnout was to a large degree influenced by unique environmental factors not shared with MDD and GAD.

Determining the drivers of population structure in a highly urbanized landscape to inform conservation planning.

PubMed

Thomassen, Henri A; Harrigan, Ryan J; Semple Delaney, Kathleen; Riley, Seth P D; Serieys, Laurel E K; Pease, Katherine; Wayne, Robert K; Smith, Thomas B

2018-02-01

Understanding the environmental contributors to population structure is of paramount importance for conservation in urbanized environments. We used spatially explicit models to determine genetic population structure under current and future environmental conditions across a highly fragmented, human-dominated environment in Southern California to assess the effects of natural ecological variation and urbanization. We focused on 7 common species with diverse habitat requirements, home-range sizes, and dispersal abilities. We quantified the relative roles of potential barriers, including natural environmental characteristics and an anthropogenic barrier created by a major highway, in shaping genetic variation. The ability to predict genetic variation in our models differed among species: 11-81% of intraspecific genetic variation was explained by environmental variables. Although an anthropogenically induced barrier (a major highway) severely restricted gene flow and movement at broad scales for some species, genetic variation seemed to be primarily driven by natural environmental heterogeneity at a local level. Our results show how assessing environmentally associated variation for multiple species under current and future climate conditions can help identify priority regions for maximizing population persistence under environmental change in urbanized regions. © 2017 Society for Conservation Biology.

Antagonistic versus non-antagonistic models of balancing selection: Characterizing the relative timescales and hitchhiking effects of partial selective sweeps

PubMed Central

Connallon, Tim; Clark, Andrew G.

2012-01-01

Antagonistically selected alleles -- those with opposing fitness effects between sexes, environments, or fitness components -- represent an important component of additive genetic variance in fitness-related traits, with stably balanced polymorphisms often hypothesized to contribute to observed quantitative genetic variation. Balancing selection hypotheses imply that intermediate-frequency alleles disproportionately contribute to genetic variance of life history traits and fitness. Such alleles may also associate with population genetic footprints of recent selection, including reduced genetic diversity and inflated linkage disequilibrium at linked, neutral sites. Here, we compare the evolutionary dynamics of different balancing selection models, and characterize the evolutionary timescale and hitchhiking effects of partial selective sweeps generated under antagonistic versus non-antagonistic (e.g., overdominant and frequency-dependent selection) processes. We show that that the evolutionary timescales of partial sweeps tend to be much longer, and hitchhiking effects are drastically weaker, under scenarios of antagonistic selection. These results predict an interesting mismatch between molecular population genetic and quantitative genetic patterns of variation. Balanced, antagonistically selected alleles are expected to contribute more to additive genetic variance for fitness than alleles maintained by classic, non-antagonistic mechanisms. Nevertheless, classical mechanisms of balancing selection are much more likely to generate strong population genetic signatures of recent balancing selection. PMID:23461340

Genetic parameters for growth characteristics of free-range chickens under univariate random regression models.

PubMed

Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B

2016-09-01

Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion. © 2016 Poultry Science Association Inc.

The shaping of genetic variation in edge-of-range populations under past and future climate change

PubMed Central

Razgour, Orly; Juste, Javier; Ibáñez, Carlos; Kiefer, Andreas; Rebelo, Hugo; Puechmaille, Sébastien J; Arlettaz, Raphael; Burke, Terry; Dawson, Deborah A; Beaumont, Mark; Jones, Gareth; Wiens, John

2013-01-01

With rates of climate change exceeding the rate at which many species are able to shift their range or adapt, it is important to understand how future changes are likely to affect biodiversity at all levels of organisation. Understanding past responses and extent of niche conservatism in climatic tolerance can help predict future consequences. We use an integrated approach to determine the genetic consequences of past and future climate changes on a bat species, Plecotus austriacus. Glacial refugia predicted by palaeo-modelling match those identified from analyses of extant genetic diversity and model-based inference of demographic history. Former refugial populations currently contain disproportionately high genetic diversity, but niche conservatism, shifts in suitable areas and barriers to migration mean that these hotspots of genetic diversity are under threat from future climate change. Evidence of population decline despite recent northward migration highlights the need to conserve leading-edge populations for spearheading future range shifts. PMID:23890483

A roadmap for the genetic analysis of renal aging

PubMed Central

Noordmans, Gerda A; Hillebrands, Jan-Luuk; van Goor, Harry; Korstanje, Ron

2015-01-01

Several studies show evidence for the genetic basis of renal disease, which renders some individuals more prone than others to accelerated renal aging. Studying the genetics of renal aging can help us to identify genes involved in this process and to unravel the underlying pathways. First, this opinion article will give an overview of the phenotypes that can be observed in age-related kidney disease. Accurate phenotyping is essential in performing genetic analysis. For kidney aging, this could include both functional and structural changes. Subsequently, this article reviews the studies that report on candidate genes associated with renal aging in humans and mice. Several loci or candidate genes have been found associated with kidney disease, but identification of the specific genetic variants involved has proven to be difficult. CUBN, UMOD, and SHROOM3 were identified by human GWAS as being associated with albuminuria, kidney function, and chronic kidney disease (CKD). These are promising examples of genes that could be involved in renal aging, and were further mechanistically evaluated in animal models. Eventually, we will provide approaches for performing genetic analysis. We should leverage the power of mouse models, as testing in humans is limited. Mouse and other animal models can be used to explain the underlying biological mechanisms of genes and loci identified by human GWAS. Furthermore, mouse models can be used to identify genetic variants associated with age-associated histological changes, of which Far2, Wisp2, and Esrrg are examples. A new outbred mouse population with high genetic diversity will facilitate the identification of genes associated with renal aging by enabling high-resolution genetic mapping while also allowing the control of environmental factors, and by enabling access to renal tissues at specific time points for histology, proteomics, and gene expression. PMID:26219736

Implications of sex-specific selection for the genetic basis of disease.

PubMed

Morrow, Edward H; Connallon, Tim

2013-12-01

Mutation and selection are thought to shape the underlying genetic basis of many common human diseases. However, both processes depend on the context in which they occur, such as environment, genetic background, or sex. Sex has widely known effects on phenotypic expression of genotype, but an analysis of how it influences the evolutionary dynamics of disease-causing variants has not yet been explored. We develop a simple population genetic model of disease susceptibility and evaluate it using a biologically plausible empirically based distribution of fitness effects among contributing mutations. The model predicts that alleles under sex-differential selection, including sexually antagonistic alleles, will disproportionately contribute to genetic variation for disease predisposition, thereby generating substantial sexual dimorphism in the genetic architecture of complex (polygenic) diseases. This is because such alleles evolve into higher population frequencies for a given effect size, relative to alleles experiencing equally strong purifying selection in both sexes. Our results provide a theoretical justification for expecting a sexually dimorphic genetic basis for variation in complex traits such as disease. Moreover, they suggest that such dimorphism is interesting - not merely something to control for - because it reflects the action of natural selection in molding the evolution of common disease phenotypes.

On the reconciliation of missing heritability for genome-wide association studies

PubMed Central

Chen, Guo-Bo

2016-01-01

The definition of heritability has been unique and clear, but its estimation and estimates vary across studies. Linear mixed model (LMM) and Haseman–Elston (HE) regression analyses are commonly used for estimating heritability from genome-wide association data. This study provides an analytical resolution that can be used to reconcile the differences between LMM and HE in the estimation of heritability given the genetic architecture, which is responsible for these differences. The genetic architecture was classified into three forms via thought experiments: (i) coupling genetic architecture that the quantitative trait loci (QTLs) in the linkage disequilibrium (LD) had a positive covariance; (ii) repulsion genetic architecture that the QTLs in the LD had a negative covariance; (iii) and neutral genetic architecture that the QTLs in the LD had a covariance with a summation of zero. The neutral genetic architecture is so far most embraced, whereas the coupling and the repulsion genetic architecture have not been well investigated. For a quantitative trait under the coupling genetic architecture, HE overestimated the heritability and LMM underestimated the heritability; under the repulsion genetic architecture, HE underestimated but LMM overestimated the heritability for a quantitative trait. These two methods gave identical results under the neutral genetic architecture. A general analytical result for the statistic estimated under HE is given regardless of genetic architecture. In contrast, the performance of LMM remained elusive, such as further depended on the ratio between the sample size and the number of markers, but LMM converged to HE with increased sample size. PMID:27436266

Combining quantitative trait loci analysis with physiological models to predict genotype-specific transpiration rates.

PubMed

Reuning, Gretchen A; Bauerle, William L; Mullen, Jack L; McKay, John K

2015-04-01

Transpiration is controlled by evaporative demand and stomatal conductance (gs ), and there can be substantial genetic variation in gs . A key parameter in empirical models of transpiration is minimum stomatal conductance (g0 ), a trait that can be measured and has a large effect on gs and transpiration. In Arabidopsis thaliana, g0 exhibits both environmental and genetic variation, and quantitative trait loci (QTL) have been mapped. We used this information to create a genetically parameterized empirical model to predict transpiration of genotypes. For the parental lines, this worked well. However, in a recombinant inbred population, the predictions proved less accurate. When based only upon their genotype at a single g0 QTL, genotypes were less distinct than our model predicted. Follow-up experiments indicated that both genotype by environment interaction and a polygenic inheritance complicate the application of genetic effects into physiological models. The use of ecophysiological or 'crop' models for predicting transpiration of novel genetic lines will benefit from incorporating further knowledge of the genetic control and degree of independence of core traits/parameters underlying gs variation. © 2014 John Wiley & Sons Ltd.

Short communication: Genetic lag represents commercial herd genetic merit more accurately than the 4-path selection model.

PubMed

Dechow, C D; Rogers, G W

2018-05-01

Expectation of genetic merit in commercial dairy herds is routinely estimated using a 4-path genetic selection model that was derived for a closed population, but commercial herds using artificial insemination sires are not closed. The 4-path model also predicts a higher rate of genetic progress in elite herds that provide artificial insemination sires than in commercial herds that use such sires, which counters other theoretical assumptions and observations of realized genetic responses. The aim of this work is to clarify whether genetic merit in commercial herds is more accurately reflected under the assumptions of the 4-path genetic response formula or by a genetic lag formula. We demonstrate by tracing the transmission of genetic merit from parents to offspring that the rate of genetic progress in commercial dairy farms is expected to be the same as that in the genetic nucleus. The lag in genetic merit between the nucleus and commercial farms is a function of sire and dam generation interval, the rate of genetic progress in elite artificial insemination herds, and genetic merit of sires and dams. To predict how strategies such as the use of young versus daughter-proven sires, culling heifers following genomic testing, or selective use of sexed semen will alter genetic merit in commercial herds, genetic merit expectations for commercial herds should be modeled using genetic lag expectations. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Distribution of lod scores in oligogenic linkage analysis.

PubMed

Williams, J T; North, K E; Martin, L J; Comuzzie, A G; Göring, H H; Blangero, J

2001-01-01

In variance component oligogenic linkage analysis it can happen that the residual additive genetic variance bounds to zero when estimating the effect of the ith quantitative trait locus. Using quantitative trait Q1 from the Genetic Analysis Workshop 12 simulated general population data, we compare the observed lod scores from oligogenic linkage analysis with the empirical lod score distribution under a null model of no linkage. We find that zero residual additive genetic variance in the null model alters the usual distribution of the likelihood-ratio statistic.

The MAX Statistic is Less Powerful for Genome Wide Association Studies Under Most Alternative Hypotheses.

PubMed

Shifflett, Benjamin; Huang, Rong; Edland, Steven D

2017-01-01

Genotypic association studies are prone to inflated type I error rates if multiple hypothesis testing is performed, e.g., sequentially testing for recessive, multiplicative, and dominant risk. Alternatives to multiple hypothesis testing include the model independent genotypic χ 2 test, the efficiency robust MAX statistic, which corrects for multiple comparisons but with some loss of power, or a single Armitage test for multiplicative trend, which has optimal power when the multiplicative model holds but with some loss of power when dominant or recessive models underlie the genetic association. We used Monte Carlo simulations to describe the relative performance of these three approaches under a range of scenarios. All three approaches maintained their nominal type I error rates. The genotypic χ 2 and MAX statistics were more powerful when testing a strictly recessive genetic effect or when testing a dominant effect when the allele frequency was high. The Armitage test for multiplicative trend was most powerful for the broad range of scenarios where heterozygote risk is intermediate between recessive and dominant risk. Moreover, all tests had limited power to detect recessive genetic risk unless the sample size was large, and conversely all tests were relatively well powered to detect dominant risk. Taken together, these results suggest the general utility of the multiplicative trend test when the underlying genetic model is unknown.

The power and robustness of maximum LOD score statistics.

PubMed

Yoo, Y J; Mendell, N R

2008-07-01

The maximum LOD score statistic is extremely powerful for gene mapping when calculated using the correct genetic parameter value. When the mode of genetic transmission is unknown, the maximum of the LOD scores obtained using several genetic parameter values is reported. This latter statistic requires higher critical value than the maximum LOD score statistic calculated from a single genetic parameter value. In this paper, we compare the power of maximum LOD scores based on three fixed sets of genetic parameter values with the power of the LOD score obtained after maximizing over the entire range of genetic parameter values. We simulate family data under nine generating models. For generating models with non-zero phenocopy rates, LOD scores maximized over the entire range of genetic parameters yielded greater power than maximum LOD scores for fixed sets of parameter values with zero phenocopy rates. No maximum LOD score was consistently more powerful than the others for generating models with a zero phenocopy rate. The power loss of the LOD score maximized over the entire range of genetic parameters, relative to the maximum LOD score calculated using the correct genetic parameter value, appeared to be robust to the generating models.

A genome-wide survey of transgenerational genetic effects in autism.

PubMed

Tsang, Kathryn M; Croen, Lisa A; Torres, Anthony R; Kharrazi, Martin; Delorenze, Gerald N; Windham, Gayle C; Yoshida, Cathleen K; Zerbo, Ousseny; Weiss, Lauren A

2013-01-01

Effects of parental genotype or parent-offspring genetic interaction are well established in model organisms for a variety of traits. However, these transgenerational genetic models are rarely studied in humans. We have utilized an autism case-control study with 735 mother-child pairs to perform genome-wide screening for maternal genetic effects and maternal-offspring genetic interaction. We used simple models of single locus parent-child interaction and identified suggestive results (P<10(-4)) that cannot be explained by main effects, but no genome-wide significant signals. Some of these maternal and maternal-child associations were in or adjacent to autism candidate genes including: PCDH9, FOXP1, GABRB3, NRXN1, RELN, MACROD2, FHIT, RORA, CNTN4, CNTNAP2, FAM135B, LAMA1, NFIA, NLGN4X, RAPGEF4, and SDK1. We attempted validation of potential autism association under maternal-specific models using maternal-paternal comparison in family-based GWAS datasets. Our results suggest that further study of parental genetic effects and parent-child interaction in autism is warranted.

Quantitative genetics of taura syndrome resistance in pacific white shrimp (penaeus vannamei): a cure model approach

PubMed Central

2011-01-01

Background In aquaculture breeding, resistance against infectious diseases is commonly assessed as time until death under exposure to a pathogen. For some diseases, a fraction of the individuals may appear as "cured" (non-susceptible), and the resulting survival time may thus be a result of two confounded underlying traits, i.e., endurance (individual hazard) and susceptibility (whether at risk or not), which may be accounted for by fitting a cure survival model. We applied a cure model to survival data of Pacific white shrimp (Penaeus vannamei) challenged with the Taura syndrome virus, which is one of the major pathogens of Panaeid shrimp species. Methods In total, 15,261 individuals of 513 full-sib families from three generations were challenge-tested in 21 separate tests (tanks). All challenge-tests were run until mortality naturally ceased. Time-until-event data were analyzed with a mixed cure survival model using Gibbs sampling, treating susceptibility and endurance as separate genetic traits. Results Overall mortality at the end of test was 28%, while 38% of the population was considered susceptible to the disease. The estimated underlying heritability was high for susceptibility (0.41 ± 0.07), but low for endurance (0.07 ± 0.03). Furthermore, endurance and susceptibility were distinct genetic traits (rg = 0.22 ± 0.25). Estimated breeding values for endurance and susceptibility were only moderately correlated (0.50), while estimated breeding values from classical models for analysis of challenge-test survival (ignoring the cured fraction) were closely correlated with estimated breeding values for susceptibility, but less correlated with estimated breeding values for endurance. Conclusions For Taura syndrome resistance, endurance and susceptibility are apparently distinct genetic traits. However, genetic evaluation of susceptibility based on the cure model showed clear associations with standard genetic evaluations that ignore the cure fraction for these data. Using the current testing design, genetic variation in observed survival time and absolute survival at the end of test were most likely dominated by genetic variation in susceptibility. If the aim is to reduce susceptibility, earlier termination of the challenge-test or back-truncation of the follow-up period should be avoided, as this may shift focus of selection towards endurance rather than susceptibility. PMID:21418636

Gene × Environment Interactions in Schizophrenia: Evidence from Genetic Mouse Models

PubMed Central

Marr, Julia; Bock, Gavin; Desbonnet, Lieve; Waddington, John

2016-01-01

The study of gene × environment, as well as epistatic interactions in schizophrenia, has provided important insight into the complex etiopathologic basis of schizophrenia. It has also increased our understanding of the role of susceptibility genes in the disorder and is an important consideration as we seek to translate genetic advances into novel antipsychotic treatment targets. This review summarises data arising from research involving the modelling of gene × environment interactions in schizophrenia using preclinical genetic models. Evidence for synergistic effects on the expression of schizophrenia-relevant endophenotypes will be discussed. It is proposed that valid and multifactorial preclinical models are important tools for identifying critical areas, as well as underlying mechanisms, of convergence of genetic and environmental risk factors, and their interaction in schizophrenia. PMID:27725886

Genetic Variance in Processing Speed Drives Variation in Aging of Spatial and Memory Abilities

ERIC Educational Resources Information Center

Finkel, Deborah; Reynolds, Chandra A.; McArdle, John J.; Hamagami, Fumiaki; Pedersen, Nancy L.

2009-01-01

Previous analyses have identified a genetic contribution to the correlation between declines with age in processing speed and higher cognitive abilities. The goal of the current analysis was to apply the biometric dual change score model to consider the possibility of temporal dynamics underlying the genetic covariance between aging trajectories…

Using multi-trait and random regression models to identify genetic variation in tolerance of pigs to Porcine Reproductive and Respiratory Syndrome virus

USDA-ARS?s Scientific Manuscript database

Background A host can adopt two response strategies to infection: resistance (reduce pathogen load) and tolerance (minimize impact of infection on performance). Both strategies may be under genetic control and could thus be targeted for genetic improvement. Although there is evidence in support of a...

Genetic and environmental melanoma models in fish

PubMed Central

Patton, E Elizabeth; Mitchell, David L; Nairn, Rodney S

2010-01-01

Experimental animal models are extremely valuable for the study of human diseases, especially those with underlying genetic components. The exploitation of various animal models, from fruitflies to mice, has led to major advances in our understanding of the etiologies of many diseases, including cancer. Cutaneous malignant melanoma is a form of cancer for which both environmental insult (i.e., UV) and hereditary predisposition are major causative factors. Fish melanoma models have been used in studies of both spontaneous and induced melanoma formation. Genetic hybrids between platyfish and swordtails, different species of the genus Xiphophorus, have been studied since the 1920s to identify genetic determinants of pigmentation and melanoma formation. Recently, transgenesis has been used to develop zebrafish and medaka models for melanoma research. This review will provide a historical perspective on the use of fish models in melanoma research, and an updated summary of current and prospective studies using these unique experimental systems. PMID:20230482

Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits.

PubMed

Feltus, F Alex

2014-06-01

Understanding the control of any trait optimally requires the detection of causal genes, gene interaction, and mechanism of action to discover and model the biochemical pathways underlying the expressed phenotype. Functional genomics techniques, including RNA expression profiling via microarray and high-throughput DNA sequencing, allow for the precise genome localization of biological information. Powerful genetic approaches, including quantitative trait locus (QTL) and genome-wide association study mapping, link phenotype with genome positions, yet genetics is less precise in localizing the relevant mechanistic information encoded in DNA. The coupling of salient functional genomic signals with genetically mapped positions is an appealing approach to discover meaningful gene-phenotype relationships. Techniques used to define this genetic-genomic convergence comprise the field of systems genetics. This short review will address an application of systems genetics where RNA profiles are associated with genetically mapped genome positions of individual genes (eQTL mapping) or as gene sets (co-expression network modules). Both approaches can be applied for knowledge independent selection of candidate genes (and possible control mechanisms) underlying complex traits where multiple, likely unlinked, genomic regions might control specific complex traits. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Association between genetic polymorphisms of interleukins and cerebral infarction risk: a meta-analysis

PubMed Central

Wang, Jiantao; Fan, Niannian; Deng, Yili; Zhu, Jie; Mei, Jing; Chen, Yao; Yang, Heng

2016-01-01

Interleukins (ILs) are the most typical inflammatory and immunoregulatory cytokines. Evidences have shown that polymorphisms in ILs are associated with cerebral infarction risk. However, the results remain inconclusive. The present study was to evaluate the role of ILs polymorphisms in cerebral infarction susceptibility. Relevant case-control studies published between January 2000 and December 2015 were searched and retrieved from the electronic databases of Web of Science, PubMed, Embase and the Chinese Biomedical Database. The odds ratio (OR) with its 95% confidence interval (CI) were employed to calculate the strength of association. A total of 55 articles including 12619 cerebral infarction patients and 14436 controls were screened out. Four ILs (IL-1, IL-6, IL-10 and IL-18) contained nine single nucleotide polymorphisms (SNPs; IL-1α −899C/T, IL-1β −511C/T and IL-1β +3953C/T; IL-6 −174G/C and −572C/G; IL-10 −819C/T and −1082A/G; IL-18 −607C/A and −137G/C). Our result showed that IL-1α −899C/T and IL-18 −607C/A (under all the genetic models), and IL-6 −572C/G (under the allelic model, heterogeneity model and dominant model) were associated with increased the risk of cerebral infarction (P<0.05). Subgroup analysis by ethnicity showed that IL-6 −174G/C polymorphism (under all the five models) and IL-10 −1082A/G polymorphism (under the allelic model and heterologous model) were significantly associated with increased the cerebral infarction risk in Asians. Other genetic polymorphisms were not related with cerebral infarction susceptibility under any genetic models. In conclusion, IL-1α −899C/T, IL-6 −572C/G and IL-18 −607C/A might be risk factors for cerebral infarction development. Further studies with well-designed and large sample size are still required. PMID:27679860

Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases

PubMed Central

Amos, Christopher I.; Bafna, Vineet; Hauser, Elizabeth R.; Hernandez, Ryan D.; Li, Chun; Liberles, David A.; McAllister, Kimberly; Moore, Jason H.; Paltoo, Dina N.; Papanicolaou, George J.; Peng, Bo; Ritchie, Marylyn D.; Rosenfeld, Gabriel; Witte, John S.

2014-01-01

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled “Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases” at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to: (i) identify opportunities, challenges and resource needs for the development and application of genetic simulation models; (ii) improve the integration of tools for modeling and analysis of simulated data; and (iii) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation. PMID:25371374

Environmental Noise, Genetic Diversity and the Evolution of Evolvability and Robustness in Model Gene Networks

PubMed Central

Steiner, Christopher F.

2012-01-01

The ability of organisms to adapt and persist in the face of environmental change is accepted as a fundamental feature of natural systems. More contentious is whether the capacity of organisms to adapt (or “evolvability”) can itself evolve and the mechanisms underlying such responses. Using model gene networks, I provide evidence that evolvability emerges more readily when populations experience positively autocorrelated environmental noise (red noise) compared to populations in stable or randomly varying (white noise) environments. Evolvability was correlated with increasing genetic robustness to effects on network viability and decreasing robustness to effects on phenotypic expression; populations whose networks displayed greater viability robustness and lower phenotypic robustness produced more additive genetic variation and adapted more rapidly in novel environments. Patterns of selection for robustness varied antagonistically with epistatic effects of mutations on viability and phenotypic expression, suggesting that trade-offs between these properties may constrain their evolutionary responses. Evolution of evolvability and robustness was stronger in sexual populations compared to asexual populations indicating that enhanced genetic variation under fluctuating selection combined with recombination load is a primary driver of the emergence of evolvability. These results provide insight into the mechanisms potentially underlying rapid adaptation as well as the environmental conditions that drive the evolution of genetic interactions. PMID:23284934

Implementation of the Realized Genomic Relationship Matrix to Open-Pollinated White Spruce Family Testing for Disentangling Additive from Nonadditive Genetic Effects

PubMed Central

Gamal El-Dien, Omnia; Ratcliffe, Blaise; Klápště, Jaroslav; Porth, Ilga; Chen, Charles; El-Kassaby, Yousry A.

2016-01-01

The open-pollinated (OP) family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates’ offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of “half-sibling” in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench) Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure. PMID:26801647

A weighted U statistic for association analyses considering genetic heterogeneity.

PubMed

Wei, Changshuai; Elston, Robert C; Lu, Qing

2016-07-20

Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity-weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally efficient for high-dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome-wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments dataset. The genome-wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Privacy-preserving genomic testing in the clinic: a model using HIV treatment

PubMed Central

McLaren, Paul J.; Raisaro, Jean Louis; Aouri, Manel; Rotger, Margalida; Ayday, Erman; Bartha, István; Delgado, Maria B.; Vallet, Yannick; Günthard, Huldrych F.; Cavassini, Matthias; Furrer, Hansjakob; Doco-Lecompte, Thanh; Marzolini, Catia; Schmid, Patrick; Di Benedetto, Caroline; Decosterd, Laurent A.; Fellay, Jacques; Hubaux, Jean-Pierre; Telenti, Amalio

2016-01-01

Purpose: The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics. Genet Med 18 8, 814–822. Methods: We genotyped 4,149 markers in HIV-positive individuals. Variants allowed for prediction of 17 traits relevant to HIV medical care, inference of patient ancestry, and imputation of human leukocyte antigen (HLA) types. Genetic data were processed under a privacy-preserving framework using homomorphic encryption, and clinical reports describing potentially actionable results were delivered to health-care providers. Genet Med 18 8, 814–822. Results: A total of 230 patients were included in the study. We demonstrated the feasibility of encrypting a large number of genetic markers, inferring patient ancestry, computing monogenic and polygenic trait risks, and reporting results under privacy-preserving conditions. The average execution time of a multimarker test on encrypted data was 865 ms on a standard computer. The proportion of tests returning potentially actionable genetic results ranged from 0 to 54%. Genet Med 18 8, 814–822. Conclusions: The model of implementation presented herein informs on strategies to deliver genomic test results for clinical care. Data encryption to ensure privacy helps to build patient trust, a key requirement on the road to genomic-based medicine. Genet Med 18 8, 814–822. PMID:26765343

Genetic-based prediction of disease traits: prediction is very difficult, especially about the future†

PubMed Central

Schrodi, Steven J.; Mukherjee, Shubhabrata; Shan, Ying; Tromp, Gerard; Sninsky, John J.; Callear, Amy P.; Carter, Tonia C.; Ye, Zhan; Haines, Jonathan L.; Brilliant, Murray H.; Crane, Paul K.; Smelser, Diane T.; Elston, Robert C.; Weeks, Daniel E.

2014-01-01

Translation of results from genetic findings to inform medical practice is a highly anticipated goal of human genetics. The aim of this paper is to review and discuss the role of genetics in medically-relevant prediction. Germline genetics presages disease onset and therefore can contribute prognostic signals that augment laboratory tests and clinical features. As such, the impact of genetic-based predictive models on clinical decisions and therapy choice could be profound. However, given that (i) medical traits result from a complex interplay between genetic and environmental factors, (ii) the underlying genetic architectures for susceptibility to common diseases are not well-understood, and (iii) replicable susceptibility alleles, in combination, account for only a moderate amount of disease heritability, there are substantial challenges to constructing and implementing genetic risk prediction models with high utility. In spite of these challenges, concerted progress has continued in this area with an ongoing accumulation of studies that identify disease predisposing genotypes. Several statistical approaches with the aim of predicting disease have been published. Here we summarize the current state of disease susceptibility mapping and pharmacogenetics efforts for risk prediction, describe methods used to construct and evaluate genetic-based predictive models, and discuss applications. PMID:24917882

Genetic change and rates of cladogenesis

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

Avise, J.C.; Ayala, F.J.

1975-12-01

Models are introduced which predict ratios of mean levels of genetic divergence in species-rich versus species-poor phylads under two competing assumptions: (1) genetic differentiation is a function of time, unrelated to the number of cladogenetic events and (2) genetic differentiation is proportional to the number of speciation events in the group. The models are simple, general, and biologically real, but not precise. They lead to qualitatively distinct predictions about levels of genetic divergence depending upon the relationship between rates of speciation and amount of genetic change. When genetic distance between species is a function of time, mean genetic distances inmore » speciose and depauperate phylads of equal evolutionary age are very similar. On the contrary, when genetic distance is a function of the number of speciations in the history of a phylad, the ratio of mean genetic distances separating species in speciose versus depauperate phylads is greater than one, and increases rapidly as the frequency of speciations in one group relative to the other increases. The models may be tested with data from natural populations to assess (1) possible correlations between rates of anagenesis and cladogenesis and (2) the amount of genetic differentiation accompanying the speciation process. The data collected in electrophoretic surveys and other kinds of studies can be used to test the predictions of the models. For this purpose genetic distances need to be measured in speciose and depauperate phylads of equal evolutionary age. The limited information presently available agrees better with the model predicting that genetic change is primarily a function of time, and is not correlated with rates of speciation. Further testing of the models is, however, required before firm conclusions can be drawn. (auth)« less

Association between Apolipoprotein C-III Gene Polymorphisms and Coronary Heart Disease: A Meta-analysis.

PubMed

Zhang, Jing-Zhan; Xie, Xiang; Ma, Yi-Tong; Zheng, Ying-Ying; Yang, Yi-Ning; Li, Xiao-Mei; Fu, Zhen-Yan; Dai, Chuan-Fang; Zhang, Ming-Ming; Yin, Guo-Ting; Liu, Fen; Chen, Bang-Dang; Gai, Min-Tao

2016-01-01

Polymorphisms in the apolipoprotein C-III (APOC3) gene have been reported to be associated with coronary heart disease (CHD), but the data so far have been conflicting. To derive a more precise estimation of these associations, we performed a meta-analysis to investigate the three main polymorphisms (SstI, T-455C, C-482T) of APOC3 in all published studies. Databases including PubMed, Web of Science, Wanfang, SinoMed and CNKI were systematically searched. The association was assessed using odds ratios (ORs) with 95% confidence intervals (CIs). The statistical analysis was performed using Review Manager 5.3.3 and Stata 12.0. A total of 31 studies have been identified. The pooled odds ratio (OR) for the association between the APOC3 gene polymorphisms and CHD and its corresponding 95% confidence interval (95% CI) were evaluated by random or fixed effect models. A statistical association between APOC3 SstI polymorphism and CHD susceptibility was observed under an allelic contrast model (P= 0.003, OR = 1.14, 95% CI = 1.05-1.24), dominant genetic model (P= 0.01, OR = 1.14, 95% CI = 1.03-1.26), and recessive genetic model (P= 0.02, OR = 1.35, 95% CI = 1.06-1.71), respectively. A significant association between the APOC3 T-455C polymorphism and CHD was also detected under an allelic contrast (P < 0.0001, OR = 1.19, 95% CI = 1.10-1.29), dominant genetic model (P= 0.0003, OR = 1.24, 95% CI = 1.11-1.39) and recessive genetic model (P= 0.04, OR = 1.30, 95% CI = 1.01-1.67). No significant association between the APOC3 C-482T polymorphism and CHD was found under an allelic model (P= 0.94, OR = 1.00, 95% CI = 0.93-1.08), dominant genetic model (P= 0.20, OR = 1.07, 95% CI = 0.97-1.18) or recessive genetic model (P= 0.13, OR = 0.90, 95% CI = 0.79-1.03). This meta-analysis revealed that the APOC3 SstI and T-455C polymorphisms significantly increase CHD susceptibility. No significant association was observed between the APOC3 C-482T polymorphism and CHD susceptibility.

Association between Apolipoprotein C-III Gene Polymorphisms and Coronary Heart Disease: A Meta-analysis

PubMed Central

Zhang, Jing-Zhan; Xie, Xiang; Ma, Yi-Tong; Zheng, Ying-Ying; Yang, Yi-Ning; Li, Xiao-Mei; Fu, Zhen-Yan; Dai, Chuan-Fang; Zhang, Ming-Ming; Yin, Guo-Ting; Liu, Fen; Chen, Bang-Dang; Gai, Min-Tao

2016-01-01

Polymorphisms in the apolipoprotein C-III (APOC3) gene have been reported to be associated with coronary heart disease (CHD), but the data so far have been conflicting. To derive a more precise estimation of these associations, we performed a meta-analysis to investigate the three main polymorphisms (SstI, T-455C, C-482T) of APOC3 in all published studies. Databases including PubMed, Web of Science, Wanfang, SinoMed and CNKI were systematically searched. The association was assessed using odds ratios (ORs) with 95% confidence intervals (CIs). The statistical analysis was performed using Review Manager 5.3.3 and Stata 12.0. A total of 31 studies have been identified. The pooled odds ratio (OR) for the association between the APOC3 gene polymorphisms and CHD and its corresponding 95% confidence interval (95% CI) were evaluated by random or fixed effect models. A statistical association between APOC3 SstI polymorphism and CHD susceptibility was observed under an allelic contrast model (P= 0.003, OR = 1.14, 95% CI = 1.05-1.24), dominant genetic model (P= 0.01, OR = 1.14, 95% CI = 1.03-1.26), and recessive genetic model (P= 0.02, OR = 1.35, 95% CI = 1.06-1.71), respectively. A significant association between the APOC3 T-455C polymorphism and CHD was also detected under an allelic contrast (P < 0.0001, OR = 1.19, 95% CI = 1.10-1.29), dominant genetic model (P= 0.0003, OR = 1.24, 95% CI = 1.11-1.39) and recessive genetic model (P= 0.04, OR = 1.30, 95% CI = 1.01-1.67). No significant association between the APOC3 C-482T polymorphism and CHD was found under an allelic model (P= 0.94, OR = 1.00, 95% CI = 0.93-1.08), dominant genetic model (P= 0.20, OR = 1.07, 95% CI = 0.97-1.18) or recessive genetic model (P= 0.13, OR = 0.90, 95% CI = 0.79-1.03). This meta-analysis revealed that the APOC3 SstI and T-455C polymorphisms significantly increase CHD susceptibility. No significant association was observed between the APOC3 C-482T polymorphism and CHD susceptibility. PMID:26816662

Controlling complexity: the clinical relevance of mouse complex genetics

PubMed Central

Schughart, Klaus; Libert, Claude; Kas, Martien J

2013-01-01

Experimental animal models are essential to obtain basic knowledge of the underlying biological mechanisms in human diseases. Here, we review major contributions to biomedical research and discoveries that were obtained in the mouse model by using forward genetics approaches and that provided key insights into the biology of human diseases and paved the way for the development of novel therapeutic approaches. PMID:23632795

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history

PubMed Central

Sparks, Jeffrey A.; Chen, Chia-Yen; Jiang, Xia; Askling, Johan; Hiraki, Linda T.; Malspeis, Susan; Klareskog, Lars; Alfredsson, Lars; Costenbader, Karen H.; Karlson, Elizabeth W.

2014-01-01

Objective To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors, and known genetic risk factors. Methods We developed and validated models for RA based on known RA risk factors, among women in two cohorts: the Nurses’ Health Study (NHS, 381 RA cases and 410 controls) and the Epidemiological Investigation of RA (EIRA, 1244 RA cases and 971 controls). Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC) in logistic regression models for the study population and for those with positive family history. The joint effect of family history with genetics, smoking, and body mass index (BMI) was evaluated using logistic regression models to estimate odds ratios (OR) for RA. Results The complete model including family history, epidemiologic risk factors, and genetics demonstrated AUCs of 0.74 for seropositive RA in NHS and 0.77 for anti-citrullinated protein antibody (ACPA)-positive RA in EIRA. Among women with positive family history, discrimination was excellent for complete models for seropositive RA in NHS (AUC 0.82) and ACPA-positive RA in EIRA (AUC 0.83). Positive family history, high genetic susceptibility, smoking, and increased BMI had an OR of 21.73 for ACPA-positive RA. Conclusions We developed models for seropositive and seronegative RA phenotypes based on family history, epidemiologic and genetic factors. Among those with positive family history, models utilizing epidemiologic and genetic factors were highly discriminatory for seropositive and seronegative RA. Assessing epidemiological and genetic factors among those with positive family history may identify individuals suitable for RA prevention strategies. PMID:24685909

EPS-LASSO: Test for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits.

PubMed

Xu, Chao; Fang, Jian; Shen, Hui; Wang, Yu-Ping; Deng, Hong-Wen

2018-01-25

Extreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in extreme phenotypic samples, EPS can boost the association power compared to random sampling. Most existing statistical methods for EPS examine the genetic factors individually, despite many quantitative traits have multiple genetic factors underlying their variation. It is desirable to model the joint effects of genetic factors, which may increase the power and identify novel quantitative trait loci under EPS. The joint analysis of genetic data in high-dimensional situations requires specialized techniques, e.g., the least absolute shrinkage and selection operator (LASSO). Although there are extensive research and application related to LASSO, the statistical inference and testing for the sparse model under EPS remain unknown. We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function. The comprehensive simulation shows EPS-LASSO outperforms existing methods with stable type I error and FDR control. EPS-LASSO can provide a consistent power for both low- and high-dimensional situations compared with the other methods dealing with high-dimensional situations. The power of EPS-LASSO is close to other low-dimensional methods when the causal effect sizes are small and is superior when the effects are large. Applying EPS-LASSO to a transcriptome-wide gene expression study for obesity reveals 10 significant body mass index associated genes. Our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors. The source code is available at https://github.com/xu1912/EPSLASSO. hdeng2@tulane.edu. Supplementary data are available at Bioinformatics online. © The Author (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Natural genetic variation profoundly regulates gene expression in immune cells and dictates susceptibility to CNS autoimmunity

PubMed Central

Bearoff, Frank; del Rio, Roxana; Case, Laure K.; Dragon, Julie A.; Nguyen-Vu, Trang; Lin, Chin-Yo; Blankenhorn, Elizabeth P.; Teuscher, Cory; Krementsov, Dimitry N.

2016-01-01

Regulation of gene expression in immune cells is known to be under genetic control, and likely contributes to susceptibility to autoimmune diseases, such as multiple sclerosis (MS). How this occurs in concert across multiple immune cell types is poorly understood. Using a mouse model that harnesses the genetic diversity of wild-derived mice, more accurately reflecting genetically diverse human populations, we provide an extensive characterization of the genetic regulation of gene expression in five different naïve immune cell types relevant to MS. The immune cell transcriptome is shown to be under profound genetic control, exhibiting diverse patterns: global, cell-specific, and sex-specific. Bioinformatic analysis of the genetically-controlled transcript networks reveals reduced cell type-specificity and inflammatory activity in wild-derived PWD/PhJ mice, compared with the conventional laboratory strain C57BL/6J. Additionally, candidate MS-GWAS genes were significantly enriched among transcripts overrepresented in C57BL/6J cells compared to PWD. These expression level differences correlate with robust differences in susceptibility to experimental autoimmune encephalomyelitis, the principal model of MS, and skewing of the encephalitogenic T cell responses. Taken together, our results provide functional insights into the genetic regulation of the immune transcriptome, and shed light on how this in turn contributes to susceptibility to autoimmune disease. PMID:27653816

DENSITY-DEPENDENT SELECTION ON CONTINUOUS CHARACTERS: A QUANTITATIVE GENETIC MODEL.

PubMed

Tanaka, Yoshinari

1996-10-01

A quantitative genetic model of density-dependent selection is presented and analysed with parameter values obtained from laboratory selection experiments conducted by Mueller and his coworkers. The ecological concept of r- and K-selection is formulated in terms of selection gradients on underlying phenotypic characters that influence the density-dependent measure of fitness. Hence the selection gradients on traits are decomposed into two components, one that changes in the direction to increase r, and one that changes in the direction to increase K. The relative importance of the two components is determined by temporal fluctuations in population density. The evolutionary rate of r and K (per-generation changes in r and K due to the genetic responses of the underlying traits) is also formulated. Numerical simulation has shown that with moderate genetic variances of the underlying characters, r and K can evolve rapidly and the evolutionary rate is influenced by synergistic interaction between characters that contribute to r and K. But strong r-selection can occur only with severe and continuous disturbances of populations so that the population density is kept low enough to prevent K-selection. © 1996 The Society for the Study of Evolution.

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits.

PubMed

van Heerwaarden, Joost; van Zanten, Martijn; Kruijer, Willem

2015-10-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation.

Panmictic and Clonal Evolution on a Single Patchy Resource Produces Polymorphic Foraging Guilds

PubMed Central

Getz, Wayne M.; Salter, Richard; Lyons, Andrew J.; Sippl-Swezey, Nicolas

2015-01-01

We develop a stochastic, agent-based model to study how genetic traits and experiential changes in the state of agents and available resources influence individuals’ foraging and movement behaviors. These behaviors are manifest as decisions on when to stay and exploit a current resource patch or move to a particular neighboring patch, based on information of the resource qualities of the patches and the anticipated level of intraspecific competition within patches. We use a genetic algorithm approach and an individual’s biomass as a fitness surrogate to explore the foraging strategy diversity of evolving guilds under clonal versus hermaphroditic sexual reproduction. We first present the resource exploitation processes, movement on cellular arrays, and genetic algorithm components of the model. We then discuss their implementation on the Nova software platform. This platform seamlessly combines the dynamical systems modeling of consumer-resource interactions with agent-based modeling of individuals moving over a landscapes, using an architecture that lays transparent the following four hierarchical simulation levels: 1.) within-patch consumer-resource dynamics, 2.) within-generation movement and competition mitigation processes, 3.) across-generation evolutionary processes, and 4.) multiple runs to generate the statistics needed for comparative analyses. The focus of our analysis is on the question of how the biomass production efficiency and the diversity of guilds of foraging strategy types, exploiting resources over a patchy landscape, evolve under clonal versus random hermaphroditic sexual reproduction. Our results indicate greater biomass production efficiency under clonal reproduction only at higher population densities, and demonstrate that polymorphisms evolve and are maintained under random mating systems. The latter result questions the notion that some type of associative mating structure is needed to maintain genetic polymorphisms among individuals exploiting a common patchy resource on an otherwise spatially homogeneous landscape. PMID:26274613

Dissecting genetic and environmental mutation signatures with model organisms.

PubMed

Segovia, Romulo; Tam, Annie S; Stirling, Peter C

2015-08-01

Deep sequencing has impacted on cancer research by enabling routine sequencing of genomes and exomes to identify genetic changes associated with carcinogenesis. Researchers can now use the frequency, type, and context of all mutations in tumor genomes to extract mutation signatures that reflect the driving mutational processes. Identifying mutation signatures, however, may not immediately suggest a mechanism. Consequently, several recent studies have employed deep sequencing of model organisms exposed to discrete genetic or environmental perturbations. These studies exploit the simpler genomes and availability of powerful genetic tools in model organisms to analyze mutation signatures under controlled conditions, forging mechanistic links between mutational processes and signatures. We discuss the power of this approach and suggest that many such studies may be on the horizon. Copyright © 2015 Elsevier Ltd. All rights reserved.

Accounting for Genotype-by-Environment Interactions and Residual Genetic Variation in Genomic Selection for Water-Soluble Carbohydrate Concentration in Wheat.

PubMed

Ovenden, Ben; Milgate, Andrew; Wade, Len J; Rebetzke, Greg J; Holland, James B

2018-05-31

Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat under field conditions. A panel of 358 varieties and breeding lines constrained for maturity was evaluated under rainfed and irrigated treatments across two locations and two years. Whole-genome marker profiles and factor analytic mixed models were used to generate genomic estimated breeding values (GEBVs) for specific environments and environment groups. Additive genetic variance was smaller than residual genetic variance for WSCC, such that genotypic values were dominated by residual genetic effects rather than additive breeding values. As a result, GEBVs were not accurate predictors of genotypic values of the extant lines, but GEBVs should be reliable selection criteria to choose parents for intermating to produce new populations. The accuracy of GEBVs for untested lines was sufficient to increase predicted genetic gain from genomic selection per unit time compared to phenotypic selection if the breeding cycle is reduced by half by the use of GEBVs in off-season generations. Further, genomic prediction accuracy depended on having phenotypic data from environments with strong correlations with target production environments to build prediction models. By combining high-density marker genotypes, stress-managed field evaluations, and mixed models that model simultaneously covariances among genotypes and covariances of complex trait performance between pairs of environments, we were able to train models with good accuracy to facilitate genetic gain from genomic selection. Copyright © 2018 Ovenden et al.

Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions.

PubMed

Petrini, J; Iung, L H S; Rodriguez, M A P; Salvian, M; Pértille, F; Rovadoscki, G A; Cassoli, L D; Coutinho, L L; Machado, P F; Wiggans, G R; Mourão, G B

2016-10-01

Information about genetic parameters is essential for selection decisions and genetic evaluation. These estimates are population specific; however, there are few studies with dairy cattle populations reared under tropical and sub-tropical conditions. Thus, the aim was to obtain estimates of heritability and genetic correlations for milk yield and quality traits using pedigree and genomic information from a Holstein population maintained in a tropical environment. Phenotypic records (n = 36 457) of 4203 cows as well as the genotypes for 57 368 single nucleotide polymorphisms from 755 of these cows were used. Covariance components were estimated using the restricted maximum likelihood method under a mixed animal model, considering a pedigree-based relationship matrix or a combined pedigree-genomic matrix. High heritabilities (around 0.30) were estimated for lactose and protein content in milk whereas moderate values (between 0.19 and 0.26) were obtained for percentages of fat, saturated fatty acids and palmitic acid in milk. Genetic correlations ranging from -0.38 to -0.13 were determined between milk yield and composition traits. The smaller estimates compared to other similar studies can be due to poor environmental conditions, which may reduce genetic variability. These results highlight the importance in using genetic parameters estimated in the population under evaluation for selection decisions. © 2016 Blackwell Verlag GmbH.

Clinical and Neurobiological Relevance of Current Animal Models of Autism Spectrum Disorders

PubMed Central

Kim, Ki Chan; Gonzales, Edson Luck; Lázaro, María T.; Choi, Chang Soon; Bahn, Geon Ho; Yoo, Hee Jeong; Shin, Chan Young

2016-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social and communication impairments, as well as repetitive and restrictive behaviors. The phenotypic heterogeneity of ASD has made it overwhelmingly difficult to determine the exact etiology and pathophysiology underlying the core symptoms, which are often accompanied by comorbidities such as hyperactivity, seizures, and sensorimotor abnormalities. To our benefit, the advent of animal models has allowed us to assess and test diverse risk factors of ASD, both genetic and environmental, and measure their contribution to the manifestation of autistic symptoms. At a broader scale, rodent models have helped consolidate molecular pathways and unify the neurophysiological mechanisms underlying each one of the various etiologies. This approach will potentially enable the stratification of ASD into clinical, molecular, and neurophenotypic subgroups, further proving their translational utility. It is henceforth paramount to establish a common ground of mechanistic theories from complementing results in preclinical research. In this review, we cluster the ASD animal models into lesion and genetic models and further classify them based on the corresponding environmental, epigenetic and genetic factors. Finally, we summarize the symptoms and neuropathological highlights for each model and make critical comparisons that elucidate their clinical and neurobiological relevance. PMID:27133257

Stochastic models for regulatory networks of the genetic toggle switch.

PubMed

Tian, Tianhai; Burrage, Kevin

2006-05-30

Bistability arises within a wide range of biological systems from the lambda phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Stochastic models for regulatory networks of the genetic toggle switch

PubMed Central

Tian, Tianhai; Burrage, Kevin

2006-01-01

Bistability arises within a wide range of biological systems from the λ phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks. PMID:16714385

Adaptive transmission disequilibrium test for family trio design.

PubMed

Yuan, Min; Tian, Xin; Zheng, Gang; Yang, Yaning

2009-01-01

The transmission disequilibrium test (TDT) is a standard method to detect association using family trio design. It is optimal for an additive genetic model. Other TDT-type tests optimal for recessive and dominant models have also been developed. Association tests using family data, including the TDT-type statistics, have been unified to a class of more comprehensive and flexable family-based association tests (FBAT). TDT-type tests have high efficiency when the genetic model is known or correctly specified, but may lose power if the model is mis-specified. Hence tests that are robust to genetic model mis-specification yet efficient are preferred. Constrained likelihood ratio test (CLRT) and MAX-type test have been shown to be efficiency robust. In this paper we propose a new efficiency robust procedure, referred to as adaptive TDT (aTDT). It uses the Hardy-Weinberg disequilibrium coefficient to identify the potential genetic model underlying the data and then applies the TDT-type test (or FBAT for general applications) corresponding to the selected model. Simulation demonstrates that aTDT is efficiency robust to model mis-specifications and generally outperforms the MAX test and CLRT in terms of power. We also show that aTDT has power close to, but much more robust, than the optimal TDT-type test based on a single genetic model. Applications to real and simulated data from Genetic Analysis Workshop (GAW) illustrate the use of our adaptive TDT.

Evidence for transgenerational metabolic programming in Drosophila

PubMed Central

Buescher, Jessica L.; Musselman, Laura P.; Wilson, Christina A.; Lang, Tieming; Keleher, Madeline; Baranski, Thomas J.; Duncan, Jennifer G.

2013-01-01

SUMMARY Worldwide epidemiologic studies have repeatedly demonstrated an association between prenatal nutritional environment, birth weight and susceptibility to adult diseases including obesity, cardiovascular disease and type 2 diabetes. Despite advances in mammalian model systems, the molecular mechanisms underlying this phenomenon are unclear, but might involve programming mechanisms such as epigenetics. Here we describe a new system for evaluating metabolic programming mechanisms using a simple, genetically tractable Drosophila model. We examined the effect of maternal caloric excess on offspring and found that a high-sugar maternal diet alters body composition of larval offspring for at least two generations, augments an obese-like phenotype under suboptimal (high-calorie) feeding conditions in adult offspring, and modifies expression of metabolic genes. Our data indicate that nutritional programming mechanisms could be highly conserved and support the use of Drosophila as a model for evaluating the underlying genetic and epigenetic contributions to this phenomenon. PMID:23649823

The Effect of an Extreme and Prolonged Population Bottleneck on Patterns of Deleterious Variation: Insights from the Greenlandic Inuit.

PubMed

Pedersen, Casper-Emil T; Lohmueller, Kirk E; Grarup, Niels; Bjerregaard, Peter; Hansen, Torben; Siegismund, Hans R; Moltke, Ida; Albrechtsen, Anders

2017-02-01

The genetic consequences of population bottlenecks on patterns of deleterious genetic variation in human populations are of tremendous interest. Based on exome sequencing of 18 Greenlandic Inuit we show that the Inuit have undergone a severe ∼20,000-year-long bottleneck. This has led to a markedly more extreme distribution of allele frequencies than seen for any other human population tested to date, making the Inuit the perfect population for investigating the effect of a bottleneck on patterns of deleterious variation. When comparing proxies for genetic load that assume an additive effect of deleterious alleles, the Inuit show, at most, a slight increase in load compared to European, East Asian, and African populations. Specifically, we observe <4% increase in the number of derived deleterious alleles in the Inuit. In contrast, proxies for genetic load under a recessive model suggest that the Inuit have a significantly higher load (20% increase or more) compared to other less bottlenecked human populations. Forward simulations under realistic models of demography support our empirical findings, showing up to a 6% increase in the genetic load for the Inuit population across all models of dominance. Further, the Inuit population carries fewer deleterious variants than other human populations, but those that are present tend to be at higher frequency than in other populations. Overall, our results show how recent demographic history has affected patterns of deleterious variants in human populations. Copyright © 2017 by the Genetics Society of America.

Studying Human Disease Genes in "Caenorhabditis Elegans": A Molecular Genetics Laboratory Project

ERIC Educational Resources Information Center

Cox-Paulson, Elisabeth A.; Grana, Theresa M.; Harris, Michelle A.; Batzli, Janet M.

2012-01-01

Scientists routinely integrate information from various channels to explore topics under study. We designed a 4-wk undergraduate laboratory module that used a multifaceted approach to study a question in molecular genetics. Specifically, students investigated whether "Caenorhabditis elegans" can be a useful model system for studying genes…

Reconsidering Clinical Staging Model: A Case of Genetic High Risk for Schizophrenia.

PubMed

Lee, Tae Young; Kim, Minah; Kim, Sung Nyun; Kwon, Jun Soo

2017-01-01

The clinical staging model is considered a useful and practical method not only in dealing with the early stage of psychosis overcoming the debate about diagnostic boundaries but also in emerging mood disorder. However, its one limitation is that it cannot discriminate the heterogeneity of individuals at clinical high risk for psychosis, but lumps them all together. Even a healthy offspring of schizophrenia can eventually show clinical symptoms and progress to schizophrenia under the influence of genetic vulnerability and environmental stress even after the peak age of onset of schizophrenia. Therefore, individuals with genetic liability of schizophrenia may require a more intensive intervention than recommended by the staging model based on current clinical status.

Education and alcohol use: A study of gene-environment interaction in young adulthood.

PubMed

Barr, Peter B; Salvatore, Jessica E; Maes, Hermine; Aliev, Fazil; Latvala, Antti; Viken, Richard; Rose, Richard J; Kaprio, Jaakko; Dick, Danielle M

2016-08-01

The consequences of heavy alcohol use remain a serious public health problem. Consistent evidence has demonstrated that both genetic and social influences contribute to alcohol use. Research on gene-environment interaction (GxE) has also demonstrated that these social and genetic influences do not act independently. Instead, certain environmental contexts may limit or exacerbate an underlying genetic predisposition. However, much of the work on GxE and alcohol use has focused on adolescence and less is known about the important environmental contexts in young adulthood. Using data from the young adult wave of the Finnish Twin Study, FinnTwin12 (N = 3402), we used biometric twin modeling to test whether education moderated genetic risk for alcohol use as assessed by drinking frequency and intoxication frequency. Education is important because it offers greater access to personal resources and helps determine one's position in the broader stratification system. Results from the twin models show that education did not moderate genetic variance components and that genetic risk was constant across levels of education. Instead, education moderated environmental variance so that under conditions of low education, environmental influences explained more of the variation in alcohol use outcomes. The implications and limitations of these results are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Education and Alcohol Use: A Study of Gene-Environment Interaction in Young Adulthood

PubMed Central

Barr, Peter B.; Salvatore, Jessica E.; Maes, Hermine; Aliev, Fazil; Latvala, Antti; Viken, Richard; Rose, Richard J.; Kaprio, Jaakko; Dick, Danielle M.

2016-01-01

The consequences of heavy alcohol use remain a serious public health problem. Consistent evidence has demonstrated that both genetic and social influences contribute to alcohol use. Research on gene-environment interaction (GxE) has also demonstrated that these social and genetic influences do not act independently. Instead, certain environmental contexts may limit or exacerbate an underlying genetic predisposition. However, much of the work on GxE and alcohol use has focused on adolescence and less is known about the important environmental contexts in young adulthood. Using data from the young adult wave of the Finnish Twin Study, FinnTwin12 (N=3,402), we used biometric twin modeling to test whether education moderated genetic risk for alcohol use as assessed by drinking frequency and intoxication frequency. Education is important because it offers greater access to personal resources and helps determine one’s position in the broader stratification system. Results from the twin models show that education did not moderate genetic variance components and that genetic risk was constant across levels of education. Instead, education moderated environmental variance so that under conditions of low education, environmental influences explained more of the variation in alcohol use outcomes. The implications and limitations of these results are discussed. PMID:27367897

The mathematical limits of genetic prediction for complex chronic disease.

PubMed

Keyes, Katherine M; Smith, George Davey; Koenen, Karestan C; Galea, Sandro

2015-06-01

Attempts at predicting individual risk of disease based on common germline genetic variation have largely been disappointing. The present paper formalises why genetic prediction at the individual level is and will continue to have limited utility given the aetiological architecture of most common complex diseases. Data were simulated on one million populations with 10 000 individuals in each populations with varying prevalences of a genetic risk factor, an interacting environmental factor and the background rate of disease. The determinant risk ratio and risk difference magnitude for the association between a gene variant and disease is a function of the prevalence of the interacting factors that activate the gene, and the background rate of disease. The risk ratio and total excess cases due to the genetic factor increase as the prevalence of interacting factors increase, and decrease as the background rate of disease increases. Germline genetic variations have high predictive capacity for individual disease only under conditions of high heritability of particular genetic sequences, plausible only under rare variant hypotheses. Under a model of common germline genetic variants that interact with other genes and/or environmental factors in order to cause disease, the predictive capacity of common genetic variants is determined by the prevalence of the factors that interact with the variant and the background rate. A focus on estimating genetic associations for the purpose of prediction without explicitly grounding such work in an understanding of modifiable (including environmentally influenced) factors will be limited in its ability to yield important insights about the risk of disease. 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.

Common genetic architecture underlying young children's food fussiness and liking for vegetables and fruit.

PubMed

Fildes, Alison; van Jaarsveld, Cornelia H M; Cooke, Lucy; Wardle, Jane; Llewellyn, Clare H

2016-04-01

Food fussiness (FF) is common in early childhood and is often associated with the rejection of nutrient-dense foods such as vegetables and fruit. FF and liking for vegetables and fruit are likely all heritable phenotypes; the genetic influence underlying FF may explain the observed genetic influence on liking for vegetables and fruit. Twin analyses make it possible to get a broad-based estimate of the extent of the shared genetic influence that underlies these traits. We quantified the extent of the shared genetic influence that underlies FF and liking for vegetables and fruit in early childhood with the use of a twin design. Data were from the Gemini cohort, which is a population-based sample of twins born in England and Wales in 2007. Parents of 3-y-old twins (n= 1330 pairs) completed questionnaire measures of their children's food preferences (liking for vegetables and fruit) and the FF scale from the Children's Eating Behavior Questionnaire. Multivariate quantitative genetic modeling was used to estimate common genetic influences that underlie FF and liking for vegetables and fruit. Genetic correlations were significant and moderate to large in size between FF and liking for both vegetables (-0.65) and fruit (-0.43), which indicated that a substantial proportion of the genes that influence FF also influence liking. Common genes that underlie FF and liking for vegetables and fruit largely explained the observed phenotypic correlations between them (68-70%). FF and liking for fruit and vegetables in young children share a large proportion of common genetic factors. The genetic influence on FF may determine why fussy children typically reject fruit and vegetables.

Applications of genetic data to improve management and conservation of river fishes and their habitats

USGS Publications Warehouse

Scribner, Kim T.; Lowe, Winsor H.; Landguth, Erin L.; Luikart, Gordon; Infante, Dana M.; Whelan, Gary; Muhlfeld, Clint C.

2015-01-01

Environmental variation and landscape features affect ecological processes in fluvial systems; however, assessing effects at management-relevant temporal and spatial scales is challenging. Genetic data can be used with landscape models and traditional ecological assessment data to identify biodiversity hotspots, predict ecosystem responses to anthropogenic effects, and detect impairments to underlying processes. We show that by combining taxonomic, demographic, and genetic data of species in complex riverscapes, managers can better understand the spatial and temporal scales over which environmental processes and disturbance influence biodiversity. We describe how population genetic models using empirical or simulated genetic data quantify effects of environmental processes affecting species diversity and distribution. Our summary shows that aquatic assessment initiatives that use standardized data sets to direct management actions can benefit from integration of genetic data to improve the predictability of disturbance–response relationships of river fishes and their habitats over a broad range of spatial and temporal scales.

The Impact of Genetic Variants for Different Physiological Characterization of Type 2 Diabetes Loci on Gestational Insulin Signaling in Nondiabetic Pregnant Chinese Women.

PubMed

Liao, Shunyao; Liu, Yunqiang; Chen, Xiaojuan; Tan, Yuande; Mei, Jie; Song, Wenzhong; Gan, Lu; Wang, Hailian; Yin, Shi; Dong, Xianjue; Chi, Shu; Deng, Shaoping

2015-11-01

We investigate the impact of genetic variants on transiently upregulated gestational insulin signaling. We recruited 1152 unrelated nondiabetic pregnant Han Chinese women (age 28.5 ± 4.1 years; body mass index [BMI] 21.4 ± 2.6 kg/m(2)) and gave them oral glucose tolerance tests. Matsuda index of insulin sensitivity, homeostatic model assessment of insulin resistance, indices of insulin disposition, early-phase insulin release, fasting state, and 0 to 120 minute's proinsulin to insulin conversion were used to dissect insulin physiological characterization. Several variants related to β-cell function were genotyped. The genetic impacts were analyzed using logistic regression under an additive model. By adjusting for maternal age, BMI, and the related interactions, the genetic variants in ABCC8, CDKAL1, CDKN2A, HNF1B, KCNJ11, and MTNR1B were detected to impact gestational insulin signaling through heterogeneous mechanisms; however, compared with that in nonpregnant metabolism, the genetic effects seem to be eminently and heavily influenced by maternal age and BMI, indicating possible particular mechanisms underlying gestational metabolism and diabetic pathogenesis. © The Author(s) 2015.

The genetics of pigment dispersion syndrome and pigmentary glaucoma.

PubMed

Lascaratos, Gerassimos; Shah, Ameet; Garway-Heath, David F

2013-01-01

We review the inheritance patterns and recent genetic advances in the study of pigment dispersion syndrome (PDS) and pigmentary glaucoma (PG). Both conditions may result from combinations of mutations in more than one gene or from common variants in many genes, each contributing small effects. We discuss the currently known genetic loci that may be related with PDS/PG in humans, the role of animal models in expanding our understanding of the genetic basis of PDS, the genetic factors underlying the risk for conversion from PDS to PG and the relationship between genetic and environmental--as well as anatomical--risk factors. Copyright © 2013 Elsevier Inc. All rights reserved.

Circulating anti-Mullerian hormone levels in adult men are under a strong genetic influence.

PubMed

Pietiläinen, Kirsi H; Kaprio, Jaakko; Vaaralahti, Kirsi; Rissanen, Aila; Raivio, Taneli

2012-01-01

The determinants of serum anti-Müllerian hormone (AMH) levels in adult men remain unclear. The objective of the study was to investigate the genetic and environmental components in determining postpubertal AMH levels in healthy men. Serum AMH levels, body mass index (BMI), and fat mass (dual energy x-ray absorptiometry) were measured in 64 healthy male (23 monozygotic and 41 dizygotic) twin pairs. Postpubertal AMH levels were highly genetically determined (broad sense heritability 0.92, 95% confidence interval 0.83-0.96). AMH correlated negatively with BMI (r = -0.26, P = 0.030) and fat mass (r = -0.23, P = 0.048). As AMH, BMI had a high heritability (0.68, 95% confidence interval 0.39-0.83), but no genetic correlation was observed between them. AMH levels in men after puberty are under a strong genetic influence. Twin modeling suggests that AMH and BMI are influenced by different sets of genes.

The genetics of human obesity.

PubMed

Xia, Qianghua; Grant, Struan F A

2013-04-01

It has long been known that there is a genetic component to obesity, and that characterizing this underlying factor would likely offer the possibility of better intervention in the future. Monogenic obesity has proved to be relatively straightforward, with a combination of linkage analysis and mouse models facilitating the identification of multiple genes. In contrast, genome-wide association studies have successfully revealed a variety of genetic loci associated with the more common form of obesity, allowing for very strong consensus on the underlying genetic architecture of the phenotype for the first time. Although a number of significant findings have been made, it appears that very little of the apparent heritability of body mass index has actually been explained to date. New approaches for data analyses and advances in technology will be required to uncover the elusive missing heritability, and to aid in the identification of the key causative genetic underpinnings of obesity. © 2013 New York Academy of Sciences.

Introgression of a Block of Genome Under Infinitesimal Selection.

PubMed

Sachdeva, Himani; Barton, Nicholas H

2018-06-12

Adaptive introgression is common in nature and can be driven by selection acting on multiple, linked genes. We explore the effects of polygenic selection on introgression under the infinitesimal model with linkage. This model assumes that the introgressing block has an effectively infinite number of loci, each with an infinitesimal effect on the trait under selection. The block is assumed to introgress under directional selection within a native population that is genetically homogeneous. We use individual-based simulations and a branching process approximation to compute various statistics of the introgressing block, and explore how these depend on parameters such as the map length and initial trait value associated with the introgressing block, the genetic variability along the block, and the strength of selection. Our results show that the introgression dynamics of a block under infinitesimal selection are qualitatively different from the dynamics of neutral introgression. We also find that in the long run, surviving descendant blocks are likely to have intermediate lengths, and clarify how their length is shaped by the interplay between linkage and infinitesimal selection. Our results suggest that it may be difficult to distinguish the long-term introgression of a block of genome with a single strongly selected locus from the introgression of a block with multiple, tightly linked and weakly selected loci. Copyright © 2018, Genetics.

No boundaries: genomes, organisms, and ecological interactions responsible for divergence and reproductive isolation.

PubMed

Etges, William J

2014-01-01

Revealing the genetic basis of traits that cause reproductive isolation, particularly premating or sexual isolation, usually involves the same challenges as most attempts at genotype-phenotype mapping and so requires knowledge of how these traits are expressed in different individuals, populations, and environments, particularly under natural conditions. Genetic dissection of speciation phenotypes thus requires understanding of the internal and external contexts in which underlying genetic elements are expressed. Gene expression is a product of complex interacting factors internal and external to the organism including developmental programs, the genetic background including nuclear-cytotype interactions, epistatic relationships, interactions among individuals or social effects, stochasticity, and prevailing variation in ecological conditions. Understanding of genomic divergence associated with reproductive isolation will be facilitated by functional expression analysis of annotated genomes in organisms with well-studied evolutionary histories, phylogenetic affinities, and known patterns of ecological variation throughout their life cycles. I review progress and prospects for understanding the pervasive role of host plant use on genetic and phenotypic expression of reproductive isolating mechanisms in cactophilic Drosophila mojavensis and suggest how this system can be used as a model for revealing the genetic basis for species formation in organisms where speciation phenotypes are under the joint influences of genetic and environmental factors. © The American Genetic Association. 2014. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The quantitative genetics of maximal and basal rates of oxygen consumption in mice.

PubMed Central

Dohm, M R; Hayes, J P; Garland, T

2001-01-01

A positive genetic correlation between basal metabolic rate (BMR) and maximal (VO(2)max) rate of oxygen consumption is a key assumption of the aerobic capacity model for the evolution of endothermy. We estimated the genetic (V(A), additive, and V(D), dominance), prenatal (V(N)), and postnatal common environmental (V(C)) contributions to individual differences in metabolic rates and body mass for a genetically heterogeneous laboratory strain of house mice (Mus domesticus). Our breeding design did not allow the simultaneous estimation of V(D) and V(N). Regardless of whether V(D) or V(N) was assumed, estimates of V(A) were negative under the full models. Hence, we fitted reduced models (e.g., V(A) + V(N) + V(E) or V(A) + V(E)) and obtained new variance estimates. For reduced models, narrow-sense heritability (h(2)(N)) for BMR was <0.1, but estimates of h(2)(N) for VO(2)max were higher. When estimated with the V(A) + V(E) model, the additive genetic covariance between VO(2)max and BMR was positive and statistically different from zero. This result offers tentative support for the aerobic capacity model for the evolution of vertebrate energetics. However, constraints imposed on the genetic model may cause our estimates of additive variance and covariance to be biased, so our results should be interpreted with caution and tested via selection experiments. PMID:11560903

Fast forward to new genes in mammalian reproduction.

PubMed

Furnes, Bjarte; Schimenti, John

2007-01-01

The study of reproductive genetics in mammals has lagged behind that of simpler and more tractable model organisms, such as D. melanogaster, C. elegans and various yeast models. Although much valuable information has been generated using these organisms, they do not model the genetic and biological complexity of mammalian reproduction. Thus, the majority of genes required for gametogenesis in mammals remain unidentified. To expand on the existing knowledge of mammalian reproductive genetics, we have carried out forward genetic screens in mice to identify infertility mutants and the underlying mutant genes. Two different approaches were used: mutagenesis of the germline in whole mice, and mutagenesis of embryonic stem cells. This was followed by two- or three-generation breeding schemes to identify pedigrees segregating infertility mutations, which were then phenotypically characterized, genetically mapped, and in some cases, positionally cloned. This whole-genome approach has generated a wide collection of mutants with defects ranging from problems with germ cell development to abnormal sperm morphology. These models have allowed us to study the genetics, as well as the physiology, of reproduction in mammals. This review focuses on describing some of the genes identified in these screens and the ongoing effort to characterize additional mutants.

Fast forward to new genes in mammalian reproduction

PubMed Central

Furnes, Bjarte; Schimenti, John

2007-01-01

The study of reproductive genetics in mammals has lagged behind that of simpler and more tractable model organisms, such as D. melanogaster, C. elegans and various yeast models. Although much valuable information has been generated using these organisms, they do not model the genetic and biological complexity of mammalian reproduction. Thus, the majority of genes required for gametogenesis in mammals remain unidentified. To expand on the existing knowledge of mammalian reproductive genetics, we have carried out forward genetic screens in mice to identify infertility mutants and the underlying mutant genes. Two different approaches were used: mutagenesis of the germline in whole mice, and mutagenesis of embryonic stem cells. This was followed by two- or three-generation breeding schemes to identify pedigrees segregating infertility mutations, which were then phenotypically characterized, genetically mapped, and in some cases, positionally cloned. This whole-genome approach has generated a wide collection of mutants with defects ranging from problems with germ cell development to abnormal sperm morphology. These models have allowed us to study the genetics, as well as the physiology, of reproduction in mammals. This review focuses on describing some of the genes identified in these screens and the ongoing effort to characterize additional mutants. PMID:16973708

Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a "gene-to-phenotype" modeling approach.

PubMed

Chenu, Karine; Chapman, Scott C; Tardieu, François; McLean, Greg; Welcker, Claude; Hammer, Graeme L

2009-12-01

Under drought, substantial genotype-environment (G x E) interactions impede breeding progress for yield. Identifying genetic controls associated with yield response is confounded by poor genetic correlations across testing environments. Part of this problem is related to our inability to account for the interplay of genetic controls, physiological traits, and environmental conditions throughout the crop cycle. We propose a modeling approach to bridge this "gene-to-phenotype" gap. For maize under drought, we simulated the impact of quantitative trait loci (QTL) controlling two key processes (leaf and silk elongation) that influence crop growth, water use, and grain yield. Substantial G x E interaction for yield was simulated for hypothetical recombinant inbred lines (RILs) across different seasonal patterns of drought. QTL that accelerated leaf elongation caused an increase in crop leaf area and yield in well-watered or preflowering water deficit conditions, but a reduction in yield under terminal stresses (as such "leafy" genotypes prematurely exhausted the water supply). The QTL impact on yield was substantially enhanced by including pleiotropic effects of these QTL on silk elongation and on consequent grain set. The simulations obtained illustrated the difficulty of interpreting the genetic control of yield for genotypes influenced only by the additive effects of QTL associated with leaf and silk growth. The results highlight the potential of integrative simulation modeling for gene-to-phenotype prediction and for exploiting G x E interactions for complex traits such as drought tolerance.

Nemesis Autonomous Test System

NASA Technical Reports Server (NTRS)

Barltrop, Kevin J.; Lee, Cin-Young; Horvath, Gregory A,; Clement, Bradley J.

2012-01-01

A generalized framework has been developed for systems validation that can be applied to both traditional and autonomous systems. The framework consists of an automated test case generation and execution system called Nemesis that rapidly and thoroughly identifies flaws or vulnerabilities within a system. By applying genetic optimization and goal-seeking algorithms on the test equipment side, a "war game" is conducted between a system and its complementary nemesis. The end result of the war games is a collection of scenarios that reveals any undesirable behaviors of the system under test. The software provides a reusable framework to evolve test scenarios using genetic algorithms using an operation model of the system under test. It can automatically generate and execute test cases that reveal flaws in behaviorally complex systems. Genetic algorithms focus the exploration of tests on the set of test cases that most effectively reveals the flaws and vulnerabilities of the system under test. It leverages advances in state- and model-based engineering, which are essential in defining the behavior of autonomous systems. It also uses goal networks to describe test scenarios.

Giftedness and Genetics: The Emergenic-Epigenetic Model and Its Implications

ERIC Educational Resources Information Center

Simonton, Dean Keith

2005-01-01

The genetic endowment underlying giftedness may operate in a far more complex manner than often expressed in most theoretical accounts of the phenomenon. First, an endowment may be emergenic. That is, a gift may consist of multiple traits (multidimensional) that are inherited in a multiplicative (configurational), rather than an additive (simple)…

Exploring Middle School Students' Conceptions of the Relationship between Genetic Inheritance and Cell Division

ERIC Educational Resources Information Center

Williams, Michelle; DeBarger, Angela Haydel; Montgomery, Beronda L.; Zhou, Xuechun; Tate, Erika

2012-01-01

This study examines students' understanding of the normative connections between key concepts of cell division, including both mitosis and meiosis, and underlying biological principles that are critical for an in-depth understanding of genetic inheritance. Using a structural equation modeling method, we examine middle school students'…

Genetic signatures of natural selection in a model invasive ascidian

NASA Astrophysics Data System (ADS)

Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin

2017-03-01

Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta.

On the validity of within-nuclear-family genetic association analysis in samples of extended families.

PubMed

Bureau, Alexandre; Duchesne, Thierry

2015-12-01

Splitting extended families into their component nuclear families to apply a genetic association method designed for nuclear families is a widespread practice in familial genetic studies. Dependence among genotypes and phenotypes of nuclear families from the same extended family arises because of genetic linkage of the tested marker with a risk variant or because of familial specificity of genetic effects due to gene-environment interaction. This raises concerns about the validity of inference conducted under the assumption of independence of the nuclear families. We indeed prove theoretically that, in a conditional logistic regression analysis applicable to disease cases and their genotyped parents, the naive model-based estimator of the variance of the coefficient estimates underestimates the true variance. However, simulations with realistic effect sizes of risk variants and variation of this effect from family to family reveal that the underestimation is negligible. The simulations also show the greater efficiency of the model-based variance estimator compared to a robust empirical estimator. Our recommendation is therefore, to use the model-based estimator of variance for inference on effects of genetic variants.

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.

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits

PubMed Central

van Zanten, Martijn

2015-01-01

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation. PMID:26496492

Pancreatic cancer cell lines as patient-derived avatars: genetic characterisation and functional utility.

PubMed

Knudsen, Erik S; Balaji, Uthra; Mannakee, Brian; Vail, Paris; Eslinger, Cody; Moxom, Christopher; Mansour, John; Witkiewicz, Agnieszka K

2018-03-01

Pancreatic ductal adenocarcinoma (PDAC) is a therapy recalcitrant disease with the worst survival rate of common solid tumours. Preclinical models that accurately reflect the genetic and biological diversity of PDAC will be important for delineating features of tumour biology and therapeutic vulnerabilities. 27 primary PDAC tumours were employed for genetic analysis and development of tumour models. Tumour tissue was used for derivation of xenografts and cell lines. Exome sequencing was performed on the originating tumour and developed models. RNA sequencing, histological and functional analyses were employed to determine the relationship of the patient-derived models to clinical presentation of PDAC. The cohort employed captured the genetic diversity of PDAC. From most cases, both cell lines and xenograft models were developed. Exome sequencing confirmed preservation of the primary tumour mutations in developed cell lines, which remained stable with extended passaging. The level of genetic conservation in the cell lines was comparable to that observed with patient-derived xenograft (PDX) models. Unlike historically established PDAC cancer cell lines, patient-derived models recapitulated the histological architecture of the primary tumour and exhibited metastatic spread similar to that observed clinically. Detailed genetic analyses of tumours and derived models revealed features of ex vivo evolution and the clonal architecture of PDAC. Functional analysis was used to elucidate therapeutic vulnerabilities of relevance to treatment of PDAC. These data illustrate that with the appropriate methods it is possible to develop cell lines that maintain genetic features of PDAC. Such models serve as important substrates for analysing the significance of genetic variants and create a unique biorepository of annotated cell lines and xenografts that were established simultaneously from same primary tumour. These models can be used to infer genetic and empirically determined therapeutic sensitivities that would be germane to the patient. 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/.

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

The evolution of phenotypes and genetic parameters under preferential mating

PubMed Central

Roff, Derek A; Fairbairn, Daphne J

2014-01-01

This article extends and adds more realism to Lande's analytical model for evolution under mate choice by using individual-based simulations in which females sample a finite number of males and the genetic architecture of the preference and preferred trait evolves. The simulations show that the equilibrium heritabilities of the preference and preferred trait and the genetic correlation between them (rG), depend critically on aspects of the mating system (the preference function, mode of mate choice, choosiness, and number of potential mates sampled), the presence or absence of natural selection on the preferred trait, and the initial genetic parameters. Under some parameter combinations, preferential mating increased the heritability of the preferred trait, providing a possible resolution for the lek paradox. The Kirkpatrick–Barton approximation for rG proved to be biased downward, but the realized genetic correlations were also low, generally <0.2. Such low values of rG indicate that coevolution of the preference and preferred trait is likely to be very slow and subject to significant stochastic variation. Lande's model accurately predicted the incidence of runaway selection in the simulations, except where preferences were relative and the preferred trait was subject to natural selection. In these cases, runaways were over- or underestimated, depending on the number of males sampled. We conclude that rapid coevolution of preferences and preferred traits is unlikely in natural populations, but that the parameter combinations most conducive to it are most likely to occur in lekking species. PMID:25077025

Genetic mixed linear models for twin survival data.

PubMed

Ha, Il Do; Lee, Youngjo; Pawitan, Yudi

2007-07-01

Twin studies are useful for assessing the relative importance of genetic or heritable component from the environmental component. In this paper we develop a methodology to study the heritability of age-at-onset or lifespan traits, with application to analysis of twin survival data. Due to limited period of observation, the data can be left truncated and right censored (LTRC). Under the LTRC setting we propose a genetic mixed linear model, which allows general fixed predictors and random components to capture genetic and environmental effects. Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models. We also propose a simple and fast computation method for dealing with large data sets. The method is illustrated by the survival data from the Swedish Twin Registry. Finally, a simulation study is carried out to evaluate its performance.

Geographical distance and local environmental conditions drive the genetic population structure of a freshwater microalga (Bathycoccaceae; Chlorophyta) in Patagonian lakes.

PubMed

Fernández, Leonardo D; Hernández, Cristián E; Schiaffino, M Romina; Izaguirre, Irina; Lara, Enrique

2017-10-01

The patterns and mechanisms underlying the genetic structure of microbial populations remain unresolved. Herein we investigated the role played by two non-mutually exclusive models (i.e. isolation by distance and isolation by environment) in shaping the genetic structure of lacustrine populations of a microalga (a freshwater Bathycoccaceae) in the Argentinean Patagonia. To our knowledge, this was the first study to investigate the genetic population structure in a South American microorganism. Population-level analyses based on ITS1-5.8S-ITS2 sequences revealed high levels of nucleotide and haplotype diversity within and among populations. Fixation index and a spatially explicit Bayesian analysis confirmed the occurrence of genetically distinct microalga populations in Patagonia. Isolation by distance and isolation by environment accounted for 38.5% and 17.7% of the genetic structure observed, respectively, whereas together these models accounted for 41% of the genetic differentiation. While our results highlighted isolation by distance and isolation by environment as important mechanisms in driving the genetic population structure of the microalga studied, none of these models (either alone or together) could explain the entire genetic differentiation observed. The unexplained variation in the genetic differentiation observed could be the result of founder events combined with rapid local adaptations, as proposed by the monopolisation hypothesis. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Autism spectrum disorder: neuropathology and animal models.

PubMed

Varghese, Merina; Keshav, Neha; Jacot-Descombes, Sarah; Warda, Tahia; Wicinski, Bridget; Dickstein, Dara L; Harony-Nicolas, Hala; De Rubeis, Silvia; Drapeau, Elodie; Buxbaum, Joseph D; Hof, Patrick R

2017-10-01

Autism spectrum disorder (ASD) has a major impact on the development and social integration of affected individuals and is the most heritable of psychiatric disorders. An increase in the incidence of ASD cases has prompted a surge in research efforts on the underlying neuropathologic processes. We present an overview of current findings in neuropathology studies of ASD using two investigational approaches, postmortem human brains and ASD animal models, and discuss the overlap, limitations, and significance of each. Postmortem examination of ASD brains has revealed global changes including disorganized gray and white matter, increased number of neurons, decreased volume of neuronal soma, and increased neuropil, the last reflecting changes in densities of dendritic spines, cerebral vasculature and glia. Both cortical and non-cortical areas show region-specific abnormalities in neuronal morphology and cytoarchitectural organization, with consistent findings reported from the prefrontal cortex, fusiform gyrus, frontoinsular cortex, cingulate cortex, hippocampus, amygdala, cerebellum and brainstem. The paucity of postmortem human studies linking neuropathology to the underlying etiology has been partly addressed using animal models to explore the impact of genetic and non-genetic factors clinically relevant for the ASD phenotype. Genetically modified models include those based on well-studied monogenic ASD genes (NLGN3, NLGN4, NRXN1, CNTNAP2, SHANK3, MECP2, FMR1, TSC1/2), emerging risk genes (CHD8, SCN2A, SYNGAP1, ARID1B, GRIN2B, DSCAM, TBR1), and copy number variants (15q11-q13 deletion, 15q13.3 microdeletion, 15q11-13 duplication, 16p11.2 deletion and duplication, 22q11.2 deletion). Models of idiopathic ASD include inbred rodent strains that mimic ASD behaviors as well as models developed by environmental interventions such as prenatal exposure to sodium valproate, maternal autoantibodies, and maternal immune activation. In addition to replicating some of the neuropathologic features seen in postmortem studies, a common finding in several animal models of ASD is altered density of dendritic spines, with the direction of the change depending on the specific genetic modification, age and brain region. Overall, postmortem neuropathologic studies with larger sample sizes representative of the various ASD risk genes and diverse clinical phenotypes are warranted to clarify putative etiopathogenic pathways further and to promote the emergence of clinically relevant diagnostic and therapeutic tools. In addition, as genetic alterations may render certain individuals more vulnerable to developing the pathological changes at the synapse underlying the behavioral manifestations of ASD, neuropathologic investigation using genetically modified animal models will help to improve our understanding of the disease mechanisms and enhance the development of targeted treatments.

Radial Domany-Kinzel models with mutation and selection

NASA Astrophysics Data System (ADS)

Lavrentovich, Maxim O.; Korolev, Kirill S.; Nelson, David R.

2013-01-01

We study the effect of spatial structure, genetic drift, mutation, and selective pressure on the evolutionary dynamics in a simplified model of asexual organisms colonizing a new territory. Under an appropriate coarse-graining, the evolutionary dynamics is related to the directed percolation processes that arise in voter models, the Domany-Kinzel (DK) model, contact process, and so on. We explore the differences between linear (flat front) expansions and the much less familiar radial (curved front) range expansions. For the radial expansion, we develop a generalized, off-lattice DK model that minimizes otherwise persistent lattice artifacts. With both simulations and analytical techniques, we study the survival probability of advantageous mutants, the spatial correlations between domains of neutral strains, and the dynamics of populations with deleterious mutations. “Inflation” at the frontier leads to striking differences between radial and linear expansions. For a colony with initial radius R0 expanding at velocity v, significant genetic demixing, caused by local genetic drift, occurs only up to a finite time t*=R0/v, after which portions of the colony become causally disconnected due to the inflating perimeter of the expanding front. As a result, the effect of a selective advantage is amplified relative to genetic drift, increasing the survival probability of advantageous mutants. Inflation also modifies the underlying directed percolation transition, introducing novel scaling functions and modifications similar to a finite-size effect. Finally, we consider radial range expansions with deflating perimeters, as might arise from colonization initiated along the shores of an island.

Economic and developmental considerations for pharmacogenomic technology.

PubMed

Vernon, John A; Johnson, Scott J; Hughen, W Keener; Trujillo, Antonio

2006-01-01

The pharmaceutical industry's core business is the innovation, development and marketing of new drugs. Pharmacogenetic (PG) testing and technology has the potential to increase a drug's value in many ways. A critical issue for the industry is whether products in development should be teamed with genetic tests that could segment the total population into responders and non-responders. In this paper we use a cost-effectiveness framework to model the strategic decision-making considerations by pharmaceutical manufacturers as they relate to drug development and the new technology of PG (the science of using genetic markers to predict drug response). In a simple, static, one-period model we consider three drug development strategies: a drug is exclusively developed and marketed to patients with a particular genetic marker; no distinguishing among patients based on the expression of a genetic marker is made (traditional approach); and a strategy whereby a drug is marketed to patients both with and without the genetic marker but there is price discrimination between the two subpopulations. We developed three main principles: revenues under a strategy targeting only the responder subpopulation will never generate more revenue than that which could have been obtained under a traditional approach; total revenues under a targeted PG strategy will be less than that under a traditional approach but higher than a naive [corrected] view would believe them to be; and a traditional [corrected] approach will earn the same total revenues as a price discrimination strategy, assuming no intermarket arbitrage. While these principles relate to the singular (and quite narrow) consideration of drug revenues, they may nevertheless partially explain why PG is not being used as widely as was predicted several years ago when the technology first became available, especially in terms of pharmaceutical manufacturer-developed tests.

Concise Review: Cardiac Disease Modeling Using Induced Pluripotent Stem Cells.

PubMed

Yang, Chunbo; Al-Aama, Jumana; Stojkovic, Miodrag; Keavney, Bernard; Trafford, Andrew; Lako, Majlinda; Armstrong, Lyle

2015-09-01

Genetic cardiac diseases are major causes of morbidity and mortality. Although animal models have been created to provide some useful insights into the pathogenesis of genetic cardiac diseases, the significant species differences and the lack of genetic information for complex genetic diseases markedly attenuate the application values of such data. Generation of induced pluripotent stem cells (iPSCs) from patient-specific specimens and subsequent derivation of cardiomyocytes offer novel avenues to study the mechanisms underlying cardiac diseases, to identify new causative genes, and to provide insights into the disease aetiology. In recent years, the list of human iPSC-based models for genetic cardiac diseases has been expanding rapidly, although there are still remaining concerns on the level of functionality of iPSC-derived cardiomyocytes and their ability to be used for modeling complex cardiac diseases in adults. This review focuses on the development of cardiomyocyte induction from pluripotent stem cells, the recent progress in heart disease modeling using iPSC-derived cardiomyocytes, and the challenges associated with understanding complex genetic diseases. To address these issues, we examine the similarity between iPSC-derived cardiomyocytes and their ex vivo counterparts and how this relates to the method used to differentiate the pluripotent stem cells into a cardiomyocyte phenotype. We progress to examine categories of congenital cardiac abnormalities that are suitable for iPSC-based disease modeling. © AlphaMed Press.

The intergenerational correlation in weight: How genetic resemblance reveals the social role of families*

PubMed Central

Martin, Molly A.

2009-01-01

According to behavioral genetics research, the intergenerational correlation in weight derives solely from shared genetic predispositions, but complete genetic determinism contradicts the scientific consensus that social and behavioral change underlies the modern obesity epidemic. To address this conundrum, this article utilizes sibling data from the National Longitudinal Study of Adolescent Health and extends structural equation sibling models to incorporate siblings’ genetic relationships to explore the role of families’ social characteristics for adolescent weight. The article is the first to demonstrate that the association between parents’ obesity and adolescent weight is both social and genetic. Furthermore, by incorporating genetic information, the shared and social origins of the correlation between inactivity and weight are better revealed. PMID:19569401

Ghrelin and eating behavior: evidence and insights from genetically-modified mouse models

PubMed Central

Uchida, Aki; Zigman, Jeffrey M.; Perelló, Mario

2013-01-01

Ghrelin is an octanoylated peptide hormone, produced by endocrine cells of the stomach, which acts in the brain to increase food intake and body weight. Our understanding of the mechanisms underlying ghrelin's effects on eating behaviors has been greatly improved by the generation and study of several genetically manipulated mouse models. These models include mice overexpressing ghrelin and also mice with genetic deletion of ghrelin, the ghrelin receptor [the growth hormone secretagogue receptor (GHSR)] or the enzyme that post-translationally modifies ghrelin [ghrelin O-acyltransferase (GOAT)]. In addition, a GHSR-null mouse model in which GHSR transcription is globally blocked but can be cell-specifically reactivated in a Cre recombinase-mediated fashion has been generated. Here, we summarize findings obtained with these genetically manipulated mice, with the aim to highlight the significance of the ghrelin system in the regulation of both homeostatic and hedonic eating, including that occurring in the setting of chronic psychosocial stress. PMID:23882175

Adaptive Topographies and Equilibrium Selection in an Evolutionary Game

PubMed Central

Osinga, Hinke M.; Marshall, James A. R.

2015-01-01

It has long been known in the field of population genetics that adaptive topographies, in which population equilibria maximise mean population fitness for a trait regardless of its genetic bases, do not exist. Whether one chooses to model selection acting on a single locus or multiple loci does matter. In evolutionary game theory, analysis of a simple and general game involving distinct roles for the two players has shown that whether strategies are modelled using a single ‘locus’ or one ‘locus’ for each role, the stable population equilibria are unchanged and correspond to the fitness-maximising evolutionary stable strategies of the game. This is curious given the aforementioned population genetical results on the importance of the genetic bases of traits. Here we present a dynamical systems analysis of the game with roles detailing how, while the stable equilibria in this game are unchanged by the number of ‘loci’ modelled, equilibrium selection may differ under the two modelling approaches. PMID:25706762

Exploring the Validity of Proposed Transgenic Animal Models of Attention-Deficit Hyperactivity Disorder (ADHD).

PubMed

de la Peña, June Bryan; Dela Peña, Irene Joy; Custodio, Raly James; Botanas, Chrislean Jun; Kim, Hee Jin; Cheong, Jae Hoon

2018-05-01

Attention-deficit/hyperactivity disorder (ADHD) is a common, behavioral, and heterogeneous neurodevelopmental condition characterized by hyperactivity, impulsivity, and inattention. Symptoms of this disorder are managed by treatment with methylphenidate, amphetamine, and/or atomoxetine. The cause of ADHD is unknown, but substantial evidence indicates that this disorder has a significant genetic component. Transgenic animals have become an essential tool in uncovering the genetic factors underlying ADHD. Although they cannot accurately reflect the human condition, they can provide insights into the disorder that cannot be obtained from human studies due to various limitations. An ideal animal model of ADHD must have face (similarity in symptoms), predictive (similarity in response to treatment or medications), and construct (similarity in etiology or underlying pathophysiological mechanism) validity. As the exact etiology of ADHD remains unclear, the construct validity of animal models of ADHD would always be limited. The proposed transgenic animal models of ADHD have substantially increased and diversified over the years. In this paper, we compiled and explored the validity of proposed transgenic animal models of ADHD. Each of the reviewed transgenic animal models has strengths and limitations. Some fulfill most of the validity criteria of an animal model of ADHD and have been extensively used, while there are others that require further validation. Nevertheless, these transgenic animal models of ADHD have provided and will continue to provide valuable insights into the genetic underpinnings of this complex disorder.

Privacy-preserving genomic testing in the clinic: a model using HIV treatment.

PubMed

McLaren, Paul J; Raisaro, Jean Louis; Aouri, Manel; Rotger, Margalida; Ayday, Erman; Bartha, István; Delgado, Maria B; Vallet, Yannick; Günthard, Huldrych F; Cavassini, Matthias; Furrer, Hansjakob; Doco-Lecompte, Thanh; Marzolini, Catia; Schmid, Patrick; Di Benedetto, Caroline; Decosterd, Laurent A; Fellay, Jacques; Hubaux, Jean-Pierre; Telenti, Amalio

2016-08-01

The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics. We genotyped 4,149 markers in HIV-positive individuals. Variants allowed for prediction of 17 traits relevant to HIV medical care, inference of patient ancestry, and imputation of human leukocyte antigen (HLA) types. Genetic data were processed under a privacy-preserving framework using homomorphic encryption, and clinical reports describing potentially actionable results were delivered to health-care providers. A total of 230 patients were included in the study. We demonstrated the feasibility of encrypting a large number of genetic markers, inferring patient ancestry, computing monogenic and polygenic trait risks, and reporting results under privacy-preserving conditions. The average execution time of a multimarker test on encrypted data was 865 ms on a standard computer. The proportion of tests returning potentially actionable genetic results ranged from 0 to 54%. The model of implementation presented herein informs on strategies to deliver genomic test results for clinical care. Data encryption to ensure privacy helps to build patient trust, a key requirement on the road to genomic-based medicine.Genet Med 18 8, 814-822.

Indirect Genetic Effects and the Spread of Infectious Disease: Are We Capturing the Full Heritable Variation Underlying Disease Prevalence?

PubMed Central

Lipschutz-Powell, Debby; Woolliams, John A.; Bijma, Piter; Doeschl-Wilson, Andrea B.

2012-01-01

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence. PMID:22768088

Genetic component of sensitivity to heat stress for nonreturn rate of Brazilian Holstein cattle.

PubMed

Santana, M L; Bignardi, A B; Stefani, G; El Faro, L

2017-08-01

The objectives of the present study were: 1) to investigate variation in the genetic component of heat stress for nonreturn rate at 56 days after first artificial insemination (NR56); 2) to identify and characterize the genotype by environment interaction (G × E) due to heat stress for NR56 of Brazilian Holstein cattle. A linear random regression model (reaction norm model) was applied to 51,748 NR56 records of 28,595 heifers and multiparous cows. The decline in NR56 due to heat stress was more pronounced in milking cows compared to heifers. The age of females at first artificial insemination and temperature-humidity index (THI) exerted an important influence on the genetic parameters of NR56. Several evidence of G × E on NR56 were found as the high slope/intercept ratio and frequent intersection of reaction norms. Additionally, the genetic correlation between NR56 at opposite extremes of the THI scale reached estimates below zero, indicating that few of the same genes are responsible for NR56 under conditions of thermoneutrality and heat stress. The genetic evaluation and selection for NR56 in Holstein cattle reared under (sub)tropical conditions should therefore take into consideration the genetic variation on age at insemination and G × E due to heat stress. Copyright © 2017 Elsevier Inc. All rights reserved.

Defining the consequences of genetic variation on a proteome–wide scale

PubMed Central

Chick, Joel M.; Munger, Steven C.; Simecek, Petr; Huttlin, Edward L.; Choi, Kwangbom; Gatti, Daniel M.; Raghupathy, Narayanan; Svenson, Karen L.; Churchill, Gary A.; Gygi, Steven P.

2016-01-01

Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we identify 2,866 protein quantitative trait loci (pQTL) with twice as many local as distant genetic variants. These data support distinct transcriptional and post-transcriptional models underlying the observed pQTL effects. Using a sensitive approach to mediation analysis, we often identified a second protein or transcript as the causal mediator of distant pQTL. Our analysis reveals an extensive network of direct protein–protein interactions. Finally, we show that local genotype can provide accurate predictions of protein abundance in an independent cohort of collaborative cross mice. PMID:27309819

The origin of Behçet's disease geoepidemiology: possible role of a dual microbial-driven genetic selection.

PubMed

Piga, Matteo; Mathieu, Alessandro

2014-01-01

It is recognised that the genetic profiles that give rise to chronic inflammatory diseases, under the influence of environmental agents, might have been implicated in the host defence mechanism against lethal infections in the past. Behçet's disease (BD) is an immune-mediated inflammatory disease, expressed as vasculitis, triggered by environmental factors in genetically susceptible individuals. We carried out a review of published data to draw up an evolutionary adaptation model, as Author's perspective, for genetic susceptibility factors and inflammatory immune response involved in BD pathogenesis. Two lethal infectious agents, Plasmodium Falciparum and Yersinia Pestis, are proposed as the putative driving forces that favoured the fixing of the major genetic susceptibility factors to BD, thus determining its geoepidemiology. Further studies are needed to confirm the validity of this evolutionary model which includes and integrates the key insights of previous hypotheses.

Testing Models for the Contributions of Genes and Environment to Developmental Change in Adolescent Depression

PubMed Central

Eaves, Lindon J.; Maes, Hermine; Silberg, Judy L.

2015-01-01

We tested two models to identify the genetic and environmental processes underlying longitudinal changes in depression among adolescents. The first assumes that observed changes in covariance structure result from the unfolding of inherent, random individual differences in the overall levels and rates of change in depression over time (random growth curves). The second assumes that observed changes are due to time-specific random effects (innovations) accumulating over time (autoregressive effects). We found little evidence of age-specific genetic effects or persistent genetic innovations. Instead, genetic effects are consistent with a gradual unfolding in the liability to depression and rates of change with increasing age. Likewise, the environment also creates significant individual differences in overall levels of depression and rates of change. However, there are also time-specific environmental experiences that persist with fidelity. The implications of these differing genetic and environmental mechanisms in the etiology of depression are considered. PMID:25894924

Testing Models for the Contributions of Genes and Environment to Developmental Change in Adolescent Depression.

PubMed

Gillespie, Nathan A; Eaves, Lindon J; Maes, Hermine; Silberg, Judy L

2015-07-01

We tested two models to identify the genetic and environmental processes underlying longitudinal changes in depression among adolescents. The first assumes that observed changes in covariance structure result from the unfolding of inherent, random individual differences in the overall levels and rates of change in depression over time (random growth curves). The second assumes that observed changes are due to time-specific random effects (innovations) accumulating over time (autoregressive effects). We found little evidence of age-specific genetic effects or persistent genetic innovations. Instead, genetic effects are consistent with a gradual unfolding in the liability to depression and rates of change with increasing age. Likewise, the environment also creates significant individual differences in overall levels of depression and rates of change. However, there are also time-specific environmental experiences that persist with fidelity. The implications of these differing genetic and environmental mechanisms in the etiology of depression are considered.

Combining genetic and physiological data to identify predictors of lifetime reproductive success and the effect of selection on these predictors on underlying fertility traits.

PubMed

Dennis, N A; Stachowicz, K; Visser, B; Hely, F S; Berg, D K; Friggens, N C; Amer, P R; Meier, S; Burke, C R

2018-04-01

Fertility of the dairy cow relies on complex interactions between genetics, physiology, and management. Mathematical modeling can combine a range of information sources to facilitate informed predictions of cow fertility in scenarios that are difficult to evaluate empirically. We have developed a stochastic model that incorporates genetic and physiological data from more than 70 published reports on a wide range of fertility-related traits in dairy cattle. The model simulates pedigree, random mating, genetically correlated traits (in the form of breeding values for traits such as hours in estrus, estrous cycle length, age at puberty, milk yield, and so on), and interacting environmental variables. This model was used to generate a large simulated data set (200,000 cows replicated 100 times) of herd records within a seasonal dairy production system (based on an average New Zealand system). Using these simulated data, we investigated the genetic component of lifetime reproductive success (LRS), which, in reality, would be impractical to assess empirically. We defined LRS as the total number of times, during her lifetime, a cow calved within the first 42 d of the calving season. Sire estimated breeding values for LRS and other traits were calculated using simulated daughter records. Daughter pregnancy rate in the first lactation (PD_1) was the strongest single predictor of a sire's genetic merit for LRS (R 2 = 0.81). A simple predictive model containing PD_1, calving date for the second season and calving rate in the first season provided a good estimate of sire LRS (R 2 = 0.97). Daughters from sires with extremely high (n = 99,995 daughters, sire LRS = +0.70) or low (n = 99,635 daughters, sire LRS = -0.73) LRS estimated breeding values were compared over a single generation. Of the 14 underlying component traits of fertility, 12 were divergent between the 2 lines. This suggests that genetic variation in female fertility has a complex and multifactorial genetic basis. When simulated phenotypes were compared, daughters of the high LRS sires (HiFERT) reached puberty 44.5 d younger and calved ∼14 d younger at each parity than daughters from low LRS sires (LoFERT). Despite having a much lower genetic potential for milk production (-400 L/lactation) than LoFERT cows, HiFERT cows produced 33% more milk over their lifetime due to additional lactations before culling. In summary, this simulation model suggests that LRS contributes substantially to cow productivity, and novel selection criteria would facilitate a more accurate prediction at a younger age. 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/).

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

2002-01-01

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

KRAS Mouse Models

PubMed Central

O’Hagan, Rónán C.; Heyer, Joerg

2011-01-01

KRAS is a potent oncogene and is mutated in about 30% of all human cancers. However, the biological context of KRAS-dependent oncogenesis is poorly understood. Genetically engineered mouse models of cancer provide invaluable tools to study the oncogenic process, and insights from KRAS-driven models have significantly increased our understanding of the genetic, cellular, and tissue contexts in which KRAS is competent for oncogenesis. Moreover, variation among tumors arising in mouse models can provide insight into the mechanisms underlying response or resistance to therapy in KRAS-dependent cancers. Hence, it is essential that models of KRAS-driven cancers accurately reflect the genetics of human tumors and recapitulate the complex tumor-stromal intercommunication that is manifest in human cancers. Here, we highlight the progress made in modeling KRAS-dependent cancers and the impact that these models have had on our understanding of cancer biology. In particular, the development of models that recapitulate the complex biology of human cancers enables translational insights into mechanisms of therapeutic intervention in KRAS-dependent cancers. PMID:21779503

A Twin Study of Normative Personality and DSM-IV Personality Disorder Criterion Counts: Evidence for Separate Genetic Influences.

PubMed

Czajkowski, Nikolai; Aggen, Steven H; Krueger, Robert F; Kendler, Kenneth S; Neale, Michael C; Knudsen, Gun Peggy; Gillespie, Nathan A; Røysamb, Espen; Tambs, Kristian; Reichborn-Kjennerud, Ted

2018-03-21

Both normative personality and DSM-IV personality disorders have been found to be heritable. However, there is limited knowledge about the extent to which the genetic and environmental influences underlying DSM personality disorders are shared with those of normative personality. The aims of this study were to assess the phenotypic similarity between normative and pathological personality and to investigate the extent to which genetic and environmental influences underlying individual differences in normative personality account for symptom variance across DSM-IV personality disorders. A large population-based sample of adult twins was assessed for DSM-IV personality disorder criteria with structured interviews at two waves spanning a 10-year interval. At the second assessment, participants also completed the Big Five Inventory, a self-report instrument assessing the five-factor normative personality model. The proportion of genetic and environmental liabilities unique to the individual personality disorder measures, and hence not shared with the five Big Five Inventory domains, were estimated by means of multivariate Cholesky twin decompositions. The median percentage of genetic liability to the 10 DSM-IV personality disorders assessed at wave 1 that was not shared with the Big Five domains was 64%, whereas for the six personality disorders that were assessed concurrently at wave 2, the median was 39%. Conversely, the median proportions of unique environmental liability in the personality disorders for wave 1 and wave 2 were 97% and 96%, respectively. The results indicate that a moderate-to-sizable proportion of the genetic influence underlying DSM-IV personality disorders is not shared with the domain constructs of the Big Five model of normative personality. Caution should be exercised in assuming that normative personality measures can serve as proxies for DSM personality disorders when investigating the etiology of these disorders.

Genetic Variation of Morphological Traits and Transpiration in an Apple Core Collection under Well-Watered Conditions: Towards the Identification of Morphotypes with High Water Use Efficiency.

PubMed

Lopez, Gerardo; Pallas, Benoît; Martinez, Sébastien; Lauri, Pierre-Éric; Regnard, Jean-Luc; Durel, Charles-Éric; Costes, Evelyne

2015-01-01

Water use efficiency (WUE) is a quantitative measurement which improvement is a major issue in the context of global warming and restrictions in water availability for agriculture. In this study, we aimed at studying the variation and genetic control of WUE and the respective role of its components (plant biomass and transpiration) in a perennial fruit crop. We explored an INRA apple core collection grown in a phenotyping platform to screen one-year-old scions for their accumulated biomass, transpiration and WUE under optimal growing conditions. Plant biomass was decompose into morphological components related to either growth or organ expansion. For each trait, nine mixed models were evaluated to account for the genetic effect and spatial heterogeneity inside the platform. The Best Linear Unbiased Predictors of genetic values were estimated after model selection. Mean broad-sense heritabilities were calculated from variance estimates. Heritability values indicated that biomass (0.76) and WUE (0.73) were under genetic control. This genetic control was lower in plant transpiration with an heritability of 0.54. Across the collection, biomass accounted for 70% of the WUE variability. A Hierarchical Ascendant Classification of the core collection indicated the existence of six groups of genotypes with contrasting morphology and WUE. Differences between morphotypes were interpreted as resulting from differences in the main processes responsible for plant growth: cell division leading to the generation of new organs and cell elongation leading to organ dimension. Although further studies will be necessary on mature trees with more complex architecture and multiple sinks such as fruits, this study is a first step for improving apple plant material for the use of water.

Genetic Variation of Morphological Traits and Transpiration in an Apple Core Collection under Well-Watered Conditions: Towards the Identification of Morphotypes with High Water Use Efficiency

PubMed Central

Lopez, Gerardo; Pallas, Benoît; Martinez, Sébastien; Lauri, Pierre-Éric; Regnard, Jean-Luc; Durel, Charles-Éric; Costes, Evelyne

2015-01-01

Water use efficiency (WUE) is a quantitative measurement which improvement is a major issue in the context of global warming and restrictions in water availability for agriculture. In this study, we aimed at studying the variation and genetic control of WUE and the respective role of its components (plant biomass and transpiration) in a perennial fruit crop. We explored an INRA apple core collection grown in a phenotyping platform to screen one-year-old scions for their accumulated biomass, transpiration and WUE under optimal growing conditions. Plant biomass was decompose into morphological components related to either growth or organ expansion. For each trait, nine mixed models were evaluated to account for the genetic effect and spatial heterogeneity inside the platform. The Best Linear Unbiased Predictors of genetic values were estimated after model selection. Mean broad-sense heritabilities were calculated from variance estimates. Heritability values indicated that biomass (0.76) and WUE (0.73) were under genetic control. This genetic control was lower in plant transpiration with an heritability of 0.54. Across the collection, biomass accounted for 70% of the WUE variability. A Hierarchical Ascendant Classification of the core collection indicated the existence of six groups of genotypes with contrasting morphology and WUE. Differences between morphotypes were interpreted as resulting from differences in the main processes responsible for plant growth: cell division leading to the generation of new organs and cell elongation leading to organ dimension. Although further studies will be necessary on mature trees with more complex architecture and multiple sinks such as fruits, this study is a first step for improving apple plant material for the use of water. PMID:26717192

Modelling the co-evolution of indirect genetic effects and inherited variability.

PubMed

Marjanovic, Jovana; Mulder, Han A; Rönnegård, Lars; Bijma, Piter

2018-03-28

When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of IGEs and variability, as the regression coefficient can respond to selection. Our simulations show that the model results in increased variability of body weight with increasing competition. When competition decreases, i.e., cooperation evolves, variability becomes significantly smaller. Hence, our model facilitates quantitative genetic studies on the relationship between IGEs and inherited variability. Moreover, our findings suggest that we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.

Appearance traits in fish farming: progress from classical genetics to genomics, providing insight into current and potential genetic improvement

PubMed Central

Colihueque, Nelson; Araneda, Cristian

2014-01-01

Appearance traits in fish, those external body characteristics that influence consumer acceptance at point of sale, have come to the forefront of commercial fish farming, as culture profitability is closely linked to management of these traits. Appearance traits comprise mainly body shape and skin pigmentation. Analysis of the genetic basis of these traits in different fish reveals significant genetic variation within populations, indicating potential for their genetic improvement. Work into ascertaining the minor or major genes underlying appearance traits for commercial fish is emerging, with substantial progress in model fish in terms of identifying genes that control body shape and skin colors. In this review, we describe research progress to date, especially with regard to commercial fish, and discuss genomic findings in model fish in order to better address the genetic basis of the traits. Given that appearance traits are important in commercial fish, the genomic information related to this issue promises to accelerate the selection process in coming years. PMID:25140172

Kinetic modeling of growth and lipid body induction in Chlorella pyrenoidosa under heterotrophic conditions.

PubMed

Sachdeva, Neha; Kumar, G Dinesh; Gupta, Ravi Prakash; Mathur, Anshu Shankar; Manikandan, B; Basu, Biswajit; Tuli, Deepak Kumar

2016-10-01

The aim of the present work was to develop a mathematical model to describe the biomass and (total) lipid productivity of Chlorella pyrenoidosa NCIM 2738 under heterotrophic conditions. Biomass growth rate was predicted by Droop's cell quota model, while changes observed in cell quota (utilization) under carbon excess conditions were used for the modeling and predicting the lipid accumulation rate. The model was simulated under non-limiting (excess) carbon and limiting nitrate concentration and validated with experimental data for the culture grown in batch (flask) mode under different nitrate concentrations. The present model incorporated two modes (growth and stressed) for the prediction of endogenous lipid synthesis/induction and aimed to predict the effect and response of the microalgae under nutrient starvation (stressed) conditions. MATLAB and Genetic Algorithm were employed for the prediction and validation of the model parameters. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genetic Analysis of Milk Yield in First-Lactation Holstein Friesian in Ethiopia: A Lactation Average vs Random Regression Test-Day Model Analysis

PubMed Central

Meseret, S.; Tamir, B.; Gebreyohannes, G.; Lidauer, M.; Negussie, E.

2015-01-01

The development of effective genetic evaluations and selection of sires requires accurate estimates of genetic parameters for all economically important traits in the breeding goal. The main objective of this study was to assess the relative performance of the traditional lactation average model (LAM) against the random regression test-day model (RRM) in the estimation of genetic parameters and prediction of breeding values for Holstein Friesian herds in Ethiopia. The data used consisted of 6,500 test-day (TD) records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013. Co-variance components were estimated using the average information restricted maximum likelihood method under single trait animal model. The estimate of heritability for first-lactation milk yield was 0.30 from LAM whilst estimates from the RRM model ranged from 0.17 to 0.29 for the different stages of lactation. Genetic correlations between different TDs in first-lactation Holstein Friesian ranged from 0.37 to 0.99. The observed genetic correlation was less than unity between milk yields at different TDs, which indicated that the assumption of LAM may not be optimal for accurate evaluation of the genetic merit of animals. A close look at estimated breeding values from both models showed that RRM had higher standard deviation compared to LAM indicating that the TD model makes efficient utilization of TD information. Correlations of breeding values between models ranged from 0.90 to 0.96 for different group of sires and cows and marked re-rankings were observed in top sires and cows in moving from the traditional LAM to RRM evaluations. PMID:26194217

Simulating the Yield Impacts of Organ-Level Quantitative Trait Loci Associated With Drought Response in Maize: A “Gene-to-Phenotype” Modeling Approach

PubMed Central

Chenu, Karine; Chapman, Scott C.; Tardieu, François; McLean, Greg; Welcker, Claude; Hammer, Graeme L.

2009-01-01

Under drought, substantial genotype–environment (G × E) interactions impede breeding progress for yield. Identifying genetic controls associated with yield response is confounded by poor genetic correlations across testing environments. Part of this problem is related to our inability to account for the interplay of genetic controls, physiological traits, and environmental conditions throughout the crop cycle. We propose a modeling approach to bridge this “gene-to-phenotype” gap. For maize under drought, we simulated the impact of quantitative trait loci (QTL) controlling two key processes (leaf and silk elongation) that influence crop growth, water use, and grain yield. Substantial G × E interaction for yield was simulated for hypothetical recombinant inbred lines (RILs) across different seasonal patterns of drought. QTL that accelerated leaf elongation caused an increase in crop leaf area and yield in well-watered or preflowering water deficit conditions, but a reduction in yield under terminal stresses (as such “leafy” genotypes prematurely exhausted the water supply). The QTL impact on yield was substantially enhanced by including pleiotropic effects of these QTL on silk elongation and on consequent grain set. The simulations obtained illustrated the difficulty of interpreting the genetic control of yield for genotypes influenced only by the additive effects of QTL associated with leaf and silk growth. The results highlight the potential of integrative simulation modeling for gene-to-phenotype prediction and for exploiting G × E interactions for complex traits such as drought tolerance. PMID:19786622

Molecular Analysis Research at Community College of Philadelphia

DTIC Science & Technology

2015-09-21

projects presented below fall under the category of "molecular genetics ", as presented in ARO Solicitation Number W911NF-12-R-0012-01. These projects...role of the GADD45 family of genes in innate immunity and sepsis. In addition to studying genetic components of the molecular response of myeloid...Equipment in left  column, procedure in right column.  kinetics of these molecular signaling pathways in genetic variants (gene KO models) has yet to

Resolving the evolutionary paradox of genetic instability: a cost-benefit analysis of DNA repair in changing environments.

PubMed

Breivik, Jarle; Gaudernack, Gustav

2004-04-09

Loss of genetic stability is a critical phenomenon in cancer and antibiotic resistance, and the prevailing dogma is that unstable cells survive because instability provides adaptive mutations. Challenging this view, we have argued that genetic instability arises because DNA repair may be a counterproductive strategy in mutagenic environments. This paradoxical relationship has also been confirmed by explicit experiments, but the underlying evolutionary principles remain controversial. This paper aims to clarify the issue, and presents a model that explains genetic instability from the basic perspective of molecular evolution and information processing.

Evaluating methods to visualize patterns of genetic differentiation on a landscape.

PubMed

House, Geoffrey L; Hahn, Matthew W

2018-05-01

With advances in sequencing technology, research in the field of landscape genetics can now be conducted at unprecedented spatial and genomic scales. This has been especially evident when using sequence data to visualize patterns of genetic differentiation across a landscape due to demographic history, including changes in migration. Two recent model-based visualization methods that can highlight unusual patterns of genetic differentiation across a landscape, SpaceMix and EEMS, are increasingly used. While SpaceMix's model can infer long-distance migration, EEMS' model is more sensitive to short-distance changes in genetic differentiation, and it is unclear how these differences may affect their results in various situations. Here, we compare SpaceMix and EEMS side by side using landscape genetics simulations representing different migration scenarios. While both methods excel when patterns of simulated migration closely match their underlying models, they can produce either un-intuitive or misleading results when the simulated migration patterns match their models less well, and this may be difficult to assess in empirical data sets. We also introduce unbundled principal components (un-PC), a fast, model-free method to visualize patterns of genetic differentiation by combining principal components analysis (PCA), which is already used in many landscape genetics studies, with the locations of sampled individuals. Un-PC has characteristics of both SpaceMix and EEMS and works well with simulated and empirical data. Finally, we introduce msLandscape, a collection of tools that streamline the creation of customizable landscape-scale simulations using the popular coalescent simulator ms and conversion of the simulated data for use with un-PC, SpaceMix and EEMS. © 2017 John Wiley & Sons Ltd.

Navigation by environmental geometry: the use of zebrafish as a model.

PubMed

Lee, Sang Ah; Vallortigara, Giorgio; Flore, Michele; Spelke, Elizabeth S; Sovrano, Valeria A

2013-10-01

Sensitivity to environmental shape in spatial navigation has been found, at both behavioural and neural levels, in virtually every species tested, starting early in development. Moreover, evidence that genetic deletions can cause selective deficits in such navigation behaviours suggests a genetic basis to navigation by environmental geometry. Nevertheless, the geometric computations underlying navigation have not been specified in any species. The present study teases apart the geometric components within the traditionally used rectangular enclosure and finds that zebrafish selectively represent distance and directional relationships between extended boundary surfaces. Similar behavioural results in geometric navigation tasks with human children provide prima facie evidence for similar underlying cognitive computations and open new doors for probing the genetic foundations that give rise to these computations.

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

PubMed Central

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C.

2014-01-01

Background and Aims Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Methods Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. Key Results To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36 % more yield than those based on markers for yield per se. Conclusions This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions. PMID:24984712

Unraveling the mechanisms of synapse formation and axon regeneration: the awesome power of C. elegans genetics.

PubMed

Jin, YiShi

2015-11-01

Since Caenorhabditis elegans was chosen as a model organism by Sydney Brenner in 1960's, genetic studies in this organism have been instrumental in discovering the function of genes and in deciphering molecular signaling network. The small size of the organism and the simple nervous system enable the complete reconstruction of the first connectome. The stereotypic developmental program and the anatomical reproducibility of synaptic connections provide a blueprint to dissect the mechanisms underlying synapse formation. Recent technological innovation using laser surgery of single axons and in vivo imaging has also made C. elegans a new model for axon regeneration. Importantly, genes regulating synaptogenesis and axon regeneration are highly conserved in function across animal phyla. This mini-review will summarize the main approaches and the key findings in understanding the mechanisms underlying the development and maintenance of the nervous system. The impact of such findings underscores the awesome power of C. elegans genetics.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

Genetic signatures of natural selection in a model invasive ascidian

PubMed Central

Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin

2017-01-01

Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta. PMID:28266616

Pulmonary Complications Resulting from Genetic Cardiovascular Disease in Two Rat Models

EPA Science Inventory

Underlying cardiovascular disease (CVD) has been considered a risk factor for exacerbation of air pollution health effects. Therefore, rodent models of CVD are increasingly used to examine mechanisms of variation in susceptibility. Pulmonary complications and altered iron homeost...

Equilibrium and nonequilibrium attractors for a discrete, selection-migration model

Treesearch

James F. Selgrade; James H. Roberds

2003-01-01

This study presents a discrete-time model for the effects of selection and immigration on the demographic and genetic compositions of a population. Under biologically reasonable conditions, it is shown that the model always has an equilibrium. Although equilibria for similar models without migration must have real eigenvalues, for this selection-migration model we...

ADAM33 polymorphisms are associated with asthma and a distinctive palm dermatoglyphic pattern

PubMed Central

XUE, WEILIN; HAN, WEI; ZHOU, ZHAO-SHAN

2013-01-01

A close correlation between asthma and palm dermatoglyphic patterns has been observed in previous studies, but the underlying genetic mechanisms have not been investigated. A disintegrin and metalloprotein-33 (ADAM33) polymorphisms are important in the development of asthma and other atopic diseases. To investigate the underlying mechanisms of the association between asthma and distinctive palm dermatoglyphic patterns, thirteen ADAM33 single-nucleotide polymorphisms (SNPs) were analyzed for the association between asthma and palm dermatoglyphic patterns in a population of 400 asthmatic patients and 200 healthy controls. Based on the results, five SNPs, rs44707 (codominant model, P=0.031; log-additive model, P=0.0084), rs2787094 (overdominant model, P=0.049), rs678881 (codominant model, P=0.028; overdominant model, P=0.0083), rs677044 (codominant model, P=0.013; log-additive model, P=0.0033) and rs512625 (dominant model, P=0.033), were associated with asthma in this population. Two SNPs, rs44707 (dominant model, P=0.042) and rs2787094 (codominant model, P=0.014; recessive model, P=0.0038), were observed in the asthma patients with the distinctive palm pattern. As rs44707 and rs2787094 are associated with asthma and a distinctive palm pattern, the data suggest that ADAM33 polymorphisms are correlated with asthma and may be the underlying genetic basis of the association between asthma and palm dermatoglyphic patterns. PMID:24141861

Upweighting rare favourable alleles increases long-term genetic gain in genomic selection programs.

PubMed

Liu, Huiming; Meuwissen, Theo H E; Sørensen, Anders C; Berg, Peer

2015-03-21

The short-term impact of using different genomic prediction (GP) models in genomic selection has been intensively studied, but their long-term impact is poorly understood. Furthermore, long-term genetic gain of genomic selection is expected to improve by using Jannink's weighting (JW) method, in which rare favourable marker alleles are upweighted in the selection criterion. In this paper, we extend the JW method by including an additional parameter to decrease the emphasis on rare favourable alleles over the time horizon, with the purpose of further improving the long-term genetic gain. We call this new method dynamic weighting (DW). The paper explores the long-term impact of different GP models with or without weighting methods. Different selection criteria were tested by simulating a population of 500 animals with truncation selection of five males and 50 females. Selection criteria included unweighted and weighted genomic estimated breeding values using the JW or DW methods, for which ridge regression (RR) and Bayesian lasso (BL) were used to estimate marker effects. The impacts of these selection criteria were compared under three genetic architectures, i.e. varying numbers of QTL for the trait and for two time horizons of 15 (TH15) or 40 (TH40) generations. For unweighted GP, BL resulted in up to 21.4% higher long-term genetic gain and 23.5% lower rate of inbreeding under TH40 than RR. For weighted GP, DW resulted in 1.3 to 5.5% higher long-term gain compared to unweighted GP. JW, however, showed a 6.8% lower long-term genetic gain relative to unweighted GP when BL was used to estimate the marker effects. Under TH40, both DW and JW obtained significantly higher genetic gain than unweighted GP. With DW, the long-term genetic gain was increased by up to 30.8% relative to unweighted GP, and also increased by 8% relative to JW, although at the expense of a lower short-term gain. Irrespective of the number of QTL simulated, BL is superior to RR in maintaining genetic variance and therefore results in higher long-term genetic gain. Moreover, DW is a promising method with which high long-term genetic gain can be expected within a fixed time frame.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.

PubMed

He, Liang; Zhbannikov, Ilya; Arbeev, Konstantin G; Yashin, Anatoliy I; Kulminski, Alexander M

2017-11-01

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10 -7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10 -7 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases. © 2017 WILEY PERIODICALS, INC.

Heritability of DUI convictions: a twin study of driving under the influence of alcohol.

PubMed

Anum, Emmanuel A; Silberg, Judy; Retchin, Sheldon M

2014-02-01

The study was undertaken to assess the relative contributions of genetic and environmental influences on drunk-driving. Driving records of a cohort of male and female twins (N = 17,360) from the Mid-Atlantic Twin Registry were examined. Structural equation models were used to estimate the magnitude of genetic and environmental effects on male and female phenotypes, and test for gender differences. There were significant gender and age effects. Compared with females, males were five times more likely to engage in driving under the influence. Among persons aged 21-49 years, the risk for drunk-driving was eight times that for those aged 50+ years and five times greater than those ≤20 years. In both males and females, aged 21-49 years, a large proportion (57%) of the variance in drunk-driving was due to genetic factors and the remaining 43% due to individual specific environmental influences. Drunk-driving is under significant genetic influence in both males and females. Our findings suggest that a different set of genes influence DUIs in men and women.

Quantitative genetic properties of four measures of deformity in yellowtail kingfish Seriola lalandi Valenciennes, 1833.

PubMed

Nguyen, N H; Whatmore, P; Miller, A; Knibb, W

2016-02-01

The main aim of this study was to estimate the heritability for four measures of deformity and their genetic associations with growth (body weight and length), carcass (fillet weight and yield) and flesh-quality (fillet fat content) traits in yellowtail kingfish Seriola lalandi. The observed major deformities included lower jaw, nasal erosion, deformed operculum and skinny fish on 480 individuals from 22 families at Clean Seas Tuna Ltd. They were typically recorded as binary traits (presence or absence) and were analysed separately by both threshold generalized models and standard animal mixed models. Consistency of the models was evaluated by calculating simple Pearson correlation of breeding values of full-sib families for jaw deformity. Genetic and phenotypic correlations among traits were estimated using a multitrait linear mixed model in ASReml. Both threshold and linear mixed model analysis showed that there is additive genetic variation in the four measures of deformity, with the estimates of heritability obtained from the former (threshold) models on liability scale ranging from 0.14 to 0.66 (SE 0.32-0.56) and from the latter (linear animal and sire) models on original (observed) scale, 0.01-0.23 (SE 0.03-0.16). When the estimates on the underlying liability were transformed to the observed scale (0, 1), they were generally consistent between threshold and linear mixed models. Phenotypic correlations among deformity traits were weak (close to zero). The genetic correlations among deformity traits were not significantly different from zero. Body weight and fillet carcass showed significant positive genetic correlations with jaw deformity (0.75 and 0.95, respectively). Genetic correlation between body weight and operculum was negative (-0.51, P < 0.05). The genetic correlations' estimates of body and carcass traits with other deformity were not significant due to their relatively high standard errors. Our results showed that there are prospects for genetic selection to improve deformity in yellowtail kingfish and that measures of deformity should be included in the recording scheme, breeding objectives and selection index in practical selective breeding programmes due to the antagonistic genetic correlations of deformed jaws with body and carcass performance. © 2015 John Wiley & Sons Ltd.

Effects of Genetic Drift and Gene Flow on the Selective Maintenance of Genetic Variation

PubMed Central

Star, Bastiaan; Spencer, Hamish G.

2013-01-01

Explanations for the genetic variation ubiquitous in natural populations are often classified by the population–genetic processes they emphasize: natural selection or mutation and genetic drift. Here we investigate models that incorporate all three processes in a spatially structured population, using what we call a construction approach, simulating finite populations under selection that are bombarded with a steady stream of novel mutations. As expected, the amount of genetic variation compared to previous models that ignored the stochastic effects of drift was reduced, especially for smaller populations and when spatial structure was most profound. By contrast, however, for higher levels of gene flow and larger population sizes, the amount of genetic variation found after many generations was greater than that in simulations without drift. This increased amount of genetic variation is due to the introduction of slightly deleterious alleles by genetic drift and this process is more efficient when migration load is higher. The incorporation of genetic drift also selects for fitness sets that exhibit allele-frequency equilibria with larger domains of attraction: they are “more stable.” Moreover, the finiteness of populations strongly influences levels of local adaptation, selection strength, and the proportion of allele-frequency vectors that can be distinguished from the neutral expectation. PMID:23457235

In defence of model-based inference in phylogeography

PubMed Central

Beaumont, Mark A.; Nielsen, Rasmus; Robert, Christian; Hey, Jody; Gaggiotti, Oscar; Knowles, Lacey; Estoup, Arnaud; Panchal, Mahesh; Corander, Jukka; Hickerson, Mike; Sisson, Scott A.; Fagundes, Nelson; Chikhi, Lounès; Beerli, Peter; Vitalis, Renaud; Cornuet, Jean-Marie; Huelsenbeck, John; Foll, Matthieu; Yang, Ziheng; Rousset, Francois; Balding, David; Excoffier, Laurent

2017-01-01

Recent papers have promoted the view that model-based methods in general, and those based on Approximate Bayesian Computation (ABC) in particular, are flawed in a number of ways, and are therefore inappropriate for the analysis of phylogeographic data. These papers further argue that Nested Clade Phylogeographic Analysis (NCPA) offers the best approach in statistical phylogeography. In order to remove the confusion and misconceptions introduced by these papers, we justify and explain the reasoning behind model-based inference. We argue that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics. We also examine the NCPA method and highlight numerous deficiencies, either when used with single or multiple loci. We further show that the ages of clades are carelessly used to infer ages of demographic events, that these ages are estimated under a simple model of panmixia and population stationarity but are then used under different and unspecified models to test hypotheses, a usage the invalidates these testing procedures. We conclude by encouraging researchers to study and use model-based inference in population genetics. PMID:29284924

Host susceptibility to malaria in human and mice: compatible approaches to identify potential resistant genes.

PubMed

Hernandez-Valladares, Maria; Rihet, Pascal; Iraqi, Fuad A

2014-01-01

There is growing evidence for human genetic factors controlling the outcome of malaria infection, while molecular basis of this genetic control is still poorly understood. Case-control and family-based studies have been carried out to identify genes underlying host susceptibility to malarial infection. Parasitemia and mild malaria have been genetically linked to human chromosomes 5q31-q33 and 6p21.3, and several immune genes located within those regions have been associated with malaria-related phenotypes. Association and linkage studies of resistance to malaria are not easy to carry out in human populations, because of the difficulty in surveying a significant number of families. Murine models have proven to be an excellent genetic tool for studying host response to malaria; their use allowed mapping 14 resistance loci, eight of them controlling parasitic levels and six controlling cerebral malaria. Once quantitative trait loci or genes have been identified, the human ortholog may then be identified. Comparative mapping studies showed that a couple of human and mouse might share similar genetically controlled mechanisms of resistance. In this way, char8, which controls parasitemia, was mapped on chromosome 11; char8 corresponds to human chromosome 5q31-q33 and contains immune genes, such as Il3, Il4, Il5, Il12b, Il13, Irf1, and Csf2. Nevertheless, part of the genetic factors controlling malaria traits might differ in both hosts because of specific host-pathogen interactions. Finally, novel genetic tools including animal models were recently developed and will offer new opportunities for identifying genetic factors underlying host phenotypic response to malaria, which will help in better therapeutic strategies including vaccine and drug development.

Operating Characteristics of Statistical Methods for Detecting Gene-by-Measured Environment Interaction in the Presence of Gene-Environment Correlation under Violations of Distributional Assumptions.

PubMed

Van Hulle, Carol A; Rathouz, Paul J

2015-02-01

Accurately identifying interactions between genetic vulnerabilities and environmental factors is of critical importance for genetic research on health and behavior. In the previous work of Van Hulle et al. (Behavior Genetics, Vol. 43, 2013, pp. 71-84), we explored the operating characteristics for a set of biometric (e.g., twin) models of Rathouz et al. (Behavior Genetics, Vol. 38, 2008, pp. 301-315), for testing gene-by-measured environment interaction (GxM) in the presence of gene-by-measured environment correlation (rGM) where data followed the assumed distributional structure. Here we explore the effects that violating distributional assumptions have on the operating characteristics of these same models even when structural model assumptions are correct. We simulated N = 2,000 replicates of n = 1,000 twin pairs under a number of conditions. Non-normality was imposed on either the putative moderator or on the ultimate outcome by ordinalizing or censoring the data. We examined the empirical Type I error rates and compared Bayesian information criterion (BIC) values. In general, non-normality in the putative moderator had little impact on the Type I error rates or BIC comparisons. In contrast, non-normality in the outcome was often mistaken for or masked GxM, especially when the outcome data were censored.

Identifying Loci Under Selection Against Gene Flow in Isolation-with-Migration Models

PubMed Central

Sousa, Vitor C.; Carneiro, Miguel; Ferrand, Nuno; Hey, Jody

2013-01-01

When divergence occurs in the presence of gene flow, there can arise an interesting dynamic in which selection against gene flow, at sites associated with population-specific adaptations or genetic incompatibilities, can cause net gene flow to vary across the genome. Loci linked to sites under selection may experience reduced gene flow and may experience genetic bottlenecks by the action of nearby selective sweeps. Data from histories such as these may be poorly fitted by conventional neutral model approaches to demographic inference, which treat all loci as equally subject to forces of genetic drift and gene flow. To allow for demographic inference in the face of such histories, as well as the identification of loci affected by selection, we developed an isolation-with-migration model that explicitly provides for variation among genomic regions in migration rates and/or rates of genetic drift. The method allows for loci to fall into any of multiple groups, each characterized by a different set of parameters, thus relaxing the assumption that all loci share the same demography. By grouping loci, the method can be applied to data with multiple loci and still have tractable dimensionality and statistical power. We studied the performance of the method using simulated data, and we applied the method to study the divergence of two subspecies of European rabbits (Oryctolagus cuniculus). PMID:23457232

Genetic Contributors to Intergenerational CAG Repeat Instability in Huntington's Disease Knock-In Mice.

PubMed

Neto, João Luís; Lee, Jong-Min; Afridi, Ali; Gillis, Tammy; Guide, Jolene R; Dempsey, Stephani; Lager, Brenda; Alonso, Isabel; Wheeler, Vanessa C; Pinto, Ricardo Mouro

2017-02-01

Huntington's disease (HD) is a neurodegenerative disorder caused by the expansion of a CAG trinucleotide repeat in exon 1 of the HTT gene. Longer repeat sizes are associated with increased disease penetrance and earlier ages of onset. Intergenerationally unstable transmissions are common in HD families, partly underlying the genetic anticipation seen in this disorder. HD CAG knock-in mouse models also exhibit a propensity for intergenerational repeat size changes. In this work, we examine intergenerational instability of the CAG repeat in over 20,000 transmissions in the largest HD knock-in mouse model breeding datasets reported to date. We confirmed previous observations that parental sex drives the relative ratio of expansions and contractions. The large datasets further allowed us to distinguish effects of paternal CAG repeat length on the magnitude and frequency of expansions and contractions, as well as the identification of large repeat size jumps in the knock-in models. Distinct degrees of intergenerational instability were observed between knock-in mice of six background strains, indicating the occurrence of trans-acting genetic modifiers. We also found that lines harboring a neomycin resistance cassette upstream of Htt showed reduced expansion frequency, indicative of a contributing role for sequences in cis, with the expanded repeat as modifiers of intergenerational instability. These results provide a basis for further understanding of the mechanisms underlying intergenerational repeat instability. Copyright © 2017 by the Genetics Society of America.

More powerful haplotype sharing by accounting for the mode of inheritance.

PubMed

Ziegler, Andreas; Ewhida, Adel; Brendel, Michael; Kleensang, André

2009-04-01

The concept of haplotype sharing (HS) has received considerable attention recently, and several haplotype association methods have been proposed. Here, we extend the work of Beckmann and colleagues [2005 Hum. Hered. 59:67-78] who derived an HS statistic (BHS) as special case of Mantel's space-time clustering approach. The Mantel-type HS statistic correlates genetic similarity with phenotypic similarity across pairs of individuals. While phenotypic similarity is measured as the mean-corrected cross product of phenotypes, we propose to incorporate information of the underlying genetic model in the measurement of the genetic similarity. Specifically, for the recessive and dominant modes of inheritance we suggest the use of the minimum and maximum of shared length of haplotypes around a marker locus for pairs of individuals. If the underlying genetic model is unknown, we propose a model-free HS Mantel statistic using the max-test approach. We compare our novel HS statistics to BHS using simulated case-control data and illustrate its use by re-analyzing data from a candidate region of chromosome 18q from the Rheumatoid Arthritis (RA) Consortium. We demonstrate that our approach is point-wise valid and superior to BHS. In the re-analysis of the RA data, we identified three regions with point-wise P-values<0.005 containing six known genes (PMIP1, MC4R, PIGN, KIAA1468, TNFRSF11A and ZCCHC2) which might be worth follow-up.

Is pigment patterning in fish skin determined by the Turing mechanism?

PubMed

Watanabe, Masakatsu; Kondo, Shigeru

2015-02-01

More than half a century ago, Alan Turing postulated that pigment patterns may arise from a mechanism that could be mathematically modeled based on the diffusion of two substances that interact with each other. Over the past 15 years, the molecular and genetic tools to verify this prediction have become available. Here, we review experimental studies aimed at identifying the mechanism underlying pigment pattern formation in zebrafish. Extensive molecular genetic studies in this model organism have revealed the interactions between the pigment cells that are responsible for the patterns. The mechanism discovered is substantially different from that predicted by the mathematical model, but it retains the property of 'local activation and long-range inhibition', a necessary condition for Turing pattern formation. Although some of the molecular details of pattern formation remain to be elucidated, current evidence confirms that the underlying mechanism is mathematically equivalent to the Turing mechanism. Copyright © 2014 Elsevier Ltd. All rights reserved.

Integrative strategies to identify candidate genes in rodent models of human alcoholism.

PubMed

Treadwell, Julie A

2006-01-01

The search for genes underlying alcohol-related behaviours in rodent models of human alcoholism has been ongoing for many years with only limited success. Recently, new strategies that integrate several of the traditional approaches have provided new insights into the molecular mechanisms underlying ethanol's actions in the brain. We have used alcohol-preferring C57BL/6J (B6) and alcohol-avoiding DBA/2J (D2) genetic strains of mice in an integrative strategy combining high-throughput gene expression screening, genetic segregation analysis, and mapping to previously published quantitative trait loci to uncover candidate genes for the ethanol-preference phenotype. In our study, 2 genes, retinaldehyde binding protein 1 (Rlbp1) and syntaxin 12 (Stx12), were found to be strong candidates for ethanol preference. Such experimental approaches have the power and the potential to greatly speed up the laborious process of identifying candidate genes for the animal models of human alcoholism.

ptk7 mutant zebrafish models of congenital and idiopathic scoliosis implicate dysregulated Wnt signalling in disease

PubMed Central

Hayes, Madeline; Gao, Xiaochong; Yu, Lisa X; Paria, Nandina; Henkelman, R. Mark; Wise, Carol A.; Ciruna, Brian

2014-01-01

Scoliosis is a complex genetic disorder of the musculoskeletal system, characterized by three-dimensional rotation of the spine. Curvatures caused by malformed vertebrae (congenital scoliosis (CS)) are apparent at birth. Spinal curvatures with no underlying vertebral abnormality (idiopathic scoliosis (IS)) most commonly manifest during adolescence. The genetic and biological mechanisms responsible for IS remain poorly understood due largely to limited experimental models. Here we describe zygotic ptk7 (Zptk7) mutant zebrafish, deficient in a critical regulator of Wnt signalling, as the first genetically defined developmental model of IS. We identify a novel sequence variant within a single IS patient that disrupts PTK7 function, consistent with a role for dysregulated Wnt activity in disease pathogenesis. Furthermore, we demonstrate that embryonic loss-of-gene function in maternal-zygotic ptk7 mutants (MZptk7) leads to vertebral anomalies associated with CS. Our data suggest novel molecular origins of, and genetic links between, congenital and idiopathic forms of disease. PMID:25182715

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

PubMed Central

Ritchie, Marylyn D; White, Bill C; Parker, Joel S; Hahn, Lance W; Moore, Jason H

2003-01-01

Background Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. Results Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. Conclusion This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases. PMID:12846935

Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection.

PubMed

Bemmels, Jordan B; Title, Pascal O; Ortego, Joaquín; Knowles, L Lacey

2016-10-01

Past climate change has caused shifts in species distributions and undoubtedly impacted patterns of genetic variation, but the biological processes mediating responses to climate change, and their genetic signatures, are often poorly understood. We test six species-specific biologically informed hypotheses about such processes in canyon live oak (Quercus chrysolepis) from the California Floristic Province. These hypotheses encompass the potential roles of climatic niche, niche multidimensionality, physiological trade-offs in functional traits, and local-scale factors (microsites and local adaptation within ecoregions) in structuring genetic variation. Specifically, we use ecological niche models (ENMs) to construct temporally dynamic landscapes where the processes invoked by each hypothesis are reflected by differences in local habitat suitabilities. These landscapes are used to simulate expected patterns of genetic variation under each model and evaluate the fit of empirical data from 13 microsatellite loci genotyped in 226 individuals from across the species range. Using approximate Bayesian computation (ABC), we obtain very strong support for two statistically indistinguishable models: a trade-off model in which growth rate and drought tolerance drive habitat suitability and genetic structure, and a model based on the climatic niche estimated from a generic ENM, in which the variables found to make the most important contribution to the ENM have strong conceptual links to drought stress. The two most probable models for explaining the patterns of genetic variation thus share a common component, highlighting the potential importance of seasonal drought in driving historical range shifts in a temperate tree from a Mediterranean climate where summer drought is common. © 2016 John Wiley & Sons Ltd.

Automatic inference of multicellular regulatory networks using informative priors.

PubMed

Sun, Xiaoyun; Hong, Pengyu

2009-01-01

To fully understand the mechanisms governing animal development, computational models and algorithms are needed to enable quantitative studies of the underlying regulatory networks. We developed a mathematical model based on dynamic Bayesian networks to model multicellular regulatory networks that govern cell differentiation processes. A machine-learning method was developed to automatically infer such a model from heterogeneous data. We show that the model inference procedure can be greatly improved by incorporating interaction data across species. The proposed approach was applied to C. elegans vulval induction to reconstruct a model capable of simulating C. elegans vulval induction under 73 different genetic conditions.

A kernel regression approach to gene-gene interaction detection for case-control studies.

PubMed

Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

A recessive genetic model and runs of homozygosity in major depressive disorder

PubMed Central

Power, Robert A.; Keller, Matthew C.; Ripke, Stephan; Abdellaoui, Abdel; Wray, Naomi R.; Sullivan, Patrick F; Breen, Gerome

2014-01-01

Genome-wide association studies (GWASs) of major depressive disorder (MDD) have yet to identify variants that surpass the threshold for genome-wide significance. A recent study reported that runs of homozygosity (ROH) are associated with schizophrenia, reflecting a novel genetic risk factor resulting from increased parental relatedness and recessive genetic effects. Here we undertake an analysis of ROH for MDD using the 9,238 MDD cases and 9,521 controls reported in a recent mega-analysis of 9 GWAS. Since evidence for association with ROH could reflect a recessive mode of action at loci, we also conducted a genome-wide association analyses under a recessive model. The genome-wide association analysis using a recessive model found no significant associations. Our analysis of ROH suggested that there was significant heterogeneity of effect across studies in effect (p=0.001), and it was associated with genotyping platform and country of origin. The results of the ROH analysis show that differences across studies can lead to conflicting systematic genome-wide differences between cases and controls that are unaccounted for by traditional covariates. They highlight the sensitivity of the ROH method to spurious associations, and the need to carefully control for potential confounds in such analyses. We found no strong evidence for a recessive model underlying MDD. PMID:24482242

Multi-scale genetic dynamic modelling II: application to synthetic biology: an algorithmic Markov chain based approach.

PubMed

Kirkilionis, Markus; Janus, Ulrich; Sbano, Luca

2011-09-01

We model in detail a simple synthetic genetic clock that was engineered in Atkinson et al. (Cell 113(5):597-607, 2003) using Escherichia coli as a host organism. Based on this engineered clock its theoretical description uses the modelling framework presented in Kirkilionis et al. (Theory Biosci. doi: 10.1007/s12064-011-0125-0 , 2011, this volume). The main goal of this accompanying article was to illustrate that parts of the modelling process can be algorithmically automatised once the model framework we called 'average dynamics' is accepted (Sbano and Kirkilionis, WMI Preprint 7/2007, 2008c; Kirkilionis and Sbano, Adv Complex Syst 13(3):293-326, 2010). The advantage of the 'average dynamics' framework is that system components (especially in genetics) can be easier represented in the model. In particular, if once discovered and characterised, specific molecular players together with their function can be incorporated. This means that, for example, the 'gene' concept becomes more clear, for example, in the way the genetic component would react under different regulatory conditions. Using the framework it has become a realistic aim to link mathematical modelling to novel tools of bioinformatics in the future, at least if the number of regulatory units can be estimated. This should hold in any case in synthetic environments due to the fact that the different synthetic genetic components are simply known (Elowitz and Leibler, Nature 403(6767):335-338, 2000; Gardner et al., Nature 403(6767):339-342, 2000; Hasty et al., Nature 420(6912):224-230, 2002). The paper illustrates therefore as a necessary first step how a detailed modelling of molecular interactions with known molecular components leads to a dynamic mathematical model that can be compared to experimental results on various levels or scales. The different genetic modules or components are represented in different detail by model variants. We explain how the framework can be used for investigating other more complex genetic systems in terms of regulation and feedback.

What underlies the diversity of brain tumors?

PubMed Central

Swartling, Fredrik J.; Hede, Sanna-Maria; Weiss, William A.

2012-01-01

Glioma and medulloblastoma represent the most commonly occurring malignant brain tumors in adults and in children respectively. Recent genomic and transcriptional approaches present a complex group of diseases, and delineate a number of molecular subgroups within tumors that share a common histopathology. Differences in cells of origin, regional niches, developmental timing and genetic events all contribute to this heterogeneity. In an attempt to recapitulate the diversity of brain tumors, an increasing array of genetically engineered mouse models (GEMMs) has been developed. These models often utilize promoters and genetic drivers from normal brain development, and can provide insight into specific cells from which these tumors originate. GEMMs show promise in both developmental biology and developmental therapeutics. This review describes numerous murine brain tumor models in the context of normal brain development, and the potential for these animals to impact brain tumor research. PMID:23085857

Under the Influence of Genetics: How Transdisciplinarity Leads Us to Rethink Social Pathways to Illness

PubMed Central

Pescosolido, Bernice A.; Perry, Brea L.; Long, J. Scott; Martin, Jack K.; Nurnberger, John I.; Hesselbrock, Victor

2015-01-01

To extend our understanding of how social structures and social processes impact behavior, sociologists have been challenged to incorporate the potential explanatory role of genetics in their models. Here, we draw propositions from three major understandings of illness causation offered by social theory – fundamental causes, social stress processes, and social safety net theories. We tailor hypotheses to the case of alcohol dependence, long considered a multifaceted problem, defying simple explanation and having both biological and social roots. After briefly reviewing current appeals for transdisciplinary research, we describe both sociological and genetic theories, and derive propositions expected under each and under a transdisciplinary theoretical frame. Analyses of a later wave of the preeminent medical science study, the Collaborative Study on the Genetics of Alcoholism (COGA), reveals a complex interplay of how the GABRA2 gene works with and against social structural factors to produce cases meeting DSM/ICD diagnoses. When both genetic and social factors are controlled, virtually equivalent effects of each remain; and, only modest evidence suggests that genetic influence works through social structural conditions and experiences. Further exploratory analyses using multiplicative terms reveal enhanced gene-environment interactions: 1) women are largely unaffected in their risk for alcohol dependence by allele status at this candidate gene; 2) family support attenuates genetic influence; 3) childhood deprivation exacerbates genetic predispositions. We discuss how these findings lead us to consider the essential intradisciplinary tension in sociological theories (i.e., the role of proximal and distal influences in social processes). Overall, our findings point to the promise of theories blending social and genetic influences by focusing directly on dynamic, networked sequences that produce different pathways to health and illness. PMID:19569404

Why do some like it hot? Genetic and environmental contributions to the pleasantness of oral pungency.

PubMed

Törnwall, Outi; Silventoinen, Karri; Kaprio, Jaakko; Tuorila, Hely

2012-10-10

Although potential environmental influences on hedonic responses to oral pungency have been identified, little is known of the possible role of genetics underlying these responses. We explored the contribution of genetic and environmental influences on the pleasantness of oral pungency and spicy foods. Respondents were young adult Finnish twins (n=331, 21-25 years), including 47 complete monozygotic and 93 dizygotic twin pairs and 51 twin individuals without their co-twin. Pleasantness and intensity of strawberry jelly spiked with capsaicin (0.0001% w/v) relative to untainted strawberry jelly were rated. Furthermore, pleasantness of spicy foods and oral pungency caused by spices were rated based on food names in a questionnaire. Respondents were grouped as non-likers, medium-likers, and likers by their pleasantness responses to capsaicin spiked jelly. The contribution of genetic and environmental factors to variation and co-variation of the pleasantness traits was analyzed using quantitative genetic modeling. The non-likers perceived oral pungency as more intense (sensory) and rated pleasantness of spicy foods and pungent sensations caused by spices (questionnaire) as less pleasant than the likers. Genetic factors accounted for 18-58% of the variation in the pleasantness of oral pungency, spicy foods and pungent sensations. The rest was due to environmental factors. All pleasantness traits (sensory and questionnaire based) were shown to share a common genetic variance. This indicates that an underlying genetic aptitude to like oral pungency, and spicy foods exists and it is expressed in these measures. The findings broaden the understanding of the diverse nature of individual food preferences and motivate further search for the underlying genetic components of oral pungency. Copyright © 2012. Published by Elsevier Inc.

Bridging the Gap: Towards a Cell-Type Specific Understanding of Neural Circuits Underlying Fear Behaviors

PubMed Central

McCullough, KM; Morrison, FG; Ressler, KJ

2016-01-01

Fear and anxiety-related disorders are remarkably common and debilitating, and are often characterized by dysregulated fear responses. Rodent models of fear learning and memory have taken great strides towards elucidating the specific neuronal circuitries underlying the learning of fear responses. The present review addresses recent research utilizing optogenetic approaches to parse circuitries underlying fear behaviors. It also highlights the powerful advances made when optogenetic techniques are utilized in a genetically defined, cell-type specific, manner. The application of next-generation genetic and sequencing approaches in a cell-type specific context will be essential for a mechanistic understanding of the neural circuitry underlying fear behavior and for the rational design of targeted, circuit specific, pharmacologic interventions for the treatment and prevention of fear-related disorders. PMID:27470092

Navigation by environmental geometry: the use of zebrafish as a model

PubMed Central

Lee, Sang Ah; Vallortigara, Giorgio; Flore, Michele; Spelke, Elizabeth S.; Sovrano, Valeria A.

2013-01-01

SUMMARY Sensitivity to environmental shape in spatial navigation has been found, at both behavioural and neural levels, in virtually every species tested, starting early in development. Moreover, evidence that genetic deletions can cause selective deficits in such navigation behaviours suggests a genetic basis to navigation by environmental geometry. Nevertheless, the geometric computations underlying navigation have not been specified in any species. The present study teases apart the geometric components within the traditionally used rectangular enclosure and finds that zebrafish selectively represent distance and directional relationships between extended boundary surfaces. Similar behavioural results in geometric navigation tasks with human children provide prima facie evidence for similar underlying cognitive computations and open new doors for probing the genetic foundations that give rise to these computations. PMID:23788708

Stochastic modelling of shifts in allele frequencies reveals a strongly polygynous mating system in the re-introduced Asiatic wild ass.

PubMed

Renan, Sharon; Greenbaum, Gili; Shahar, Naama; Templeton, Alan R; Bouskila, Amos; Bar-David, Shirli

2015-04-01

Small populations are prone to loss of genetic variation and hence to a reduction in their evolutionary potential. Therefore, studying the mating system of small populations and its potential effects on genetic drift and genetic diversity is of high importance for their viability assessments. The traditional method for studying genetic mating systems is paternity analysis. Yet, as small populations are often rare and elusive, the genetic data required for paternity analysis are frequently unavailable. The endangered Asiatic wild ass (Equus hemionus), like all equids, displays a behaviourally polygynous mating system; however, the level of polygyny has never been measured genetically in wild equids. Combining noninvasive genetic data with stochastic modelling of shifts in allele frequencies, we developed an alternative approach to paternity analysis for studying the genetic mating system of the re-introduced Asiatic wild ass in the Negev Desert, Israel. We compared the shifts in allele frequencies (as a measure of genetic drift) that have occurred in the wild ass population since re-introduction onset to simulated scenarios under different proportions of mating males. We revealed a strongly polygynous mating system in which less than 25% of all males participate in the mating process each generation. This strongly polygynous mating system and its potential effect on the re-introduced population's genetic diversity could have significant consequences for the long-term persistence of the population in the Negev. The stochastic modelling approach and the use of allele-frequency shifts can be further applied to systems that are affected by genetic drift and for which genetic data are limited. © 2015 John Wiley & Sons Ltd.

Genetically informed ecological niche models improve climate change predictions.

PubMed

Ikeda, Dana H; Max, Tamara L; Allan, Gerard J; Lau, Matthew K; Shuster, Stephen M; Whitham, Thomas G

2017-01-01

We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change. © 2016 John Wiley & Sons Ltd.

The role of crossover operator in evolutionary-based approach to the problem of genetic code optimization.

PubMed

Błażej, Paweł; Wnȩtrzak, Małgorzata; Mackiewicz, Paweł

2016-12-01

One of theories explaining the present structure of canonical genetic code assumes that it was optimized to minimize harmful effects of amino acid replacements resulting from nucleotide substitutions and translational errors. A way to testify this concept is to find the optimal code under given criteria and compare it with the canonical genetic code. Unfortunately, the huge number of possible alternatives makes it impossible to find the optimal code using exhaustive methods in sensible time. Therefore, heuristic methods should be applied to search the space of possible solutions. Evolutionary algorithms (EA) seem to be ones of such promising approaches. This class of methods is founded both on mutation and crossover operators, which are responsible for creating and maintaining the diversity of candidate solutions. These operators possess dissimilar characteristics and consequently play different roles in the process of finding the best solutions under given criteria. Therefore, the effective searching for the potential solutions can be improved by applying both of them, especially when these operators are devised specifically for a given problem. To study this subject, we analyze the effectiveness of algorithms for various combinations of mutation and crossover probabilities under three models of the genetic code assuming different restrictions on its structure. To achieve that, we adapt the position based crossover operator for the most restricted model and develop a new type of crossover operator for the more general models. The applied fitness function describes costs of amino acid replacement regarding their polarity. Our results indicate that the usage of crossover operators can significantly improve the quality of the solutions. Moreover, the simulations with the crossover operator optimize the fitness function in the smaller number of generations than simulations without this operator. The optimal genetic codes without restrictions on their structure minimize the costs about 2.7 times better than the canonical genetic code. Interestingly, the optimal codes are dominated by amino acids characterized by polarity close to its average value for all amino acids. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Learning genetic inquiry through the use, revision, and justification of explanatory models

NASA Astrophysics Data System (ADS)

Cartier, Jennifer Lorraine

Central to the process of inquiry in science is the construction and assessment of models that can be used to explain (and in some cases, predict) natural phenomena. This dissertation is a qualitative study of student learning in a high school biology course that was designed to give students opportunities to learn about genetic inquiry in part by providing them with authentic experiences doing inquiry in the discipline. With the aid of a computer program that generates populations of "fruit flies", the students in this class worked in groups structured like scientific communities to build, revise, and defend explanatory models for various inheritance phenomena. Analysis of the ways in which the first cohort of students assessed their inheritance models revealed that all students assessed models based upon empirical fit (data/model match). However, in contrast to the practice of scientists and despite explicit instruction, students did not consistently apply conceptual assessment criteria to their models. That is, they didn't seek consistency between underlying concepts or processes in their models and those of other important genetic models, such as meiosis. This is perhaps in part because they lacked an understanding of models as conceptual rather than physical entities. Subsequently, the genetics curriculum was altered in order to create more opportunities for students to address epistemological issues associated with model assessment throughout the course. The second cohort of students' understanding of models changed over the nine-week period: initially the majority of students equated scientific models with "proof" (generally physical) of "theories"; at the end of the course, most students demonstrated understanding of the conceptual nature of scientific models and the need to justify such knowledge according to both its empirical utility and conceptual consistency. Through model construction and assessment (i.e. scientific inquiry), students were able to come to a rich understanding of both the central concepts of transmission genetics and important epistemological aspects of genetic practice.

An integrative, translational approach to understanding rare and orphan genetically based diseases

PubMed Central

Hoehndorf, Robert; Schofield, Paul N.; Gkoutos, Georgios V.

2013-01-01

PhenomeNet is an approach for integrating phenotypes across species and identifying candidate genes for genetic diseases based on the similarity between a disease and animal model phenotypes. In contrast to ‘guilt-by-association’ approaches, PhenomeNet relies exclusively on the comparison of phenotypes to suggest candidate genes, and can, therefore, be applied to study the molecular basis of rare and orphan diseases for which the molecular basis is unknown. In addition to disease phenotypes from the Online Mendelian Inheritance in Man (OMIM) database, we have now integrated the clinical signs from Orphanet into PhenomeNet. We demonstrate that our approach can efficiently identify known candidate genes for genetic diseases in Orphanet and OMIM. Furthermore, we find evidence that mutations in the HIP1 gene might cause Bassoe syndrome, a rare disorder with unknown genetic aetiology. Our results demonstrate that integration and computational analysis of human disease and animal model phenotypes using PhenomeNet has the potential to reveal novel insights into the pathobiology underlying genetic diseases. PMID:23853703

Modelling female fertility traits in beef cattle using linear and non-linear models.

PubMed

Naya, H; Peñagaricano, F; Urioste, J I

2017-06-01

Female fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h 2  < 0.08 and r < 0.13, for linear models; h 2  > 0.23 and r > 0.24, for non-linear models). While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. © 2017 Blackwell Verlag GmbH.

Additive Genetic Variability and the Bayesian Alphabet

PubMed Central

Gianola, Daniel; de los Campos, Gustavo; Hill, William G.; Manfredi, Eduardo; Fernando, Rohan

2009-01-01

The use of all available molecular markers in statistical models for prediction of quantitative traits has led to what could be termed a genomic-assisted selection paradigm in animal and plant breeding. This article provides a critical review of some theoretical and statistical concepts in the context of genomic-assisted genetic evaluation of animals and crops. First, relationships between the (Bayesian) variance of marker effects in some regression models and additive genetic variance are examined under standard assumptions. Second, the connection between marker genotypes and resemblance between relatives is explored, and linkages between a marker-based model and the infinitesimal model are reviewed. Third, issues associated with the use of Bayesian models for marker-assisted selection, with a focus on the role of the priors, are examined from a theoretical angle. The sensitivity of a Bayesian specification that has been proposed (called “Bayes A”) with respect to priors is illustrated with a simulation. Methods that can solve potential shortcomings of some of these Bayesian regression procedures are discussed briefly. PMID:19620397

Nemo: an evolutionary and population genetics programming framework.

PubMed

Guillaume, Frédéric; Rougemont, Jacques

2006-10-15

Nemo is an individual-based, genetically explicit and stochastic population computer program for the simulation of population genetics and life-history trait evolution in a metapopulation context. It comes as both a C++ programming framework and an executable program file. Its object-oriented programming design gives it the flexibility and extensibility needed to implement a large variety of forward-time evolutionary models. It provides developers with abstract models allowing them to implement their own life-history traits and life-cycle events. Nemo offers a large panel of population models, from the Island model to lattice models with demographic or environmental stochasticity and a variety of already implemented traits (deleterious mutations, neutral markers and more), life-cycle events (mating, dispersal, aging, selection, etc.) and output operators for saving data and statistics. It runs on all major computer platforms including parallel computing environments. The source code, binaries and documentation are available under the GNU General Public License at http://nemo2.sourceforge.net.

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.

From psychiatric disorders to animal models: a bidirectional and dimensional approach

PubMed Central

Donaldson, Zoe. R.; Hen, René

2014-01-01

Psychiatric genetics research is bidirectional in nature, with human and animal studies becoming more closely integrated as techniques for genetic manipulations allow for more subtle exploration of disease phenotypes. This synergy, however, highlights the importance of considering the way in which we approach the genotype-phenotype relationship. In particular, the nosological divide of psychiatric illness, while clinically relevant, is not directly translatable in animal models. For instance, mice will never fully re-capitulate the broad criteria for many psychiatric disorders; nor will they have guilty ruminations, suicidal thoughts, or rapid speech. Instead, animal models have been and continue to provide a means to explore dimensions of psychiatric disorders in order to identify neural circuits and mechanisms underlying disease-relevant phenotypes. Thus, the genetic investigation of psychiatric illness will yield the greatest insights if efforts continue to identify and utilize biologically valid phenotypes across species. In this review we discuss the progress to date and the future efforts that will enhance translation between human and animal studies, including the identification of intermediate phenotypes that can be studied across species, as well as the importance of refined modeling of human disease-associated genetic variation in mice and other animal models. PMID:24650688

Coalescent Modelling Suggests Recent Secondary-Contact of Cryptic Penguin Species

PubMed Central

Grosser, Stefanie; Burridge, Christopher P.; Peucker, Amanda J.; Waters, Jonathan M.

2015-01-01

Molecular genetic analyses present powerful tools for elucidating demographic and biogeographic histories of taxa. Here we present genetic evidence showing a dynamic history for two cryptic lineages within Eudyptula, the world's smallest penguin. Specifically, we use a suite of genetic markers to reveal that two congeneric taxa ('Australia' and 'New Zealand') co-occur in southern New Zealand, with only low levels of hybridization. Coalescent modelling suggests that the Australian little penguin only recently expanded into southern New Zealand. Analyses conducted under time-dependent molecular evolutionary rates lend support to the hypothesis of recent anthropogenic turnover, consistent with shifts detected in several other New Zealand coastal vertebrate taxa. This apparent turnover event highlights the dynamic nature of the region’s coastal ecosystem. PMID:26675310

Coalescent Modelling Suggests Recent Secondary-Contact of Cryptic Penguin Species.

PubMed

Grosser, Stefanie; Burridge, Christopher P; Peucker, Amanda J; Waters, Jonathan M

2015-01-01

Molecular genetic analyses present powerful tools for elucidating demographic and biogeographic histories of taxa. Here we present genetic evidence showing a dynamic history for two cryptic lineages within Eudyptula, the world's smallest penguin. Specifically, we use a suite of genetic markers to reveal that two congeneric taxa ('Australia' and 'New Zealand') co-occur in southern New Zealand, with only low levels of hybridization. Coalescent modelling suggests that the Australian little penguin only recently expanded into southern New Zealand. Analyses conducted under time-dependent molecular evolutionary rates lend support to the hypothesis of recent anthropogenic turnover, consistent with shifts detected in several other New Zealand coastal vertebrate taxa. This apparent turnover event highlights the dynamic nature of the region's coastal ecosystem.

The infinitesimal model: Definition, derivation, and implications.

PubMed

Barton, N H; Etheridge, A M; Véber, A

2017-12-01

Our focus here is on the infinitesimal model. In this model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed within families as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. Thus, the variance that segregates within families is not perturbed by selection, and can be predicted from the variance components. This does not necessarily imply that the trait distribution across the whole population should be Gaussian, and indeed selection or population structure may have a substantial effect on the overall trait distribution. One of our main aims is to identify some general conditions on the allelic effects for the infinitesimal model to be accurate. We first review the long history of the infinitesimal model in quantitative genetics. Then we formulate the model at the phenotypic level in terms of individual trait values and relationships between individuals, but including different evolutionary processes: genetic drift, recombination, selection, mutation, population structure, …. We give a range of examples of its application to evolutionary questions related to stabilising selection, assortative mating, effective population size and response to selection, habitat preference and speciation. We provide a mathematical justification of the model as the limit as the number M of underlying loci tends to infinity of a model with Mendelian inheritance, mutation and environmental noise, when the genetic component of the trait is purely additive. We also show how the model generalises to include epistatic effects. We prove in particular that, within each family, the genetic components of the individual trait values in the current generation are indeed normally distributed with a variance independent of ancestral traits, up to an error of order 1∕M. Simulations suggest that in some cases the convergence may be as fast as 1∕M. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

The Sphagnome Project: enabling ecological and evolutionary insights through a genus-level sequencing project

DOE PAGES

Weston, David J.; Turetsky, Merritt R.; Johnson, Matthew G.; ...

2017-10-27

Considerable progress has been made in ecological and evolutionary genetics with studies demonstrating how genes underlying plant and microbial traits can influence adaptation and even ‘extend’ to influence community structure and ecosystem level processes. The progress in this area is limited to model systems with deep genetic and genomic resources that often have negligible ecological impact or interest. Therefore, important linkages between genetic adaptations and their consequences at organismal and ecological scales are often lacking. We introduce the Sphagnome Project, which incorporates genomics into a long-running history of Sphagnum research that has documented unparalleled contributions to peatland ecology, carbon sequestration,more » biogeochemistry, microbiome research, niche construction, and ecosystem engineering. The Sphagnome Project encompasses a genus-level sequencing effort that represents a new type of model system driven not only by genetic tractability, but by ecologically relevant questions and hypotheses.« less

The interaction of host genetics and disease processes in chronic livestock disease: a simulation model of ovine footrot.

PubMed

Russell, V N L; Green, L E; Bishop, S C; Medley, G F

2013-03-01

A stochastic, individual-based, simulation model of footrot in a flock of 200 ewes was developed that included flock demography, disease processes, host genetic variation for traits influencing infection and disease processes, and bacterial contamination of the environment. Sensitivity analyses were performed using ANOVA to examine the contribution of unknown parameters to outcome variation. The infection rate and bacterial death rate were the most significant factors determining the observed prevalence of footrot, as well as the heritability of resistance. The dominance of infection parameters in determining outcomes implies that observational data cannot be used to accurately estimate the strength of genetic control of underlying traits describing the infection process, i.e. resistance. Further work will allow us to address the potential for genetic selection to control ovine footrot. Copyright © 2012 Elsevier B.V. All rights reserved.

The Sphagnome Project: enabling ecological and evolutionary insights through a genus-level sequencing project

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

Weston, David J.; Turetsky, Merritt R.; Johnson, Matthew G.

Considerable progress has been made in ecological and evolutionary genetics with studies demonstrating how genes underlying plant and microbial traits can influence adaptation and even ‘extend’ to influence community structure and ecosystem level processes. The progress in this area is limited to model systems with deep genetic and genomic resources that often have negligible ecological impact or interest. Therefore, important linkages between genetic adaptations and their consequences at organismal and ecological scales are often lacking. We introduce the Sphagnome Project, which incorporates genomics into a long-running history of Sphagnum research that has documented unparalleled contributions to peatland ecology, carbon sequestration,more » biogeochemistry, microbiome research, niche construction, and ecosystem engineering. The Sphagnome Project encompasses a genus-level sequencing effort that represents a new type of model system driven not only by genetic tractability, but by ecologically relevant questions and hypotheses.« less

The Sphagnome Project: enabling ecological and evolutionary insights through a genus-level sequencing project.

PubMed

Weston, David J; Turetsky, Merritt R; Johnson, Matthew G; Granath, Gustaf; Lindo, Zoë; Belyea, Lisa R; Rice, Steven K; Hanson, David T; Engelhardt, Katharina A M; Schmutz, Jeremy; Dorrepaal, Ellen; Euskirchen, Eugénie S; Stenøien, Hans K; Szövényi, Péter; Jackson, Michelle; Piatkowski, Bryan T; Muchero, Wellington; Norby, Richard J; Kostka, Joel E; Glass, Jennifer B; Rydin, Håkan; Limpens, Juul; Tuittila, Eeva-Stiina; Ullrich, Kristian K; Carrell, Alyssa; Benscoter, Brian W; Chen, Jin-Gui; Oke, Tobi A; Nilsson, Mats B; Ranjan, Priya; Jacobson, Daniel; Lilleskov, Erik A; Clymo, R S; Shaw, A Jonathan

2018-01-01

Considerable progress has been made in ecological and evolutionary genetics with studies demonstrating how genes underlying plant and microbial traits can influence adaptation and even 'extend' to influence community structure and ecosystem level processes. Progress in this area is limited to model systems with deep genetic and genomic resources that often have negligible ecological impact or interest. Thus, important linkages between genetic adaptations and their consequences at organismal and ecological scales are often lacking. Here we introduce the Sphagnome Project, which incorporates genomics into a long-running history of Sphagnum research that has documented unparalleled contributions to peatland ecology, carbon sequestration, biogeochemistry, microbiome research, niche construction, and ecosystem engineering. The Sphagnome Project encompasses a genus-level sequencing effort that represents a new type of model system driven not only by genetic tractability, but by ecologically relevant questions and hypotheses. © 2017 UT-Battelle New Phytologist © 2017 New Phytologist Trust.

Factors affecting commercial application of embryo technologies in dairy cattle in Europe--a modelling approach.

PubMed

van Arendonk, Johan A M; Bijma, Piter

2003-01-15

Reproductive techniques have a major impact on the structure of breeding programmes, the rate of genetic gain and dissemination of genetic gain in populations. This manuscript reviews the impact of reproductive technologies on the underlying components of genetic gain and inbreeding with special reference to the role of female reproductive technology. Evaluation of alternative breeding schemes should be based on genetic gain while constraining inbreeding. Optimum breeding schemes can be characterised by: decreased importance of sib information; increased accuracy at the expense of intensity; and a factorial mating strategy. If large-scale embryo cloning becomes feasible, this will have a small impact on the rate of genetic gain but will have a large impact on the structure of breeding programmes.

Predicting human genetic interactions from cancer genome evolution.

PubMed

Lu, Xiaowen; Megchelenbrink, Wout; Notebaart, Richard A; Huynen, Martijn A

2015-01-01

Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

Human X-chromosome inactivation pattern distributions fit a model of genetically influenced choice better than models of completely random choice

PubMed Central

Renault, Nisa K E; Pritchett, Sonja M; Howell, Robin E; Greer, Wenda L; Sapienza, Carmen; Ørstavik, Karen Helene; Hamilton, David C

2013-01-01

In eutherian mammals, one X-chromosome in every XX somatic cell is transcriptionally silenced through the process of X-chromosome inactivation (XCI). Females are thus functional mosaics, where some cells express genes from the paternal X, and the others from the maternal X. The relative abundance of the two cell populations (X-inactivation pattern, XIP) can have significant medical implications for some females. In mice, the ‘choice' of which X to inactivate, maternal or paternal, in each cell of the early embryo is genetically influenced. In humans, the timing of XCI choice and whether choice occurs completely randomly or under a genetic influence is debated. Here, we explore these questions by analysing the distribution of XIPs in large populations of normal females. Models were generated to predict XIP distributions resulting from completely random or genetically influenced choice. Each model describes the discrete primary distribution at the onset of XCI, and the continuous secondary distribution accounting for changes to the XIP as a result of development and ageing. Statistical methods are used to compare models with empirical data from Danish and Utah populations. A rigorous data treatment strategy maximises information content and allows for unbiased use of unphased XIP data. The Anderson–Darling goodness-of-fit statistics and likelihood ratio tests indicate that a model of genetically influenced XCI choice better fits the empirical data than models of completely random choice. PMID:23652377

Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean.

PubMed

Fang, Chao; Ma, Yanming; Wu, Shiwen; Liu, Zhi; Wang, Zheng; Yang, Rui; Hu, Guanghui; Zhou, Zhengkui; Yu, Hong; Zhang, Min; Pan, Yi; Zhou, Guoan; Ren, Haixiang; Du, Weiguang; Yan, Hongrui; Wang, Yanping; Han, Dezhi; Shen, Yanting; Liu, Shulin; Liu, Tengfei; Zhang, Jixiang; Qin, Hao; Yuan, Jia; Yuan, Xiaohui; Kong, Fanjiang; Liu, Baohui; Li, Jiayang; Zhang, Zhiwu; Wang, Guodong; Zhu, Baoge; Tian, Zhixi

2017-08-24

Soybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding. To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits. This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.

Panel 4: Recent Advances in Otitis Media in Molecular Biology, Biochemistry, Genetics, and Animal Models

PubMed Central

Li, Jian-Dong; Hermansson, Ann; Ryan, Allen F.; Bakaletz, Lauren O.; Brown, Steve D.; Cheeseman, Michael T.; Juhn, Steven K.; Jung, Timothy T. K.; Lim, David J.; Lim, Jae Hyang; Lin, Jizhen; Moon, Sung-Kyun; Post, J. Christopher

2014-01-01

Background Otitis media (OM) is the most common childhood bacterial infection and also the leading cause of conductive hearing loss in children. Currently, there is an urgent need for developing novel therapeutic agents for treating OM based on full understanding of molecular pathogenesis in the areas of molecular biology, biochemistry, genetics, and animal model studies in OM. Objective To provide a state-of-the-art review concerning recent advances in OM in the areas of molecular biology, biochemistry, genetics, and animal model studies and to discuss the future directions of OM studies in these areas. Data Sources and Review Methods A structured search of the current literature (since June 2007). The authors searched PubMed for published literature in the areas of molecular biology, biochemistry, genetics, and animal model studies in OM. Results Over the past 4 years, significant progress has been made in the areas of molecular biology, biochemistry, genetics, and animal model studies in OM. These studies brought new insights into our understanding of the molecular and biochemical mechanisms underlying the molecular pathogenesis of OM and helped identify novel therapeutic targets for OM. Conclusions and Implications for Practice Our understanding of the molecular pathogenesis of OM has been significantly advanced, particularly in the areas of inflammation, innate immunity, mucus overproduction, mucosal hyperplasia, middle ear and inner ear interaction, genetics, genome sequencing, and animal model studies. Although these studies are still in their experimental stages, they help identify new potential therapeutic targets. Future preclinical and clinical studies will help to translate these exciting experimental research findings into clinical applications. PMID:23536532

Nurture Net of Nature: Re-Evaluating the Role of Shared Environments in Academic Achievement and Verbal Intelligence

PubMed Central

Daw, Jonathan; Guo, Guang; Harris, Kathie Mullan

2016-01-01

Prominent authors in the behavioral genetics tradition have long argued that shared environments do not meaningfully shape intelligence and academic achievement. However, we argue that these conclusions are erroneous due to large violations of the additivity assumption underlying behavioral genetics methods – that sources of genetic and shared and nonshared environmental variance are independent and non-interactive. This is compounded in some cases by the theoretical equation of the effective and objective environments, where the former is defined by whether siblings are made more or less similar, and the latter by whether siblings are equally subject to the environmental characteristic in question. Using monozygotic twin fixed effects models, which compare outcomes among genetically identical pairs, we show that many characteristics of objectively shared environments significantly moderate the effects of nonshared environments on adolescent academic achievement and verbal intelligence, violating the additivity assumption of behavioral genetic methods. Importantly, these effects would be categorized as nonshared environmental influences in standard twin models despite their roots in shared environments. These findings should encourage caution among those who claim that the frequently trivial variance attributed to shared environments in behavioral genetic models means that families, schools, and neighborhoods do not meaningfully influence these outcomes. PMID:26004471

Nurture net of nature: Re-evaluating the role of shared environments in academic achievement and verbal intelligence.

PubMed

Daw, Jonathan; Guo, Guang; Harris, Kathie Mullan

2015-07-01

Prominent authors in the behavioral genetics tradition have long argued that shared environments do not meaningfully shape intelligence and academic achievement. However, we argue that these conclusions are erroneous due to large violations of the additivity assumption underlying behavioral genetics methods - that sources of genetic and shared and nonshared environmental variance are independent and non-interactive. This is compounded in some cases by the theoretical equation of the effective and objective environments, where the former is defined by whether siblings are made more or less similar, and the latter by whether siblings are equally subject to the environmental characteristic in question. Using monozygotic twin fixed effects models, which compare outcomes among genetically identical pairs, we show that many characteristics of objectively shared environments significantly moderate the effects of nonshared environments on adolescent academic achievement and verbal intelligence, violating the additivity assumption of behavioral genetic methods. Importantly, these effects would be categorized as nonshared environmental influences in standard twin models despite their roots in shared environments. These findings should encourage caution among those who claim that the frequently trivial variance attributed to shared environments in behavioral genetic models means that families, schools, and neighborhoods do not meaningfully influence these outcomes. Copyright © 2015. Published by Elsevier Inc.

Inference on the Genetic Basis of Eye and Skin Color in an Admixed Population via Bayesian Linear Mixed Models.

PubMed

Lloyd-Jones, Luke R; Robinson, Matthew R; Moser, Gerhard; Zeng, Jian; Beleza, Sandra; Barsh, Gregory S; Tang, Hua; Visscher, Peter M

2017-06-01

Genetic association studies in admixed populations are underrepresented in the genomics literature, with a key concern for researchers being the adequate control of spurious associations due to population structure. Linear mixed models (LMMs) are well suited for genome-wide association studies (GWAS) because they account for both population stratification and cryptic relatedness and achieve increased statistical power by jointly modeling all genotyped markers. Additionally, Bayesian LMMs allow for more flexible assumptions about the underlying distribution of genetic effects, and can concurrently estimate the proportion of phenotypic variance explained by genetic markers. Using three recently published Bayesian LMMs, Bayes R, BSLMM, and BOLT-LMM, we investigate an existing data set on eye ( n = 625) and skin ( n = 684) color from Cape Verde, an island nation off West Africa that is home to individuals with a broad range of phenotypic values for eye and skin color due to the mix of West African and European ancestry. We use simulations to demonstrate the utility of Bayesian LMMs for mapping loci and studying the genetic architecture of quantitative traits in admixed populations. The Bayesian LMMs provide evidence for two new pigmentation loci: one for eye color ( AHRR ) and one for skin color ( DDB1 ). Copyright © 2017 by the Genetics Society of America.

A test of genetic models for the evolutionary maintenance of same-sex sexual behaviour.

PubMed

Hoskins, Jessica L; Ritchie, Michael G; Bailey, Nathan W

2015-06-22

The evolutionary maintenance of same-sex sexual behaviour (SSB) has received increasing attention because it is perceived to be an evolutionary paradox. The genetic basis of SSB is almost wholly unknown in non-human animals, though this is key to understanding its persistence. Recent theoretical work has yielded broadly applicable predictions centred on two genetic models for SSB: overdominance and sexual antagonism. Using Drosophila melanogaster, we assayed natural genetic variation for male SSB and empirically tested predictions about the mode of inheritance and fitness consequences of alleles influencing its expression. We screened 50 inbred lines derived from a wild population for male-male courtship and copulation behaviour, and examined crosses between the lines for evidence of overdominance and antagonistic fecundity selection. Consistent variation among lines revealed heritable genetic variation for SSB, but the nature of the genetic variation was complex. Phenotypic and fitness variation was consistent with expectations under overdominance, although predictions of the sexual antagonism model were also supported. We found an unexpected and strong paternal effect on the expression of SSB, suggesting possible Y-linkage of the trait. Our results inform evolutionary genetic mechanisms that might maintain low but persistently observed levels of male SSB in D. melanogaster, but highlight a need for broader taxonomic representation in studies of its evolutionary causes. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Evolutionary genetics of host shifts in herbivorous insects: insights from the age of genomics.

PubMed

Vertacnik, Kim L; Linnen, Catherine R

2017-02-01

Adaptation to different host taxa is a key driver of insect diversification. Herbivorous insects are classic models for ecological and evolutionary research, but it is recent advances in sequencing, statistics, and molecular technologies that have cleared the way for investigations into the proximate genetic mechanisms underlying host shifts. In this review, we discuss how genome-scale data are revealing-at resolutions previously unimaginable-the genetic architecture of host-use traits, the causal loci underlying host shifts, and the predictability of host-use evolution. Collectively, these studies are providing novel insights into longstanding questions about host-use evolution. On the basis of this synthesis, we suggest that different host-use traits are likely to differ in their genetic architecture (number of causal loci and the nature of their genetic correlations) and genetic predictability (extent of gene or mutation reuse), indicating that any conclusions about the causes and consequences of host-use evolution will depend heavily on which host-use traits are investigated. To draw robust conclusions and identify general patterns in host-use evolution, we argue that investigation of diverse host-use traits and identification of causal genes and mutations should be the top priorities for future studies on the evolutionary genetics of host shifts. © 2017 New York Academy of Sciences.

Oceanographic connectivity and environmental correlates of genetic structuring in Atlantic herring in the Baltic Sea

PubMed Central

Teacher, Amber GF; André, Carl; Jonsson, Per R; Merilä, Juha

2013-01-01

Marine fish often show little genetic structuring in neutral marker genes, and Atlantic herring (Clupea harengus) in the Baltic Sea are no exception; historically, very low levels of population differentiation (FST ≍ 0.002) have been found, despite a high degree of interpopulation environmental heterogeneity in salinity and temperature. Recent exome sequencing and SNP studies have however shown that many loci are under selection in this system. Here, we combined population genetic analyses of a large number of transcriptome-derived microsatellite markers with oceanographic modelling to investigate genetic differentiation and connectivity in Atlantic herring at a relatively fine scale within the Baltic Sea. We found evidence for weak but robust and significant genetic structuring (FST = 0.008) explainable by oceanographic connectivity. Genetic differentiation was also associated with site differences in temperature and salinity, with the result driven by the locus Her14 which appears to be under directional selection (FST = 0.08). The results show that Baltic herring are genetically structured within the Baltic Sea, and highlight the role of oceanography and environmental factors in explaining this structuring. The results also have implications for the management of herring fisheries, the most economically important fishery in the Baltic Sea, suggesting that the current fisheries management units may be in need of revision. PMID:23745145

The Nile Rat (Arvicanthis niloticus) as a Superior Carbohydrate-Sensitive Model for Type 2 Diabetes Mellitus (T2DM)

PubMed Central

Landstrom, Michelle; Luu, Alice; Hayes, K. C.

2018-01-01

Type II diabetes mellitus (T2DM) is a multifactorial disease involving complex genetic and environmental interactions. No single animal model has so far mirrored all the characteristics or complications of diabetes in humans. Since this disease represents a chronic nutritional insult based on a diet bearing a high glycemic load, the ideal model should recapitulate the underlying dietary issues. Most rodent models have three shortcomings: (1) they are genetically or chemically modified to produce diabetes; (2) unlike humans, most require high-fat feeding; (3) and they take too long to develop diabetes. By contrast, Nile rats develop diabetes rapidly (8–10 weeks) with high-carbohydrate (hiCHO) diets, similar to humans, and are protected by high fat (with low glycemic load) intake. This review describes diabetes progression in the Nile rat, including various aspects of breeding, feeding, and handling for best experimental outcomes. The diabetes is characterized by a striking genetic permissiveness influencing hyperphagia and hyperinsulinemia; random blood glucose is the best index of disease progression; and kidney failure with chronic morbidity and death are outcomes, all of which mimic uncontrolled T2DM in humans. Non-alcoholic fatty liver disease (NAFLD), also described in diabetic humans, results from hepatic triglyceride and cholesterol accumulation associated with rising blood glucose. Protection is afforded by low glycemic load diets rich in certain fibers or polyphenols. Accordingly, the Nile rat provides a unique opportunity to identify the nutritional factors and underlying genetic and molecular mechanisms that characterize human T2DM. PMID:29463026

Comparison of Two New Mouse Models of Polygenic Type 2 Diabetes at the Jackson Laboratory, NONcNZO10Lt/J and TALLYHO/JngJ

PubMed Central

Leiter, Edward H.; Strobel, Marjorie; Schultz, David; Schile, Andrew; Reifsnyder, Peter C.

2013-01-01

This review compares two novel polygenic mouse models of type 2 diabetes (T2D), TALLYHO/JngJ and NONcNZO10/LtJ, and contrasts both with the well-known C57BLKS/J-Leprdb (db/db) monogenic diabesity model. We posit that the new polygenic models are more representative of the “garden variety” obesity underlying human T2D in terms of their polygenetic rather than monogenic etiology. Moreover, the clinical phenotypes in these new models are less extreme, for example, more moderated development of obesity coupled with less extreme endocrine disturbances. The more progressive development of obesity produces a maturity-onset development of hyperglycemia in contrast to the juvenile-onset diabetes observed in the morbidly obese db/db model. Unlike the leptin receptor-deficient db/db models with central leptin resistance, the new models develop a progressive peripheral leptin resistance and are able to maintain reproductive function. Although the T2D pathophysiology in both TALLYHO/JngJ and NONcNZO10/LtJ is remarkably similar, their genetic etiologies are clearly different, underscoring the genetic heterogeneity underlying T2D in humans. PMID:23671854

Comparison of Two New Mouse Models of Polygenic Type 2 Diabetes at the Jackson Laboratory, NONcNZO10Lt/J and TALLYHO/JngJ.

PubMed

Leiter, Edward H; Strobel, Marjorie; O'Neill, Adam; Schultz, David; Schile, Andrew; Reifsnyder, Peter C

2013-01-01

This review compares two novel polygenic mouse models of type 2 diabetes (T2D), TALLYHO/JngJ and NONcNZO10/LtJ, and contrasts both with the well-known C57BLKS/J-Lepr(db) (db/db) monogenic diabesity model. We posit that the new polygenic models are more representative of the "garden variety" obesity underlying human T2D in terms of their polygenetic rather than monogenic etiology. Moreover, the clinical phenotypes in these new models are less extreme, for example, more moderated development of obesity coupled with less extreme endocrine disturbances. The more progressive development of obesity produces a maturity-onset development of hyperglycemia in contrast to the juvenile-onset diabetes observed in the morbidly obese db/db model. Unlike the leptin receptor-deficient db/db models with central leptin resistance, the new models develop a progressive peripheral leptin resistance and are able to maintain reproductive function. Although the T2D pathophysiology in both TALLYHO/JngJ and NONcNZO10/LtJ is remarkably similar, their genetic etiologies are clearly different, underscoring the genetic heterogeneity underlying T2D in humans.

Population genetics inference for longitudinally-sampled mutants under strong selection.

PubMed

Lacerda, Miguel; Seoighe, Cathal

2014-11-01

Longitudinal allele frequency data are becoming increasingly prevalent. Such samples permit statistical inference of the population genetics parameters that influence the fate of mutant variants. To infer these parameters by maximum likelihood, the mutant frequency is often assumed to evolve according to the Wright-Fisher model. For computational reasons, this discrete model is commonly approximated by a diffusion process that requires the assumption that the forces of natural selection and mutation are weak. This assumption is not always appropriate. For example, mutations that impart drug resistance in pathogens may evolve under strong selective pressure. Here, we present an alternative approximation to the mutant-frequency distribution that does not make any assumptions about the magnitude of selection or mutation and is much more computationally efficient than the standard diffusion approximation. Simulation studies are used to compare the performance of our method to that of the Wright-Fisher and Gaussian diffusion approximations. For large populations, our method is found to provide a much better approximation to the mutant-frequency distribution when selection is strong, while all three methods perform comparably when selection is weak. Importantly, maximum-likelihood estimates of the selection coefficient are severely attenuated when selection is strong under the two diffusion models, but not when our method is used. This is further demonstrated with an application to mutant-frequency data from an experimental study of bacteriophage evolution. We therefore recommend our method for estimating the selection coefficient when the effective population size is too large to utilize the discrete Wright-Fisher model. Copyright © 2014 by the Genetics Society of America.

Developmental-genetic effects on level and change in childhood fears of twins during adolescence.

PubMed

Eaves, Lindon J; Silberg, Judy L

2008-11-01

If the adaptive significance of specific fears changes with age, the genetic contribution to individual differences may be lowest at the age of greatest salience. The roles of genes and environment in the developmental-genetic trajectory of five common childhood fears are explored in 1094 like-sex pairs of male and female monozygotic and dizygotic twins assessed on up to three occasions during adolescence (ages 8-18 years). Dichotomous self-ratings of a cluster of five correlated fears from Ollendick's schedule of fears (FSSC-R) were extracted for subjects at each occasion of assessment. The effects of genes and environment on overall level of fears and rates of adolescent decline were explored by fitting an item-response theory ('IRT') model that allowed for individual genetic and environmental differences in initial fear level ('intercept') and rates of adolescent change ('slope') across the repeated waves of measurement. Different forms of the model were explored using Markov Chain Monte Carlo (MCMC) methods to derive the posterior distribution of subject and item parameters from the raw responses. Additive genetic differences affect the common factor underlying the five fear-items. The same genes also affect rates of change with age, especially in boys. Male adolescents with higher overall genetic predisposition to childhood fears tended to show slower recovery with age than subjects with relatively low initial values. Thus, the genetic variance apparently increases with age. This finding is consistent with a prediction that the regulation of genetic differences will be strongest, and thus the additive genetic variance will be smallest, at the age when the particular stimulus is most salient. Items differed in the extent to which they were sensitive to underlying random differences in the rate of developmental change. Individual differences in rates of change with age were more marked in boys than girls.

The big five personality traits: psychological entities or statistical constructs?

PubMed

Franić, Sanja; Borsboom, Denny; Dolan, Conor V; Boomsma, Dorret I

2014-11-01

The present study employed multivariate genetic item-level analyses to examine the ontology and the genetic and environmental etiology of the Big Five personality dimensions, as measured by the NEO Five Factor Inventory (NEO-FFI) [Costa and McCrae, Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO-FFI) professional manual, 1992; Hoekstra et al., NEO personality questionnaires NEO-PI-R, NEO-FFI: manual, 1996]. Common and independent pathway model comparison was used to test whether the five personality dimensions fully mediate the genetic and environmental effects on the items, as would be expected under the realist interpretation of the Big Five. In addition, the dimensionalities of the latent genetic and environmental structures were examined. Item scores of a population-based sample of 7,900 adult twins (including 2,805 complete twin pairs; 1,528 MZ and 1,277 DZ) on the Dutch version of the NEO-FFI were analyzed. Although both the genetic and the environmental covariance components display a 5-factor structure, applications of common and independent pathway modeling showed that they do not comply with the collinearity constraints entailed in the common pathway model. Implications for the substantive interpretation of the Big Five are discussed.

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

Burger, Brian T.; Imam, Saheed; Scarborough, Matthew J.

Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosyntheticmore » growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species.« less

Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster.

PubMed

Morgante, Fabio; Sørensen, Peter; Sorensen, Daniel A; Maltecca, Christian; Mackay, Trudy F C

2015-05-06

Individuals of the same genotype do not have the same phenotype for quantitative traits when reared under common macro-environmental conditions, a phenomenon called micro-environmental plasticity. Genetic variation in micro-environmental plasticity is assumed in models of the evolution of phenotypic variance, and is important in applied breeding and personalized medicine. Here, we quantified genetic variation for micro-environmental plasticity for three quantitative traits in the inbred, sequenced lines of the Drosophila melanogaster Genetic Reference Panel. We found substantial genetic variation for micro-environmental plasticity for all traits, with broad sense heritabilities of the same magnitude or greater than those of trait means. Micro-environmental plasticity is not correlated with residual segregating variation, is trait-specific, and has genetic correlations with trait means ranging from zero to near unity. We identified several candidate genes associated with micro-environmental plasticity of startle response, including Drosophila Hsp90, setting the stage for future genetic dissection of this phenomenon.

Role of genetics in adapting forests under climate change: lessons learned from common garden experiments in central Europe

NASA Astrophysics Data System (ADS)

Chakraborty, Debojyoti; Schueler, Silvio

2017-04-01

Adaptive management aiming at reducing vulnerability and enhancing the resilience of forested ecosystems is a key to preserving the potential of forests to provide multiple ecosystem services under climate change. Planting alternative or non native tree species adapted to future conditions and also utilizing the genetic variation within tree species has also been suggested as an important adaptive management strategy under climate change. Therefore, knowledge on suitable provenances/populations is a key issue. Provenance trial experiments, where several populations of a species are planted in a particular climate or throughout an appropriate climatic gradient offers a great opportunity to understand adaptive genetic variation within a tree species. These trials were primarily established, for identifying populations with desired growth and fitness characteristics. Due to the increasing interest in climate change, such trials were revisited to understand the relation between growth performance and climate and to recommend suitable populations for future conditions. Here we present the lessons learned from provenance trials of Norway spruce and Douglas -fir in central Europe. With data from provenance trials planted across a wide range of environmental conditions in central Europe we developed multivariate models, Universal Response Functions (URFs). The URFs predict growth performance as a function of climate of planting locations (i.e. environmental factors) and provenance/ population origin (i.e. genetic factors). The flexibility of the URFs as a decision making tool is remarkable. The model can be used as to identify suitable planting material for a give site, and vice versa and also as a species distribution model (SDM) with integrated genetic variation. Under current and climate change scenarios, the URFs were applied to predict populations with higher growth performance in central Europe and also as species distribution models for Douglas-fir (Pseudotsuga menziesii [Mirbel] Franco) and Norway spruce (Picea abies (L.) Karst). For both Douglas-fir and Norway spruce wide variation in growth performance were detected. Populations of Douglas-fir identified by the URFs to be optimum for central Europe current climate and climate change scenarios originate from western Cascades and coastal areas of British Columbia, Washington and Oregon. The current seed stands of Douglas-fir in North America, providing planting materials for Central Europe under the legal framework of the Organization for Economic Cooperation and Development (OECD) were found to be suitable for under future conditions. In case of Norway spruce provenances originating from warm and drier regions of south east Europe were found to be suitable for central Europe under future conditions. Even though calibrated with data from Central Europe, when applied as SDMs, the URFs predicted the observed occurrence of Douglas-fir in its native range in North America with reasonable accuracy compared to contemporary SDMs developed in North America. For both Douglas-fir and Norway spruce significant variation in habitat suitability was found depending on the planted population or seed source indicating the role of intraspecific variation in buffering effects of climate change.

GENES AS INSTRUMENTS FOR STUDYING RISK BEHAVIOR EFFECTS: AN APPLICATION TO MATERNAL SMOKING AND OROFACIAL CLEFTS

PubMed Central

Jugessur, Astanand; Murray, Jeffrey C.; Moreno, Lina; Wilcox, Allen; Lie, Rolv T.

2011-01-01

This study uses instrumental variable (IV) models with genetic instruments to assess the effects of maternal smoking on the child’s risk of orofacial clefts (OFC), a common birth defect. The study uses genotypic variants in neurotransmitter and detoxification genes relateded to smoking as instruments for cigarette smoking before and during pregnancy. Conditional maximum likelihood and two-stage IV probit models are used to estimate the IV model. The data are from a population-level sample of affected and unaffected children in Norway. The selected genetic instruments generally fit the IV assumptions but may be considered “weak” in predicting cigarette smoking. We find that smoking before and during pregnancy increases OFC risk substantially under the IV model (by about 4–5 times at the sample average smoking rate). This effect is greater than that found with classical analytic models. This may be because the usual models are not able to consider self-selection into smoking based on unobserved confounders, or it may to some degree reflect limitations of the instruments. Inference based on weak-instrument robust confidence bounds is consistent with standard inference. Genetic instruments may provide a valuable approach to estimate the “causal” effects of risk behaviors with genetic-predisposing factors (such as smoking) on health and socioeconomic outcomes. PMID:22102793

Modern spandrels: the roles of genetic drift, gene flow and natural selection in the evolution of parallel clines.

PubMed

Santangelo, James S; Johnson, Marc T J; Ness, Rob W

2018-05-16

Urban environments offer the opportunity to study the role of adaptive and non-adaptive evolutionary processes on an unprecedented scale. While the presence of parallel clines in heritable phenotypic traits is often considered strong evidence for the role of natural selection, non-adaptive evolutionary processes can also generate clines, and this may be more likely when traits have a non-additive genetic basis due to epistasis. In this paper, we use spatially explicit simulations modelled according to the cyanogenesis (hydrogen cyanide, HCN) polymorphism in white clover ( Trifolium repens ) to examine the formation of phenotypic clines along urbanization gradients under varying levels of drift, gene flow and selection. HCN results from an epistatic interaction between two Mendelian-inherited loci. Our results demonstrate that the genetic architecture of this trait makes natural populations susceptible to decreases in HCN frequencies via drift. Gradients in the strength of drift across a landscape resulted in phenotypic clines with lower frequencies of HCN in strongly drifting populations, giving the misleading appearance of deterministic adaptive changes in the phenotype. Studies of heritable phenotypic change in urban populations should generate null models of phenotypic evolution based on the genetic architecture underlying focal traits prior to invoking selection's role in generating adaptive differentiation. © 2018 The Author(s).

Association study between variants in LHCGR DENND1A and THADA with preeclampsia risk in Han Chinese populations.

PubMed

Zhang, Ya-Jie; Li, Lei; Wang, Zhen-Jing; Zhang, Xiao-Jing; Zhao, Han; Zhao, Yan; Wang, Xie-Tong; Li, Chang-Zhong; Wan, Ji-Peng

2018-05-17

To evaluate the association between preeclampsia and three single nucleotide polymorphisms (rs13405728 in LHCGR gene; rs13429458 in THADA gene, and rs2479106 in DENND1A gene) which were identified to be genetic variants of polycystic ovary syndrome (PCOS) by genome-wide association study in Han Chinese populations. A total of 784 northern Han Chinese women (378 controls and 406 cases) were genotyped for the three genetic variants by polymerase chain reaction and direct sequencing. Unconditional logistic regression analysis was used to adjust the impact of prepregnancy body mass index, primiparas, and maternal age. No significant difference was found in the allele frequencies of the three genetic variants between cases and controls (p > .05), but genotype frequency of the SNP rs2479106 was significantly differ between cases and controls when analyzed under recessive models (p = .02). There was also a substantial difference in the genotype frequencies of the SNP rs13429458 between cases and controls under additive models (p = .01). Genetic variants of PCOS (rs13405728 in LHCGR gene; rs13429458 in THADA gene and rs2479106 in DENND1A gene) may not be involved in the development of preeclampsia in Han Chinese women.

Genetic information and ecosystem health: arguments for the application of chaos theory to identify boundary conditions for ecosystem management.

PubMed Central

Stomp, A M

1994-01-01

To meet the demands for goods and services of an exponentially growing human population, global ecosystems will come under increasing human management. The hallmark of successful ecosystem management will be long-term ecosystem stability. Ecosystems and the genetic information and processes which underlie interactions of organisms with the environment in populations and communities exhibit behaviors which have nonlinear characteristics. Nonlinear mathematical formulations describing deterministic chaos have been used successfully to model such systems in physics, chemistry, economics, physiology, and epidemiology. This approach can be extended to ecotoxicology and can be used to investigate how changes in genetic information determine the behavior of populations and communities. This article seeks to provide the arguments for such an approach and to give initial direction to the search for the boundary conditions within which lies ecosystem stability. The identification of a theoretical framework for ecotoxicology and the parameters which drive the underlying model is a critical component in the formulation of a prioritized research agenda and appropriate ecosystem management policy and regulation. PMID:7713038

The integration of quantitative genetics, paleontology, and neontology reveals genetic underpinnings of primate dental evolution.

PubMed

Hlusko, Leslea J; Schmitt, Christopher A; Monson, Tesla A; Brasil, Marianne F; Mahaney, Michael C

2016-08-16

Developmental genetics research on mice provides a relatively sound understanding of the genes necessary and sufficient to make mammalian teeth. However, mouse dentitions are highly derived compared with human dentitions, complicating the application of these insights to human biology. We used quantitative genetic analyses of data from living nonhuman primates and extensive osteological and paleontological collections to refine our assessment of dental phenotypes so that they better represent how the underlying genetic mechanisms actually influence anatomical variation. We identify ratios that better characterize the output of two dental genetic patterning mechanisms for primate dentitions. These two newly defined phenotypes are heritable with no measurable pleiotropic effects. When we consider how these two phenotypes vary across neontological and paleontological datasets, we find that the major Middle Miocene taxonomic shift in primate diversity is characterized by a shift in these two genetic outputs. Our results build on the mouse model by combining quantitative genetics and paleontology, and thereby elucidate how genetic mechanisms likely underlie major events in primate evolution.

Genetics of human hydrocephalus

PubMed Central

Williams, Michael A.; Rigamonti, Daniele

2006-01-01

Human hydrocephalus is a common medical condition that is characterized by abnormalities in the flow or resorption of cerebrospinal fluid (CSF), resulting in ventricular dilatation. Human hydrocephalus can be classified into two clinical forms, congenital and acquired. Hydrocephalus is one of the complex and multifactorial neurological disorders. A growing body of evidence indicates that genetic factors play a major role in the pathogenesis of hydrocephalus. An understanding of the genetic components and mechanism of this complex disorder may offer us significant insights into the molecular etiology of impaired brain development and an accumulation of the cerebrospinal fluid in cerebral compartments during the pathogenesis of hydrocephalus. Genetic studies in animal models have started to open the way for understanding the underlying pathology of hydrocephalus. At least 43 mutants/loci linked to hereditary hydrocephalus have been identified in animal models and humans. Up to date, 9 genes associated with hydrocephalus have been identified in animal models. In contrast, only one such gene has been identified in humans. Most of known hydrocephalus gene products are the important cytokines, growth factors or related molecules in the cellular signal pathways during early brain development. The current molecular genetic evidence from animal models indicate that in the early development stage, impaired and abnormal brain development caused by abnormal cellular signaling and functioning, all these cellular and developmental events would eventually lead to the congenital hydrocephalus. Owing to our very primitive knowledge of the genetics and molecular pathogenesis of human hydrocephalus, it is difficult to evaluate whether data gained from animal models can be extrapolated to humans. Initiation of a large population genetics study in humans will certainly provide invaluable information about the molecular and cellular etiology and the developmental mechanisms of human hydrocephalus. This review summarizes the recent findings on this issue among human and animal models, especially with reference to the molecular genetics, pathological, physiological and cellular studies, and identifies future research directions. PMID:16773266

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.

PubMed

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C

2014-09-01

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait 'total crop nitrogen uptake' (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10-36 % more yield than those based on markers for yield per se. This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Variability in Ozone-Induced Pulmonary Injury and Inflammation in Healthy and Cardiovascular Compromised Rat Models

EPA Science Inventory

The molecular bases for variability in air pollutant-induced pulmonary injury due to underlying cardiovascular (CVD) and/or metabolic diseases are unknown. We hypothesized that healthy and genetic CVD-prone rat models will exhibit exacerbated response to acute ozone exposure depe...

Adaptive genetic potential of coniferous forest tree species under climate change: implications for sustainable forest management

NASA Astrophysics Data System (ADS)

Mihai, Georgeta; Birsan, Marius-Victor; Teodosiu, Maria; Dumitrescu, Alexandru; Daia, Mihai; Mirancea, Ionel; Ivanov, Paula; Alin, Alexandru

2017-04-01

Mountain ecosystems are extremely vulnerable to climate change. The real potential for adaptation depends upon the existence of a wide genetic diversity in trees populations, upon the adaptive genetic variation, respectively. Genetic diversity offers the guarantee that forest species can survive, adapt and evolve under the influence of changing environmental conditions. The aim of this study is to evaluate the genetic diversity and adaptive genetic potential of two local species - Norway spruce and European silver fir - in the context of regional climate change. Based on data from a long-term provenance experiments network and climate variables spanning over more than 50 years, we have investigated the impact of climatic factors on growth performance and adaptation of tree species. Our results indicate that climatic and geographic factors significantly affect forest site productivity. Mean annual temperature and annual precipitation amount were found to be statistically significant explanatory variables. Combining the additive genetic model with the analysis of nuclear markers we obtained different images of the genetic structure of tree populations. As genetic indicators we used: gene frequencies, genetic diversity, genetic differentiation, genetic variance, plasticity. Spatial genetic analyses have allowed identifying the genetic centers holding high genetic diversity which will be valuable sources of gene able to buffer the negative effects of future climate change. Correlations between the marginal populations and in the optimal vegetation, between the level of genetic diversity and ecosystem stability, will allow the assessment of future risks arising from current genetic structure. Therefore, the strategies for sustainable forest management have to rely on the adaptive genetic variation and local adaptation of the valuable genetic resources. This work was realized within the framework of the project GENCLIM (Evaluating the adaptive potential of the main coniferous species for a sustainable forest management in the context of climate change), financed by the Executive Agency for Higher Education, Research, Development and Innovation Funding, grant number PN-II-PC-PCCA-2013-4-0695.

An Approximate Markov Model for the Wright-Fisher Diffusion and Its Application to Time Series Data.

PubMed

Ferrer-Admetlla, Anna; Leuenberger, Christoph; Jensen, Jeffrey D; Wegmann, Daniel

2016-06-01

The joint and accurate inference of selection and demography from genetic data is considered a particularly challenging question in population genetics, since both process may lead to very similar patterns of genetic diversity. However, additional information for disentangling these effects may be obtained by observing changes in allele frequencies over multiple time points. Such data are common in experimental evolution studies, as well as in the comparison of ancient and contemporary samples. Leveraging this information, however, has been computationally challenging, particularly when considering multilocus data sets. To overcome these issues, we introduce a novel, discrete approximation for diffusion processes, termed mean transition time approximation, which preserves the long-term behavior of the underlying continuous diffusion process. We then derive this approximation for the particular case of inferring selection and demography from time series data under the classic Wright-Fisher model and demonstrate that our approximation is well suited to describe allele trajectories through time, even when only a few states are used. We then develop a Bayesian inference approach to jointly infer the population size and locus-specific selection coefficients with high accuracy and further extend this model to also infer the rates of sequencing errors and mutations. We finally apply our approach to recent experimental data on the evolution of drug resistance in influenza virus, identifying likely targets of selection and finding evidence for much larger viral population sizes than previously reported. Copyright © 2016 by the Genetics Society of America.

Use of genetic data to infer population-specific ecological and phenotypic traits from mixed aggregations

USGS Publications Warehouse

Moran, Paul; Bromaghin, Jeffrey F.; Masuda, Michele

2014-01-01

Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions.

Use of Genetic Data to Infer Population-Specific Ecological and Phenotypic Traits from Mixed Aggregations

PubMed Central

Moran, Paul; Bromaghin, Jeffrey F.; Masuda, Michele

2014-01-01

Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions. PMID:24905464

A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under e-supply chain environment.

PubMed

Li, Yanhui; Guo, Hao; Wang, Lin; Fu, Jing

2013-01-01

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

Landscape genetics as a tool for conservation planning: predicting the effects of landscape change on gene flow.

PubMed

van Strien, Maarten J; Keller, Daniela; Holderegger, Rolf; Ghazoul, Jaboury; Kienast, Felix; Bolliger, Janine

2014-03-01

For conservation managers, it is important to know whether landscape changes lead to increasing or decreasing gene flow. Although the discipline of landscape genetics assesses the influence of landscape elements on gene flow, no studies have yet used landscape-genetic models to predict gene flow resulting from landscape change. A species that has already been severely affected by landscape change is the large marsh grasshopper (Stethophyma grossum), which inhabits moist areas in fragmented agricultural landscapes in Switzerland. From transects drawn between all population pairs within maximum dispersal distance (< 3 km), we calculated several measures of landscape composition as well as some measures of habitat configuration. Additionally, a complete sampling of all populations in our study area allowed incorporating measures of population topology. These measures together with the landscape metrics formed the predictor variables in linear models with gene flow as response variable (F(ST) and mean pairwise assignment probability). With a modified leave-one-out cross-validation approach, we selected the model with the highest predictive accuracy. With this model, we predicted gene flow under several landscape-change scenarios, which simulated construction, rezoning or restoration projects, and the establishment of a new population. For some landscape-change scenarios, significant increase or decrease in gene flow was predicted, while for others little change was forecast. Furthermore, we found that the measures of population topology strongly increase model fit in landscape genetic analysis. This study demonstrates the use of predictive landscape-genetic models in conservation and landscape planning.

Random regression models to account for the effect of genotype by environment interaction due to heat stress on the milk yield of Holstein cows under tropical conditions.

PubMed

Santana, Mário L; Bignardi, Annaiza Braga; Pereira, Rodrigo Junqueira; Menéndez-Buxadera, Alberto; El Faro, Lenira

2016-02-01

The present study had the following objectives: to compare random regression models (RRM) considering the time-dependent (days in milk, DIM) and/or temperature × humidity-dependent (THI) covariate for genetic evaluation; to identify the effect of genotype by environment interaction (G×E) due to heat stress on milk yield; and to quantify the loss of milk yield due to heat stress across lactation of cows under tropical conditions. A total of 937,771 test-day records from 3603 first lactations of Brazilian Holstein cows obtained between 2007 and 2013 were analyzed. An important reduction in milk yield due to heat stress was observed for THI values above 66 (-0.23 kg/day/THI). Three phases of milk yield loss were identified during lactation, the most damaging one at the end of lactation (-0.27 kg/day/THI). Using the most complex RRM, the additive genetic variance could be altered simultaneously as a function of both DIM and THI values. This model could be recommended for the genetic evaluation taking into account the effect of G×E. The response to selection in the comfort zone (THI ≤ 66) is expected to be higher than that obtained in the heat stress zone (THI > 66) of the animals. The genetic correlations between milk yield in the comfort and heat stress zones were less than unity at opposite extremes of the environmental gradient. Thus, the best animals for milk yield in the comfort zone are not necessarily the best in the zone of heat stress and, therefore, G×E due to heat stress should not be neglected in the genetic evaluation.

Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio.

PubMed

Lloyd-Jones, Luke R; Robinson, Matthew R; Yang, Jian; Visscher, Peter M

2018-04-01

Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure ( e.g. , a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects. Copyright © 2018 by the Genetics Society of America.

Molecular Genetics of Pigment Dispersion Syndrome and Pigmentary Glaucoma: New Insights into Mechanisms

PubMed Central

2018-01-01

We explore the ideas and advances surrounding the genetic basis of pigment dispersion syndrome (PDS) and pigmentary glaucoma (PG). As PG is the leading cause of nontraumatic blindness in young adults and current tailored interventions have proven ineffective, a better understanding of the underlying causes of PDS, PG, and their relationship is essential. Despite PDS being a subclinical disease, a large proportion of patients progress to PG with associated vision loss. Decades of research have supported a genetic component both for PDS and conversion to PG. We review the body of evidence supporting a genetic basis in humans and animal models and reevaluate classical mechanisms of PDS/PG considering this new evidence. PMID:29780638

Molecular Genetics of Pigment Dispersion Syndrome and Pigmentary Glaucoma: New Insights into Mechanisms.

PubMed

Lahola-Chomiak, Adrian A; Walter, Michael A

2018-01-01

We explore the ideas and advances surrounding the genetic basis of pigment dispersion syndrome (PDS) and pigmentary glaucoma (PG). As PG is the leading cause of nontraumatic blindness in young adults and current tailored interventions have proven ineffective, a better understanding of the underlying causes of PDS, PG, and their relationship is essential. Despite PDS being a subclinical disease, a large proportion of patients progress to PG with associated vision loss. Decades of research have supported a genetic component both for PDS and conversion to PG. We review the body of evidence supporting a genetic basis in humans and animal models and reevaluate classical mechanisms of PDS/PG considering this new evidence.

Association mapping reveals the genetic architecture of tomato response to water deficit: focus on major fruit quality traits

PubMed Central

Albert, Elise; Segura, Vincent; Gricourt, Justine; Bonnefoi, Julien; Derivot, Laurent; Causse, Mathilde

2016-01-01

Water scarcity constitutes a crucial constraint for agriculture productivity. High-throughput approaches in model plant species identified hundreds of genes potentially involved in survival under drought, but few having beneficial effects on quality and yield. Nonetheless, controlled water deficit may improve fruit quality through higher concentration of flavor compounds. The underlying genetic determinants are still poorly known. In this study, we phenotyped 141 highly diverse small fruit tomato accessions for 27 traits under two contrasting watering conditions. A subset of 55 accessions exhibited increased metabolite contents and maintained yield under water deficit. Using 6100 single nucleotide polymorphisms (SNPs), association mapping revealed 31, 41, and 44 quantitative trait loci (QTLs) under drought, control, and both conditions, respectively. Twenty-five additional QTLs were interactive between conditions, emphasizing the interest in accounting for QTLs by watering regime interactions in fruit quality improvement. Combining our results with the loci previously identified in a biparental progeny resulted in 11 common QTLs and contributed to a first detailed characterization of the genetic determinants of response to water deficit in tomato. Major QTLs for fruit quality traits were dissected and candidate genes were proposed using expression and polymorphism data. The outcomes provide a basis for fruit quality improvement under deficit irrigation while limiting yield losses. PMID:27856709

The power to detect linkage in complex disease by means of simple LOD-score analyses.

PubMed Central

Greenberg, D A; Abreu, P; Hodge, S E

1998-01-01

Maximum-likelihood analysis (via LOD score) provides the most powerful method for finding linkage when the mode of inheritance (MOI) is known. However, because one must assume an MOI, the application of LOD-score analysis to complex disease has been questioned. Although it is known that one can legitimately maximize the maximum LOD score with respect to genetic parameters, this approach raises three concerns: (1) multiple testing, (2) effect on power to detect linkage, and (3) adequacy of the approximate MOI for the true MOI. We evaluated the power of LOD scores to detect linkage when the true MOI was complex but a LOD score analysis assumed simple models. We simulated data from 14 different genetic models, including dominant and recessive at high (80%) and low (20%) penetrances, intermediate models, and several additive two-locus models. We calculated LOD scores by assuming two simple models, dominant and recessive, each with 50% penetrance, then took the higher of the two LOD scores as the raw test statistic and corrected for multiple tests. We call this test statistic "MMLS-C." We found that the ELODs for MMLS-C are >=80% of the ELOD under the true model when the ELOD for the true model is >=3. Similarly, the power to reach a given LOD score was usually >=80% that of the true model, when the power under the true model was >=60%. These results underscore that a critical factor in LOD-score analysis is the MOI at the linked locus, not that of the disease or trait per se. Thus, a limited set of simple genetic models in LOD-score analysis can work well in testing for linkage. PMID:9718328

The power to detect linkage in complex disease by means of simple LOD-score analyses.

PubMed

Greenberg, D A; Abreu, P; Hodge, S E

1998-09-01

Maximum-likelihood analysis (via LOD score) provides the most powerful method for finding linkage when the mode of inheritance (MOI) is known. However, because one must assume an MOI, the application of LOD-score analysis to complex disease has been questioned. Although it is known that one can legitimately maximize the maximum LOD score with respect to genetic parameters, this approach raises three concerns: (1) multiple testing, (2) effect on power to detect linkage, and (3) adequacy of the approximate MOI for the true MOI. We evaluated the power of LOD scores to detect linkage when the true MOI was complex but a LOD score analysis assumed simple models. We simulated data from 14 different genetic models, including dominant and recessive at high (80%) and low (20%) penetrances, intermediate models, and several additive two-locus models. We calculated LOD scores by assuming two simple models, dominant and recessive, each with 50% penetrance, then took the higher of the two LOD scores as the raw test statistic and corrected for multiple tests. We call this test statistic "MMLS-C." We found that the ELODs for MMLS-C are >=80% of the ELOD under the true model when the ELOD for the true model is >=3. Similarly, the power to reach a given LOD score was usually >=80% that of the true model, when the power under the true model was >=60%. These results underscore that a critical factor in LOD-score analysis is the MOI at the linked locus, not that of the disease or trait per se. Thus, a limited set of simple genetic models in LOD-score analysis can work well in testing for linkage.

The threshold vs LNT showdown: Dose rate findings exposed flaws in the LNT model part 2. How a mistake led BEIR I to adopt LNT

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

Calabrese, Edward J., E-mail: edwardc@schoolph.uma

This paper reveals that nearly 25 years after the used Russell's dose-rate data to support the adoption of the linear-no-threshold (LNT) dose response model for genetic and cancer risk assessment, Russell acknowledged a significant under-reporting of the mutation rate of the historical control group. This error, which was unknown to BEIR I, had profound implications, leading it to incorrectly adopt the LNT model, which was a decision that profoundly changed the course of risk assessment for radiation and chemicals to the present. -- Highlights: • The BEAR I Genetics Panel made an error in denying dose rate for mutation. •more » The BEIR I Genetics Subcommittee attempted to correct this dose rate error. • The control group used for risk assessment by BEIR I is now known to be in error. • Correcting this error contradicts the LNT, supporting a threshold model.« less

Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.

1997-01-01

The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium.

PubMed

Fischer, Christine; Kuchenbäcker, Karoline; Engel, Christoph; Zachariae, Silke; Rhiem, Kerstin; Meindl, Alfons; Rahner, Nils; Dikow, Nicola; Plendl, Hansjörg; Debatin, Irmgard; Grimm, Tiemo; Gadzicki, Dorothea; Flöttmann, Ricarda; Horvath, Judit; Schröck, Evelin; Stock, Friedrich; Schäfer, Dieter; Schwaab, Ira; Kartsonaki, Christiana; Mavaddat, Nasim; Schlegelberger, Brigitte; Antoniou, Antonis C; Schmutzler, Rita

2013-06-01

Risk prediction models are widely used in clinical genetic counselling. Despite their frequent use, the genetic risk models BOADICEA, BRCAPRO, IBIS and extended Claus model (eCLAUS), used to estimate BRCA1/2 mutation carrier probabilities, have never been comparatively evaluated in a large sample from central Europe. Additionally, a novel version of BOADICEA that incorporates tumour pathology information has not yet been validated. Using data from 7352 German families we estimated BRCA1/2 carrier probabilities under each model and compared their discrimination and calibration. The incremental value of using pathology information in BOADICEA was assessed in a subsample of 4928 pedigrees with available data on breast tumour molecular markers oestrogen receptor, progesterone receptor and human epidermal growth factor 2. BRCAPRO (area under receiver operating characteristic curve (AUC)=0.80 (95% CI 0.78 to 0.81)) and BOADICEA (AUC=0.79 (0.78-0.80)), had significantly higher diagnostic accuracy than IBIS and eCLAUS (p<0.001). The AUC increased when pathology information was used in BOADICEA: AUC=0.81 (95% CI 0.80 to 0.83, p<0.001). At carrier thresholds of 10% and 15%, the net reclassification index was +3.9% and +5.4%, respectively, when pathology was included in the model. Overall, calibration was best for BOADICEA and worst for eCLAUS. With eCLAUS, twice as many mutation carriers were predicted than observed. Our results support the use of BRCAPRO and BOADICEA for decision making regarding genetic testing for BRCA1/2 mutations. However, model calibration has to be improved for this population. eCLAUS should not be used for estimating mutation carrier probabilities in clinical settings. Whenever possible, breast tumour molecular marker information should be taken into account.

Targeted treatment trials for tuberous sclerosis and autism: no longer a dream.

PubMed

Sahin, Mustafa

2012-10-01

Genetic disorders that present with a high incidence of autism spectrum disorders (ASD) offer tremendous potential both for elucidating the underlying neurobiology of ASD and identifying therapeutic drugs and/or drug targets. As a result, clinical trials for genetic disorders associated with ASD are no longer a hope for the future but rather an exciting reality whose time has come. Tuberous sclerosis complex (TSC) is one such genetic disorder that presents with ASD, epilepsy, and intellectual disability. Cell culture and mouse model experiments have identified the mTOR pathway as a therapeutic target in this disease. This review summarizes the advantages of using TSC as model of ASD and the recent advances in the translational and clinical treatment trials in TSC. Copyright © 2012 Elsevier Ltd. All rights reserved.

New frontiers in the study of human cultural and genetic evolution.

PubMed

Ross, Cody T; Richerson, Peter J

2014-12-01

In this review, we discuss the dynamic linkages between culture and the genetic evolution of the human species. We begin by briefly describing the framework of gene-culture coevolutionary (or dual-inheritance) models for human evolutionary change. Until recently, the literature on gene-culture coevolution was composed primarily of mathematical models and formalized theory describing the complex dynamics underlying human behavior, adaptation, and technological evolution, but had little empirical support concerning genetics. The rapid progress in the fields of molecular genetics and genomics, however, is now providing the kinds of data needed to produce rich empirical support for gene-culture coevolutionary models. We briefly outline how theoretical and methodological progress in genome sciences has provided ways for the strength of selection on genes to be evaluated, and then outline how evidence of selection on several key genes can be directly linked to human cultural practices. We then describe some exciting new directions in the empirical study of gene-culture coevolution, and conclude with a discussion of the role of gene-culture evolutionary models in the future integration of medical, biological, and social sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

An integrated analysis of genes and functional pathways for aggression in human and rodent models.

PubMed

Zhang-James, Yanli; Fernàndez-Castillo, Noèlia; Hess, Jonathan L; Malki, Karim; Glatt, Stephen J; Cormand, Bru; Faraone, Stephen V

2018-06-01

Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression.

Natural variation and the capacity to adapt to ocean acidification in the keystone sea urchin Strongylocentrotus purpuratus.

PubMed

Kelly, Morgan W; Padilla-Gamiño, Jacqueline L; Hofmann, Gretchen E

2013-08-01

A rapidly growing body of literature documents the potential negative effects of CO2 -driven ocean acidification (OA) on marine organisms. However, nearly all this work has focused on the effects of future conditions on modern populations, neglecting the role of adaptation. Rapid evolution can alter demographic responses to environmental change, ultimately affecting the likelihood of population persistence, but the capacity for adaptation will differ among populations and species. Here, we measure the capacity of the ecologically important purple sea urchin Strongylocentrotus purpuratus to adapt to OA, using a breeding experiment to estimate additive genetic variance for larval size (an important component of fitness) under future high-pCO2 /low-pH conditions. Although larvae reared under future conditions were smaller than those reared under present-day conditions, we show that there is also abundant genetic variation for body size under elevated pCO2 , indicating that this trait can evolve. The observed heritability of size was 0.40 ± 0.32 (95% CI) under low pCO2 , and 0.50 ± 0.30 under high-pCO2 conditions. Accounting for the observed genetic variation in models of future larval size and demographic rates substantially alters projections of performance for this species in the future ocean. Importantly, our model shows that after incorporating the effects of adaptation, the OA-driven decrease in population growth rate is up to 50% smaller, than that predicted by the 'no-adaptation' scenario. Adults used in the experiment were collected from two sites on the coast of the Northeast Pacific that are characterized by different pH regimes, as measured by autonomous sensors. Comparing results between sites, we also found subtle differences in larval size under high-pCO2 rearing conditions, consistent with local adaptation to carbonate chemistry in the field. These results suggest that spatially varying selection may help to maintain genetic variation necessary for adaptation to future OA. © 2013 John Wiley & Sons Ltd.

Mapping eQTL Networks with Mixed Graphical Markov Models

PubMed Central

Tur, Inma; Roverato, Alberto; Castelo, Robert

2014-01-01

Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene–gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes. PMID:25271303

Paternal antisocial behavior and sons' cognitive ability: a population-based quasiexperimental study.

PubMed

Latvala, Antti; Kuja-Halkola, Ralf; Långström, Niklas; Lichtenstein, Paul

2015-01-01

Parents' antisocial behavior is associated with developmental risks for their offspring, but its effects on their children's cognitive ability are unknown. We used linked Swedish register data for a large sample of adolescent men (N = 1,177,173) and their parents to estimate associations between fathers' criminal-conviction status and sons' cognitive ability assessed at compulsory military conscription. Mechanisms behind the association were tested in children-of-siblings models across three types of sibling fathers with increasing genetic relatedness (half-siblings, full siblings, and monozygotic twins) and in quantitative genetic models. Sons whose fathers had a criminal conviction had lower cognitive ability than sons whose fathers had no conviction (any crime: Cohen's d = -0.28; violent crime: Cohen's d = -0.49). As models adjusted for more genetic factors, the association was gradually reduced and eventually eliminated. Nuclear-family environmental factors did not contribute to the association. Our results suggest that the association between men's antisocial behavior and their children's cognitive ability is not causal but is due mostly to underlying genetic factors. © The Author(s) 2014.

A unified genetic association test robust to latent population structure for a count phenotype.

PubMed

Song, Minsun

2018-06-04

Confounding caused by latent population structure in genome-wide association studies has been a big concern despite the success of genome-wide association studies at identifying genetic variants associated with complex diseases. In particular, because of the growing interest in association mapping using count phenotype data, it would be interesting to develop a testing framework for genetic associations that is immune to population structure when phenotype data consist of count measurements. Here, I propose a solution for testing associations between single nucleotide polymorphisms and a count phenotype in the presence of an arbitrary population structure. I consider a classical range of models for count phenotype data. Under these models, a unified test for genetic associations that protects against confounding was derived. An algorithm was developed to efficiently estimate the parameters that are required to fit the proposed model. I illustrate the proposed approach using simulation studies and an empirical study. Both simulated and real-data examples suggest that the proposed method successfully corrects population structure. Copyright © 2018 John Wiley & Sons, Ltd.

A heuristic model on the role of plasticity in adaptive evolution: plasticity increases adaptation, population viability and genetic variation.

PubMed

Gomez-Mestre, Ivan; Jovani, Roger

2013-11-22

An ongoing new synthesis in evolutionary theory is expanding our view of the sources of heritable variation beyond point mutations of fixed phenotypic effects to include environmentally sensitive changes in gene regulation. This expansion of the paradigm is necessary given ample evidence for a heritable ability to alter gene expression in response to environmental cues. In consequence, single genotypes are often capable of adaptively expressing different phenotypes in different environments, i.e. are adaptively plastic. We present an individual-based heuristic model to compare the adaptive dynamics of populations composed of plastic or non-plastic genotypes under a wide range of scenarios where we modify environmental variation, mutation rate and costs of plasticity. The model shows that adaptive plasticity contributes to the maintenance of genetic variation within populations, reduces bottlenecks when facing rapid environmental changes and confers an overall faster rate of adaptation. In fluctuating environments, plasticity is favoured by selection and maintained in the population. However, if the environment stabilizes and costs of plasticity are high, plasticity is reduced by selection, leading to genetic assimilation, which could result in species diversification. More broadly, our model shows that adaptive plasticity is a common consequence of selection under environmental heterogeneity, and hence a potentially common phenomenon in nature. Thus, taking adaptive plasticity into account substantially extends our view of adaptive evolution.

A null model for microbial diversification

PubMed Central

Straub, Timothy J.

2017-01-01

Whether prokaryotes (Bacteria and Archaea) are naturally organized into phenotypically and genetically cohesive units comparable to animal or plant species remains contested, frustrating attempts to estimate how many such units there might be, or to identify the ecological roles they play. Analyses of gene sequences in various closely related prokaryotic groups reveal that sequence diversity is typically organized into distinct clusters, and processes such as periodic selection and extensive recombination are understood to be drivers of cluster formation (“speciation”). However, observed patterns are rarely compared with those obtainable with simple null models of diversification under stochastic lineage birth and death and random genetic drift. Via a combination of simulations and analyses of core and phylogenetic marker genes, we show that patterns of diversity for the genera Escherichia, Neisseria, and Borrelia are generally indistinguishable from patterns arising under a null model. We suggest that caution should thus be taken in interpreting observed clustering as a result of selective evolutionary forces. Unknown forces do, however, appear to play a role in Helicobacter pylori, and some individual genes in all groups fail to conform to the null model. Taken together, we recommend the presented birth−death model as a null hypothesis in prokaryotic speciation studies. It is only when the real data are statistically different from the expectations under the null model that some speciation process should be invoked. PMID:28630293

Genetical genomics of Populus leaf shape variation

DOE PAGES

Drost, Derek R.; Puranik, Swati; Novaes, Evandro; ...

2015-06-30

Leaf morphology varies extensively among plant species and is under strong genetic control. Mutagenic screens in model systems have identified genes and established molecular mechanisms regulating leaf initiation, development, and shape. However, it is not known whether this diversity across plant species is related to naturally occurring variation at these genes. Quantitative trait locus (QTL) analysis has revealed a polygenic control for leaf shape variation in different species suggesting that loci discovered by mutagenesis may only explain part of the naturally occurring variation in leaf shape. Here we undertook a genetical genomics study in a poplar intersectional pseudo-backcross pedigree tomore » identify genetic factors controlling leaf shape. Here, the approach combined QTL discovery in a genetic linkage map anchored to the Populus trichocarpa reference genome sequence and transcriptome analysis.« less

More grain per drop of water: Screening rice genotype for physiological parameters of drought tolerance

NASA Astrophysics Data System (ADS)

Massanelli, J.; Meadows-McDonnell, M.; Konzelman, C.; Moon, J. B.; Kumar, A.; Thomas, J.; Pereira, A.; Naithani, K. J.

2016-12-01

Meeting agricultural water demands is becoming progressively difficult due to population growth and changes in climate. Breeding stress-resilient crops is a viable solution, as information about genetic variation and their role in stress tolerance is becoming available due to advancement in technology. In this study we screened eight diverse rice genotypes for photosynthetic capacity under greenhouse conditions. These include the Asian rice (Oryza sativa) genotypes, drought sensitive Nipponbare, and a transgenic line overexpressing the HYR gene in Nipponbare; six genotypes (Vandana, Bengal, Nagina-22, Glaberrima, Kaybonnet, Ai Chueh Ta Pai Ku) and an African rice O. glaberrima, all selected for varying levels of drought tolerance. We collected CO2 and light response curve data under well-watered and simulated drought conditions in greenhouse. From these curves we estimated photosynthesis model parameters, such as the maximum carboxylation rate (Vcmax), the maximum electron transport rate (Jmax), the maximum gross photosynthesis rate, daytime respiration (Rd), and quantum yield (f). Our results suggest that O. glaberrima and Nipponbare were the most sensitive to drought because Vcmax and Pgmax declined under drought conditions; other drought tolerant genotypes did not show significant changes in these model parameters. Our integrated approach, combining genetic information and photosynthesis modeling, shows promise to quantify drought response parameters and improve crop yield under drought stress conditions.

Analysis of the Multi Strategy Goal Programming for Micro-Grid Based on Dynamic ant Genetic Algorithm

NASA Astrophysics Data System (ADS)

Qiu, J. P.; Niu, D. X.

Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.

Symbiotic conversations are revealed under genetic interrogation

PubMed Central

Ruby, Edward G.

2013-01-01

The recent development and application of molecular genetics to the symbionts of invertebrate animal species have advanced our knowledge of the biochemical communication that occurs between the host and its bacterial symbionts. In particular, the ability to manipulate these associations experimentally by introducing genetic variants of the symbionts into naive hosts has allowed the discovery of novel colonization mechanisms and factors. In addition, the role of the symbionts in inducing normal host development has been revealed, and its molecular basis described. In this Review, I discuss many of these developments, focusing on what has been discovered in five well-understood model systems. PMID:18794913

Translating genome wide association study results to associations among common diseases: in silico study with an electronic medical record.

PubMed

Anand, Vibha; Rosenman, Marc B; Downs, Stephen M

2013-09-01

To develop a map of disease associations exclusively using two publicly available genetic sources: the catalog of single nucleotide polymorphisms (SNPs) from the HapMap, and the catalog of Genome Wide Association Studies (GWAS) from the NHGRI, and to evaluate it with a large, long-standing electronic medical record (EMR). A computational model, In Silico Bayesian Integration of GWAS (IsBIG), was developed to learn associations among diseases using a Bayesian network (BN) framework, using only genetic data. The IsBIG model (I-Model) was re-trained using data from our EMR (M-Model). Separately, another clinical model (C-Model) was learned from this training dataset. The I-Model was compared with both the M-Model and the C-Model for power to discriminate a disease given other diseases using a test dataset from our EMR. Area under receiver operator characteristics curve was used as a performance measure. Direct associations between diseases in the I-Model were also searched in the PubMed database and in classes of the Human Disease Network (HDN). On the basis of genetic information alone, the I-Model linked a third of diseases from our EMR. When compared to the M-Model, the I-Model predicted diseases given other diseases with 94% specificity, 33% sensitivity, and 80% positive predictive value. The I-Model contained 117 direct associations between diseases. Of those associations, 20 (17%) were absent from the searches of the PubMed database; one of these was present in the C-Model. Of the direct associations in the I-Model, 7 (35%) were absent from disease classes of HDN. Using only publicly available genetic sources we have mapped associations in GWAS to a human disease map using an in silico approach. Furthermore, we have validated this disease map using phenotypic data from our EMR. Models predicting disease associations on the basis of known genetic associations alone are specific but not sensitive. Genetic data, as it currently exists, can only explain a fraction of the risk of a disease. Our approach makes a quantitative statement about disease variation that can be explained in an EMR on the basis of genetic associations described in the GWAS. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Stochastic noncooperative and cooperative evolutionary game strategies of a population of biological networks under natural selection.

PubMed

Chen, Bor-Sen; Yeh, Chin-Hsun

2017-12-01

We review current static and dynamic evolutionary game strategies of biological networks and discuss the lack of random genetic variations and stochastic environmental disturbances in these models. To include these factors, a population of evolving biological networks is modeled as a nonlinear stochastic biological system with Poisson-driven genetic variations and random environmental fluctuations (stimuli). To gain insight into the evolutionary game theory of stochastic biological networks under natural selection, the phenotypic robustness and network evolvability of noncooperative and cooperative evolutionary game strategies are discussed from a stochastic Nash game perspective. The noncooperative strategy can be transformed into an equivalent multi-objective optimization problem and is shown to display significantly improved network robustness to tolerate genetic variations and buffer environmental disturbances, maintaining phenotypic traits for longer than the cooperative strategy. However, the noncooperative case requires greater effort and more compromises between partly conflicting players. Global linearization is used to simplify the problem of solving nonlinear stochastic evolutionary games. Finally, a simple stochastic evolutionary model of a metabolic pathway is simulated to illustrate the procedure of solving for two evolutionary game strategies and to confirm and compare their respective characteristics in the evolutionary process. Copyright © 2017 Elsevier B.V. All rights reserved.

The Impact of Population Demography and Selection on the Genetic Architecture of Complex Traits

PubMed Central

Lohmueller, Kirk E.

2014-01-01

Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequency, and effect sizes of mutations that contribute risk to complex traits. Because researchers are performing exome sequencing studies aimed at uncovering the role of low-frequency variants in the risk of complex traits, this topic is of critical importance. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant associations with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant associations detected. These findings suggest recent population history may be an important factor influencing the power of association tests and in accounting for the missing heritability of certain complex traits. PMID:24875776

Clinical-genetic model predicts incident impulse control disorders in Parkinson's disease.

PubMed

Kraemmer, Julia; Smith, Kara; Weintraub, Daniel; Guillemot, Vincent; Nalls, Mike A; Cormier-Dequaire, Florence; Moszer, Ivan; Brice, Alexis; Singleton, Andrew B; Corvol, Jean-Christophe

2016-10-01

Impulse control disorders (ICD) are commonly associated with dopamine replacement therapy (DRT) in patients with Parkinson's disease (PD). Our aims were to estimate ICD heritability and to predict ICD by a candidate genetic multivariable panel in patients with PD. Data from de novo patients with PD, drug-naïve and free of ICD behaviour at baseline, were obtained from the Parkinson's Progression Markers Initiative cohort. Incident ICD behaviour was defined as positive score on the Questionnaire for Impulsive-Compulsive Disorders in PD. ICD heritability was estimated by restricted maximum likelihood analysis on whole exome sequencing data. 13 candidate variants were selected from the DRD2, DRD3, DAT1, COMT, DDC, GRIN2B, ADRA2C, SERT, TPH2, HTR2A, OPRK1 and OPRM1 genes. ICD prediction was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC) curves. Among 276 patients with PD included in the analysis, 86% started DRT, 40% were on dopamine agonists (DA), 19% reported incident ICD behaviour during follow-up. We found heritability of this symptom to be 57%. Adding genotypes from the 13 candidate variants significantly increased ICD predictability (AUC=76%, 95% CI (70% to 83%)) compared to prediction based on clinical variables only (AUC=65%, 95% CI (58% to 73%), p=0.002). The clinical-genetic prediction model reached highest accuracy in patients initiating DA therapy (AUC=87%, 95% CI (80% to 93%)). OPRK1, HTR2A and DDC genotypes were the strongest genetic predictive factors. Our results show that adding a candidate genetic panel increases ICD predictability, suggesting potential for developing clinical-genetic models to identify patients with PD at increased risk of ICD development and guide DRT management. 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/

Using probability modelling and genetic parentage assignment to test the role of local mate availability in mating system variation.

PubMed

Blyton, Michaela D J; Banks, Sam C; Peakall, Rod; Lindenmayer, David B

2012-02-01

The formal testing of mating system theories with empirical data is important for evaluating the relative importance of different processes in shaping mating systems in wild populations. Here, we present a generally applicable probability modelling framework to test the role of local mate availability in determining a population's level of genetic monogamy. We provide a significance test for detecting departures in observed mating patterns from model expectations based on mate availability alone, allowing the presence and direction of behavioural effects to be inferred. The assessment of mate availability can be flexible and in this study it was based on population density, sex ratio and spatial arrangement. This approach provides a useful tool for (1) isolating the effect of mate availability in variable mating systems and (2) in combination with genetic parentage analyses, gaining insights into the nature of mating behaviours in elusive species. To illustrate this modelling approach, we have applied it to investigate the variable mating system of the mountain brushtail possum (Trichosurus cunninghami) and compared the model expectations with the outcomes of genetic parentage analysis over an 18-year study. The observed level of monogamy was higher than predicted under the model. Thus, behavioural traits, such as mate guarding or selective mate choice, may increase the population level of monogamy. We show that combining genetic parentage data with probability modelling can facilitate an improved understanding of the complex interactions between behavioural adaptations and demographic dynamics in driving mating system variation. © 2011 Blackwell Publishing Ltd.

Geographic and ecologic distributions of the Anopheles gambiae complex predicted using a genetic algorithm.

PubMed

Levine, Rebecca S; Peterson, A Townsend; Benedict, Mark Q

2004-02-01

The distribution of the Anopheles gambiae complex of malaria vectors in Africa is uncertain due to under-sampling of vast regions. We use ecologic niche modeling to predict the potential distribution of three members of the complex (A. gambiae, A. arabiensis, and A. quadriannulatus) and demonstrate the statistical significance of the models. Predictions correspond well to previous estimates, but provide detail regarding spatial discontinuities in the distribution of A. gambiae s.s. that are consistent with population genetic studies. Our predictions also identify large areas of Africa where the presence of A. arabiensis is predicted, but few specimens have been obtained, suggesting under-sampling of the species. Finally, we project models developed from African distribution data for the late 1900s into the past and to South America to determine retrospectively whether the deadly 1929 introduction of A. gambiae sensu lato into Brazil was more likely that of A. gambiae sensu stricto or A. arabiensis.

Probability distribution of haplotype frequencies under the two-locus Wright-Fisher model by diffusion approximation.

PubMed

Boitard, Simon; Loisel, Patrice

2007-05-01

The probability distribution of haplotype frequencies in a population, and the way it is influenced by genetical forces such as recombination, selection, random drift ...is a question of fundamental interest in population genetics. For large populations, the distribution of haplotype frequencies for two linked loci under the classical Wright-Fisher model is almost impossible to compute because of numerical reasons. However the Wright-Fisher process can in such cases be approximated by a diffusion process and the transition density can then be deduced from the Kolmogorov equations. As no exact solution has been found for these equations, we developed a numerical method based on finite differences to solve them. It applies to transient states and models including selection or mutations. We show by several tests that this method is accurate for computing the conditional joint density of haplotype frequencies given that no haplotype has been lost. We also prove that it is far less time consuming than other methods such as Monte Carlo simulations.

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

PubMed

Song, Minsun; Wheeler, William; Caporaso, Neil E; Landi, Maria Teresa; Chatterjee, Nilanjan

2018-03-01

Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package. © 2017 WILEY PERIODICALS, INC.

Population genetics of autopolyploids under a mixed mating model and the estimation of selfing rate.

PubMed

Hardy, Olivier J

2016-01-01

Nowadays, the population genetics analysis of autopolyploid species faces many difficulties due to (i) limited development of population genetics tools under polysomic inheritance, (ii) difficulties to assess allelic dosage when genotyping individuals and (iii) a form of inbreeding resulting from the mechanism of 'double reduction'. Consequently, few data analysis computer programs are applicable to autopolyploids. To contribute bridging this gap, this article first derives theoretical expectations for the inbreeding and identity disequilibrium coefficients under polysomic inheritance in a mixed mating model. Moment estimators of these coefficients are proposed when exact genotypes or just markers phenotypes (i.e. allelic dosage unknown) are available. This led to the development of estimators of the selfing rate based on adult genotypes or phenotypes and applicable to any even-ploidy level. Their statistical performances and robustness were assessed by numerical simulations. Contrary to inbreeding-based estimators, the identity disequilibrium-based estimator using phenotypes is robust (absolute bias generally < 0.05), even in the presence of double reduction, null alleles or biparental inbreeding due to isolation by distance. A fairly good precision of the selfing rate estimates (root mean squared error < 0.1) is already achievable using a sample of 30-50 individuals phenotyped at 10 loci bearing 5-10 alleles each, conditions reachable using microsatellite markers. Diallelic markers (e.g. SNP) can also perform satisfactorily in diploids and tetraploids but more polymorphic markers are necessary for higher ploidy levels. The method is implemented in the software SPAGeDi and should contribute to reduce the lack of population genetics tools applicable to autopolyploids. © 2015 John Wiley & Sons Ltd.

Genetic evidence in the mouse solidifies the calcium hypothesis of myofiber death in muscular dystrophy

PubMed Central

Burr, A R; Molkentin, J D

2015-01-01

Muscular dystrophy (MD) refers to a clinically and genetically heterogeneous group of degenerative muscle disorders characterized by progressive muscle wasting and often premature death. Although the primary defect underlying most forms of MD typically results from a loss of sarcolemmal integrity, the secondary molecular mechanisms leading to muscle degeneration and myofiber necrosis is debated. One hypothesis suggests that elevated or dysregulated cytosolic calcium is the common transducing event, resulting in myofiber necrosis in MD. Previous measurements of resting calcium levels in myofibers from dystrophic animal models or humans produced equivocal results. However, recent studies in genetically altered mouse models have largely solidified the calcium hypothesis of MD, such that models with artificially elevated calcium in skeletal muscle manifest fulminant dystrophic-like disease, whereas models with enhanced calcium clearance or inhibited calcium influx are resistant to myofiber death and MD. Here, we will review the field and the recent cadre of data from genetically altered mouse models, which we propose have collectively mostly proven the hypothesis that calcium is the primary effector of myofiber necrosis in MD. This new consensus on calcium should guide future selection of drugs to be evaluated in clinical trials as well as gene therapy-based approaches. PMID:26088163

Genetic structure and signatures of selection in grey reef sharks (Carcharhinus amblyrhynchos).

PubMed

Momigliano, P; Harcourt, R; Robbins, W D; Jaiteh, V; Mahardika, G N; Sembiring, A; Stow, A

2017-09-01

With overfishing reducing the abundance of marine predators in multiple marine ecosystems, knowledge of genetic structure and local adaptation may provide valuable information to assist sustainable management. Despite recent technological advances, most studies on sharks have used small sets of neutral markers to describe their genetic structure. We used 5517 nuclear single-nucleotide polymorphisms (SNPs) and a mitochondrial DNA (mtDNA) gene to characterize patterns of genetic structure and detect signatures of selection in grey reef sharks (Carcharhinus amblyrhynchos). Using samples from Australia, Indonesia and oceanic reefs in the Indian Ocean, we established that large oceanic distances represent barriers to gene flow, whereas genetic differentiation on continental shelves follows an isolation by distance model. In Australia and Indonesia differentiation at nuclear SNPs was weak, with coral reefs acting as stepping stones maintaining connectivity across large distances. Differentiation of mtDNA was stronger, and more pronounced in females, suggesting sex-biased dispersal. Four independent tests identified a set of loci putatively under selection, indicating that grey reef sharks in eastern Australia are likely under different selective pressures to those in western Australia and Indonesia. Genetic distances averaged across all loci were uncorrelated with genetic distances calculated from outlier loci, supporting the conclusion that different processes underpin genetic divergence in these two data sets. This pattern of heterogeneous genomic differentiation, suggestive of local adaptation, has implications for the conservation of grey reef sharks; furthermore, it highlights that marine species showing little genetic differentiation at neutral loci may exhibit patterns of cryptic genetic structure driven by local selection.

Joint genetic analysis of hippocampal size in mouse and human identifies a novel gene linked to neurodegenerative disease.

PubMed

Ashbrook, David G; Williams, Robert W; Lu, Lu; Stein, Jason L; Hibar, Derrek P; Nichols, Thomas E; Medland, Sarah E; Thompson, Paul M; Hager, Reinmar

2014-10-03

Variation in hippocampal volume has been linked to significant differences in memory, behavior, and cognition among individuals. To identify genetic variants underlying such differences and associated disease phenotypes, multinational consortia such as ENIGMA have used large magnetic resonance imaging (MRI) data sets in human GWAS studies. In addition, mapping studies in mouse model systems have identified genetic variants for brain structure variation with great power. A key challenge is to understand how genetically based differences in brain structure lead to the propensity to develop specific neurological disorders. We combine the largest human GWAS of brain structure with the largest mammalian model system, the BXD recombinant inbred mouse population, to identify novel genetic targets influencing brain structure variation that are linked to increased risk for neurological disorders. We first use a novel cross-species, comparative analysis using mouse and human genetic data to identify a candidate gene, MGST3, associated with adult hippocampus size in both systems. We then establish the coregulation and function of this gene in a comprehensive systems-analysis. We find that MGST3 is associated with hippocampus size and is linked to a group of neurodegenerative disorders, such as Alzheimer's.

Should I Perform Genetic Testing? A Qualitative Look into the Decision Making Considerations of Religious Israeli Undergraduate Students.

PubMed

Siani, Merav; Assaraf, Orit Ben-Zvi

2016-10-01

The aim of this study is to draw a picture of the concerns that guide the decision making of Israeli religious undergraduate students and the complex considerations they take into account while facing the need to have genetic testing or to attend a genetic counseling session. We examined how the religious affiliation of the students influences their perceptions toward genetics and how these are expressed. Qualitative data were collected from 51 semi-structured interviews with students, in which recurring themes were identified using 'thematic analysis.' The codes from the thematic analysis were obtained according to 'grounded theory'. Our results show that religious undergraduate students' decision making in these issues is influenced by factors that fall under three main categories: knowledge and perceptions, values, and norms. In order to include all the components of influence, we created the Triple C model: "Culture influences Choices towards genetic Counseling" which aims to generalize the complex decision making considerations that we detected. Our model places religion, as part of culture, as its central point of influence that impacts all three of the main categories we detected. It also traces the bidirectional influences that each of these main categories have on one another. Using this model may help identify the sociocultural differences between different types of patients, helping genetic counselors to better assist them in addressing their genetic status by tailoring the counseling more specifically to the patient's cultural uniqueness.

Unravelling the genetic differentiation among varieties of the Neotropical savanna tree Hancornia speciosa Gomes.

PubMed

Collevatti, Rosane G; Rodrigues, Eduardo E; Vitorino, Luciana C; Lima-Ribeiro, Matheus S; Chaves, Lázaro J; Telles, Mariana P C

2018-04-20

Spatial distribution of species genetic diversity is often driven by geographical distance (isolation by distance) or environmental conditions (isolation by environment), especially under climate change scenarios such as Quaternary glaciations. Here, we used coalescent analyses coupled with ecological niche modelling (ENM), spatially explicit quantile regression analyses and the multiple matrix regression with randomization (MMRR) approach to unravel the patterns of genetic differentiation in the widely distributed Neotropical savanna tree, Hancornia speciosa (Apocynaceae). Due to its high morphological differentiation, the species was originally classified into six botanical varieties by Monachino, and has recently been recognized as only two varieties by Flora do Brasil 2020. Thus, H. speciosa is a good biological model for learning about evolution of phenotypic plasticity under genetic and ecological effects, and predicting their responses to changing environmental conditions. We sampled 28 populations (777 individuals) of Monachino's four varieties of H. speciosa and used seven microsatellite loci to genotype them. Bayesian clustering showed five distinct genetic groups (K = 5) with high admixture among Monachino's varieties, mainly among populations in the central area of the species geographical range. Genetic differentiation among Monachino's varieties was lower than the genetic differentiation among populations within varieties, with higher within-population inbreeding. A high historical connectivity among populations of the central Cerrado shown by coalescent analyses may explain the high admixture among varieties. In addition, areas of higher climatic suitability also presented higher genetic diversity in such a way that the wide historical refugium across central Brazil might have promoted the long-term connectivity among populations. Yet, FST was significantly related to geographic distances, but not to environmental distances, and coalescent analyses and ENM predicted a demographical scenario of quasi-stability through time. Our findings show that demographical history and isolation by distance, but not isolation by environment, drove genetic differentiation of populations. Finally, the genetic clusters do not support the two recently recognized botanical varieties of H. speciosa, but partially support Monachino's classification at least for the four sampled varieties, similar to morphological variation.

Designing Mouse Behavioral Tasks Relevant to Autistic-Like Behaviors

ERIC Educational Resources Information Center

Crawley, Jacqueline N.

2004-01-01

The importance of genetic factors in autism has prompted the development of mutant mouse models to advance our understanding of biological mechanisms underlying autistic behaviors. Mouse models of human neuropsychiatric diseases are designed to optimize (1) face validity, i.e., resemblance to the human symptoms; (2) construct validity, i.e.,…

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

USGS Publications Warehouse

Chen, C.; Xia, J.; Liu, J.; Feng, G.

2006-01-01

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.

Further evidence for the increased power of LOD scores compared with nonparametric methods.

PubMed

Durner, M; Vieland, V J; Greenberg, D A

1999-01-01

In genetic analysis of diseases in which the underlying model is unknown, "model free" methods-such as affected sib pair (ASP) tests-are often preferred over LOD-score methods, although LOD-score methods under the correct or even approximately correct model are more powerful than ASP tests. However, there might be circumstances in which nonparametric methods will outperform LOD-score methods. Recently, Dizier et al. reported that, in some complex two-locus (2L) models, LOD-score methods with segregation analysis-derived parameters had less power to detect linkage than ASP tests. We investigated whether these particular models, in fact, represent a situation that ASP tests are more powerful than LOD scores. We simulated data according to the parameters specified by Dizier et al. and analyzed the data by using a (a) single locus (SL) LOD-score analysis performed twice, under a simple dominant and a recessive mode of inheritance (MOI), (b) ASP methods, and (c) nonparametric linkage (NPL) analysis. We show that SL analysis performed twice and corrected for the type I-error increase due to multiple testing yields almost as much linkage information as does an analysis under the correct 2L model and is more powerful than either the ASP method or the NPL method. We demonstrate that, even for complex genetic models, the most important condition for linkage analysis is that the assumed MOI at the disease locus being tested is approximately correct, not that the inheritance of the disease per se is correctly specified. In the analysis by Dizier et al., segregation analysis led to estimates of dominance parameters that were grossly misspecified for the locus tested in those models in which ASP tests appeared to be more powerful than LOD-score analyses.

The effects of intraspecific competition and stabilizing selection on a polygenic trait.

PubMed Central

Bürger, Reinhard; Gimelfarb, Alexander

2004-01-01

The equilibrium properties of an additive multilocus model of a quantitative trait under frequency- and density-dependent selection are investigated. Two opposing evolutionary forces are assumed to act: (i) stabilizing selection on the trait, which favors genotypes with an intermediate phenotype, and (ii) intraspecific competition mediated by that trait, which favors genotypes whose effect on the trait deviates most from that of the prevailing genotypes. Accordingly, fitnesses of genotypes have a frequency-independent component describing stabilizing selection and a frequency- and density-dependent component modeling competition. We study how the equilibrium structure, in particular, number, degree of polymorphism, and genetic variance of stable equilibria, is affected by the strength of frequency dependence, and what role the number of loci, the amount of recombination, and the demographic parameters play. To this end, we employ a statistical and numerical approach, complemented by analytical results, and explore how the equilibrium properties averaged over a large number of genetic systems with a given number of loci and average amount of recombination depend on the ecological and demographic parameters. We identify two parameter regions with a transitory region in between, in which the equilibrium properties of genetic systems are distinctively different. These regions depend on the strength of frequency dependence relative to pure stabilizing selection and on the demographic parameters, but not on the number of loci or the amount of recombination. We further study the shape of the fitness function observed at equilibrium and the extent to which the dynamics in this model are adaptive, and we present examples of equilibrium distributions of genotypic values under strong frequency dependence. Consequences for the maintenance of genetic variation, the detection of disruptive selection, and models of sympatric speciation are discussed. PMID:15280253

Association between KCNJ11 gene polymorphisms and risk of type 2 diabetes mellitus in East Asian populations: a meta-analysis in 42,573 individuals.

PubMed

Yang, Lijuan; Zhou, Xianghai; Luo, Yingying; Sun, Xiuqin; Tang, Yong; Guo, Wulan; Han, Xueyao; Ji, Linong

2012-01-01

A number of studies have been performed to identify the association between potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) gene and type 2 diabetes mellitus (T2DM) in East Asian populations, with inconsistent results. The main aim of this work was to evaluate more precisely the genetic influence of KCNJ11 on T2DM in East Asian populations by means of a meta-analysis. We identified 20 articles for qualitative analysis and 16 were eligible for quantitative analysis (meta-analysis) by database searching up to May 2010. The association was assessed under different genetic models, and the pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated. The allelic and genotypic contrast demonstrated that the association between KCNJ11 and T2DM was significant for rs5210. However, not all results for rs5215 and rs5218 showed significant associations. For rs5219, the combined ORs (95% CIs) for allelic contrast, dominant and recessive models contrast (with allelic frequency and genotypic distribution data) were 1.139 (1.093-1.188), 1.177 (1.099-1.259) and 1.207 (1.094-1.332), respectively (random effect model). The analysis on the most completely adjusted ORs (95% CIs) by the covariates of rs5219 all presented significant associations under different genetic models. Population-stratified analysis (Korean, Japanese and Chinese) and sensitivity analysis verified the significant results. Cumulative meta-analysis including publication time and sample size illustrated the exaggerated genetic effect in the earliest studies. Heterogeneity and publication bias were assessed. Our study verified that single nucleotide polymorphisms (SNPs) of KCNJ11 gene were significantly associated with the risk of T2DM in East Asian populations.

A Common Missense Variant in the Neuregulin1 Gene is associated with Both Schizophrenia and Sudden Cardiac Death

PubMed Central

Huertas-Vazquez, Adriana; Teodorescu, Carmen; Reinier, Kyndaron; Uy-Evanado, Audrey; Chugh, Harpriya; Jerger, Katherine; Ayala, Jo; Gunson, Karen; Jui, Jonathan; Newton-Cheh, Christopher; Albert, Christine M.; Chugh, Sumeet S.

2013-01-01

Background Both schizophrenia and epilepsy have been linked to increased risk of sudden cardiac death (SCD). We hypothesized that DNA variants within genes previously associated with schizophrenia and epilepsy may contribute to an increased risk of SCD. Objective To investigate the contribution to SCD susceptibility of DNA variants previously implicated in schizophrenia and epilepsy. Methods From the ongoing Oregon Sudden Unexpected Death Study, comparisons were performed among 340 SCD cases presenting with ventricular fibrillation and 342 controls. We tested for association between 17 SNPs mapped to 14 loci previously implicated in schizophrenia and epilepsy using logistic regression, assuming additive, dominant and recessive genetic models. Results The minor allele of the non-synonymous SNP rs10503929 within the Neuregulin 1 gene (NRG1) was associated with SCD under all three investigated models, with the strongest association for the recessive genetic model (recessive P=4.01×10−5, OR= 4.04; additive P=2.84×10−7, OR= 1.9 and dominant P=9.01×10−6, OR= 2.06). To validate our findings, we further explored the association of this variant in the Harvard Cohort SCD study. The SNP rs10503929 was associated with an increased risk of SCD under the recessive genetic model (P=0.0005, OR= 2.7). This missense variation causes a methionine to threonine change and functional effects are currently unknown. Conclusions The observed association between a schizophrenia-related NRG1 variant and SCD may represent the first evidence of coexisting genetic susceptibility between two conditions that have an established clinical overlap. Further investigation is warranted to explore the molecular mechanisms of this variant in the pathogenesis of SCD. PMID:23524320

Neutral null models for diversity in serial transfer evolution experiments.

PubMed

Harpak, Arbel; Sella, Guy

2014-09-01

Evolution experiments with microorganisms coupled with genome-wide sequencing now allow for the systematic study of population genetic processes under a wide range of conditions. In learning about these processes in natural, sexual populations, neutral models that describe the behavior of diversity and divergence summaries have played a pivotal role. It is therefore natural to ask whether neutral models, suitably modified, could be useful in the context of evolution experiments. Here, we introduce coalescent models for polymorphism and divergence under the most common experimental evolution assay, a serial transfer experiment. This relatively simple setting allows us to address several issues that could affect diversity patterns in evolution experiments, whether selection is operating or not: the transient behavior of neutral polymorphism in an experiment beginning from a single clone, the effects of randomness in the timing of cell division and noisiness in population size in the dilution stage. In our analyses and discussion, we emphasize the implications for experiments aimed at measuring diversity patterns and making inferences about population genetic processes based on these measurements. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Optimizing Fukushima Emissions Through Pattern Matching and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Simpson, M. D.; Philip, C. S.; Baskett, R.

2017-12-01

Hazardous conditions during the Fukushima Daiichi nuclear power plant (NPP) accident hindered direct observations of the emissions of radioactive materials into the atmosphere. A wide range of emissions are estimated from bottom-up studies using reactor inventories and top-down approaches based on inverse modeling. We present a new inverse modeling estimate of cesium-137 emitted from the Fukushima NPP. Our estimate considers weather uncertainty through a large ensemble of Weather Research and Forecasting model simulations and uses the FLEXPART atmospheric dispersion model to transport and deposit cesium. The simulations are constrained by observations of the spatial distribution of cumulative cesium deposited on the surface of Japan through April 2, 2012. Multiple spatial metrics are used to quantify differences between observed and simulated deposition patterns. In order to match the observed pattern, we use a multi-objective genetic algorithm to optimize the time-varying emissions. We find that large differences with published bottom-up estimates are required to explain the observations. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models.

PubMed

Chen, Han; Wang, Chaolong; Conomos, Matthew P; Stilp, Adrienne M; Li, Zilin; Sofer, Tamar; Szpiro, Adam A; Chen, Wei; Brehm, John M; Celedón, Juan C; Redline, Susan; Papanicolaou, George J; Thornton, Timothy A; Laurie, Cathy C; Rice, Kenneth; Lin, Xihong

2016-04-07

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM's constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs. Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Matching oceanography and genetics at the basin scale. Seascape connectivity of the Mediterranean shore crab in the Adriatic Sea.

PubMed

Schiavina, M; Marino, I A M; Zane, L; Melià, P

2014-11-01

Investigating the interactions between the physical environment and early life history is crucial to understand the mechanisms that shape the genetic structure of marine populations. Here, we assessed the genetic differentiation in a species with larval dispersal, the Mediterranean shore crab (Carcinus aestuarii) in the Adriatic Sea (central Mediterranean), and we investigated the role of oceanic circulation in shaping population structure. To this end, we screened 11 polymorphic microsatellite loci from 431 individuals collected at eight different sites. We found a weak, yet significant, genetic structure into three major clusters: a northern Adriatic group, a central Adriatic group and one group including samples from southern Adriatic and Ionian seas. Genetic analyses were compared, under a seascape genetics approach, with estimates of potential larval connectivity obtained with a coupled physical-biological model that integrates a water circulation model and a description of biological traits affecting dispersal. The cross-validation of the results of the two approaches supported the view that genetic differentiation reflects an oceanographic subdivision of the Adriatic Sea into three subbasins, with circulation patterns allowing the exchange of larvae through permanent connections linking north Adriatic sites and ephemeral connections like those linking the central Adriatic with northern and southern locations. © 2014 John Wiley & Sons Ltd.

Predicting Risk of Type 2 Diabetes Mellitus with Genetic Risk Models on the Basis of Established Genome-wide Association Markers: A Systematic Review

PubMed Central

Bao, Wei; Hu, Frank B.; Rong, Shuang; Rong, Ying; Bowers, Katherine; Schisterman, Enrique F.; Liu, Liegang; Zhang, Cuilin

2013-01-01

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker–based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55–0.68), which did not differ appreciably by study design, sample size, participants’ race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor–based models (median AUC, 0.79 (range, 0.63–0.91) vs. median AUC, 0.78 (range, 0.63–0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants’ race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance. PMID:24008910

Potential and limits for rapid genetic adaptation to warming in a Great Barrier Reef coral.

PubMed

Matz, Mikhail V; Treml, Eric A; Aglyamova, Galina V; Bay, Line K

2018-04-01

Can genetic adaptation in reef-building corals keep pace with the current rate of sea surface warming? Here we combine population genomics, biophysical modeling, and evolutionary simulations to predict future adaptation of the common coral Acropora millepora on the Great Barrier Reef (GBR). Genomics-derived migration rates were high (0.1-1% of immigrants per generation across half the latitudinal range of the GBR) and closely matched the biophysical model of larval dispersal. Both genetic and biophysical models indicated the prevalence of southward migration along the GBR that would facilitate the spread of heat-tolerant alleles to higher latitudes as the climate warms. We developed an individual-based metapopulation model of polygenic adaptation and parameterized it with population sizes and migration rates derived from the genomic analysis. We find that high migration rates do not disrupt local thermal adaptation, and that the resulting standing genetic variation should be sufficient to fuel rapid region-wide adaptation of A. millepora populations to gradual warming over the next 20-50 coral generations (100-250 years). Further adaptation based on novel mutations might also be possible, but this depends on the currently unknown genetic parameters underlying coral thermal tolerance and the rate of warming realized. Despite this capacity for adaptation, our model predicts that coral populations would become increasingly sensitive to random thermal fluctuations such as ENSO cycles or heat waves, which corresponds well with the recent increase in frequency of catastrophic coral bleaching events.

Between-Region Genetic Divergence Reflects the Mode and Tempo of Tumor Evolution

PubMed Central

Sun, Ruping; Hu, Zheng; Sottoriva, Andrea; Graham, Trevor A.; Harpak, Arbel; Ma, Zhicheng; Fischer, Jared M.; Shibata, Darryl; Curtis, Christina

2017-01-01

Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multi-region sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, revealing different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, accumulate intra-tumor heterogeneity, and ultimately how they may be more effectively treated. PMID:28581503

Methods for quantifying simple gravity sensing in Drosophila melanogaster.

PubMed

Inagaki, Hidehiko K; Kamikouchi, Azusa; Ito, Kei

2010-01-01

Perception of gravity is essential for animals: most animals possess specific sense organs to detect the direction of the gravitational force. Little is known, however, about the molecular and neural mechanisms underlying their behavioral responses to gravity. Drosophila melanogaster, having a rather simple nervous system and a large variety of molecular genetic tools available, serves as an ideal model for analyzing the mechanisms underlying gravity sensing. Here we describe an assay to measure simple gravity responses of flies behaviorally. This method can be applied for screening genetic mutants of gravity perception. Furthermore, in combination with recent genetic techniques to silence or activate selective sets of neurons, it serves as a powerful tool to systematically identify neural substrates required for the proper behavioral responses to gravity. The assay requires 10 min to perform, and two experiments can be performed simultaneously, enabling 12 experiments per hour.

A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

PubMed Central

Guo, Hao; Fu, Jing

2013-01-01

Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment. PMID:24489489

Navigating the Interface Between Landscape Genetics and Landscape Genomics.

PubMed

Storfer, Andrew; Patton, Austin; Fraik, Alexandra K

2018-01-01

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used.

Navigating the Interface Between Landscape Genetics and Landscape Genomics

PubMed Central

Storfer, Andrew; Patton, Austin; Fraik, Alexandra K.

2018-01-01

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used. PMID:29593776

Breeding objectives for indigenous chicken: model development and application to different production systems.

PubMed

Okeno, Tobias O; Magothe, Thomas M; Kahi, Alexander K; Peters, Kurt J

2013-01-01

A bio-economic model was developed to evaluate the utilisation of indigenous chickens (IC) under different production systems accounting for the risk attitude of the farmers. The model classified the production systems into three categories based on the level of management: free-range system (FRS), where chickens were left to scavenge for feed resources with no supplementation and healthcare; intensive system (IS), where the chickens were permanently confined and supplied with rationed feed and healthcare; and semi-intensive system (SIS), a hybrid of FRS and IS, where the chickens were partially confined, supplemented with rationed feeds, provided with healthcare and allowed to scavenge within the homestead or in runs. The model allows prediction of the live weights and feed intake at different stages in the life cycle of the IC and can compute the profitability of each production system using both traditional and risk-rated profit models. The input parameters used in the model represent a typical IC production system in developing countries but are flexible and therefore can be modified to suit specific situations and simulate profitability and costs of other poultry species production systems. The model has the capability to derive the economic values as changes in the genetic merit of the biological parameter results in marginal changes in profitability and costs of the production systems. The results suggested that utilisation of IC in their current genetic merit and production environment is more profitable under FRS and SIS but not economically viable under IS.

Determining causes of genetic isolation in a large carnivore (Ursus americanus) population to direct contemporary conservation measures

PubMed Central

Obbard, Martyn E.; Harnden, Matthew; McConnell, Sabine; Howe, Eric J.; Burrows, Frank G.; White, Bradley N.; Kyle, Christopher J.

2017-01-01

The processes leading to genetic isolation influence a population’s local extinction risk, and should thus be identified before conservation actions are implemented. Natural or human-induced circumstances can result in historical or contemporary barriers to gene flow and/or demographic bottlenecks. Distinguishing between these hypotheses can be achieved by comparing genetic diversity and differentiation in isolated vs. continuous neighboring populations. In Ontario, American black bears (Ursus americanus) are continuously distributed, genetically diverse, and exhibit an isolation-by-distance structuring pattern, except on the Bruce Peninsula (BP). To identify the processes that led to the genetic isolation of BP black bears, we modelled various levels of historical and contemporary migration and population size reductions using forward simulations. We compared simulation results with empirical genetic indices from Ontario black bear populations under different levels of geographic isolation, and conducted additional simulations to determine if translocations could help achieve genetic restoration. From a genetic standpoint, conservation concerns for BP black bears are warranted because our results show that: i) a recent demographic bottleneck associated with recently reduced migration best explains the low genetic diversity on the BP; and ii) under sustained isolation, BP black bears could lose between 70% and 80% of their rare alleles within 100 years. Although restoring migration corridors would be the most effective method to enhance long-term genetic diversity and prevent inbreeding, it is unrealistic to expect connectivity to be re-established. Current levels of genetic diversity could be maintained by successfully translocating 10 bears onto the peninsula every 5 years. Such regular translocations may be more practical than landscape restoration, because areas connecting the peninsula to nearby mainland black bear populations have been irreversibly modified by humans, and form strong barriers to movement. PMID:28235066

Advances in Genetical Genomics of Plants

PubMed Central

Joosen, R.V.L.; Ligterink, W.; Hilhorst, H.W.M.; Keurentjes, J.J.B.

2009-01-01

Natural variation provides a valuable resource to study the genetic regulation of quantitative traits. In quantitative trait locus (QTL) analyses this variation, captured in segregating mapping populations, is used to identify the genomic regions affecting these traits. The identification of the causal genes underlying QTLs is a major challenge for which the detection of gene expression differences is of major importance. By combining genetics with large scale expression profiling (i.e. genetical genomics), resulting in expression QTLs (eQTLs), great progress can be made in connecting phenotypic variation to genotypic diversity. In this review we discuss examples from human, mouse, Drosophila, yeast and plant research to illustrate the advances in genetical genomics, with a focus on understanding the regulatory mechanisms underlying natural variation. With their tolerance to inbreeding, short generation time and ease to generate large families, plants are ideal subjects to test new concepts in genetics. The comprehensive resources which are available for Arabidopsis make it a favorite model plant but genetical genomics also found its way to important crop species like rice, barley and wheat. We discuss eQTL profiling with respect to cis and trans regulation and show how combined studies with other ‘omics’ technologies, such as metabolomics and proteomics may further augment current information on transcriptional, translational and metabolomic signaling pathways and enable reconstruction of detailed regulatory networks. The fast developments in the ‘omics’ area will offer great potential for genetical genomics to elucidate the genotype-phenotype relationships for both fundamental and applied research. PMID:20514216

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

PubMed Central

Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

2016-01-01

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

PubMed

Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo

2017-01-05

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.

Genetic disruptions of Drosophila Pavlovian learning leave extinction learning intact.

PubMed

Qin, H; Dubnau, J

2010-03-01

Individuals who experience traumatic events may develop persistent posttraumatic stress disorder. Patients with this disorder are commonly treated with exposure therapy, which has had limited long-term success. In experimental neurobiology, fear extinction is a model for exposure therapy. In this behavioral paradigm, animals are repeatedly exposed in a safe environment to the fearful stimulus, which leads to greatly reduced fear. Studying animal models of extinction already has lead to better therapeutic strategies and development of new candidate drugs. Lack of a powerful genetic model of extinction, however, has limited progress in identifying underlying molecular and genetic factors. In this study, we established a robust behavioral paradigm to study the short-term effect (acquisition) of extinction in Drosophila melanogaster. We focused on the extinction of olfactory aversive 1-day memory with a task that has been the main workhorse for genetics of memory in flies. Using this paradigm, we show that extinction can inhibit each of two genetically distinct forms of consolidated memory. We then used a series of single-gene mutants with known impact on associative learning to examine the effects on extinction. We find that extinction is intact in each of these mutants, suggesting that extinction learning relies on different molecular mechanisms than does Pavlovian learning.

Plasticity of genetic interactions in metabolic networks of yeast.

PubMed

Harrison, Richard; Papp, Balázs; Pál, Csaba; Oliver, Stephen G; Delneri, Daniela

2007-02-13

Why are most genes dispensable? The impact of gene deletions may depend on the environment (plasticity), the presence of compensatory mechanisms (mutational robustness), or both. Here, we analyze the interaction between these two forces by exploring the condition-dependence of synthetic genetic interactions that define redundant functions and alternative pathways. We performed systems-level flux balance analysis of the yeast (Saccharomyces cerevisiae) metabolic network to identify genetic interactions and then tested the model's predictions with in vivo gene-deletion studies. We found that the majority of synthetic genetic interactions are restricted to certain environmental conditions, partly because of the lack of compensation under some (but not all) nutrient conditions. Moreover, the phylogenetic cooccurrence of synthetically interacting pairs is not significantly different from random expectation. These findings suggest that these gene pairs have at least partially independent functions, and, hence, compensation is only a byproduct of their evolutionary history. Experimental analyses that used multiple gene deletion strains not only confirmed predictions of the model but also showed that investigation of false predictions may both improve functional annotation within the model and also lead to the discovery of higher-order genetic interactions. Our work supports the view that functional redundancy may be more apparent than real, and it offers a unified framework for the evolution of environmental adaptation and mutational robustness.

CRISPR-Barcoding for Intratumor Genetic Heterogeneity Modeling and Functional Analysis of Oncogenic Driver Mutations.

PubMed

Guernet, Alexis; Mungamuri, Sathish Kumar; Cartier, Dorthe; Sachidanandam, Ravi; Jayaprakash, Anitha; Adriouch, Sahil; Vezain, Myriam; Charbonnier, Françoise; Rohkin, Guy; Coutant, Sophie; Yao, Shen; Ainani, Hassan; Alexandre, David; Tournier, Isabelle; Boyer, Olivier; Aaronson, Stuart A; Anouar, Youssef; Grumolato, Luca

2016-08-04

Intratumor genetic heterogeneity underlies the ability of tumors to evolve and adapt to different environmental conditions. Using CRISPR/Cas9 technology and specific DNA barcodes, we devised a strategy to recapitulate and trace the emergence of subpopulations of cancer cells containing a mutation of interest. We used this approach to model different mechanisms of lung cancer cell resistance to EGFR inhibitors and to assess effects of combined drug therapies. By overcoming intrinsic limitations of current approaches, CRISPR-barcoding also enables investigation of most types of genetic modifications, including repair of oncogenic driver mutations. Finally, we used highly complex barcodes inserted at a specific genome location as a means of simultaneously tracing the fates of many thousands of genetically labeled cancer cells. CRISPR-barcoding is a straightforward and highly flexible method that should greatly facilitate the functional investigation of specific mutations, in a context that closely mimics the complexity of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Possibility of modifying the growth trajectory in Raeini Cashmere goat.

PubMed

Ghiasi, Heydar; Mokhtari, M S

2018-03-27

The objective of this study was to investigate the possibility of modifying the growth trajectory in Raeini Cashmere goat breed. In total, 13,193 records on live body weight collected from 4788 Raeini Cashmere goats were used. According to Akanke's information criterion (AIC), the sing-trait random regression model included fourth-order Legendre polynomial for direct and maternal genetic effect; maternal and individual permanent environmental effect was the best model for estimating (co)variance components. The matrices of eigenvectors for (co)variances between random regression coefficients of direct additive genetic were used to calculate eigenfunctions, and different eigenvector indices were also constructed. The obtained results showed that the first eigenvalue explained 79.90% of total genetic variance. Therefore, changing the body weights applying the first eigenfunction will be obtained rapidly. Selection based on the first eigenvector will cause favorable positive genetic gains for all body weight considered from birth to 12 months of age. For modifying the growth trajectory in Raeini Cashmere goat, the selection should be based on the second eigenfunction. The second eigenvalue accounted for 14.41% of total genetic variance for body weights that is low in comparison with genetic variance explained by the first eigenvalue. The complex patterns of genetic change in growth trajectory observed under the third and fourth eigenfunction and low amount of genetic variance explained by the third and fourth eigenvalues.

Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis.

PubMed

Conley, Samantha

2017-12-01

The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

The Mouse Lemur, a Genetic Model Organism for Primate Biology, Behavior, and Health.

PubMed

Ezran, Camille; Karanewsky, Caitlin J; Pendleton, Jozeph L; Sholtz, Alex; Krasnow, Maya R; Willick, Jason; Razafindrakoto, Andriamahery; Zohdy, Sarah; Albertelli, Megan A; Krasnow, Mark A

2017-06-01

Systematic genetic studies of a handful of diverse organisms over the past 50 years have transformed our understanding of biology. However, many aspects of primate biology, behavior, and disease are absent or poorly modeled in any of the current genetic model organisms including mice. We surveyed the animal kingdom to find other animals with advantages similar to mice that might better exemplify primate biology, and identified mouse lemurs ( Microcebus spp.) as the outstanding candidate. Mouse lemurs are prosimian primates, roughly half the genetic distance between mice and humans. They are the smallest, fastest developing, and among the most prolific and abundant primates in the world, distributed throughout the island of Madagascar, many in separate breeding populations due to habitat destruction. Their physiology, behavior, and phylogeny have been studied for decades in laboratory colonies in Europe and in field studies in Malagasy rainforests, and a high quality reference genome sequence has recently been completed. To initiate a classical genetic approach, we developed a deep phenotyping protocol and have screened hundreds of laboratory and wild mouse lemurs for interesting phenotypes and begun mapping the underlying mutations, in collaboration with leading mouse lemur biologists. We also seek to establish a mouse lemur gene "knockout" library by sequencing the genomes of thousands of mouse lemurs to identify null alleles in most genes from the large pool of natural genetic variants. As part of this effort, we have begun a citizen science project in which students across Madagascar explore the remarkable biology around their schools, including longitudinal studies of the local mouse lemurs. We hope this work spawns a new model organism and cultivates a deep genetic understanding of primate biology and health. We also hope it establishes a new and ethical method of genetics that bridges biological, behavioral, medical, and conservation disciplines, while providing an example of how hands-on science education can help transform developing countries. Copyright © 2017 by the Genetics Society of America.

Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources

NASA Astrophysics Data System (ADS)

Cantelli, A.; D'Orta, F.; Cattini, A.; Sebastianelli, F.; Cedola, L.

2015-08-01

A computational model is developed for retrieving the positions and the emission rates of unknown pollution sources, under steady state conditions, starting from the measurements of the concentration of the pollutants. The approach is based on the minimization of a fitness function employing a genetic algorithm paradigm. The model is tested considering both pollutant concentrations generated through a Gaussian model in 25 points in a 3-D test case domain (1000m × 1000m × 50 m) and experimental data such as the Prairie Grass field experiments data in which about 600 receptors were located along five concentric semicircle arcs and the Fusion Field Trials 2007. The results show that the computational model is capable to efficiently retrieve up to three different unknown sources.

MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data.

PubMed

Fan, Yu; Xi, Liu; Hughes, Daniel S T; Zhang, Jianjun; Zhang, Jianhua; Futreal, P Andrew; Wheeler, David A; Wang, Wenyi

2016-08-24

Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We develop MuSE ( http://bioinformatics.mdanderson.org/main/MuSE ), Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base. MuSE adopts a sample-specific error model that reflects the underlying tumor heterogeneity to greatly improve the overall accuracy. We demonstrate the accuracy of MuSE in calling subclonal mutations in the context of large-scale tumor sequencing projects using whole exome and whole genome sequencing.

IBSEM: An Individual-Based Atlantic Salmon Population Model.

PubMed

Castellani, Marco; Heino, Mikko; Gilbey, John; Araki, Hitoshi; Svåsand, Terje; Glover, Kevin A

2015-01-01

Ecology and genetics can influence the fate of individuals and populations in multiple ways. However, to date, few studies consider them when modelling the evolutionary trajectory of populations faced with admixture with non-local populations. For the Atlantic salmon, a model incorporating these elements is urgently needed because many populations are challenged with gene-flow from non-local and domesticated conspecifics. We developed an Individual-Based Salmon Eco-genetic Model (IBSEM) to simulate the demographic and population genetic change of an Atlantic salmon population through its entire life-cycle. Processes such as growth, mortality, and maturation are simulated through stochastic procedures, which take into account environmental variables as well as the genotype of the individuals. IBSEM is based upon detailed empirical data from salmon biology, and parameterized to reproduce the environmental conditions and the characteristics of a wild population inhabiting a Norwegian river. Simulations demonstrated that the model consistently and reliably reproduces the characteristics of the population. Moreover, in absence of farmed escapees, the modelled populations reach an evolutionary equilibrium that is similar to our definition of a 'wild' genotype. We assessed the sensitivity of the model in the face of assumptions made on the fitness differences between farm and wild salmon, and evaluated the role of straying as a buffering mechanism against the intrusion of farm genes into wild populations. These results demonstrate that IBSEM is able to capture the evolutionary forces shaping the life history of wild salmon and is therefore able to model the response of populations under environmental and genetic stressors.

Explaining the increasing heritability of cognitive ability across development: a meta-analysis of longitudinal twin and adoption studies.

PubMed

Briley, Daniel A; Tucker-Drob, Elliot M

2013-09-01

Genes account for increasing proportions of variation in cognitive ability across development, but the mechanisms underlying these increases remain unclear. We conducted a meta-analysis of longitudinal behavioral genetic studies spanning infancy to adolescence. We identified relevant data from 16 articles with 11 unique samples containing a total of 11,500 twin and sibling pairs who were all reared together and measured at least twice between the ages of 6 months and 18 years. Longitudinal behavioral genetic models were used to estimate the extent to which early genetic influences on cognition were amplified over time and the extent to which innovative genetic influences arose with time. Results indicated that in early childhood, innovative genetic influences predominate but that innovation quickly diminishes, and amplified influences account for increasing heritability following age 8 years.

Multisynchronization of Coupled Heterogeneous Genetic Oscillator Networks via Partial Impulsive Control.

PubMed

He, Ding-Xin; Ling, Guang; Guan, Zhi-Hong; Hu, Bin; Liao, Rui-Quan

2018-02-01

This paper focuses on the collective dynamics of multisynchronization among heterogeneous genetic oscillators under a partial impulsive control strategy. The coupled nonidentical genetic oscillators are modeled by differential equations with uncertainties. The definition of multisynchronization is proposed to describe some more general synchronization behaviors in the real. Considering that each genetic oscillator consists of a large number of biochemical molecules, we design a more manageable impulsive strategy for dynamic networks to achieve multisynchronization. Not all the molecules but only a small fraction of them in each genetic oscillator are controlled at each impulsive instant. Theoretical analysis of multisynchronization is carried out by the control theory approach, and a sufficient condition of partial impulsive controller for multisynchronization with given error bounds is established. At last, numerical simulations are exploited to demonstrate the effectiveness of our results.

Population Structure, Genetic Diversity and Molecular Marker-Trait Association Analysis for High Temperature Stress Tolerance in Rice

PubMed Central

Barik, Saumya Ranjan; Sahoo, Ambika; Mohapatra, Sudipti; Nayak, Deepak Kumar; Mahender, Anumalla; Meher, Jitandriya; Anandan, Annamalai

2016-01-01

Rice exhibits enormous genetic diversity, population structure and molecular marker-traits associated with abiotic stress tolerance to high temperature stress. A set of breeding lines and landraces representing 240 germplasm lines were studied. Based on spikelet fertility percent under high temperature, tolerant genotypes were broadly classified into four classes. Genetic diversity indicated a moderate level of genetic base of the population for the trait studied. Wright’s F statistic estimates showed a deviation of Hardy-Weinberg expectation in the population. The analysis of molecular variance revealed 25 percent variation between population, 61 percent among individuals and 14 percent within individuals in the set. The STRUCTURE analysis categorized the entire population into three sub-populations and suggested that most of the landraces in each sub-population had a common primary ancestor with few admix individuals. The composition of materials in the panel showed the presence of many QTLs representing the entire genome for the expression of tolerance. The strongly associated marker RM547 tagged with spikelet fertility under stress and the markers like RM228, RM205, RM247, RM242, INDEL3 and RM314 indirectly controlling the high temperature stress tolerance were detected through both mixed linear model and general linear model TASSEL analysis. These markers can be deployed as a resource for marker-assisted breeding program of high temperature stress tolerance. PMID:27494320

Population Structure, Genetic Diversity and Molecular Marker-Trait Association Analysis for High Temperature Stress Tolerance in Rice.

PubMed

Pradhan, Sharat Kumar; Barik, Saumya Ranjan; Sahoo, Ambika; Mohapatra, Sudipti; Nayak, Deepak Kumar; Mahender, Anumalla; Meher, Jitandriya; Anandan, Annamalai; Pandit, Elssa

2016-01-01

Rice exhibits enormous genetic diversity, population structure and molecular marker-traits associated with abiotic stress tolerance to high temperature stress. A set of breeding lines and landraces representing 240 germplasm lines were studied. Based on spikelet fertility percent under high temperature, tolerant genotypes were broadly classified into four classes. Genetic diversity indicated a moderate level of genetic base of the population for the trait studied. Wright's F statistic estimates showed a deviation of Hardy-Weinberg expectation in the population. The analysis of molecular variance revealed 25 percent variation between population, 61 percent among individuals and 14 percent within individuals in the set. The STRUCTURE analysis categorized the entire population into three sub-populations and suggested that most of the landraces in each sub-population had a common primary ancestor with few admix individuals. The composition of materials in the panel showed the presence of many QTLs representing the entire genome for the expression of tolerance. The strongly associated marker RM547 tagged with spikelet fertility under stress and the markers like RM228, RM205, RM247, RM242, INDEL3 and RM314 indirectly controlling the high temperature stress tolerance were detected through both mixed linear model and general linear model TASSEL analysis. These markers can be deployed as a resource for marker-assisted breeding program of high temperature stress tolerance.

A Genome-Wide Scan for Evidence of Selection in a Maize Population Under Long-Term Artificial Selection for Ear Number

PubMed Central

Beissinger, Timothy M.; Hirsch, Candice N.; Vaillancourt, Brieanne; Deshpande, Shweta; Barry, Kerrie; Buell, C. Robin; Kaeppler, Shawn M.; Gianola, Daniel; de Leon, Natalia

2014-01-01

A genome-wide scan to detect evidence of selection was conducted in the Golden Glow maize long-term selection population. The population had been subjected to selection for increased number of ears per plant for 30 generations, with an empirically estimated effective population size ranging from 384 to 667 individuals and an increase of more than threefold in the number of ears per plant. Allele frequencies at >1.2 million single-nucleotide polymorphism loci were estimated from pooled whole-genome resequencing data, and FST values across sliding windows were employed to assess divergence between the population preselection and the population postselection. Twenty-eight highly divergent regions were identified, with half of these regions providing gene-level resolution on potentially selected variants. Approximately 93% of the divergent regions do not demonstrate a significant decrease in heterozygosity, which suggests that they are not approaching fixation. Also, most regions display a pattern consistent with a soft-sweep model as opposed to a hard-sweep model, suggesting that selection mostly operated on standing genetic variation. For at least 25% of the regions, results suggest that selection operated on variants located outside of currently annotated coding regions. These results provide insights into the underlying genetic effects of long-term artificial selection and identification of putative genetic elements underlying number of ears per plant in maize. PMID:24381334

Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster

PubMed Central

Morgante, Fabio; Sørensen, Peter; Sorensen, Daniel A.; Maltecca, Christian; Mackay, Trudy F. C.

2015-01-01

Individuals of the same genotype do not have the same phenotype for quantitative traits when reared under common macro-environmental conditions, a phenomenon called micro-environmental plasticity. Genetic variation in micro-environmental plasticity is assumed in models of the evolution of phenotypic variance, and is important in applied breeding and personalized medicine. Here, we quantified genetic variation for micro-environmental plasticity for three quantitative traits in the inbred, sequenced lines of the Drosophila melanogaster Genetic Reference Panel. We found substantial genetic variation for micro-environmental plasticity for all traits, with broad sense heritabilities of the same magnitude or greater than those of trait means. Micro-environmental plasticity is not correlated with residual segregating variation, is trait-specific, and has genetic correlations with trait means ranging from zero to near unity. We identified several candidate genes associated with micro-environmental plasticity of startle response, including Drosophila Hsp90, setting the stage for future genetic dissection of this phenomenon. PMID:25943032

Convergent synaptic and circuit substrates underlying autism genetic risks.

PubMed

McGee, Aaron; Li, Guohui; Lu, Zhongming; Qiu, Shenfeng

2014-02-01

There has been a surge of diagnosis of autism spectrum disorders (ASD) over the past decade. While large, high powered genome screening studies of children with ASD have identified numerous genetic risk factors, research efforts to understanding how each of these risk factors contributes to the development autism has met with limited success. Revealing the mechanisms by which these genetic risk factors affect brain development and predispose a child to autism requires mechanistic understanding of the neurobiological changes underlying this devastating group of developmental disorders at multifaceted molecular, cellular and system levels. It has been increasingly clear that the normal trajectory of neurodevelopment is compromised in autism, in multiple domains as much as aberrant neuronal production, growth, functional maturation, patterned connectivity, and balanced excitation and inhibition of brain networks. Many autism risk factors identified in humans have been now reconstituted in experimental mouse models to allow mechanistic interrogation of the biological role of the risk gene. Studies utilizing these mouse models have revealed that underlying the enormous heterogeneity of perturbed cellular events, mechanisms directing synaptic and circuit assembly may provide a unifying explanation for the pathophysiological changes and behavioral endophenotypes seen in autism, although synaptic perturbations are far from being the only alterations relevant for ASD. In this review, we discuss synaptic and circuit abnormalities obtained from several prevalent mouse models, particularly those reflecting syndromic forms of ASD that are caused by single gene perturbations. These compiled results reveal that ASD risk genes contribute to proper signaling of the developing gene networks that maintain synaptic and circuit homeostasis, which is fundamental to normal brain development.

Natural selection and inheritance of breeding time and clutch size in the collared flycatcher.

PubMed

Sheldon, B C; Kruuk, L E B; Merilä, J

2003-02-01

Many characteristics of organisms in free-living populations appear to be under directional selection, possess additive genetic variance, and yet show no evolutionary response to selection. Avian breeding time and clutch size are often-cited examples of such characters. We report analyses of inheritance of, and selection on, these traits in a long-term study of a wild population of the collared flycatcher Ficedula albicollis. We used mixed model analysis with REML estimation ("animal models") to make full use of the information in complex multigenerational pedigrees. Heritability of laying date, but not clutch size, was lower than that estimated previously using parent-offspring regressions, although for both traits there was evidence of substantial additive genetic variance (h2 = 0.19 and 0.29, respectively). Laying date and clutch size were negatively genetically correlated (rA = -0.41 +/- 0.09), implying that selection on one of the traits would cause a correlated response in the other, but there was little evidence to suggest that evolution of either trait would be constrained by correlations with other phenotypic characters. Analysis of selection on these traits in females revealed consistent strong directional fecundity selection for earlier breeding at the level of the phenotype (beta = -0.28 +/- 0.03), but little evidence for stabilising selection on breeding time. We found no evidence that clutch size was independently under selection. Analysis of fecundity selection on breeding values for laying date, estimated from an animal model, indicated that selection acts directly on additive genetic variance underlying breeding time (beta = -0.20 +/- 0.04), but not on clutch size (beta = 0.03 +/- 0.05). In contrast, selection on laying date via adult female survival fluctuated in sign between years, and was opposite in sign for selection on phenotypes (negative) and breeding values (positive). Our data thus suggest that any evolutionary response to selection on laying date is partially constrained by underlying life-history trade-offs, and illustrate the difficulties in using purely phenotypic measures and incomplete fitness estimates to assess evolution of life-history trade-offs. We discuss some of the difficulties associated with understanding the evolution of laying date and clutch size in natural populations.

Expanding a dynamic flux balance model of yeast fermentation to genome-scale

PubMed Central

2011-01-01

Background Yeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints. Results Appropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol. Conclusion A useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations. PMID:21595919

Prediction of gene expression with cis-SNPs using mixed models and regularization methods.

PubMed

Zeng, Ping; Zhou, Xiang; Huang, Shuiping

2017-05-11

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R 2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R 2  ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R 2  ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

Combining Genome-Scale Experimental and Computational Methods To Identify Essential Genes in Rhodobacter sphaeroides

DOE PAGES

Burger, Brian T.; Imam, Saheed; Scarborough, Matthew J.; ...

2017-06-06

Rhodobacter sphaeroides is one of the best-studied alphaproteobacteria from biochemical, genetic, and genomic perspectives. To gain a better systems-level understanding of this organism, we generated a large transposon mutant library and used transposon sequencing (Tn-seq) to identify genes that are essential under several growth conditions. Using newly developed Tn-seq analysis software (TSAS), we identified 493 genes as essential for aerobic growth on a rich medium. We then used the mutant library to identify conditionally essential genes under two laboratory growth conditions, identifying 85 additional genes required for aerobic growth in a minimal medium and 31 additional genes required for photosyntheticmore » growth. In all instances, our analyses confirmed essentiality for many known genes and identified genes not previously considered to be essential. We used the resulting Tn-seq data to refine and improve a genome-scale metabolic network model (GEM) for R. sphaeroides. Together, we demonstrate how genetic, genomic, and computational approaches can be combined to obtain a systems-level understanding of the genetic framework underlying metabolic diversity in bacterial species.« less

Genetic, environmental, and epigenetic factors in the development of personality disturbance.

PubMed

Depue, Richard A

2009-01-01

A dimensional model of personality disturbance is presented that is defined by extreme values on interacting subsets of seven major personality traits. Being at the extreme has marked effects on the threshold for eliciting those traits under stimulus conditions: that is, the extent to which the environment affects the neurobiological functioning underlying the traits. To explore the nature of development of extreme values on these traits, each trait is discussed in terms of three major issues: (a) the neurobiological variables associated with the trait, (b) individual variation in this neurobiology as a function of genetic polymorphisms, and (c) the effects of environmental adversity on these neurobiological variables through the action of epigenetic processes. It is noted that gene-environment interaction appears to be dependent on two main factors: (a) both genetic and environmental variables appear to have the most profound and enduring effects when they exert their effects during early postnatal periods, times when the forebrain is undergoing exuberant experience-expectant dendritic and axonal growth; and (b) environmental effects on neurobiology are strongly modified by individual differences in "traitlike" functioning of neurobiological variables. A model of the nature of the interaction between environmental and neurobiological variables in the development of personality disturbance is presented.

HpQTL: a geometric morphometric platform to compute the genetic architecture of heterophylly.

PubMed

Sun, Lidan; Wang, Jing; Zhu, Xuli; Jiang, Libo; Gosik, Kirk; Sang, Mengmeng; Sun, Fengsuo; Cheng, Tangren; Zhang, Qixiang; Wu, Rongling

2017-02-15

Heterophylly, i.e. morphological changes in leaves along the axis of an individual plant, is regarded as a strategy used by plants to cope with environmental change. However, little is known of the extent to which heterophylly is controlled by genes and how each underlying gene exerts its effect on heterophyllous variation. We described a geometric morphometric model that can quantify heterophylly in plants and further constructed an R-based computing platform by integrating this model into a genetic mapping and association setting. The platform, named HpQTL, allows specific quantitative trait loci mediating heterophyllous variation to be mapped throughout the genome. The statistical properties of HpQTL were examined and validated via computer simulation. Its biological relevance was demonstrated by results from a real data analysis of heterophylly in a wood plant, mei (Prunus mume). HpQTL provides a powerful tool to analyze heterophylly and its underlying genetic architecture in a quantitative manner. It also contributes a new approach for genome-wide association studies aimed to dissect the programmed regulation of plant development and evolution. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Linkage studies on chromosome 22 in familial schizophrenia

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

Vallada, H.P.; Gill, M.; Sham, P.

1995-04-24

As part of a systematic search for a major genetic locus for schizophrenia we have examined chromosome 22 using 14 highly polymorphic markers in 23 disease pedigrees. The markers were distributed at an average distance of 6.6 cM, covering 70-80% of the chromosome. We analyzed the data by the lod score method using five plausible genetic models ranging from dominant to recessive, after testing the power of our sample under the same genetic parameters. The most positive lod score found was 1.51 under a recessive model for the marker D22S278, which is insufficient to conclude linkage. However, an excess ofmore » shared alleles in affected siblings (P < .01) was found for both D22S278 and D22S283. For D22S278, the A statistic was equal to the lod score (1.51) and therefore did not provide additional evidence for linkage allowing for heterogeneity, but the Liang statistic was more significant (P = .002). Our results suggest the possibility that the region around D22S278 and D22S283 contains a gene which contributes to the etiology of schizophrenia. 60 refs., 1 fig., 5 tabs.« less

Differential influences of local subpopulations on regional diversity and differentiation for greater sage-grouse (Centrocercus urophasianus)

USGS Publications Warehouse

Row, Jeffery R.; Oyler-McCance, Sara J.; Fedy, Brad C.

2016-01-01

The distribution of spatial genetic variation across a region can shape evolutionary dynamics and impact population persistence. Local population dynamics and among-population dispersal rates are strong drivers of this spatial genetic variation, yet for many species we lack a clear understanding of how these population processes interact in space to shape within-species genetic variation. Here, we used extensive genetic and demographic data from 10 subpopulations of greater sage-grouse to parameterize a simulated approximate Bayesian computation (ABC) model and (i) test for regional differences in population density and dispersal rates for greater sage-grouse subpopulations in Wyoming, and (ii) quantify how these differences impact subpopulation regional influence on genetic variation. We found a close match between observed and simulated data under our parameterized model and strong variation in density and dispersal rates across Wyoming. Sensitivity analyses suggested that changes in dispersal (via landscape resistance) had a greater influence on regional differentiation, whereas changes in density had a greater influence on mean diversity across all subpopulations. Local subpopulations, however, varied in their regional influence on genetic variation. Decreases in the size and dispersal rates of central populations with low overall and net immigration (i.e. population sources) had the greatest negative impact on genetic variation. Overall, our results provide insight into the interactions among demography, dispersal and genetic variation and highlight the potential of ABC to disentangle the complexity of regional population dynamics and project the genetic impact of changing conditions.

Data Sufficiency Assessment and Pumping Test Design for Groundwater Prediction Using Decision Theory and Genetic Algorithms

NASA Astrophysics Data System (ADS)

McPhee, J.; William, Y. W.

2005-12-01

This work presents a methodology for pumping test design based on the reliability requirements of a groundwater model. Reliability requirements take into consideration the application of the model results in groundwater management, expressed in this case as a multiobjective management model. The pumping test design is formulated as a mixed-integer nonlinear programming (MINLP) problem and solved using a combination of genetic algorithm (GA) and gradient-based optimization. Bayesian decision theory provides a formal framework for assessing the influence of parameter uncertainty over the reliability of the proposed pumping test. The proposed methodology is useful for selecting a robust design that will outperform all other candidate designs under most potential 'true' states of the system

Association mapping reveals the genetic architecture of tomato response to water deficit: focus on major fruit quality traits.

PubMed

Albert, Elise; Segura, Vincent; Gricourt, Justine; Bonnefoi, Julien; Derivot, Laurent; Causse, Mathilde

2016-12-01

Water scarcity constitutes a crucial constraint for agriculture productivity. High-throughput approaches in model plant species identified hundreds of genes potentially involved in survival under drought, but few having beneficial effects on quality and yield. Nonetheless, controlled water deficit may improve fruit quality through higher concentration of flavor compounds. The underlying genetic determinants are still poorly known. In this study, we phenotyped 141 highly diverse small fruit tomato accessions for 27 traits under two contrasting watering conditions. A subset of 55 accessions exhibited increased metabolite contents and maintained yield under water deficit. Using 6100 single nucleotide polymorphisms (SNPs), association mapping revealed 31, 41, and 44 quantitative trait loci (QTLs) under drought, control, and both conditions, respectively. Twenty-five additional QTLs were interactive between conditions, emphasizing the interest in accounting for QTLs by watering regime interactions in fruit quality improvement. Combining our results with the loci previously identified in a biparental progeny resulted in 11 common QTLs and contributed to a first detailed characterization of the genetic determinants of response to water deficit in tomato. Major QTLs for fruit quality traits were dissected and candidate genes were proposed using expression and polymorphism data. The outcomes provide a basis for fruit quality improvement under deficit irrigation while limiting yield losses. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping.

PubMed

Thoen, Manus P M; Davila Olivas, Nelson H; Kloth, Karen J; Coolen, Silvia; Huang, Ping-Ping; Aarts, Mark G M; Bac-Molenaar, Johanna A; Bakker, Jaap; Bouwmeester, Harro J; Broekgaarden, Colette; Bucher, Johan; Busscher-Lange, Jacqueline; Cheng, Xi; Fradin, Emilie F; Jongsma, Maarten A; Julkowska, Magdalena M; Keurentjes, Joost J B; Ligterink, Wilco; Pieterse, Corné M J; Ruyter-Spira, Carolien; Smant, Geert; Testerink, Christa; Usadel, Björn; van Loon, Joop J A; van Pelt, Johan A; van Schaik, Casper C; van Wees, Saskia C M; Visser, Richard G F; Voorrips, Roeland; Vosman, Ben; Vreugdenhil, Dick; Warmerdam, Sonja; Wiegers, Gerrie L; van Heerwaarden, Joost; Kruijer, Willem; van Eeuwijk, Fred A; Dicke, Marcel

2017-02-01

Plants are exposed to combinations of various biotic and abiotic stresses, but stress responses are usually investigated for single stresses only. Here, we investigated the genetic architecture underlying plant responses to 11 single stresses and several of their combinations by phenotyping 350 Arabidopsis thaliana accessions. A set of 214 000 single nucleotide polymorphisms (SNPs) was screened for marker-trait associations in genome-wide association (GWA) analyses using tailored multi-trait mixed models. Stress responses that share phytohormonal signaling pathways also share genetic architecture underlying these responses. After removing the effects of general robustness, for the 30 most significant SNPs, average quantitative trait locus (QTL) effect sizes were larger for dual stresses than for single stresses. Plants appear to deploy broad-spectrum defensive mechanisms influencing multiple traits in response to combined stresses. Association analyses identified QTLs with contrasting and with similar responses to biotic vs abiotic stresses, and below-ground vs above-ground stresses. Our approach allowed for an unprecedented comprehensive genetic analysis of how plants deal with a wide spectrum of stress conditions. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

PubMed

Wolen, Aaron R; Miles, Michael F

2012-01-01

For complex disorders such as alcoholism, identifying the genes linked to these diseases and their specific roles is difficult. Traditional genetic approaches, such as genetic association studies (including genome-wide association studies) and analyses of quantitative trait loci (QTLs) in both humans and laboratory animals already have helped identify some candidate genes. However, because of technical obstacles, such as the small impact of any individual gene, these approaches only have limited effectiveness in identifying specific genes that contribute to complex diseases. The emerging field of systems biology, which allows for analyses of entire gene networks, may help researchers better elucidate the genetic basis of alcoholism, both in humans and in animal models. Such networks can be identified using approaches such as high-throughput molecular profiling (e.g., through microarray-based gene expression analyses) or strategies referred to as genetical genomics, such as the mapping of expression QTLs (eQTLs). Characterization of gene networks can shed light on the biological pathways underlying complex traits and provide the functional context for identifying those genes that contribute to disease development.

Genetic Basis of Body Color and Spotting Pattern in Redheaded Pine Sawfly Larvae (Neodiprion lecontei).

PubMed

Linnen, Catherine R; O'Quin, Claire T; Shackleford, Taylor; Sears, Connor R; Lindstedt, Carita

2018-05-01

Pigmentation has emerged as a premier model for understanding the genetic basis of phenotypic evolution, and a growing catalog of color loci is starting to reveal biases in the mutations, genes, and genetic architectures underlying color variation in the wild. However, existing studies have sampled a limited subset of taxa, color traits, and developmental stages. To expand the existing sample of color loci, we performed QTL mapping analyses on two types of larval pigmentation traits that vary among populations of the redheaded pine sawfly ( Neodiprion lecontei ): carotenoid-based yellow body color and melanin-based spotting pattern. For both traits, our QTL models explained a substantial proportion of phenotypic variation and suggested a genetic architecture that is neither monogenic nor highly polygenic. Additionally, we used our linkage map to anchor the current N. lecontei genome assembly. With these data, we identified promising candidate genes underlying (1) a loss of yellow pigmentation in populations in the mid-Atlantic/northeastern United States [C locus-associated membrane protein homologous to a mammalian HDL receptor-2 gene ( Cameo2 ) and lipid transfer particle apolipoproteins II and I gene ( apoLTP-II/I )], and (2) a pronounced reduction in black spotting in Great Lakes populations [members of the yellow gene family, tyrosine hydroxylase gene ( pale ), and dopamine N -acetyltransferase gene ( Dat )]. Several of these genes also contribute to color variation in other wild and domesticated taxa. Overall, our findings are consistent with the hypothesis that predictable genes of large effect contribute to color evolution in nature. Copyright © 2018 by the Genetics Society of America.

Population-genetic models of sex-limited genomic imprinting.

PubMed

Kelly, S Thomas; Spencer, Hamish G

2017-06-01

Genomic imprinting is a form of epigenetic modification involving parent-of-origin-dependent gene expression, usually the inactivation of one gene copy in some tissues, at least, for some part of the diploid life cycle. Occurring at a number of loci in mammals and flowering plants, this mode of non-Mendelian expression can be viewed more generally as parentally-specific differential gene expression. The effects of natural selection on genetic variation at imprinted loci have previously been examined in a several population-genetic models. Here we expand the existing one-locus, two-allele population-genetic models of viability selection with genomic imprinting to include sex-limited imprinting, i.e., imprinted expression occurring only in one sex, and differential viability between the sexes. We first consider models of complete inactivation of either parental allele and these models are subsequently generalized to incorporate differential expression. Stable polymorphic equilibrium was possible without heterozygote advantage as observed in some prior models of imprinting in both sexes. In contrast to these latter models, in the sex-limited case it was critical whether the paternally inherited or maternally inherited allele was inactivated. The parental origin of inactivated alleles had a different impact on how the population responded to the different selection pressures between the sexes. Under the same fitness parameters, imprinting in the other sex altered the number of possible equilibrium states and their stability. When the parental origin of imprinted alleles and the sex in which they are inactive differ, an allele cannot be inactivated in consecutive generations. The system dynamics became more complex with more equilibrium points emerging. Our results show that selection can interact with epigenetic factors to maintain genetic variation in previously unanticipated ways. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamical behaviour of a discrete selection-migration model with arbitrary dominance

Treesearch

James F. Selgrade; Jordan West Bostic; James H. Roberds

2009-01-01

To study the effects of immigration of genes (possibly transgenic) into a natural population, a one-island selection-migration model with density-dependent regulation is used to track allele frequency and population size. The existence and uniqueness of a polymorphic genetic equilibrium is proved under a general assumption about dominance in fitnesses. Also, conditions...

The Equilibrium Allele Frequency Distribution for a Population with Reproductive Skew

PubMed Central

Der, Ricky; Plotkin, Joshua B.

2014-01-01

We study the population genetics of two neutral alleles under reversible mutation in a model that features a skewed offspring distribution, called the Λ-Fleming–Viot process. We describe the shape of the equilibrium allele frequency distribution as a function of the model parameters. We show that the mutation rates can be uniquely identified from this equilibrium distribution, but the form of the offspring distribution cannot itself always be so identified. We introduce an estimator for the mutation rate that is consistent, independent of the form of reproductive skew. We also introduce a two-allele infinite-sites version of the Λ-Fleming–Viot process, and we use it to study how reproductive skew influences standing genetic diversity in a population. We derive asymptotic formulas for the expected number of segregating sites as a function of sample size and offspring distribution. We find that the Wright–Fisher model minimizes the equilibrium genetic diversity, for a given mutation rate and variance effective population size, compared to all other Λ-processes. PMID:24473932

Transposons As Tools for Functional Genomics in Vertebrate Models.

PubMed

Kawakami, Koichi; Largaespada, David A; Ivics, Zoltán

2017-11-01

Genetic tools and mutagenesis strategies based on transposable elements are currently under development with a vision to link primary DNA sequence information to gene functions in vertebrate models. By virtue of their inherent capacity to insert into DNA, transposons can be developed into powerful tools for chromosomal manipulations. Transposon-based forward mutagenesis screens have numerous advantages including high throughput, easy identification of mutated alleles, and providing insight into genetic networks and pathways based on phenotypes. For example, the Sleeping Beauty transposon has become highly instrumental to induce tumors in experimental animals in a tissue-specific manner with the aim of uncovering the genetic basis of diverse cancers. Here, we describe a battery of mutagenic cassettes that can be applied in conjunction with transposon vectors to mutagenize genes, and highlight versatile experimental strategies for the generation of engineered chromosomes for loss-of-function as well as gain-of-function mutagenesis for functional gene annotation in vertebrate models, including zebrafish, mice, and rats. Copyright © 2017 Elsevier Ltd. All rights reserved.

Replication of genetic associations as pseudoreplication due to shared genealogy.

PubMed

Rosenberg, Noah A; Vanliere, Jenna M

2009-09-01

The genotypes of individuals in replicate genetic association studies have some level of correlation due to shared descent in the complete pedigree of all living humans. As a result of this genealogical sharing, replicate studies that search for genotype-phenotype associations using linkage disequilibrium between marker loci and disease-susceptibility loci can be considered as "pseudoreplicates" rather than true replicates. We examine the size of the pseudoreplication effect in association studies simulated from evolutionary models of the history of a population, evaluating the excess probability that both of a pair of studies detect a disease association compared to the probability expected under the assumption that the two studies are independent. Each of nine combinations of a demographic model and a penetrance model leads to a detectable pseudoreplication effect, suggesting that the degree of support that can be attributed to a replicated genetic association result is less than that which can be attributed to a replicated result in a context of true independence.

Replication of genetic associations as pseudoreplication due to shared genealogy

PubMed Central

Rosenberg, Noah A.; VanLiere, Jenna M.

2009-01-01

The genotypes of individuals in replicate genetic association studies have some level of correlation due to shared descent in the complete pedigree of all living humans. As a result of this genealogical sharing, replicate studies that search for genotype-phenotype associations using linkage disequilibrium between marker loci and disease-susceptibility loci can be considered “pseudoreplicates” rather than true replicates. We examine the size of the pseudoreplication effect in association studies simulated from evolutionary models of the history of a population, evaluating the excess probability that both of a pair of studies detect a disease association compared to the probability expected under the assumption that the two studies are independent. Each of nine combinations of a demographic model and a penetrance model leads to a detectable pseudoreplication effect, suggesting that the degree of support that can be attributed to a replicated genetic association result is less than that which can be attributed to a replicated result in a context of true independence. PMID:19191270

Novel gene function revealed by mouse mutagenesis screens for models of age-related disease.

PubMed

Potter, Paul K; Bowl, Michael R; Jeyarajan, Prashanthini; Wisby, Laura; Blease, Andrew; Goldsworthy, Michelle E; Simon, Michelle M; Greenaway, Simon; Michel, Vincent; Barnard, Alun; Aguilar, Carlos; Agnew, Thomas; Banks, Gareth; Blake, Andrew; Chessum, Lauren; Dorning, Joanne; Falcone, Sara; Goosey, Laurence; Harris, Shelley; Haynes, Andy; Heise, Ines; Hillier, Rosie; Hough, Tertius; Hoslin, Angela; Hutchison, Marie; King, Ruairidh; Kumar, Saumya; Lad, Heena V; Law, Gemma; MacLaren, Robert E; Morse, Susan; Nicol, Thomas; Parker, Andrew; Pickford, Karen; Sethi, Siddharth; Starbuck, Becky; Stelma, Femke; Cheeseman, Michael; Cross, Sally H; Foster, Russell G; Jackson, Ian J; Peirson, Stuart N; Thakker, Rajesh V; Vincent, Tonia; Scudamore, Cheryl; Wells, Sara; El-Amraoui, Aziz; Petit, Christine; Acevedo-Arozena, Abraham; Nolan, Patrick M; Cox, Roger; Mallon, Anne-Marie; Brown, Steve D M

2016-08-18

Determining the genetic bases of age-related disease remains a major challenge requiring a spectrum of approaches from human and clinical genetics to the utilization of model organism studies. Here we report a large-scale genetic screen in mice employing a phenotype-driven discovery platform to identify mutations resulting in age-related disease, both late-onset and progressive. We have utilized N-ethyl-N-nitrosourea mutagenesis to generate pedigrees of mutagenized mice that were subject to recurrent screens for mutant phenotypes as the mice aged. In total, we identify 105 distinct mutant lines from 157 pedigrees analysed, out of which 27 are late-onset phenotypes across a range of physiological systems. Using whole-genome sequencing we uncover the underlying genes for 44 of these mutant phenotypes, including 12 late-onset phenotypes. These genes reveal a number of novel pathways involved with age-related disease. We illustrate our findings by the recovery and characterization of a novel mouse model of age-related hearing loss.

Novel gene function revealed by mouse mutagenesis screens for models of age-related disease

PubMed Central

Potter, Paul K.; Bowl, Michael R.; Jeyarajan, Prashanthini; Wisby, Laura; Blease, Andrew; Goldsworthy, Michelle E.; Simon, Michelle M.; Greenaway, Simon; Michel, Vincent; Barnard, Alun; Aguilar, Carlos; Agnew, Thomas; Banks, Gareth; Blake, Andrew; Chessum, Lauren; Dorning, Joanne; Falcone, Sara; Goosey, Laurence; Harris, Shelley; Haynes, Andy; Heise, Ines; Hillier, Rosie; Hough, Tertius; Hoslin, Angela; Hutchison, Marie; King, Ruairidh; Kumar, Saumya; Lad, Heena V.; Law, Gemma; MacLaren, Robert E.; Morse, Susan; Nicol, Thomas; Parker, Andrew; Pickford, Karen; Sethi, Siddharth; Starbuck, Becky; Stelma, Femke; Cheeseman, Michael; Cross, Sally H.; Foster, Russell G.; Jackson, Ian J.; Peirson, Stuart N.; Thakker, Rajesh V.; Vincent, Tonia; Scudamore, Cheryl; Wells, Sara; El-Amraoui, Aziz; Petit, Christine; Acevedo-Arozena, Abraham; Nolan, Patrick M.; Cox, Roger; Mallon, Anne-Marie; Brown, Steve D. M.

2016-01-01

Determining the genetic bases of age-related disease remains a major challenge requiring a spectrum of approaches from human and clinical genetics to the utilization of model organism studies. Here we report a large-scale genetic screen in mice employing a phenotype-driven discovery platform to identify mutations resulting in age-related disease, both late-onset and progressive. We have utilized N-ethyl-N-nitrosourea mutagenesis to generate pedigrees of mutagenized mice that were subject to recurrent screens for mutant phenotypes as the mice aged. In total, we identify 105 distinct mutant lines from 157 pedigrees analysed, out of which 27 are late-onset phenotypes across a range of physiological systems. Using whole-genome sequencing we uncover the underlying genes for 44 of these mutant phenotypes, including 12 late-onset phenotypes. These genes reveal a number of novel pathways involved with age-related disease. We illustrate our findings by the recovery and characterization of a novel mouse model of age-related hearing loss. PMID:27534441

Genetic and phenotypic variations of inherited retinal diseases in dogs: the power of within- and across-breed studies

PubMed Central

Acland, Gregory M.

2014-01-01

Considerable clinical and molecular variations have been known in retinal blinding diseases in man and also in dogs. Different forms of retinal diseases occur in specific breed(s) caused by mutations segregating within each isolated breeding population. While molecular studies to find genes and mutations underlying retinal diseases in dogs have benefited largely from the phenotypic and genetic uniformity within a breed, within- and across-breed variations have often played a key role in elucidating the molecular basis. The increasing knowledge of phenotypic, allelic, and genetic heterogeneities in canine retinal degeneration has shown that the overall picture is rather more complicated than initially thought. Over the past 20 years, various approaches have been developed and tested to search for genes and mutations underlying genetic traits in dogs, depending on the availability of genetic tools and sample resources. Candidate gene, linkage analysis, and genome-wide association studies have so far identified 24 mutations in 18 genes underlying retinal diseases in at least 58 dog breeds. Many of these genes have been associated with retinal diseases in humans, thus providing opportunities to study the role in pathogenesis and in normal vision. Application in therapeutic interventions such as gene therapy has proven successful initially in a naturally occurring dog model followed by trials in human patients. Other genes whose human homologs have not been associated with retinal diseases are potential candidates to explain equivalent human diseases and contribute to the understanding of their function in vision. PMID:22065099

Genetic and phenotypic variations of inherited retinal diseases in dogs: the power of within- and across-breed studies.

PubMed

Miyadera, Keiko; Acland, Gregory M; Aguirre, Gustavo D

2012-02-01

Considerable clinical and molecular variations have been known in retinal blinding diseases in man and also in dogs. Different forms of retinal diseases occur in specific breed(s) caused by mutations segregating within each isolated breeding population. While molecular studies to find genes and mutations underlying retinal diseases in dogs have benefited largely from the phenotypic and genetic uniformity within a breed, within- and across-breed variations have often played a key role in elucidating the molecular basis. The increasing knowledge of phenotypic, allelic, and genetic heterogeneities in canine retinal degeneration has shown that the overall picture is rather more complicated than initially thought. Over the past 20 years, various approaches have been developed and tested to search for genes and mutations underlying genetic traits in dogs, depending on the availability of genetic tools and sample resources. Candidate gene, linkage analysis, and genome-wide association studies have so far identified 24 mutations in 18 genes underlying retinal diseases in at least 58 dog breeds. Many of these genes have been associated with retinal diseases in humans, thus providing opportunities to study the role in pathogenesis and in normal vision. Application in therapeutic interventions such as gene therapy has proven successful initially in a naturally occurring dog model followed by trials in human patients. Other genes whose human homologs have not been associated with retinal diseases are potential candidates to explain equivalent human diseases and contribute to the understanding of their function in vision.

Selection on plant male function genes identifies candidates for reproductive isolation of yellow monkeyflowers.

PubMed

Aagaard, Jan E; George, Renee D; Fishman, Lila; Maccoss, Michael J; Swanson, Willie J

2013-01-01

Understanding the genetic basis of reproductive isolation promises insight into speciation and the origins of biological diversity. While progress has been made in identifying genes underlying barriers to reproduction that function after fertilization (post-zygotic isolation), we know much less about earlier acting pre-zygotic barriers. Of particular interest are barriers involved in mating and fertilization that can evolve extremely rapidly under sexual selection, suggesting they may play a prominent role in the initial stages of reproductive isolation. A significant challenge to the field of speciation genetics is developing new approaches for identification of candidate genes underlying these barriers, particularly among non-traditional model systems. We employ powerful proteomic and genomic strategies to study the genetic basis of conspecific pollen precedence, an important component of pre-zygotic reproductive isolation among yellow monkeyflowers (Mimulus spp.) resulting from male pollen competition. We use isotopic labeling in combination with shotgun proteomics to identify more than 2,000 male function (pollen tube) proteins within maternal reproductive structures (styles) of M. guttatus flowers where pollen competition occurs. We then sequence array-captured pollen tube exomes from a large outcrossing population of M. guttatus, and identify those genes with evidence of selective sweeps or balancing selection consistent with their role in pollen competition. We also test for evidence of positive selection on these genes more broadly across yellow monkeyflowers, because a signal of adaptive divergence is a common feature of genes causing reproductive isolation. Together the molecular evolution studies identify 159 pollen tube proteins that are candidate genes for conspecific pollen precedence. Our work demonstrates how powerful proteomic and genomic tools can be readily adapted to non-traditional model systems, allowing for genome-wide screens towards the goal of identifying the molecular basis of genetically complex traits.

Sexually antagonistic polymorphism in simultaneous hermaphrodites

PubMed Central

Jordan, Crispin Y.; Connallon, Tim

2015-01-01

In hermaphrodites, pleiotropic genetic tradeoffs between female and male reproductive functions can lead to sexually antagonistic (SA) selection, where individual alleles have conflicting fitness effects on each sex function. While an extensive theory of SA selection exists for dioecious species, these results have not been generalized to hermaphrodites. We develop population genetic models of SA selection in simultaneous hermaphrodites, and evaluate effects of dominance, selection on each sex function, self-fertilization, and population size, on the maintenance of polymorphism. Under obligate outcrossing, hermaphrodite model predictions converge exactly with those of dioecious populations. Self-fertilization in hermaphrodites generates three points of divergence with dioecious theory. First, opportunities for stable polymorphism decline sharply and become less sensitive to dominance with increased selfing. Second, selfing introduces an asymmetry in the relative importance of selection through male versus female reproductive functions, expands the parameter space favorable for the evolutionary invasion of female-beneficial alleles, and restricts invasion criteria for male-beneficial alleles. Finally, contrary to models of unconditionally beneficial alleles, selfing decreases genetic hitchhiking effects of invading SA alleles, and should therefore decrease these population genetic signals of SA polymorphisms. We discuss implications of SA selection in hermaphrodites, including its potential role in the evolution of “selfing syndromes”. PMID:25311368

Multivariate Cholesky models of human female fertility patterns in the NLSY.

PubMed

Rodgers, Joseph Lee; Bard, David E; Miller, Warren B

2007-03-01

Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.

Impact of QTL minor allele frequency on genomic evaluation using real genotype data and simulated phenotypes in Japanese Black cattle.

PubMed

Uemoto, Yoshinobu; Sasaki, Shinji; Kojima, Takatoshi; Sugimoto, Yoshikazu; Watanabe, Toshio

2015-11-19

Genetic variance that is not captured by single nucleotide polymorphisms (SNPs) is due to imperfect linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTLs), and the extent of LD between SNPs and QTLs depends on different minor allele frequencies (MAF) between them. To evaluate the impact of MAF of QTLs on genomic evaluation, we performed a simulation study using real cattle genotype data. In total, 1368 Japanese Black cattle and 592,034 SNPs (Illumina BovineHD BeadChip) were used. We simulated phenotypes using real genotypes under different scenarios, varying the MAF categories, QTL heritability, number of QTLs, and distribution of QTL effect. After generating true breeding values and phenotypes, QTL heritability was estimated and the prediction accuracy of genomic estimated breeding value (GEBV) was assessed under different SNP densities, prediction models, and population size by a reference-test validation design. The extent of LD between SNPs and QTLs in this population was higher in the QTLs with high MAF than in those with low MAF. The effect of MAF of QTLs depended on the genetic architecture, evaluation strategy, and population size in genomic evaluation. In genetic architecture, genomic evaluation was affected by the MAF of QTLs combined with the QTL heritability and the distribution of QTL effect. The number of QTL was not affected on genomic evaluation if the number of QTL was more than 50. In the evaluation strategy, we showed that different SNP densities and prediction models affect the heritability estimation and genomic prediction and that this depends on the MAF of QTLs. In addition, accurate QTL heritability and GEBV were obtained using denser SNP information and the prediction model accounted for the SNPs with low and high MAFs. In population size, a large sample size is needed to increase the accuracy of GEBV. The MAF of QTL had an impact on heritability estimation and prediction accuracy. Most genetic variance can be captured using denser SNPs and the prediction model accounted for MAF, but a large sample size is needed to increase the accuracy of GEBV under all QTL MAF categories.

The genetic basis of female multiple mating in a polyandrous livebearing fish

PubMed Central

Evans, Jonathan P; Gasparini, Clelia

2013-01-01

The widespread occurrence of female multiple mating (FMM) demands evolutionary explanation, particularly in the light of the costs of mating. One explanation encapsulated by “good sperm” and “sexy-sperm” (GS-SS) theoretical models is that FMM facilitates sperm competition, thus ensuring paternity by males that pass on genes for elevated sperm competitiveness to their male offspring. While support for this component of GS-SS theory is accumulating, a second but poorly tested assumption of these models is that there should be corresponding heritable genetic variation in FMM – the proposed mechanism of postcopulatory preferences underlying GS-SS models. Here, we conduct quantitative genetic analyses on paternal half-siblings to test this component of GS-SS theory in the guppy (Poecilia reticulata), a freshwater fish with some of the highest known rates of FMM in vertebrates. As with most previous quantitative genetic analyses of FMM in other species, our results reveal high levels of phenotypic variation in this trait and a correspondingly low narrow-sense heritability (h2 = 0.11). Furthermore, although our analysis of additive genetic variance in FMM was not statistically significant (probably owing to limited statistical power), the ensuing estimate of mean-standardized additive genetic variance (IA = 0.7) was nevertheless relatively low compared with estimates published for life-history traits across a broad range of taxa. Our results therefore add to a growing body of evidence that FMM is characterized by relatively low additive genetic variation, thus apparently contradicting GS-SS theory. However, we qualify this conclusion by drawing attention to potential deficiencies in most designs (including ours) that have tested for genetic variation in FMM, particularly those that fail to account for intersexual interactions that underlie FMM in many systems. PMID:23403856

Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios1[OPEN

PubMed Central

Millet, Emilie J.; Welcker, Claude; Kruijer, Willem; Negro, Sandra; Coupel-Ledru, Aude; Laborde, Jacques; Bauland, Cyril; Praud, Sebastien; Presterl, Thomas; Usadel, Björn; Charcosset, Alain; Van Eeuwijk, Fred; Tardieu, François

2016-01-01

Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years × 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments. PMID:27436830

Estimating time since infection in early homogeneous HIV-1 samples using a poisson model

PubMed Central

2010-01-01

Background The occurrence of a genetic bottleneck in HIV sexual or mother-to-infant transmission has been well documented. This results in a majority of new infections being homogeneous, i.e., initiated by a single genetic strain. Early after infection, prior to the onset of the host immune response, the viral population grows exponentially. In this simple setting, an approach for estimating evolutionary and demographic parameters based on comparison of diversity measures is a feasible alternative to the existing Bayesian methods (e.g., BEAST), which are instead based on the simulation of genealogies. Results We have devised a web tool that analyzes genetic diversity in acutely infected HIV-1 patients by comparing it to a model of neutral growth. More specifically, we consider a homogeneous infection (i.e., initiated by a unique genetic strain) prior to the onset of host-induced selection, where we can assume a random accumulation of mutations. Previously, we have shown that such a model successfully describes about 80% of sexual HIV-1 transmissions provided the samples are drawn early enough in the infection. Violation of the model is an indicator of either heterogeneous infections or the initiation of selection. Conclusions When the underlying assumptions of our model (homogeneous infection prior to selection and fast exponential growth) are met, we are under a very particular scenario for which we can use a forward approach (instead of backwards in time as provided by coalescent methods). This allows for more computationally efficient methods to derive the time since the most recent common ancestor. Furthermore, the tool performs statistical tests on the Hamming distance frequency distribution, and outputs summary statistics (mean of the best fitting Poisson distribution, goodness of fit p-value, etc). The tool runs within minutes and can readily accommodate the tens of thousands of sequences generated through new ultradeep pyrosequencing technologies. The tool is available on the LANL website. PMID:20973976

Alleles versus mutations: Understanding the evolution of genetic architecture requires a molecular perspective on allelic origins.

PubMed

Remington, David L

2015-12-01

Perspectives on the role of large-effect quantitative trait loci (QTL) in the evolution of complex traits have shifted back and forth over the past few decades. Different sets of studies have produced contradictory insights on the evolution of genetic architecture. I argue that much of the confusion results from a failure to distinguish mutational and allelic effects, a limitation of using the Fisherian model of adaptive evolution as the lens through which the evolution of adaptive variation is examined. A molecular-based perspective reveals that allelic differences can involve the cumulative effects of many mutations plus intragenic recombination, a model that is supported by extensive empirical evidence. I discuss how different selection regimes could produce very different architectures of allelic effects under a molecular-based model, which may explain conflicting insights on genetic architecture from studies of variation within populations versus between divergently selected populations. I address shortcomings of genome-wide association study (GWAS) practices in light of more suitable models of allelic evolution, and suggest alternate GWAS strategies to generate more valid inferences about genetic architecture. Finally, I discuss how adopting more suitable models of allelic evolution could help redirect research on complex trait evolution toward addressing more meaningful questions in evolutionary biology. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Measuring genetic distances between breeds: use of some distances in various short term evolution models

PubMed Central

Laval, Guillaume; SanCristobal, Magali; Chevalet, Claude

2002-01-01

Many works demonstrate the benefits of using highly polymorphic markers such as microsatellites in order to measure the genetic diversity between closely related breeds. But it is sometimes difficult to decide which genetic distance should be used. In this paper we review the behaviour of the main distances encountered in the literature in various divergence models. In the first part, we consider that breeds are populations in which the assumption of equilibrium between drift and mutation is verified. In this case some interesting distances can be expressed as a function of divergence time, t, and therefore can be used to construct phylogenies. Distances based on allele size distribution (such as (δμ)2 and derived distances), taking a mutation model of microsatellites, the Stepwise Mutation Model, specifically into account, exhibit large variance and therefore should not be used to accurately infer phylogeny of closely related breeds. In the last section, we will consider that breeds are small populations and that the divergence times between them are too small to consider that the observed diversity is due to mutations: divergence is mainly due to genetic drift. Expectation and variance of distances were calculated as a function of the Wright-Malécot inbreeding coefficient, F. Computer simulations performed under this divergence model show that the Reynolds distance [57]is the best method for very closely related breeds. PMID:12270106

Genetically engineered mouse models in oncology research and cancer medicine.

PubMed

Kersten, Kelly; de Visser, Karin E; van Miltenburg, Martine H; Jonkers, Jos

2017-02-01

Genetically engineered mouse models (GEMMs) have contributed significantly to the field of cancer research. In contrast to cancer cell inoculation models, GEMMs develop de novo tumors in a natural immune-proficient microenvironment. Tumors arising in advanced GEMMs closely mimic the histopathological and molecular features of their human counterparts, display genetic heterogeneity, and are able to spontaneously progress toward metastatic disease. As such, GEMMs are generally superior to cancer cell inoculation models, which show no or limited heterogeneity and are often metastatic from the start. Given that GEMMs capture both tumor cell-intrinsic and cell-extrinsic factors that drive de novo tumor initiation and progression toward metastatic disease, these models are indispensable for preclinical research. GEMMs have successfully been used to validate candidate cancer genes and drug targets, assess therapy efficacy, dissect the impact of the tumor microenvironment, and evaluate mechanisms of drug resistance. In vivo validation of candidate cancer genes and therapeutic targets is further accelerated by recent advances in genetic engineering that enable fast-track generation and fine-tuning of GEMMs to more closely resemble human patients. In addition, aligning preclinical tumor intervention studies in advanced GEMMs with clinical studies in patients is expected to accelerate the development of novel therapeutic strategies and their translation into the clinic. © 2016 The Authors. Published under the terms of the CC BY 4.0 license.

Genetic Contributors to Intergenerational CAG Repeat Instability in Huntington’s Disease Knock-In Mice

PubMed Central

Neto, João Luís; Lee, Jong-Min; Afridi, Ali; Gillis, Tammy; Guide, Jolene R.; Dempsey, Stephani; Lager, Brenda; Alonso, Isabel; Wheeler, Vanessa C.; Pinto, Ricardo Mouro

2017-01-01

Huntington’s disease (HD) is a neurodegenerative disorder caused by the expansion of a CAG trinucleotide repeat in exon 1 of the HTT gene. Longer repeat sizes are associated with increased disease penetrance and earlier ages of onset. Intergenerationally unstable transmissions are common in HD families, partly underlying the genetic anticipation seen in this disorder. HD CAG knock-in mouse models also exhibit a propensity for intergenerational repeat size changes. In this work, we examine intergenerational instability of the CAG repeat in over 20,000 transmissions in the largest HD knock-in mouse model breeding datasets reported to date. We confirmed previous observations that parental sex drives the relative ratio of expansions and contractions. The large datasets further allowed us to distinguish effects of paternal CAG repeat length on the magnitude and frequency of expansions and contractions, as well as the identification of large repeat size jumps in the knock-in models. Distinct degrees of intergenerational instability were observed between knock-in mice of six background strains, indicating the occurrence of trans-acting genetic modifiers. We also found that lines harboring a neomycin resistance cassette upstream of Htt showed reduced expansion frequency, indicative of a contributing role for sequences in cis, with the expanded repeat as modifiers of intergenerational instability. These results provide a basis for further understanding of the mechanisms underlying intergenerational repeat instability. PMID:27913616

Rapid changes in genetic architecture of behavioural syndromes following colonization of a novel environment.

PubMed

Karlsson Green, K; Eroukhmanoff, F; Harris, S; Pettersson, L B; Svensson, E I

2016-01-01

Behavioural syndromes, that is correlated behaviours, may be a result from adaptive correlational selection, but in a new environmental setting, the trait correlation might act as an evolutionary constraint. However, knowledge about the quantitative genetic basis of behavioural syndromes, and the stability and evolvability of genetic correlations under different ecological conditions, is limited. We investigated the quantitative genetic basis of correlated behaviours in the freshwater isopod Asellus aquaticus. In some Swedish lakes, A. aquaticus has recently colonized a novel habitat and diverged into two ecotypes, presumably due to habitat-specific selection from predation. Using a common garden approach and animal model analyses, we estimated quantitative genetic parameters for behavioural traits and compared the genetic architecture between the ecotypes. We report that the genetic covariance structure of the behavioural traits has been altered in the novel ecotype, demonstrating divergence in behavioural correlations. Thus, our study confirms that genetic correlations behind behaviours can change rapidly in response to novel selective environments. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.

Whole Body Plethysmography Reveals Differential Ventilatory Responses to Ozone in Rat Models of Cardiovascular Disease

EPA Science Inventory

Increasingly, urban air pollution is recognized as an important determinant of cardiovascular disease. Host susceptibility to air pollution can vary due to genetic predisposition and underlying disease. To elucidate key factors of host ...

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

PubMed Central

Bustos-Korts, Daniela; Malosetti, Marcos; Chapman, Scott; Biddulph, Ben; van Eeuwijk, Fred

2016-01-01

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel. PMID:27672112

Use of Longitudinal Data in Genetic Studies in the Genome-wide Association Studies Era: Summary of Group 14

PubMed Central

Kerner, Berit; North, Kari E; Fallin, M Daniele

2010-01-01

Participants analyzed actual and simulated longitudinal data from the Framingham Heart Study for various metabolic and cardiovascular traits. The genetic information incorporated into these investigations ranged from selected single-nucleotide polymorphisms to genome-wide association arrays. Genotypes were incorporated using a broad range of methodological approaches including conditional logistic regression, linear mixed models, generalized estimating equations, linear growth curve estimation, growth modeling, growth mixture modeling, population attributable risk fraction based on survival functions under the proportional hazards models, and multivariate adaptive splines for the analysis of longitudinal data. The specific scientific questions addressed by these different approaches also varied, ranging from a more precise definition of the phenotype, bias reduction in control selection, estimation of effect sizes and genotype associated risk, to direct incorporation of genetic data into longitudinal modeling approaches and the exploration of population heterogeneity with regard to longitudinal trajectories. The group reached several overall conclusions: 1) The additional information provided by longitudinal data may be useful in genetic analyses. 2) The precision of the phenotype definition as well as control selection in nested designs may be improved, especially if traits demonstrate a trend over time or have strong age-of-onset effects. 3) Analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multi-factorial diseases. 4) Estimation of the population impact of genomic risk variants could be more precise. The challenges and computational complexity demanded by genome-wide single-nucleotide polymorphism data were also discussed. PMID:19924713

Using Zebrafish to Test the Genetic Basis of Human Craniofacial Diseases.

PubMed

Machado, R Grecco; Eames, B Frank

2017-10-01

Genome-wide association studies (GWASs) opened an innovative and productive avenue to investigate the molecular basis of human craniofacial disease. However, GWASs identify candidate genes only; they do not prove that any particular one is the functional villain underlying disease or just an unlucky genomic bystander. Genetic manipulation of animal models is the best approach to reveal which genetic loci identified from human GWASs are functionally related to specific diseases. The purpose of this review is to discuss the potential of zebrafish to resolve which candidate genetic loci are mechanistic drivers of craniofacial diseases. Many anatomic, embryonic, and genetic features of craniofacial development are conserved among zebrafish and mammals, making zebrafish a good model of craniofacial diseases. Also, the ability to manipulate gene function in zebrafish was greatly expanded over the past 20 y, enabling systems such as Gateway Tol2 and CRISPR-Cas9 to test gain- and loss-of-function alleles identified from human GWASs in coding and noncoding regions of DNA. With the optimization of genetic editing methods, large numbers of candidate genes can be efficiently interrogated. Finding the functional villains that underlie diseases will permit new treatments and prevention strategies and will increase understanding of how gene pathways operate during normal development.

Rheumatoid arthritis: identifying and characterising polymorphisms using rat models

PubMed Central

2016-01-01

ABSTRACT Rheumatoid arthritis is a chronic inflammatory joint disorder characterised by erosive inflammation of the articular cartilage and by destruction of the synovial joints. It is regulated by both genetic and environmental factors, and, currently, there is no preventative treatment or cure for this disease. Genome-wide association studies have identified ∼100 new loci associated with rheumatoid arthritis, in addition to the already known locus within the major histocompatibility complex II region. However, together, these loci account for only a modest fraction of the genetic variance associated with this disease and very little is known about the pathogenic roles of most of the risk loci identified. Here, we discuss how rat models of rheumatoid arthritis are being used to detect quantitative trait loci that regulate different arthritic traits by genetic linkage analysis and to positionally clone the underlying causative genes using congenic strains. By isolating specific loci on a fixed genetic background, congenic strains overcome the challenges of genetic heterogeneity and environmental interactions associated with human studies. Most importantly, congenic strains allow functional experimental studies be performed to investigate the pathological consequences of natural genetic polymorphisms, as illustrated by the discovery of several major disease genes that contribute to arthritis in rats. We discuss how these advances have provided new biological insights into arthritis in humans. PMID:27736747

Genetic and maternal effect influences on viability of common frog tadpoles under different environmental conditions.

PubMed

Pakkasmaa, S; Merilä, J; O'Hara, R B

2003-08-01

The influence of environmental stress on the expression of genetic and maternal effects on the viability traits has seldom been assessed in wild vertebrates. We have estimated genetic and maternal effects on the viability (viz probability of survival, probability of being deformed, and body size and shape) of common frog, Rana temporaria, tadpoles under stressful (low pH) and nonstressful (neutral pH) environmental conditions. A Bayesian analysis using generalized linear mixed models was applied to data from a factorial laboratory experiment. The expression of additive genetic variance was independent of pH treatments, and all traits were significantly heritable (survival: h2 approximately 0.08; deformities: h2 approximately 0.26; body size: h2 approximately 0.12; body shape: h2 approximately 0.14). Likewise, nonadditive genetic contributions to variation in all traits were significant, independent of pH treatments and typically of magnitude similar to the additive genetic effects. Maternal effects were large for all traits, especially for viability itself, and their expression was partly dependent on the environment. In the case of body size, the maternal effects were mediated largely through egg size. In general, the results give little evidence for the conjecture that environmental stress created by low pH would impact strongly on the genetic architecture of fitness-related traits in frogs, and hamper adaptation to stress caused by acidification. The low heritabilities and high dominance contributions conform to the pattern typical for traits subject to relatively strong directional selection.

Longitudinal analysis of direct and indirect effects on average daily gain in rabbits using a structured antedependence model.

PubMed

David, Ingrid; Sánchez, Juan-Pablo; Piles, Miriam

2018-05-10

Indirect genetic effects (IGE) are important components of various traits in several species. Although the intensity of social interactions between partners likely vary over time, very few genetic studies have investigated how IGE vary over time for traits under selection in livestock species. To overcome this issue, our aim was: (1) to analyze longitudinal records of average daily gain (ADG) in rabbits subjected to a 5-week period of feed restriction using a structured antedependence (SAD) model that includes IGE and (2) to evaluate, by simulation, the response to selection when IGE are present and genetic evaluation is based on a SAD model that includes IGE or not. The direct genetic variance for ADG (g/d) increased from week 1 to 3 [from 8.03 to 13.47 (g/d) 2 ] and then decreased [6.20 (g/d) 2 at week 5], while the indirect genetic variance decreased from week 1 to 4 [from 0.43 to 0.22 (g/d) 2 ]. The correlation between the direct genetic effects of different weeks was moderate to high (ranging from 0.46 to 0.86) and tended to decrease with time interval between measurements. The same trend was observed for IGE for weeks 2 to 5 (correlations ranging from 0.62 to 0.91). Estimates of the correlation between IGE of week 1 and IGE of the other weeks did not follow the same pattern and correlations were lower. Estimates of correlations between direct and indirect effects were negative at all times. After seven generations of simulated selection, the increase in ADG from selection on EBV from a SAD model that included IGE was higher (~ 30%) than when those effects were omitted. Indirect genetic effects are larger just after mixing animals at weaning than later in the fattening period, probably because of the establishment of social hierarchy that is generally observed at that time. Accounting for IGE in the selection criterion maximizes genetic progress.

The Mouse Lemur, a Genetic Model Organism for Primate Biology, Behavior, and Health

PubMed Central

Ezran, Camille; Karanewsky, Caitlin J.; Pendleton, Jozeph L.; Sholtz, Alex; Krasnow, Maya R.; Willick, Jason; Razafindrakoto, Andriamahery; Zohdy, Sarah; Albertelli, Megan A.; Krasnow, Mark A.

2017-01-01

Systematic genetic studies of a handful of diverse organisms over the past 50 years have transformed our understanding of biology. However, many aspects of primate biology, behavior, and disease are absent or poorly modeled in any of the current genetic model organisms including mice. We surveyed the animal kingdom to find other animals with advantages similar to mice that might better exemplify primate biology, and identified mouse lemurs (Microcebus spp.) as the outstanding candidate. Mouse lemurs are prosimian primates, roughly half the genetic distance between mice and humans. They are the smallest, fastest developing, and among the most prolific and abundant primates in the world, distributed throughout the island of Madagascar, many in separate breeding populations due to habitat destruction. Their physiology, behavior, and phylogeny have been studied for decades in laboratory colonies in Europe and in field studies in Malagasy rainforests, and a high quality reference genome sequence has recently been completed. To initiate a classical genetic approach, we developed a deep phenotyping protocol and have screened hundreds of laboratory and wild mouse lemurs for interesting phenotypes and begun mapping the underlying mutations, in collaboration with leading mouse lemur biologists. We also seek to establish a mouse lemur gene “knockout” library by sequencing the genomes of thousands of mouse lemurs to identify null alleles in most genes from the large pool of natural genetic variants. As part of this effort, we have begun a citizen science project in which students across Madagascar explore the remarkable biology around their schools, including longitudinal studies of the local mouse lemurs. We hope this work spawns a new model organism and cultivates a deep genetic understanding of primate biology and health. We also hope it establishes a new and ethical method of genetics that bridges biological, behavioral, medical, and conservation disciplines, while providing an example of how hands-on science education can help transform developing countries. PMID:28592502

Modeling mania in preclinical settings: a comprehensive review

PubMed Central

Sharma, Ajaykumar N.; Fries, Gabriel R.; Galvez, Juan F.; Valvassori, Samira S.; Soares, Jair C.; Carvalho, André F.; Quevedo, Joao

2015-01-01

The current pathophysiological understanding of mechanisms leading to onset and progression of bipolar manic episodes remains limited. At the same time, available animal models for mania have limited face, construct, and predictive validities. Additionally, these models fail to encompass recent pathophysiological frameworks of bipolar disorder (BD), e.g. neuroprogression. Therefore, there is a need to search for novel preclinical models for mania that could comprehensively address these limitations. Herein we review the history, validity, and caveats of currently available animal models for mania. We also review new genetic models for mania, namely knockout mice for genes involved in neurotransmission, synapse formation, and intracellular signaling pathways. Furthermore, we review recent trends in preclinical models for mania that may aid in the comprehension of mechanisms underlying the neuroprogressive and recurring nature of BD. In conclusion, the validity of animal models for mania remains limited. Nevertheless, novel (e.g. genetic) animal models as well as adaptation of existing paradigms hold promise. PMID:26545487

Predicting type 2 diabetes using genetic and environmental risk factors in a multi-ethnic Malaysian cohort.

PubMed

Abdullah, N; Abdul Murad, N A; Mohd Haniff, E A; Syafruddin, S E; Attia, J; Oldmeadow, C; Kamaruddin, M A; Abd Jalal, N; Ismail, N; Ishak, M; Jamal, R; Scott, R J; Holliday, E G

2017-08-01

Malaysia has a high and rising prevalence of type 2 diabetes (T2D). While environmental (non-genetic) risk factors for the disease are well established, the role of genetic variations and gene-environment interactions remain understudied in this population. This study aimed to estimate the relative contributions of environmental and genetic risk factors to T2D in Malaysia and also to assess evidence for gene-environment interactions that may explain additional risk variation. This was a case-control study including 1604 Malays, 1654 Chinese and 1728 Indians from the Malaysian Cohort Project. The proportion of T2D risk variance explained by known genetic and environmental factors was assessed by fitting multivariable logistic regression models and evaluating McFadden's pseudo R 2 and the area under the receiver-operating characteristic curve (AUC). Models with and without the genetic risk score (GRS) were compared using the log likelihood ratio Chi-squared test and AUCs. Multiplicative interaction between genetic and environmental risk factors was assessed via logistic regression within and across ancestral groups. Interactions were assessed for the GRS and its 62 constituent variants. The models including environmental risk factors only had pseudo R 2 values of 16.5-28.3% and AUC of 0.75-0.83. Incorporating a genetic score aggregating 62 T2D-associated risk variants significantly increased the model fit (likelihood ratio P-value of 2.50 × 10 -4 -4.83 × 10 -12 ) and increased the pseudo R 2 by about 1-2% and AUC by 1-3%. None of the gene-environment interactions reached significance after multiple testing adjustment, either for the GRS or individual variants. For individual variants, 33 out of 310 tested associations showed nominal statistical significance with 0.001 < P < 0.05. This study suggests that known genetic risk variants contribute a significant but small amount to overall T2D risk variation in Malaysian population groups. If gene-environment interactions involving common genetic variants exist, they are likely of small effect, requiring substantially larger samples for detection. Copyright © 2017 The Royal Society for Public Health. All rights reserved.

Gene-environment interactions and construct validity in preclinical models of psychiatric disorders.

PubMed

Burrows, Emma L; McOmish, Caitlin E; Hannan, Anthony J

2011-08-01

The contributions of genetic risk factors to susceptibility for brain disorders are often so closely intertwined with environmental factors that studying genes in isolation cannot provide the full picture of pathogenesis. With recent advances in our understanding of psychiatric genetics and environmental modifiers we are now in a position to develop more accurate animal models of psychiatric disorders which exemplify the complex interaction of genes and environment. Here, we consider some of the insights that have emerged from studying the relationship between defined genetic alterations and environmental factors in rodent models. A key issue in such animal models is the optimization of construct validity, at both genetic and environmental levels. Standard housing of laboratory mice and rats generally includes ad libitum food access and limited opportunity for physical exercise, leading to metabolic dysfunction under control conditions, and thus reducing validity of animal models with respect to clinical populations. A related issue, of specific relevance to neuroscientists, is that most standard-housed rodents have limited opportunity for sensory and cognitive stimulation, which in turn provides reduced incentive for complex motor activity. Decades of research using environmental enrichment has demonstrated beneficial effects on brain and behavior in both wild-type and genetically modified rodent models, relative to standard-housed littermate controls. One interpretation of such studies is that environmentally enriched animals more closely approximate average human levels of cognitive and sensorimotor stimulation, whereas the standard housing currently used in most laboratories models a more sedentary state of reduced mental and physical activity and abnormal stress levels. The use of such standard housing as a single environmental variable may limit the capacity for preclinical models to translate into successful clinical trials. Therefore, there is a need to optimize 'environmental construct validity' in animal models, while maintaining comparability between laboratories, so as to ensure optimal scientific and medical outcomes. Utilizing more sophisticated models to elucidate the relative contributions of genetic and environmental factors will allow for improved construct, face and predictive validity, thus facilitating the identification of novel therapeutic targets. Copyright © 2010 Elsevier Inc. All rights reserved.

Comparison of linear, skewed-linear, and proportional hazard models for the analysis of lambing interval in Ripollesa ewes.

PubMed

Casellas, J; Bach, R

2012-06-01

Lambing interval is a relevant reproductive indicator for sheep populations under continuous mating systems, although there is a shortage of selection programs accounting for this trait in the sheep industry. Both the historical assumption of small genetic background and its unorthodox distribution pattern have limited its implementation as a breeding objective. In this manuscript, statistical performances of 3 alternative parametrizations [i.e., symmetric Gaussian mixed linear (GML) model, skew-Gaussian mixed linear (SGML) model, and piecewise Weibull proportional hazard (PWPH) model] have been compared to elucidate the preferred methodology to handle lambing interval data. More specifically, flock-by-flock analyses were performed on 31,986 lambing interval records (257.3 ± 0.2 d) from 6 purebred Ripollesa flocks. Model performances were compared in terms of deviance information criterion (DIC) and Bayes factor (BF). For all flocks, PWPH models were clearly preferred; they generated a reduction of 1,900 or more DIC units and provided BF estimates larger than 100 (i.e., PWPH models against linear models). These differences were reduced when comparing PWPH models with different number of change points for the baseline hazard function. In 4 flocks, only 2 change points were required to minimize the DIC, whereas 4 and 6 change points were needed for the 2 remaining flocks. These differences demonstrated a remarkable degree of heterogeneity across sheep flocks that must be properly accounted for in genetic evaluation models to avoid statistical biases and suboptimal genetic trends. Within this context, all 6 Ripollesa flocks revealed substantial genetic background for lambing interval with heritabilities ranging between 0.13 and 0.19. This study provides the first evidence of the suitability of PWPH models for lambing interval analysis, clearly discarding previous parametrizations focused on mixed linear models.

The genetic basis of panic and phobic anxiety disorders.

PubMed

Smoller, Jordan W; Gardner-Schuster, Erica; Covino, Jennifer

2008-05-15

Panic disorder and phobic anxiety disorders are common disorders that are often chronic and disabling. Genetic epidemiologic studies have documented that these disorders are familial and moderately heritable. Linkage studies have implicated several chromosomal regions that may harbor susceptibility genes; however, candidate gene association studies have not established a role for any specific loci to date. Increasing evidence from family and genetic studies suggests that genes underlying these disorders overlap and transcend diagnostic boundaries. Heritable forms of anxious temperament, anxiety-related personality traits and neuroimaging assays of fear circuitry may represent intermediate phenotypes that predispose to panic and phobic disorders. The identification of specific susceptibility variants will likely require much larger sample sizes and the integration of insights from genetic analyses of animal models and intermediate phenotypes. Copyright 2008 Wiley-Liss, Inc.

Genetic and environmental influences on individual differences in emotion regulation and its relation to working memory in toddlerhood.

PubMed

Wang, Manjie; Saudino, Kimberly J

2013-12-01

This is the first study to explore genetic and environmental contributions to individual differences in emotion regulation in toddlers, and the first to examine the genetic and environmental etiology underlying the association between emotion regulation and working memory. In a sample of 304 same-sex twin pairs (140 MZ, 164 DZ) at age 3, emotion regulation was assessed using the Behavior Rating Scale of the Bayley Scales of Infant Development (BRS; Bayley, 1993), and working memory was measured by the visually cued recall (VCR) task (Zelazo, Jacques, Burack, & Frye, 2002) and several memory tasks from the Mental Scale of the BSID. Based on model-fitting analyses, both emotion regulation and working memory were significantly influenced by genetic and nonshared environmental factors. Shared environmental effects were significant for working memory, but not for emotion regulation. Only genetic factors significantly contributed to the covariation between emotion regulation and working memory.

Correlation not Causation: The Relationship between Personality Traits and Political Ideologies

PubMed Central

Verhulst, Brad; Eaves, Lindon J.; Hatemi, Peter K.

2013-01-01

The assumption in the personality and politics literature is that a person's personality motivates them to develop certain political attitudes later in life. This assumption is founded on the simple correlation between the two constructs and the observation that personality traits are genetically influenced and develop in infancy, whereas political preferences develop later in life. Work in psychology, behavioral genetics, and recently political science, however, has demonstrated that political preferences also develop in childhood and are equally influenced by genetic factors. These findings cast doubt on the assumed causal relationship between personality and politics. Here we test the causal relationship between personality traits and political attitudes using a direction of causation structural model on a genetically informative sample. The results suggest that personality traits do not cause people to develop political attitudes; rather, the correlation between the two is a function of an innate common underlying genetic factor. PMID:22400142

Genetic and Environmental Influences on Individual Differences in Emotion Regulation and Its Relation to Working Memory in Toddlerhood

PubMed Central

Wang, Manjie; Saudino, Kimberly J.

2014-01-01

This is the first study to explore genetic and environmental contributions to individual differences in emotion regulation in toddlers, and the first to examine the genetic and environmental etiology underlying the association between emotion regulation and working memory. In a sample of 304 same-sex twin pairs (140 MZ, 164 DZ) at age 3, emotion regulation was assessed using the Behavior Rating Scale of the Bayley Scales of Infant Development (BRS; Bayley, 1993), and working memory was measured by the visually cued recall (VCR) task (Zelazo et al., 2002) and several memory tasks from the Mental Scale of BSID. Based on model-fitting analyses, both emotion regulation and working memory were significantly influenced by genetic and nonshared environmental factors. Shared environmental effects were significant for working memory, but not for emotion regulation. Only genetic factors significantly contributed to the covariation between emotion regulation and working memory. PMID:24098922

Cost-effectiveness of one-time genetic testing to minimize lifetime adverse drug reactions.

PubMed

Alagoz, O; Durham, D; Kasirajan, K

2016-04-01

We evaluated the cost-effectiveness of one-time pharmacogenomic testing for preventing adverse drug reactions (ADRs) over a patient's lifetime. We developed a Markov-based Monte Carlo microsimulation model to represent the ADR events in the lifetime of each patient. The base-case considered a 40-year-old patient. We measured health outcomes in life years (LYs) and quality-adjusted LYs (QALYs) and estimated costs using 2013 US$. In the base-case, one-time genetic testing had an incremental cost-effectiveness ratio (ICER) of $43,165 (95% confidence interval (CI) is ($42,769,$43,561)) per additional LY and $53,680 per additional QALY (95% CI is ($53,182,$54,179)), hence under the base-case one-time genetic testing is cost-effective. The ICER values were most sensitive to the average probability of death due to ADR, reduction in ADR rate due to genetic testing, mean ADR rate and cost of genetic testing.

Correlation not causation: the relationship between personality traits and political ideologies.

PubMed

Verhulst, Brad; Eaves, Lindon J; Hatemi, Peter K

2012-01-01

The assumption in the personality and politics literature is that a person's personality motivates them to develop certain political attitudes later in life. This assumption is founded on the simple correlation between the two constructs and the observation that personality traits are genetically influenced and develop in infancy, whereas political preferences develop later in life. Work in psychology, behavioral genetics, and recently political science, however, has demonstrated that political preferences also develop in childhood and are equally influenced by genetic factors. These findings cast doubt on the assumed causal relationship between personality and politics. Here we test the causal relationship between personality traits and political attitudes using a direction of causation structural model on a genetically informative sample. The results suggest that personality traits do not cause people to develop political attitudes; rather, the correlation between the two is a function of an innate common underlying genetic factor.

Genetic and environmental influences on the transmission of parental depression to children's depression and conduct disturbance: an extended Children of Twins study.

PubMed

Silberg, Judy L; Maes, Hermine; Eaves, Lindon J

2010-06-01

Despite the increased risk of depression and conduct problems in children of depressed parents, the mechanism by which parental depression affects their children's behavioral and emotional functioning is not well understood. The present study was undertaken to determine whether parental depression represents a genuine environmental risk factor in children's psychopathology, or whether children's depression/conduct can be explained as a secondary consequence of the genetic liability transmitted from parents to their offspring. Children of Twins (COT) data collected on 2,674 adult female and male twins, their spouses, and 2,940 of their children were used to address whether genetic and/or family environmental factors best account for the association between depression in parents and depression and conduct problems in their children. Data collected on juvenile twins from the Virginia Twin Study of Adolescent Behavioral Development (VTSABD) were also included to estimate child-specific genetic and environmental influences apart from those effects arising from the transmission of the parental depression itself. The fit of alternative Children of Twin models were evaluated using the statistical program Mx. The most compelling model for the association between parental and juvenile depression was a model of direct environmental risk. Both family environmental and genetic factors accounted for the association between parental depression and child conduct disturbance. These findings illustrate how a genetically mediated behavior such as parental depression can have both an environmental and genetic impact on children's behavior. We find developmentally specific genetic factors underlying risk to juvenile and adult depression. A shared genetic liability influences both parental depression and juvenile conduct disturbance, implicating child conduct disturbance (CD) as an early indicator of genetic risk for depression in adulthood. In summary, our analyses demonstrate differences in the impact of parental depression on different forms of child psychopathology, and at various stages of development.

Genetic and environmental influences on the transmission of parental depression to children’s depression and conduct disturbance: An extended Children of Twins study

PubMed Central

Silberg, Judy L.; Maes, Hermine; Eaves, Lindon J.

2010-01-01

Background Despite the increased risk of depression and conduct problems in children of depressed parents, the mechanism by which parental depression affects their children’s behavioral and emotional functioning is not well understood. The present study was undertaken to determine whether parental depression represents a genuine environmental risk factor in children’s psychopathology, or whether children’s depression/conduct can be explained as a secondary consequence of the genetic liability transmitted from parents to their offspring. Methods Children of Twins (COT) data collected on 2,674 adult female and male twins, their spouses, and 2,940 of their children were used to address whether genetic and/or family environmental factors best account for the association between depression in parents and depression and conduct problems in their children. Data collected on juvenile twins from the Virginia Twin Study of Adolescent Behavioral Development (VTSABD) were also included to estimate child-specific genetic and environmental influences apart from those effects arising from the transmission of the parental depression itself. The fit of alternative Children of Twin models were evaluated using the statistical program Mx. Results The most compelling model for the association between parental and juvenile depression was a model of direct environmental risk. Both family environmental and genetic factors accounted for the association between parental depression and child conduct disturbance. Conclusions These findings illustrate how a genetically mediated behavior such as parental depression can have both an environmental and genetic impact on children’s behavior. We find developmentally specific genetic factors underlying risk to juvenile and adult depression. A shared genetic liability influence both parental depression and juvenile conduct disturbance, implicating child CD as an early indicator of genetic risk for depression in adulthood. In summary, our analyses demonstrate differences in the impact of parental depression on different forms of child psychopathology, and at various stages of development. PMID:20163497

The efficiency of close inbreeding to reduce genetic adaptation to captivity

PubMed Central

Theodorou, K; Couvet, D

2015-01-01

Although ex situ conservation is indispensable for thousands of species, captive breeding is associated with negative genetic changes: loss of genetic variance and genetic adaptation to captivity that is deleterious in the wild. We used quantitative genetic individual-based simulations to model the effect of genetic management on the evolution of a quantitative trait and the associated fitness of wild-born individuals that are brought to captivity. We also examined the feasibility of the breeding strategies under a scenario of a large number of loci subject to deleterious mutations. We compared two breeding strategies: repeated half-sib mating and a method of minimizing mean coancestry (referred to as gc/mc). Our major finding was that half-sib mating is more effective in reducing genetic adaptation to captivity than the gc/mc method. Moreover, half-sib mating retains larger allelic and adaptive genetic variance. Relative to initial standing variation, the additive variance of the quantitative trait increased under half-sib mating during the sojourn in captivity. Although fragmentation into smaller populations improves the efficiency of the gc/mc method, half-sib mating still performs better in the scenarios tested. Half-sib mating shows two caveats that could mitigate its beneficial effects: low heterozygosity and high risk of extinction when populations are of low fecundity and size and one of the following conditions are met: (i) the strength of selection in captivity is comparable with that in the wild, (ii) deleterious mutations are numerous and only slightly deleterious. Experimental validation of half-sib mating is therefore needed for the advancement of captive breeding programs. PMID:25052417

New tools for the analysis of glial cell biology in Drosophila.

PubMed

Awasaki, Takeshi; Lee, Tzumin

2011-09-01

Because of its genetic, molecular, and behavioral tractability, Drosophila has emerged as a powerful model system for studying molecular and cellular mechanisms underlying the development and function of nervous systems. The Drosophila nervous system has fewer neurons and exhibits a lower glia:neuron ratio than is seen in vertebrate nervous systems. Despite the simplicity of the Drosophila nervous system, glial organization in flies is as sophisticated as it is in vertebrates. Furthermore, fly glial cells play vital roles in neural development and behavior. In addition, powerful genetic tools are continuously being created to explore cell function in vivo. In taking advantage of these features, the fly nervous system serves as an excellent model system to study general aspects of glial cell development and function in vivo. In this article, we review and discuss advanced genetic tools that are potentially useful for understanding glial cell biology in Drosophila. Copyright © 2011 Wiley-Liss, Inc.

Behavioral and Genetic Evidence for GIRK Channels in the CNS: Role in Physiology, Pathophysiology, and Drug Addiction.

PubMed

Mayfield, Jody; Blednov, Yuri A; Harris, R Adron

2015-01-01

G protein-coupled inwardly rectifying potassium (GIRK) channels are widely expressed throughout the brain and mediate the inhibitory effects of many neurotransmitters. As a result, these channels are important for normal CNS function and have also been implicated in Down syndrome, Parkinson's disease, psychiatric disorders, epilepsy, and drug addiction. Knockout mouse models have provided extensive insight into the significance of GIRK channels under these conditions. This review examines the behavioral and genetic evidence from animal models and genetic association studies in humans linking GIRK channels with CNS disorders. We further explore the possibility that subunit-selective modulators and other advanced research tools will be instrumental in establishing the role of individual GIRK subunits in drug addiction and other relevant CNS diseases and in potentially advancing treatment options for these disorders. © 2015 Elsevier Inc. All rights reserved.

Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

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

De Ruyck, Kim, E-mail: kim.deruyck@UGent.be; Sabbe, Nick; Oberije, Cary

2011-10-01

Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidatemore » genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade {>=}2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.« less

Coalescence and genetic diversity in sexual populations under selection.

PubMed

Neher, Richard A; Kessinger, Taylor A; Shraiman, Boris I

2013-09-24

In sexual populations, selection operates neither on the whole genome, which is repeatedly taken apart and reassembled by recombination, nor on individual alleles that are tightly linked to the chromosomal neighborhood. The resulting interference between linked alleles reduces the efficiency of selection and distorts patterns of genetic diversity. Inference of evolutionary history from diversity shaped by linked selection requires an understanding of these patterns. Here, we present a simple but powerful scaling analysis identifying the unit of selection as the genomic "linkage block" with a characteristic length, , determined in a self-consistent manner by the condition that the rate of recombination within the block is comparable to the fitness differences between different alleles of the block. We find that an asexual model with the strength of selection tuned to that of the linkage block provides an excellent description of genetic diversity and the site frequency spectra compared with computer simulations. This linkage block approximation is accurate for the entire spectrum of strength of selection and is particularly powerful in scenarios with many weakly selected loci. The latter limit allows us to characterize coalescence, genetic diversity, and the speed of adaptation in the infinitesimal model of quantitative genetics.

Maintenance of Genetic Variability under Strong Stabilizing Selection: A Two-Locus Model

PubMed Central

Gavrilets, S.; Hastings, A.

1993-01-01

We study a two locus model with additive contributions to the phenotype to explore the relationship between stabilizing selection and recombination. We show that if the double heterozygote has the optimum phenotype and the contributions of the loci to the trait are different, then any symmetric stabilizing selection fitness function can maintain genetic variability provided selection is sufficiently strong relative to linkage. We present results of a detailed analysis of the quadratic fitness function which show that selection need not be extremely strong relative to recombination for the polymorphic equilibria to be stable. At these polymorphic equilibria the mean value of the trait, in general, is not equal to the optimum phenotype, there exists a large level of negative linkage disequilibrium which ``hides'' additive genetic variance, and different equilibria can be stable simultaneously. We analyze dependence of different characteristics of these equilibria on the location of optimum phenotype, on the difference in allelic effect, and on the strength of selection relative to recombination. Our overall result that stabilizing selection does not necessarily eliminate genetic variability is compatible with some experimental results where the lines subject to strong stabilizing selection did not have significant reductions in genetic variability. PMID:8514145

A matching-allele model explains host resistance to parasites.

PubMed

Luijckx, Pepijn; Fienberg, Harris; Duneau, David; Ebert, Dieter

2013-06-17

The maintenance of genetic variation and sex despite its costs has long puzzled biologists. A popular idea, the Red Queen Theory, is that under rapid antagonistic coevolution between hosts and their parasites, the formation of new rare host genotypes through sex can be advantageous as it creates host genotypes to which the prevailing parasite is not adapted. For host-parasite coevolution to lead to an ongoing advantage for rare genotypes, parasites should infect specific host genotypes and hosts should resist specific parasite genotypes. The most prominent genetics capturing such specificity are matching-allele models (MAMs), which have the key feature that resistance for two parasite genotypes can reverse by switching one allele at one host locus. Despite the lack of empirical support, MAMs have played a central role in the theoretical development of antagonistic coevolution, local adaptation, speciation, and sexual selection. Using genetic crosses, we show that resistance of the crustacean Daphnia magna against the parasitic bacterium Pasteuria ramosa follows a MAM. Simulation results show that the observed genetics can explain the maintenance of genetic variation and contribute to the maintenance of sex in the facultatively sexual host as predicted by the Red Queen Theory. Copyright © 2013 Elsevier Ltd. All rights reserved.

Genetic demographic networks: Mathematical model and applications.

PubMed

Kimmel, Marek; Wojdyła, Tomasz

2016-10-01

Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise distributions of alleles, in the case of haploid non-recombining loci such as mitochondrial and Y-chromosome loci in humans. Copyright © 2016 Elsevier Inc. All rights reserved.

SOME USES OF MODELS OF QUANTITATIVE GENETIC SELECTION IN SOCIAL SCIENCE.

PubMed

Weight, Michael D; Harpending, Henry

2017-01-01

The theory of selection of quantitative traits is widely used in evolutionary biology, agriculture and other related fields. The fundamental model known as the breeder's equation is simple, robust over short time scales, and it is often possible to estimate plausible parameters. In this paper it is suggested that the results of this model provide useful yardsticks for the description of social traits and the evaluation of transmission models. The differences on a standard personality test between samples of Old Order Amish and Indiana rural young men from the same county and the decline of homicide in Medieval Europe are used as illustrative examples of the overall approach. It is shown that the decline of homicide is unremarkable under a threshold model while the differences between rural Amish and non-Amish young men are too large to be a plausible outcome of simple genetic selection in which assortative mating by affiliation is equivalent to truncation selection.

Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere1

PubMed Central

Hill, Kristine; Porco, Silvana; Lobet, Guillaume; Zappala, Susan; Mooney, Sacha; Draye, Xavier; Bennett, Malcolm J.

2013-01-01

Genetic and genomic approaches in model organisms have advanced our understanding of root biology over the last decade. Recently, however, systems biology and modeling have emerged as important approaches, as our understanding of root regulatory pathways has become more complex and interpreting pathway outputs has become less intuitive. To relate root genotype to phenotype, we must move beyond the examination of interactions at the genetic network scale and employ multiscale modeling approaches to predict emergent properties at the tissue, organ, organism, and rhizosphere scales. Understanding the underlying biological mechanisms and the complex interplay between systems at these different scales requires an integrative approach. Here, we describe examples of such approaches and discuss the merits of developing models to span multiple scales, from network to population levels, and to address dynamic interactions between plants and their environment. PMID:24143806

Advanced transgenic approaches to understand alcohol-related phenotypes in animals.

PubMed

Bilbao, Ainhoa

2013-01-01

During the past two decades, the use of genetically manipulated animal models in alcohol research has greatly improved the understanding of the mechanisms underlying alcohol addiction. In this chapter, we present an overview of the progress made in this field by summarizing findings obtained from studies of mice harboring global and conditional mutations in genes that influence alcohol-related phenotypes. The first part reviews behavioral paradigms for modeling the different phases of the alcohol addiction cycle and other alcohol-induced behavioral phenotypes in mice. The second part reviews the current data available using genetic models targeting the main neurotransmitter and neuropeptide systems involved in the reinforcement and stress pathways, focusing on the phenotypes modeling the alcohol addiction cycle. Finally, the third part will discuss the current findings and future directions, and proposes advanced transgenic mouse models for their potential use in alcohol research.

Genetic linkage study of bipolar disorder and the serotonin transporter

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

Kelsoe, J.R.; Morison, M.; Mroczkowski-Parker, Z.

1996-04-09

The serotonin transporter (HTT) is an important candidate gene for the genetic transmission of bipolar disorder. It is the site of action of many antidepressants, and plays a key role in the regulation of serotonin neurotransmission. Many studies of affectively ill patients have found abnormalities in serotonin metabolism, and dysregulation of the transporter itself. The human serotonin transporter has been recently cloned and mapped to chromosome 17. We have identified a PstI RFLP at the HTT locus, and here report our examination of this polymorphism for possible linkage to bipolar disorder. Eighteen families were examined from three populations: the Oldmore » Order Amish, Iceland, and the general North American population. In addition to HTT, three other microsatellite markers were examined, which span an interval known to contain HTT. Linkage analyses were conducted under both dominant and recessive models, as well as both narrow (bipolar only) and broad (bipolar + recurrent unipolar) diagnostic models. Linkage could be excluded to HTT under all models examined. Linkage to the interval spanned by the microsatellites was similarly excluded under the dominant models. In two individual families, maximum lod scores of 1.02 and 0.84 were obtained at D17S798 and HTT, respectively. However, these data overall do not support the presence of a susceptibility locus for bipolar disorder near the serotonin transporter. 20 refs., 2 tabs.« less

Pleiotropic Models of Polygenic Variation, Stabilizing Selection, and Epistasis

PubMed Central

Gavrilets, S.; de-Jong, G.

1993-01-01

We show that in polymorphic populations many polygenic traits pleiotropically related to fitness are expected to be under apparent ``stabilizing selection'' independently of the real selection acting on the population. This occurs, for example, if the genetic system is at a stable polymorphic equilibrium determined by selection and the nonadditive contributions of the loci to the trait value either are absent, or are random and independent of those to fitness. Stabilizing selection is also observed if the polygenic system is at an equilibrium determined by a balance between selection and mutation (or migration) when both additive and nonadditive contributions of the loci to the trait value are random and independent of those to fitness. We also compare different viability models that can maintain genetic variability at many loci with respect to their ability to account for the strong stabilizing selection on an additive trait. Let V(m) be the genetic variance supplied by mutation (or migration) each generation, V(g) be the genotypic variance maintained in the population, and n be the number of the loci influencing fitness. We demonstrate that in mutation (migration)-selection balance models the strength of apparent stabilizing selection is order V(m)/V(g). In the overdominant model and in the symmetric viability model the strength of apparent stabilizing selection is approximately 1/(2n) that of total selection on the whole phenotype. We show that a selection system that involves pairwise additive by additive epistasis in maintaining variability can lead to a lower genetic load and genetic variance in fitness (approximately 1/(2n) times) than an equivalent selection system that involves overdominance. We show that, in the epistatic model, the apparent stabilizing selection on an additive trait can be as strong as the total selection on the whole phenotype. PMID:8325491

An assessment of the reliability of quantitative genetics estimates in study systems with high rate of extra-pair reproduction and low recruitment.

PubMed

Bourret, A; Garant, D

2017-03-01

Quantitative genetics approaches, and particularly animal models, are widely used to assess the genetic (co)variance of key fitness related traits and infer adaptive potential of wild populations. Despite the importance of precision and accuracy of genetic variance estimates and their potential sensitivity to various ecological and population specific factors, their reliability is rarely tested explicitly. Here, we used simulations and empirical data collected from an 11-year study on tree swallow (Tachycineta bicolor), a species showing a high rate of extra-pair paternity and a low recruitment rate, to assess the importance of identity errors, structure and size of the pedigree on quantitative genetic estimates in our dataset. Our simulations revealed an important lack of precision in heritability and genetic-correlation estimates for most traits, a low power to detect significant effects and important identifiability problems. We also observed a large bias in heritability estimates when using the social pedigree instead of the genetic one (deflated heritabilities) or when not accounting for an important cause of resemblance among individuals (for example, permanent environment or brood effect) in model parameterizations for some traits (inflated heritabilities). We discuss the causes underlying the low reliability observed here and why they are also likely to occur in other study systems. Altogether, our results re-emphasize the difficulties of generalizing quantitative genetic estimates reliably from one study system to another and the importance of reporting simulation analyses to evaluate these important issues.

The capture of heritable variation for genetic quality through social competition.

PubMed

Wolf, Jason B; Harris, W Edwin; Royle, Nick J

2008-09-01

In theory, females of many species choose mates based on traits that are indicators of male genetic quality. A fundamental question in evolutionary biology is why genetic variation for such indicator traits persists despite strong persistent selection imposed by female preference, which is known as the lek paradox. One potential solution to the lek paradox suggests that the traits that are targets of mate choice should evolve condition-dependent expression and that condition should have a large genetic variance. Condition is expected to exhibit high genetic variance because it is affected by a large number of physiological processes and hence, condition-dependent traits should 'capture' variation contributed by a large number of loci. We suggest that a potentially important cause of variation in condition is competition for limited resources. Here, we discuss a pair of models to analyze the evolutionary genetics of traits affected by success in social competition for resources. We show that competition can contribute to genetic variation of 'competition-dependent' traits that have fundamentally different evolutionary properties than other sources of variation. Competition dependence can make traits honest indicators of genetic quality by revealing the relative competitive ability of males, can provide a component of heritable variation that does not contribute to trait evolution, and can help maintain heritable variation under directional selection. Here we provide a general introduction to the concept of competition dependence and briefly introduce two models to demonstrate the potential evolutionary consequences of competition-dependent trait expression.

Characterization of demographic expansions from pairwise comparisons of linked microsatellite haplotypes.

PubMed

Navascués, Miguel; Hardy, Olivier J; Burgarella, Concetta

2009-03-01

This work extends the methods of demographic inference based on the distribution of pairwise genetic differences between individuals (mismatch distribution) to the case of linked microsatellite data. Population genetics theory describes the distribution of mutations among a sample of genes under different demographic scenarios. However, the actual number of mutations can rarely be deduced from DNA polymorphisms. The inclusion of mutation models in theoretical predictions can improve the performance of statistical methods. We have developed a maximum-pseudolikelihood estimator for the parameters that characterize a demographic expansion for a series of linked loci evolving under a stepwise mutation model. Those loci would correspond to DNA polymorphisms of linked microsatellites (such as those found on the Y chromosome or the chloroplast genome). The proposed method was evaluated with simulated data sets and with a data set of chloroplast microsatellites that showed signal for demographic expansion in a previous study. The results show that inclusion of a mutational model in the analysis improves the estimates of the age of expansion in the case of older expansions.

The probability of monophyly of a sample of gene lineages on a species tree

PubMed Central

Mehta, Rohan S.; Bryant, David; Rosenberg, Noah A.

2016-01-01

Monophyletic groups—groups that consist of all of the descendants of a most recent common ancestor—arise naturally as a consequence of descent processes that result in meaningful distinctions between organisms. Aspects of monophyly are therefore central to fields that examine and use genealogical descent. In particular, studies in conservation genetics, phylogeography, population genetics, species delimitation, and systematics can all make use of mathematical predictions under evolutionary models about features of monophyly. One important calculation, the probability that a set of gene lineages is monophyletic under a two-species neutral coalescent model, has been used in many studies. Here, we extend this calculation for a species tree model that contains arbitrarily many species. We study the effects of species tree topology and branch lengths on the monophyly probability. These analyses reveal new behavior, including the maintenance of nontrivial monophyly probabilities for gene lineage samples that span multiple species and even for lineages that do not derive from a monophyletic species group. We illustrate the mathematical results using an example application to data from maize and teosinte. PMID:27432988

Zebrafish Models of Prader-Willi Syndrome: Fast Track to Pharmacotherapeutics

PubMed Central

Spikol, Emma D.; Laverriere, Caroline E.; Robnett, Maya; Carter, Gabriela; Wolfe, Erin; Glasgow, Eric

2016-01-01

Prader-Willi syndrome (PWS) is a rare genetic neurodevelopmental disorder characterized by an insatiable appetite, leading to chronic overeating and obesity. Additional features include short stature, intellectual disability, behavioral problems and incomplete sexual development. Although significant progress has been made in understanding the genetic basis of PWS, the mechanisms underlying the pathogenesis of the disorder remain poorly understood. Treatment for PWS consists mainly of palliative therapies; curative therapies are sorely needed. Zebrafish, Danio rerio, represent a promising way forward for elucidating physiological problems such as obesity and identifying new pharmacotherapeutic options for PWS. Over the last decade, an increased appreciation for the highly conserved biology among vertebrates and the ability to perform high-throughput drug screening has seen an explosion in the use of zebrafish for disease modeling and drug discovery. Here, we review recent advances in developing zebrafish models of human disease. Aspects of zebrafish genetics and physiology that are relevant to PWS will be discussed, and the advantages and disadvantages of zebrafish models will be contrasted with current animal models for this syndrome. Finally, we will present a paradigm for drug screening in zebrafish that is potentially the fastest route for identifying and delivering curative pharmacotherapies to PWS patients. PMID:27857842

An etiologic prediction model incorporating biomarkers to predict the bladder cancer risk associated with occupational exposure to aromatic amines: a pilot study.

PubMed

Mastrangelo, Giuseppe; Carta, Angela; Arici, Cecilia; Pavanello, Sofia; Porru, Stefano

2017-01-01

No etiological prediction model incorporating biomarkers is available to predict bladder cancer risk associated with occupational exposure to aromatic amines. Cases were 199 bladder cancer patients. Clinical, laboratory and genetic data were predictors in logistic regression models (full and short) in which the dependent variable was 1 for 15 patients with aromatic amines related bladder cancer and 0 otherwise. The receiver operating characteristics approach was adopted; the area under the curve was used to evaluate discriminatory ability of models. Area under the curve was 0.93 for the full model (including age, smoking and coffee habits, DNA adducts, 12 genotypes) and 0.86 for the short model (including smoking, DNA adducts, 3 genotypes). Using the "best cut-off" of predicted probability of a positive outcome, percentage of cases correctly classified was 92% (full model) against 75% (short model). Cancers classified as "positive outcome" are those to be referred for evaluation by an occupational physician for etiological diagnosis; these patients were 28 (full model) or 60 (short model). Using 3 genotypes instead of 12 can double the number of patients with suspect of aromatic amine related cancer, thus increasing costs of etiologic appraisal. Integrating clinical, laboratory and genetic factors, we developed the first etiologic prediction model for aromatic amine related bladder cancer. Discriminatory ability was excellent, particularly for the full model, allowing individualized predictions. Validation of our model in external populations is essential for practical use in the clinical setting.

A mixed model for the relationship between climate and human cranial form.

PubMed

Katz, David C; Grote, Mark N; Weaver, Timothy D

2016-08-01

We expand upon a multivariate mixed model from quantitative genetics in order to estimate the magnitude of climate effects in a global sample of recent human crania. In humans, genetic distances are correlated with distances based on cranial form, suggesting that population structure influences both genetic and quantitative trait variation. Studies controlling for this structure have demonstrated significant underlying associations of cranial distances with ecological distances derived from climate variables. However, to assess the biological importance of an ecological predictor, estimates of effect size and uncertainty in the original units of measurement are clearly preferable to significance claims based on units of distance. Unfortunately, the magnitudes of ecological effects are difficult to obtain with distance-based methods, while models that produce estimates of effect size generally do not scale to high-dimensional data like cranial shape and form. Using recent innovations that extend quantitative genetics mixed models to highly multivariate observations, we estimate morphological effects associated with a climate predictor for a subset of the Howells craniometric dataset. Several measurements, particularly those associated with cranial vault breadth, show a substantial linear association with climate, and the multivariate model incorporating a climate predictor is preferred in model comparison. Previous studies demonstrated the existence of a relationship between climate and cranial form. The mixed model quantifies this relationship concretely. Evolutionary questions that require population structure and phylogeny to be disentangled from potential drivers of selection may be particularly well addressed by mixed models. Am J Phys Anthropol 160:593-603, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

IBSEM: An Individual-Based Atlantic Salmon Population Model

PubMed Central

Castellani, Marco; Heino, Mikko; Gilbey, John; Araki, Hitoshi; Svåsand, Terje; Glover, Kevin A.

2015-01-01

Ecology and genetics can influence the fate of individuals and populations in multiple ways. However, to date, few studies consider them when modelling the evolutionary trajectory of populations faced with admixture with non-local populations. For the Atlantic salmon, a model incorporating these elements is urgently needed because many populations are challenged with gene-flow from non-local and domesticated conspecifics. We developed an Individual-Based Salmon Eco-genetic Model (IBSEM) to simulate the demographic and population genetic change of an Atlantic salmon population through its entire life-cycle. Processes such as growth, mortality, and maturation are simulated through stochastic procedures, which take into account environmental variables as well as the genotype of the individuals. IBSEM is based upon detailed empirical data from salmon biology, and parameterized to reproduce the environmental conditions and the characteristics of a wild population inhabiting a Norwegian river. Simulations demonstrated that the model consistently and reliably reproduces the characteristics of the population. Moreover, in absence of farmed escapees, the modelled populations reach an evolutionary equilibrium that is similar to our definition of a ‘wild’ genotype. We assessed the sensitivity of the model in the face of assumptions made on the fitness differences between farm and wild salmon, and evaluated the role of straying as a buffering mechanism against the intrusion of farm genes into wild populations. These results demonstrate that IBSEM is able to capture the evolutionary forces shaping the life history of wild salmon and is therefore able to model the response of populations under environmental and genetic stressors. PMID:26383256

Exploratory subsetting of autism families based on savant skills improves evidence of genetic linkage to 15q11-q13.

PubMed

Nurmi, Erika L; Dowd, Michael; Tadevosyan-Leyfer, Ovsanna; Haines, Jonathan L; Folstein, Susan E; Sutcliffe, James S

2003-07-01

Autism displays a remarkably high heritability but a complex genetic etiology. One approach to identifying susceptibility loci under these conditions is to define more homogeneous subsets of families on the basis of genetically relevant phenotypic or biological characteristics that vary from case to case. The authors performed a principal components analysis, using items from the Autism Diagnostic Interview, which resulted in six clusters of variables, five of which showed significant sib-sib correlation. The utility of these phenotypic subsets was tested in an exploratory genetic analysis of the autism candidate region on chromosome 15q11-q13. When the Collaborative Linkage Study of Autism sample was divided, on the basis of mean proband score for the "savant skills" cluster, the heterogeneity logarithm of the odds under a recessive model at D15S511, within the GABRB3 gene, increased from 0.6 to 2.6 in the subset of families in which probands had greater savant skills. These data are consistent with the genetic contribution of a 15q locus to autism susceptibility in a subset of affected individuals exhibiting savant skills. Similar types of skills have been noted in individuals with Prader-Willi syndrome, which results from deletions of this chromosomal region.

Genetic variability in calving success in Aberdeen Angus cows under extensive recording.

PubMed

Urioste, J I; Chang, Y M; Naya, H; Gianola, D

2007-09-01

Data from 2032 Uruguayan Aberdeen Angus cows under extensive management and recording practices were analysed with Bayesian threshold-liability sire models, to assess genetic variability in calving success (CS), defined as a different binary trait for each of the second (CS2), third (CS3) and fourth (CS4) calving opportunities. Sire (herd) variances ranged from 0.08 to 0.11 (0.10 to 0.20) and heritability from 0.27 to 0.35, with large credibility intervals. Correlations between herd effects on CS at different calving opportunities were positive. Genetic correlation between CS2 and CS4 was positive (0.68), whereas those involving adjacent calving opportunities (CS2-CS3 and CS3-CS4) were negative, at -0.39 and -0.54, respectively. The residual correlation CS2-CS3 was negative (-0.32). The extent of uncertainty associated with the posterior estimates of the parameters was further evaluated through simulation, assuming different true values (-0.4, -0.2, +0.2 and +0.4) for the genetic correlations and changes in the degree of belief parameters of the inverse Wishart priors for the sire covariance matrix. Although inferences were not sharp enough, CS appears to be moderately heritable. The quality of data recording should be improved, in order to effect genetic improvement in female fertility.

Sexual selection and genetic colour polymorphisms in animals.

PubMed

Wellenreuther, Maren; Svensson, Erik I; Hansson, Bengt

2014-11-01

Genetic colour polymorphisms are widespread across animals and often subjected to complex selection regimes. Traditionally, colour morphs were used as simple visual markers to measure allele frequency changes in nature, selection, population divergence and speciation. With advances in sequencing technology and analysis methods, several model systems are emerging where the molecular targets of selection are being described. Here, we discuss recent studies on the genetics of sexually selected colour polymorphisms, aiming at (i) reviewing the evidence of sexual selection on colour polymorphisms, (ii) highlighting the genetic architecture, molecular and developmental basis underlying phenotypic colour diversification and (iii) discuss how the maintenance of such polymorphisms might be facilitated or constrained by these. Studies of the genetic architecture of colour polymorphism point towards the importance of tight clustering of colour loci with other trait loci, such as in the case of inversions and supergene structures. Other interesting findings include linkage between colour loci and mate preferences or sex determination, and the role of introgression and regulatory variation in fuelling polymorphisms. We highlight that more studies are needed that explicitly integrate fitness consequences of sexual selection on colour with the underlying molecular targets of colour to gain insights into the evolutionary consequences of sexual selection on polymorphism maintenance. © 2014 John Wiley & Sons Ltd.

Wild rodents as a model to discover genes and pathways underlying natural variation in infectious disease susceptibility.

PubMed

Turner, A K; Paterson, S

2013-11-01

Individuals vary in their susceptibility to infectious disease, and it is now well established that host genetic factors form a major component of this variation. The discovery of genes underlying susceptibility has the potential to lead to improved disease control, through the identification and management of vulnerable individuals and the discovery of novel therapeutic targets. Laboratory rodents have proved invaluable for ascertaining the function of genes involved in immunity to infection. However, these captive animals experience conditions very different to the natural environment, lacking the genetic diversity and environmental pressures characteristic of natural populations, including those of humans. It has therefore often proved difficult to translate basic laboratory research to the real world. In order to further our understanding of the genetic basis of infectious disease resistance, and the evolutionary forces that drive variation in susceptibility, we propose that genetic research traditionally conducted on laboratory animals is expanded to the more ecologically valid arena of natural populations. In this article, we highlight the potential of using wild rodents as a new resource for biomedical research, to link the functional genetic knowledge gained from laboratory rodents with the variation in infectious disease susceptibility observed in humans and other natural populations. © 2013 John Wiley & Sons Ltd.

External validity of a hierarchical dimensional model of child and adolescent psychopathology: Tests using confirmatory factor analyses and multivariate behavior genetic analyses.

PubMed

Waldman, Irwin D; Poore, Holly E; van Hulle, Carol; Rathouz, Paul J; Lahey, Benjamin B

2016-11-01

Several recent studies of the hierarchical phenotypic structure of psychopathology have identified a General psychopathology factor in addition to the more expected specific Externalizing and Internalizing dimensions in both youth and adult samples and some have found relevant unique external correlates of this General factor. We used data from 1,568 twin pairs (599 MZ & 969 DZ) age 9 to 17 to test hypotheses for the underlying structure of youth psychopathology and the external validity of the higher-order factors. Psychopathology symptoms were assessed via structured interviews of caretakers and youth. We conducted phenotypic analyses of competing structural models using Confirmatory Factor Analysis and used Structural Equation Modeling and multivariate behavior genetic analyses to understand the etiology of the higher-order factors and their external validity. We found that both a General factor and specific Externalizing and Internalizing dimensions are necessary for characterizing youth psychopathology at both the phenotypic and etiologic levels, and that the 3 higher-order factors differed substantially in the magnitudes of their underlying genetic and environmental influences. Phenotypically, the specific Externalizing and Internalizing dimensions were slightly negatively correlated when a General factor was included, which reflected a significant inverse correlation between the nonshared environmental (but not genetic) influences on Internalizing and Externalizing. We estimated heritability of the general factor of psychopathology for the first time. Its moderate heritability suggests that it is not merely an artifact of measurement error but a valid construct. The General, Externalizing, and Internalizing factors differed in their relations with 3 external validity criteria: mother's smoking during pregnancy, parent's harsh discipline, and the youth's association with delinquent peers. Multivariate behavior genetic analyses supported the external validity of the 3 higher-order factors by suggesting that the General, Externalizing, and Internalizing factors were correlated with peer delinquency and parent's harsh discipline for different etiologic reasons. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Resistance Management Research Status

EPA Science Inventory

Long-term sustainability of genetically modified corn expressing Bt relies on the validity of assumptions underlying IRM models used by the EPA and the ability of EPA to monitor, detect and react to insect resistance when it develops. The EPA is developing a multi-tiered approac...

The Genetic and Environmental Factors Underlying Hypospadias

PubMed Central

Pask, Andrew; Heloury, Yves; Sinclair, Andrew H.

2016-01-01

Hypospadias results from a failure of urethral closure in the male phallus and affects 1 in 200–300 boys. It is thought to be due to a combination of genetic and environmental factors. The development of the penis progresses in 2 stages: an initial hormone-independent phase and a secondary hormone-dependent phase. Here, we review the molecular pathways that contribute to each of these stages, drawing on studies from both human and mouse models. Hypospadias can occur when normal development of the phallus is disrupted, and we provide evidence that mutations in genes underlying this developmental process are causative. Finally, we discuss the environmental factors that may contribute to hypospadias and their potential immediate and transgen erational epigenetic impacts. PMID:26613581

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.

Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

NASA Astrophysics Data System (ADS)

Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

2018-04-01

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

Projected climate changes threaten ancient refugia of kelp forests in the North Atlantic.

PubMed

Assis, Jorge; Araújo, Miguel B; Serrão, Ester A

2018-01-01

Intraspecific genetic variability is critical for species adaptation and evolution and yet it is generally overlooked in projections of the biological consequences of climate change. We ask whether ongoing climate changes can cause the loss of important gene pools from North Atlantic relict kelp forests that persisted over glacial-interglacial cycles. We use ecological niche modelling to predict genetic diversity hotspots for eight species of large brown algae with different thermal tolerances (Arctic to warm temperate), estimated as regions of persistence throughout the Last Glacial Maximum (20,000 YBP), the warmer Mid-Holocene (6,000 YBP), and the present. Changes in the genetic diversity within ancient refugia were projected for the future (year 2100) under two contrasting climate change scenarios (RCP2.6 and RCP8.5). Models predicted distributions that matched empirical distributions in cross-validation, and identified distinct refugia at the low latitude ranges, which largely coincide among species with similar ecological niches. Transferred models into the future projected polewards expansions and substantial range losses in lower latitudes, where richer gene pools are expected (in Nova Scotia and Iberia for cold affinity species and Gibraltar, Alboran, and Morocco for warm-temperate species). These effects were projected for both scenarios but were intensified under the extreme RCP8.5 scenario, with the complete borealization (circum-Arctic colonization) of kelp forests, the redistribution of the biogeographical transitional zones of the North Atlantic, and the erosion of global gene pools across all species. As the geographic distribution of genetic variability is unknown for most marine species, our results represent a baseline for identification of locations potentially rich in unique phylogeographic lineages that are also climatic relics in threat of disappearing. © 2017 John Wiley & Sons Ltd.

Studying parents and grandparents to assess genetic contributions to early-onset disease.

PubMed

Weinberg, Clarice R

2003-02-01

Suppose DNA is available from affected individuals, their parents, and their grandparents. Particularly for early-onset diseases, maternally mediated genetic effects can play a role, because the mother determines the prenatal environment. The proposed maximum-likelihood approach for the detection of apparent transmission distortion treats the triad consisting of the affected individual and his or her two parents as the outcome, conditioning on grandparental mating types. Under a null model in which the allele under study does not confer susceptibility, either through linkage or directly, and when there are no maternally mediated genetic effects, conditional probabilities for specific triads are easily derived. A log-linear model permits a likelihood-ratio test (LRT) and allows the estimation of relative penetrances. The proposed approach is robust against genetic population stratification. Missing-data methods permit the inclusion of incomplete families, even if the missing person is the affected grandchild, as is the case when an induced abortion has followed the detection of a malformation. When screening multiple markers, one can begin by genotyping only the grandparents and the affected grandchildren. LRTs based on conditioning on grandparental mating types (i.e., ignoring the parents) have asymptotic relative efficiencies that are typically >150% (per family), compared with tests based on parents. A test for asymmetry in the number of copies carried by maternal versus paternal grandparents yields an LRT specific to maternal effects. One can then genotype the parents for only the genes that passed the initial screen. Conditioning on both the grandparents' and the affected grandchild's genotypes, a third log-linear model captures the remaining information, in an independent LRT for maternal effects.

Joint effects of pleiotropic selection and stabilizing selection on the maintenance of quantitative genetic variation at mutation-selection balance.

PubMed Central

Zhang, Xu-Sheng; Hill, William G

2002-01-01

In quantitative genetics, there are two basic "conflicting" observations: abundant polygenic variation and strong stabilizing selection that should rapidly deplete that variation. This conflict, although having attracted much theoretical attention, still stands open. Two classes of model have been proposed: real stabilizing selection directly on the metric trait under study and apparent stabilizing selection caused solely by the deleterious pleiotropic side effects of mutations on fitness. Here these models are combined and the total stabilizing selection observed is assumed to derive simultaneously through these two different mechanisms. Mutations have effects on a metric trait and on fitness, and both effects vary continuously. The genetic variance (V(G)) and the observed strength of total stabilizing selection (V(s,t)) are analyzed with a rare-alleles model. Both kinds of selection reduce V(G) but their roles in depleting it are not independent: The magnitude of pleiotropic selection depends on real stabilizing selection and such dependence is subject to the shape of the distributions of mutational effects. The genetic variation maintained thus depends on the kurtosis as well as the variance of mutational effects: All else being equal, V(G) increases with increasing leptokurtosis of mutational effects on fitness, while for a given distribution of mutational effects on fitness, V(G) decreases with increasing leptokurtosis of mutational effects on the trait. The V(G) and V(s,t) are determined primarily by real stabilizing selection while pleiotropic effects, which can be large, have only a limited impact. This finding provides some promise that a high heritability can be explained under strong total stabilizing selection for what are regarded as typical values of mutation and selection parameters. PMID:12242254

Predicting Gene Structure Changes Resulting from Genetic Variants via Exon Definition Features.

PubMed

Majoros, William H; Holt, Carson; Campbell, Michael S; Ware, Doreen; Yandell, Mark; Reddy, Timothy E

2018-04-25

Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed, and produce functional proteins. We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and noncoding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or noncoding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products, and we propose that they may commonly act as cryptic factors in disease. The software is available from geneprediction.org/SGRF. bmajoros@duke.edu. Supplementary information is available at Bioinformatics online.

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Han, Dandan; Xu, Pao; Yang, Runqing

2017-11-02

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

PubMed

Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A

2013-08-01

As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity.

PubMed

Mühlenbruch, Kristin; Jeppesen, Charlotte; Joost, Hans-Georg; Boeing, Heiner; Schulze, Matthias B

2013-01-01

Genome-wide association studies have identified numerous single nucleotide polymorphisms associated with type 2 diabetes through the past years. In previous studies, the usefulness of these genetic markers for prediction of diabetes was found to be limited. However, differences may exist between substrata of the population according to the presence of major diabetes risk factors. This study aimed to investigate the added predictive value of genetic information (42 single nucleotide polymorphisms) in subgroups of sex, age, family history of diabetes, and obesity. A case-cohort study (random subcohort N = 1,968; incident cases: N = 578) within the European Prospective Investigation into Cancer and Nutrition Potsdam study was used. Prediction models without and with genetic information were evaluated in terms of the area under the receiver operating characteristic curve and the integrated discrimination improvement. Stratified analyses included subgroups of sex, age (<50 or ≥50 years), family history (positive if either father or mother or a sibling has/had diabetes), and obesity (BMI< or ≥30 kg/m(2)). A genetic risk score did not improve prediction above classic and metabolic markers, but - compared to a non-invasive prediction model - genetic information slightly improved the area under the receiver operating characteristic curve (difference [95%-CI]: 0.007 [0.002-0.011]). Stratified analyses showed stronger improvement in the older age group (0.010 [0.002-0.018]), the group with a positive family history (0.012 [0.000-0.023]) and among obese participants (0.015 [-0.005-0.034]) compared to the younger participants (0.005 [-0.004-0.014]), participants with a negative family history (0.003 [-0.001-0.008]) and non-obese (0.007 [0.000-0.014]), respectively. No difference was found between men and women. There was no incremental value of genetic information compared to standard non-invasive and metabolic markers. Our study suggests that inclusion of genetic variants in diabetes risk prediction might be useful for subgroups with already manifest risk factors such as older age, a positive family history and obesity.

An alternative covariance estimator to investigate genetic heterogeneity in populations.

PubMed

Heslot, Nicolas; Jannink, Jean-Luc

2015-11-26

For genomic prediction and genome-wide association studies (GWAS) using mixed models, covariance between individuals is estimated using molecular markers. Based on the properties of mixed models, using available molecular data for prediction is optimal if this covariance is known. Under this assumption, adding individuals to the analysis should never be detrimental. However, some empirical studies showed that increasing training population size decreased prediction accuracy. Recently, results from theoretical models indicated that even if marker density is high and the genetic architecture of traits is controlled by many loci with small additive effects, the covariance between individuals, which depends on relationships at causal loci, is not always well estimated by the whole-genome kinship. We propose an alternative covariance estimator named K-kernel, to account for potential genetic heterogeneity between populations that is characterized by a lack of genetic correlation, and to limit the information flow between a priori unknown populations in a trait-specific manner. This is similar to a multi-trait model and parameters are estimated by REML and, in extreme cases, it can allow for an independent genetic architecture between populations. As such, K-kernel is useful to study the problem of the design of training populations. K-kernel was compared to other covariance estimators or kernels to examine its fit to the data, cross-validated accuracy and suitability for GWAS on several datasets. It provides a significantly better fit to the data than the genomic best linear unbiased prediction model and, in some cases it performs better than other kernels such as the Gaussian kernel, as shown by an empirical null distribution. In GWAS simulations, alternative kernels control type I errors as well as or better than the classical whole-genome kinship and increase statistical power. No or small gains were observed in cross-validated prediction accuracy. This alternative covariance estimator can be used to gain insight into trait-specific genetic heterogeneity by identifying relevant sub-populations that lack genetic correlation between them. Genetic correlation can be 0 between identified sub-populations by performing automatic selection of relevant sets of individuals to be included in the training population. It may also increase statistical power in GWAS.

The Tumor Necrosis Factor α (-308 A/G) Polymorphism Is Associated with Cystic Fibrosis in Mexican Patients

PubMed Central

Sanchez-Dominguez, Celia N.; Reyes-Lopez, Miguel A.; Bustamante, Adriana; Cerda-Flores, Ricardo M.; Villalobos-Torres, Maria del C.; Gallardo-Blanco, Hugo L.; Rojas-Martinez, Augusto; Martinez-Rodriguez, Herminia G.; Barrera-Saldaña, Hugo A.; Ortiz-Lopez, Rocio

2014-01-01

Environmental and genetic factors may modify or contribute to the phenotypic differences observed in multigenic and monogenic diseases, such as cystic fibrosis (CF). An analysis of modifier genes can be helpful for estimating patient prognosis and directing preventive care. The aim of this study is to determine the association between seven genetic variants of four modifier genes and CF by comparing their corresponding allelic and genotypic frequencies in CF patients (n = 81) and control subjects (n = 104). Genetic variants of MBL2 exon 1 (A, B, C and D), the IL-8 promoter (−251 A/T), the TNFα promoter (TNF1/TNF2), and SERPINA1 (PI*Z and PI*S) were tested in CF patients and control subjects from northeastern Mexico by PCR-RFLP. Results The TNF2 allele (P = 0.012, OR 3.43, 95% CI 1.25–9.38) was significantly associated with CF under the dominant and additive models but was not associated with CF under the recessive model. This association remained statistically significant after adjusting for multiple tests using the Bonferroni correction (P = 0.0482). The other tested variants and genotypes did not show any association with the disease. Conclusion An analysis of seven genetic variants of four modifier genes showed that one variant, the TNF2 allele, appears to be significantly associated with CF in Mexican patients. PMID:24603877

Genes and gene networks implicated in aggression related behaviour.

PubMed

Malki, Karim; Pain, Oliver; Du Rietz, Ebba; Tosto, Maria Grazia; Paya-Cano, Jose; Sandnabba, Kenneth N; de Boer, Sietse; Schalkwyk, Leonard C; Sluyter, Frans

2014-10-01

Aggressive behaviour is a major cause of mortality and morbidity. Despite of moderate heritability estimates, progress in identifying the genetic factors underlying aggressive behaviour has been limited. There are currently three genetic mouse models of high and low aggression created using selective breeding. This is the first study to offer a global transcriptomic characterization of the prefrontal cortex across all three genetic mouse models of aggression. A systems biology approach has been applied to transcriptomic data across the three pairs of selected inbred mouse strains (Turku Aggressive (TA) and Turku Non-Aggressive (TNA), Short Attack Latency (SAL) and Long Attack Latency (LAL) mice and North Carolina Aggressive (NC900) and North Carolina Non-Aggressive (NC100)), providing novel insight into the neurobiological mechanisms and genetics underlying aggression. First, weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly correlated genes associated with aggression. Probe sets belonging to gene modules uncovered by WGCNA were carried forward for network analysis using ingenuity pathway analysis (IPA). The RankProd non-parametric algorithm was then used to statistically evaluate expression differences across the genes belonging to modules significantly associated with aggression. IPA uncovered two pathways, involving NF-kB and MAPKs. The secondary RankProd analysis yielded 14 differentially expressed genes, some of which have previously been implicated in pathways associated with aggressive behaviour, such as Adrbk2. The results highlighted plausible candidate genes and gene networks implicated in aggression-related behaviour.

Association between vascular endothelial growth factor polymorphism and recurrent pregnancy loss: A systematic review and meta-analysis.

PubMed

Sun, Yaling; Chen, Min; Mao, Benyu; Cheng, Xianglin; Zhang, Xianping; Xu, Chuanxin

2017-04-01

Some studies have reported that vascular endothelial growth factor (VEGF) genetic polymorphisms are associated with recurrent pregnancy loss (RPL), but the results are controversial. This study is aimed to quantify the strength of this association. A systematic review of the published literature from Medline, Springer, and China National Knowledge Infra structure (CNKI) databases was conducted and investigations of VEGF genetic polymorphisms in RPL were selected. We estimated the pooled odds ratio (OR) to assess this possible association. Fifteen case-control studies comprising 2702 cases and 2667 controls and including five genetic polymorphisms (rs3025039, rs833061, rs15703060, rs2010963 and rs699947) were eligible for this meta-analysis. The overall analysis suggested that only two genetic polymorphisms (rs1570360, rs3025039) were associated with increased risk of RPL. A significant increased risk between VEGF rs1570360 polymorphism and RPL was only found under the dominant model in Caucasians (OR=1.70, 95% CI 1.02-2.82, P=0.04). Whereas, we found that VEGF rs3025039 polymorphism was significantly associated with RPL both under the dominant and recessive model in East Asians, and their summary odd ratios and 95% CIs were 1.26, 1.04-1.53, P=0.02 and 2.94, 1.80-4.83, P=0, respectively. This meta-analysis showed that only rs1570360 (especially in Caucasians) and rs3025039 (especially in East Asians) may be risk factors for RPL. Copyright © 2017 Elsevier B.V. All rights reserved.

A deterministic computer simulation model of life-cycle lamb and wool production.

PubMed

Wang, C T; Dickerson, G E

1991-11-01

A deterministic mathematical computer model was developed to simulate effects on life-cycle efficiency of lamb and wool production from genetic improvement of performance traits under alternative management systems. Genetic input parameters can be varied for age at puberty, length of anestrus, fertility, precocity of fertility, number born, milk yield, mortality, growth rate, body fat, and wool growth. Management options include mating systems, lambing intervals, feeding levels, creep feeding, weaning age, marketing age or weight, and culling policy. Simulated growth of animals is linear from birth to inflection point, then slows asymptotically to specified mature empty BW and fat content when nutrition is not limiting. The ME intake requirement to maintain normal condition is calculated daily or weekly for maintenance, protein and fat deposition, wool growth, gestation, and lactation. Simulated feed intake is the minimum of availability, DM physical limit, or ME physiological limit. Tissue catabolism occurs when intake is below the requirement for essential functions. Mortality increases when BW is depressed. Equations developed for calculations of biological functions were validated with published and unpublished experimental data. Lifetime totals are accumulated for TDN, DM, and protein intake and for market lamb equivalent output values of empty body or carcass lean and wool from both lambs and ewes. These measures of efficiency for combinations of genetic, management, and marketing variables can provide the relative economic weighting of traits needed to derive optimal criteria for genetic selection among and within breeds under defined industry production systems.

A Lens for Evaluating Genetic Information Governance Models: Balancing Equity, Efficiency and Sustainability.

PubMed

Skorve, Espen; Vassilakopoulou, Polyxeni; Aanestad, Margunn; Grünfeld, Thomas

2017-01-01

This paper draws from the literature on collective action and the governance of the commons to address the governance of genetic data on variants of specific genes. Specifically, the data arrangements under study relate to the BRCA genes (BRCA1 and BRCA2) which are linked to breast and ovarian cancer. These data are stored in global genetic data repositories and accessed by researchers and clinicians, from both public and private institutions. The current BRCA data arrangements are fragmented and politicized as there are multiple tensions around data ownership and sharing. Three key principles are proposed for forming and evaluating data governance arrangements in the field. These principles are: equity, efficiency and sustainability.

Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection.

PubMed

Urbanowicz, Ryan J; Kiralis, Jeff; Fisher, Jonathan M; Moore, Jason H

2012-09-26

Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, a reliable metric is needed for model selection. We evaluate three metrics as predictors of relative model detection difficulty derived from previous works: (1) Penetrance table variance (PTV), (2) customized odds ratio (COR), and (3) our own Ease of Detection Measure (EDM), calculated from the penetrance values and respective genotype frequencies of each simulated genetic model. We evaluate the reliability of these metrics across three very different data search algorithms, each with the capacity to detect epistatic interactions. We find that a model's EDM and COR are each stronger predictors of model detection success than heritability. This study formally identifies and evaluates metrics which quantify model detection difficulty. We utilize these metrics to intelligently select models from a population of potential architectures. This allows for an improved simulation study design which accounts for differences in detection difficulty attributed to model architecture. We implement the calculation and utilization of EDM and COR into GAMETES, an algorithm which rapidly and precisely generates pure, strict, n-locus epistatic models.

Non-optimal microbial response to antibiotics underlies suppressive drug interactions

PubMed Central

Bollenbach, Tobias; Quan, Selwyn; Chait, Remy; Kishony, Roy

2010-01-01

SUMMARY Antibiotics inhibiting translation can increase bacterial growth rate in the presence of DNA synthesis inhibitors. Here, we show that this extreme type of drug antagonism, termed suppression, results from non-optimal regulation of ribosomal genes, leading to sub-maximal growth in the presence of DNA stress. Using GFP-tagged transcription reporters in Escherichia coli, we find that ribosomal genes are not directly regulated by DNA stress, leading to an imbalance between cellular DNA and protein content. Sequential deletion of up to 6 of the 7 ribosomal RNA operons corrects this imbalance and leads to improved survival and growth under DNA synthesis inhibition. Further, this genetic manipulation completely removes the suppressive drug interaction. Mathematical modeling shows that non-optimal regulation of ribosome synthesis under DNA stress can be explained as a side-effect of optimal growth-rate-dependent regulation in different nutrient environments. Together, these results reveal the genetic mechanism underlying an important class of suppressive drug interactions. PMID:19914165

Insertional engineering of chromosomes with Sleeping Beauty transposition: an overview.

PubMed

Grabundzija, Ivana; Izsvák, Zsuzsanna; Ivics, Zoltán

2011-01-01

Novel genetic tools and mutagenesis strategies based on the Sleeping Beauty (SB) transposable element are currently under development with a vision to link primary DNA sequence information to gene functions in vertebrate models. By virtue of its inherent capacity to insert into DNA, the SB transposon can be developed into powerful tools for chromosomal manipulations. Mutagenesis screens based on SB have numerous advantages including high throughput and easy identification of mutated alleles. Forward genetic approaches based on insertional mutagenesis by engineered SB transposons have the advantage of providing insight into genetic networks and pathways based on phenotype. Indeed, the SB transposon has become a highly instrumental tool to induce tumors in experimental animals in a tissue-specific -manner with the aim of uncovering the genetic basis of diverse cancers. Here, we describe a battery of mutagenic cassettes that can be applied in conjunction with SB transposon vectors to mutagenize genes, and highlight versatile experimental strategies for the generation of engineered chromosomes for loss-of-function as well as gain-of-function mutagenesis for functional gene annotation in vertebrate models.

Distinct Genetic Architectures for Male and Female Inflorescence Traits of Maize

PubMed Central

Brown, Patrick J.; Upadyayula, Narasimham; Mahone, Gregory S.; Tian, Feng; Bradbury, Peter J.; Myles, Sean; Holland, James B.; Flint-Garcia, Sherry; McMullen, Michael D.; Buckler, Edward S.; Rocheford, Torbert R.

2011-01-01

We compared the genetic architecture of thirteen maize morphological traits in a large population of recombinant inbred lines. Four traits from the male inflorescence (tassel) and three traits from the female inflorescence (ear) were measured and studied using linkage and genome-wide association analyses and compared to three flowering and three leaf traits previously studied in the same population. Inflorescence loci have larger effects than flowering and leaf loci, and ear effects are larger than tassel effects. Ear trait models also have lower predictive ability than tassel, flowering, or leaf trait models. Pleiotropic loci were identified that control elongation of ear and tassel, consistent with their common developmental origin. For these pleiotropic loci, the ear effects are larger than tassel effects even though the same causal polymorphisms are likely involved. This implies that the observed differences in genetic architecture are not due to distinct features of the underlying polymorphisms. Our results support the hypothesis that genetic architecture is a function of trait stability over evolutionary time, since the traits that changed most during the relatively recent domestication of maize have the largest effects. PMID:22125498

A meta-analysis reveals a positive correlation between genetic diversity metrics and environmental status in the long-lived seagrass Posidonia oceanica.

PubMed

Jahnke, Marlene; Olsen, Jeanine L; Procaccini, Gabriele

2015-05-01

The seagrass Posidonia oceanica is a key engineering species structuring coastal marine systems throughout much of the Mediterranean basin. Its decline is of concern, leading to the search for short- and long-term indicators of seagrass health. Using ArcGIS maps from a recent, high-resolution (1-4 km) modelling study of 18 disturbance factors affecting coastal marine systems across the Mediterranean (Micheli et al. 2013, http://globalmarine.nceas.ucsb.edu/mediterranean/), we tested for correlations with genetic diversity metrics (allelic diversity, genotypic/clonal diversity and heterozygosity) in a meta-analysis of 56 meadows. Contrary to initial predictions, weak but significantly positive correlations were found for commercial shipping, organic pollution (pesticides) and cumulative impact. This counterintuitive finding suggests greater resistance and resilience of individuals with higher genetic and genotypic diversity under disturbance (at least for a time) and/or increased sexual reproduction under an intermediate disturbance model. We interpret the absence of low and medium levels of genetic variation at impacted locations as probable local extinctions of individuals that already exceeded their resistance capacity. Alternatively, high diversity at high-impact sites is likely a temporal artefact, reflecting the mismatch with pre-environmental impact conditions, especially because flowering and sexual recruitment are seldom observed. While genetic diversity metrics are a valuable tool for restoration and mitigation, caution must be exercised in the interpretation of correlative patterns as found in this study, because the exceptional longevity of individuals creates a temporal mismatch that may falsely suggest good meadow health status, while gradual deterioration of allelic diversity might go unnoticed. © 2015 John Wiley & Sons Ltd.

Natural CMT2 Variation Is Associated With Genome-Wide Methylation Changes and Temperature Seasonality

PubMed Central

Shen, Xia; De Jonge, Jennifer; Forsberg, Simon K. G.; Pettersson, Mats E.; Sheng, Zheya; Hennig, Lars; Carlborg, Örjan

2014-01-01

As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we used public data from two collections of A. thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites. We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele. This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods. Sixteen novel loci were found, including an association between Chromomethylase 2 (CMT2) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele. Cmt2 mutants were shown to be more tolerant to heat-stress, suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures, potentially through differential allelic plasticity to temperature-stress. PMID:25503602

Application of genetic algorithm to land use optimization for non-point source pollution control based on CLUE-S and SWAT

NASA Astrophysics Data System (ADS)

Wang, Qingrui; Liu, Ruimin; Men, Cong; Guo, Lijia

2018-05-01

The genetic algorithm (GA) was combined with the Conversion of Land Use and its Effect at Small regional extent (CLUE-S) model to obtain an optimized land use pattern for controlling non-point source (NPS) pollution. The performance of the combination was evaluated. The effect of the optimized land use pattern on the NPS pollution control was estimated by the Soil and Water Assessment Tool (SWAT) model and an assistant map was drawn to support the land use plan for the future. The Xiangxi River watershed was selected as the study area. Two scenarios were used to simulate the land use change. Under the historical trend scenario (Markov chain prediction), the forest area decreased by 2035.06 ha, and was mainly converted into paddy and dryland area. In contrast, under the optimized scenario (genetic algorithm (GA) prediction), up to 3370 ha of dryland area was converted into forest area. Spatially, the conversion of paddy and dryland into forest occurred mainly in the northwest and southeast of the watershed, where the slope land occupied a large proportion. The organic and inorganic phosphorus loads decreased by 3.6% and 3.7%, respectively, in the optimized scenario compared to those in the historical trend scenario. GA showed a better performance in optimized land use prediction. A comparison of the land use patterns in 2010 under the real situation and in 2020 under the optimized situation showed that Shennongjia and Shuiyuesi should convert 1201.76 ha and 1115.33 ha of dryland into forest areas, respectively, which represented the greatest changes in all regions in the watershed. The results of this study indicated that GA and the CLUE-S model can be used to optimize the land use patterns in the future and that SWAT can be used to evaluate the effect of land use optimization on non-point source pollution control. These methods may provide support for land use plan of an area.

GENETIC STRUCTURE OF TRIATOMA INFESTANS POPULATIONS IN RURAL COMMUNITIES OF SANTIAGO DEL ESTERO, NORTHERN ARGENTINA

PubMed Central

Marcet, PL; Mora, MS; Cutrera, AP; Jones, L; Gürtler, RE; Kitron, U; Dotson, EM

2008-01-01

To gain an understanding of the genetic structure and dispersal dynamics of T. infestans populations, we analyzed the multilocus genotype of 10 microsatellite loci for 352 T. infestans collected in 21 houses of 11 rural communities in October 2002. Genetic structure was analyzed at the community and house compound levels. Analysis revealed that vector control actions affected the genetic structure of T. infestans populations. Bug populations from communities under sustained vector control (core area) were highly structured and genetic differentiation between neighboring house compounds was significant. In contrast, bug populations from communities with sporadic vector control actions were more homogeneous and lacked defined genetic clusters. Genetic differentiation between population pairs did not fit a model of isolation by distance at the microgeographical level. Evidence consistent with flight or walking bug dispersal was detected within and among communities, dispersal was more female-biased in the core area and results suggested that houses received immigrants from more than one source. Putative sources and mechanisms of re-infestation are described. These data may be use to design improved vector control strategies PMID:18773972

Smoking and caffeine consumption: a genetic analysis of their association.

PubMed

Treur, Jorien L; Taylor, Amy E; Ware, Jennifer J; Nivard, Michel G; Neale, Michael C; McMahon, George; Hottenga, Jouke-Jan; Baselmans, Bart M L; Boomsma, Dorret I; Munafò, Marcus R; Vink, Jacqueline M

2017-07-01

Smoking and caffeine consumption show a strong positive correlation, but the mechanism underlying this association is unclear. Explanations include shared genetic/environmental factors or causal effects. This study employed three methods to investigate the association between smoking and caffeine. First, bivariate genetic models were applied to data of 10 368 twins from the Netherlands Twin Register in order to estimate genetic and environmental correlations between smoking and caffeine use. Second, from the summary statistics of meta-analyses of genome-wide association studies on smoking and caffeine, the genetic correlation was calculated by LD-score regression. Third, causal effects were tested using Mendelian randomization analysis in 6605 Netherlands Twin Register participants and 5714 women from the Avon Longitudinal Study of Parents and Children. Through twin modelling, a genetic correlation of r0.47 and an environmental correlation of r0.30 were estimated between current smoking (yes/no) and coffee use (high/low). Between current smoking and total caffeine use, this was r0.44 and r0.00, respectively. LD-score regression also indicated sizeable genetic correlations between smoking and coffee use (r0.44 between smoking heaviness and cups of coffee per day, r0.28 between smoking initiation and coffee use and r0.25 between smoking persistence and coffee use). Consistent with the relatively high genetic correlations and lower environmental correlations, Mendelian randomization provided no evidence for causal effects of smoking on caffeine or vice versa. Genetic factors thus explain most of the association between smoking and caffeine consumption. These findings suggest that quitting smoking may be more difficult for heavy caffeine consumers, given their genetic susceptibility. © 2016 The Authors.Addiction Biology published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.

Smoking and caffeine consumption: a genetic analysis of their association

PubMed Central

Taylor, Amy E.; Ware, Jennifer J.; Nivard, Michel G.; Neale, Michael C.; McMahon, George; Hottenga, Jouke‐Jan; Baselmans, Bart M. L.; Boomsma, Dorret I.; Munafò, Marcus R.; Vink, Jacqueline M.

2016-01-01

Abstract Smoking and caffeine consumption show a strong positive correlation, but the mechanism underlying this association is unclear. Explanations include shared genetic/environmental factors or causal effects. This study employed three methods to investigate the association between smoking and caffeine. First, bivariate genetic models were applied to data of 10 368 twins from the Netherlands Twin Register in order to estimate genetic and environmental correlations between smoking and caffeine use. Second, from the summary statistics of meta‐analyses of genome‐wide association studies on smoking and caffeine, the genetic correlation was calculated by LD‐score regression. Third, causal effects were tested using Mendelian randomization analysis in 6605 Netherlands Twin Register participants and 5714 women from the Avon Longitudinal Study of Parents and Children. Through twin modelling, a genetic correlation of r0.47 and an environmental correlation of r0.30 were estimated between current smoking (yes/no) and coffee use (high/low). Between current smoking and total caffeine use, this was r0.44 and r0.00, respectively. LD‐score regression also indicated sizeable genetic correlations between smoking and coffee use (r0.44 between smoking heaviness and cups of coffee per day, r0.28 between smoking initiation and coffee use and r0.25 between smoking persistence and coffee use). Consistent with the relatively high genetic correlations and lower environmental correlations, Mendelian randomization provided no evidence for causal effects of smoking on caffeine or vice versa. Genetic factors thus explain most of the association between smoking and caffeine consumption. These findings suggest that quitting smoking may be more difficult for heavy caffeine consumers, given their genetic susceptibility. PMID:27027469

Characterizing Resilience and Growth Among Soldiers: A Trajectory Study

DTIC Science & Technology

2012-04-01

casualties of cruel environments or bad genetics . Positive psychology challenges the assumptions of the disease model. It calls for as much focus on...by psychologists, under several different rubrics : dispositional optimism by Carver and Scheier (1981), hope by Snyder (2000), and explanatory style

Resistance Management Research Status-May 2008

EPA Science Inventory

Long-term sustainability of genetically modified corn expressing Bt relies on the validity of assumptions underlying IRM models used by the EPA and the ability of EPA to monitor, detect and react to insect resistance when it develops. The EPA is developing a multi-tiered approac...

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change

USGS Publications Warehouse

Sork, Victoria L.; Davis, Frank W.; Westfall, Robert; Flint, Alan L.; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

2010-01-01

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata N??e, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions. ?? 2010 Blackwell Publishing Ltd.

Influence of Pleistocene glacial/interglacial cycles on the genetic structure of the mistletoe cactus Rhipsalis baccifera (Cactaceae) in Mesoamerica.

PubMed

Ornelas, Juan Francisco; Rodríguez-Gómez, Flor

2015-01-01

Phylogeographical work on cloud forest-adapted species provides inconsistent evidence on cloud forest dynamics during glacial cycles. A study of Rhipsalis baccifera (Cactaceae), a bird-dispersed epiphytic mistletoe cactus, was conducted to investigate genetic variation at sequence data from nuclear [internal transcribed spacer (ITS), 677 bp] and chloroplast (rpl32-trnL, 1092bp) DNA for 154 individuals across the species range in Mesoamerica to determine if such patterns are consistent with the expansion/contraction model of cloud forest during glacial cycles. We conducted population and spatial genetic analyses as well as gene flow and divergence time estimates between 24 populations comprising the distribution of R. baccifera in Mexico and Guatemala to gain insight of the evolutionary history of these populations, and a complementary species distribution modeling approach to frame information derived from the genetic analyses into an explicit paleoecological context. The results revealed a phylogeographical break at the Isthmus of Tehuantepec, and high levels of genetic diversity among populations and cloud forest areas. Despite the genetic differentiation of some R. baccifera populations, the widespread ITS ribotypes suggest effective nuclear gene flow via pollen and population differentiation shown by the rpl32-trnL suggests more restricted seed flow. Predictions of species distribution models under past last glacial maximum (LGM) climatic conditions and a significant signal of demographic expansion suggest that R. baccifera populations experienced a range expansion tracking the conditions of the cloud forest distribution and shifted to the lowlands with population connectivity during the LGM. © The American Genetic Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change.

PubMed

Sork, Victoria L; Davis, Frank W; Westfall, Robert; Flint, Alan; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

2010-09-01

Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata Née, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

PubMed Central

Hu, Leland S.; Ning, Shuluo; Eschbacher, Jennifer M.; Baxter, Leslie C.; Gaw, Nathan; Ranjbar, Sara; Plasencia, Jonathan; Dueck, Amylou C.; Peng, Sen; Smith, Kris A.; Nakaji, Peter; Karis, John P.; Quarles, C. Chad; Wu, Teresa; Loftus, Joseph C.; Jenkins, Robert B.; Sicotte, Hugues; Kollmeyer, Thomas M.; O'Neill, Brian P.; Elmquist, William; Hoxworth, Joseph M.; Frakes, David; Sarkaria, Jann; Swanson, Kristin R.; Tran, Nhan L.; Li, Jing; Mitchell, J. Ross

2017-01-01

Background Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology. PMID:27502248

The Ptch1DL mouse: a new model to study lambdoid craniosynostosis and basal cell nevus syndrome associated skeletal defects

PubMed Central

Feng, Weiguo; Choi, Irene; Clouthier, David E.; Niswander, Lee; Williams, Trevor

2013-01-01

Mouse models provide valuable opportunities for probing the underlying pathology of human birth defects. Employing an ENU-based screen for recessive mutations affecting craniofacial anatomy we isolated a mouse strain, Dogface-like (DL), with abnormal skull and snout morphology. Examination of the skull indicated that these mice developed craniosynostosis of the lambdoid suture. Further analysis revealed skeletal defects related to the pathology of basal cell nevus syndrome (BCNS) including defects in development of the limbs, scapula, ribcage, secondary palate, cranial base, and cranial vault. In humans, BCNS is often associated with mutations in the Hedgehog receptor PTCH1 and genetic mapping in DL identified a point mutation at a splice donor site in Ptch1. Using genetic complementation analysis we determined that DL is a hypomorphic allele of Ptch1, leading to increased Hedgehog signaling. Two aberrant transcripts are generated by the mutated Ptch1DL gene, which would be predicted to reduce significantly the levels of functional Patched1 protein. This new Ptch1 allele broadens the mouse genetic reagents available to study the Hedgehog pathway and provides a valuable means to study the underlying skeletal abnormalities in BCNS. In addition, these results strengthen the connection between elevated Hedgehog signaling and craniosynostosis. PMID:23897749

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling.

PubMed

Odegård, J; Jensen, J; Madsen, P; Gianola, D; Klemetsdal, G; Heringstad, B

2003-11-01

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow's udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (Pm). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of Pm and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.

Recurrent bottlenecks in the malaria life cycle obscure signals of positive selection.

PubMed

Chang, Hsiao-Han; Hartl, Daniel L

2015-02-01

Detecting signals of selection in the genome of malaria parasites is a key to identify targets for drug and vaccine development. Malaria parasites have a unique life cycle alternating between vector and host organism with a population bottleneck at each transition. These recurrent bottlenecks could influence the patterns of genetic diversity and the power of existing population genetic tools to identify sites under positive selection. We therefore simulated the site-frequency spectrum of a beneficial mutant allele through time under the malaria life cycle. We investigated the power of current population genetic methods to detect positive selection based on the site-frequency spectrum as well as temporal changes in allele frequency. We found that a within-host selective advantage is difficult to detect using these methods. Although a between-host transmission advantage could be detected, the power is decreased when compared with the classical Wright-Fisher (WF) population model. Using an adjusted null site-frequency spectrum that takes the malaria life cycle into account, the power of tests based on the site-frequency spectrum to detect positive selection is greatly improved. Our study demonstrates the importance of considering the life cycle in genetic analysis, especially in parasites with complex life cycles.

[Genetically modified food and allergies - an update].

PubMed

Niemann, Birgit; Pöting, Annette; Braeuning, Albert; Lampen, Alfonso

2016-07-01

Approval by the European Commission is mandatory for placing genetically modified plants as food or feed on the market in member states of the European Union (EU). The approval is preceded by a safety assessment based on the guidance of the European Food Safety Authority EFSA. The assessment of allergenicity of genetically modified plants and their newly expressed proteins is an integral part of this assessment process. Guidance documents for the assessment of allergenicity are currently under revision. For this purpose, an expert workshop was conducted in Brussels on June 17, 2015. There, methodological improvements for the assessment of coeliac disease-causing properties of proteins, as well as the use of complex models for in vitro digestion of proteins were discussed. Using such techniques a refinement of the current, proven system of allergenicity assessment of genetically modified plants can be achieved.

Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth.

PubMed

Baker, Robert L; Leong, Wen Fung; An, Nan; Brock, Marcus T; Rubin, Matthew J; Welch, Stephen; Weinig, Cynthia

2018-02-01

We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements. Characterizing the genetic basis of traits that vary during ontogeny and affect plant performance is a major goal in evolutionary biology and agronomy. Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa recombinant inbred lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation (A max ) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of A max , because these two indices were genetically correlated with A max across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including A max improves model fit over the initial model. The mtci and pri2 indices also outperform direct A max measurements. Of particular importance for evolutionary biologists and plant breeders, hierarchical Bayesian models estimating FVT parameters improve heritabilities compared to frequentist approaches.

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

Evolution of branched regulatory genetic pathways: directional selection on pleiotropic loci accelerates developmental system drift.

PubMed

Johnson, Norman A; Porter, Adam H

2007-01-01

Developmental systems are regulated by a web of interacting loci. One common and useful approach in studying the evolution of development is to focus on classes of interacting elements within these systems. Here, we use individual-based simulations to study the evolution of traits controlled by branched developmental pathways involving three loci, where one locus regulates two different traits. We examined the system under a variety of selective regimes. In the case where one branch was under stabilizing selection and the other under directional selection, we observed "developmental system drift": the trait under stabilizing selection showed little phenotypic change even though the loci underlying that trait showed considerable evolutionary divergence. This occurs because the pleiotropic locus responds to directional selection and compensatory mutants are then favored in the pathway under stabilizing selection. Though developmental system drift may be caused by other mechanisms, it seems likely that it is accelerated by the same underlying genetic mechanism as that producing the Dobzhansky-Muller incompatibilities that lead to speciation in both linear and branched pathways. We also discuss predictions of our model for developmental system drift and how different selective regimes affect probabilities of speciation in the branched pathway system.

Cell models of arrhythmogenic cardiomyopathy: advances and opportunities

PubMed Central

Stadiotti, Ilaria; Perrucci, Gianluca L.; Tondo, Claudio; Pompilio, Giulio

2017-01-01

ABSTRACT Arrhythmogenic cardiomyopathy is a rare genetic disease that is mostly inherited as an autosomal dominant trait. It is associated predominantly with mutations in desmosomal genes and is characterized by the replacement of the ventricular myocardium with fibrous fatty deposits, arrhythmias and a high risk of sudden death. In vitro studies have contributed to our understanding of the pathogenic mechanisms underlying this disease, including its genetic determinants, as well as its cellular, signaling and molecular defects. Here, we review what is currently known about the pathogenesis of arrhythmogenic cardiomyopathy and focus on the in vitro models that have advanced our understanding of the disease. Finally, we assess the potential of established and innovative cell platforms for elucidating unknown aspects of this disease, and for screening new potential therapeutic agents. This appraisal of in vitro models of arrhythmogenic cardiomyopathy highlights the discoveries made about this disease and the uses of these models for future basic and therapeutic research. PMID:28679668

Chromosome landmarks as tools to study the genome of Arabidopsis thaliana.

PubMed

Siroky, J

2008-01-01

The model plant Arabidopsis thaliana has long been used for genetic, cellular and molecular studies. Whereas this plant was used as a model of genetics in the 1940's, the first cytogenetic observation of A. thaliana chromosomes was published in the beginning of the 20th century. Although Arabidopsis was not originally considered to be a good plant model for cytogenetics due to smallness of its genome, the number of published chromosome studies has expanded enormously in recent years. The advent of fluorescence in situ hybridization techniques on meiotic chromosomes together with indirect immuno-fluorescence localization of key chromosomal and nuclear proteins and wide accessibility of Arabidopsis mutants have resulted in a synergistic boost in Arabidopsis cytogenetics. In comparison to other plant species, the small genome with under-represented DNA repeats together with a small number of chromosomes makes this model plant easy to comprehend for a cytologist. 2008 S. Karger AG, Basel

Mouse models for gastric cancer: Matching models to biological questions

PubMed Central

Poh, Ashleigh R; O'Donoghue, Robert J J

2016-01-01

Abstract Gastric cancer is the third leading cause of cancer‐related mortality worldwide. This is in part due to the asymptomatic nature of the disease, which often results in late‐stage diagnosis, at which point there are limited treatment options. Even when treated successfully, gastric cancer patients have a high risk of tumor recurrence and acquired drug resistance. It is vital to gain a better understanding of the molecular mechanisms underlying gastric cancer pathogenesis to facilitate the design of new‐targeted therapies that may improve patient survival. A number of chemically and genetically engineered mouse models of gastric cancer have provided significant insight into the contribution of genetic and environmental factors to disease onset and progression. This review outlines the strengths and limitations of current mouse models of gastric cancer and their relevance to the pre‐clinical development of new therapeutics. PMID:26809278

Modeling a model: Mouse genetics, 22q11.2 Deletion Syndrome, and disorders of cortical circuit development

PubMed Central

Meechan, Daniel W.; Maynard, Thomas M.; Fernandez, Alejandra; Karpinski, Beverly A.; Rothblat, Lawrence A.; LaMantia, Anthony S.

2015-01-01

Understanding the developmental etiology of autistic spectrum disorders, attention deficit/hyperactivity disorder and schizophrenia remains a major challenge for establishing new diagnostic and therapeutic approaches to these common, difficult-to-treat diseases that compromise neural circuits in the cerebral cortex. One aspect of this challenge is the breadth and overlap of ASD, ADHD, and SCZ deficits; another is the complexity of mutations associated with each, and a third is the difficulty of analyzing disrupted development in at-risk or affected human fetuses. The identification of distinct genetic syndromes that include behavioral deficits similar to those in ASD, ADHC and SCZ provides a critical starting point for meeting this challenge. We summarize clinical and behavioral impairments in children and adults with one such genetic syndrome, the 22q11.2 Deletion Syndrome, routinely called 22q11DS, caused by micro-deletions of between 1.5 and 3.0 MB on human chromosome 22. Among many syndromic features, including cardiovascular and craniofacial anomalies, 22q11DS patients have a high incidence of brain structural, functional, and behavioral deficits that reflect cerebral cortical dysfunction and fall within the spectrum that defines ASD, ADHD, and SCZ. We show that developmental pathogenesis underlying this apparent genetic “model” syndrome in patients can be defined and analyzed mechanistically using genomically accurate mouse models of the deletion that causes 22q11DS. We conclude that “modeling a model”, in this case 22q11DS as a model for idiopathic ASD, ADHD and SCZ, as well as other behavioral disorders like anxiety frequently seen in 22q11DS patients, in genetically engineered mice provides a foundation for understanding the causes and improving diagnosis and therapy for these disorders of cortical circuit development. PMID:25866365

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions.

PubMed

Chatterjee, Nilanjan; Kalaylioglu, Zeynep; Moslehi, Roxana; Peters, Ulrike; Wacholder, Sholom

2006-12-01

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

Predicting the effects of climate change on population connectivity and genetic diversity of an imperiled freshwater mussel, Cumberlandia monodonta (Bivalvia: Margaritiferidae), in riverine systems.

PubMed

Inoue, Kentaro; Berg, David J

2017-01-01

In the face of global climate change, organisms may respond to temperature increases by shifting their ranges poleward or to higher altitudes. However, the direction of range shifts in riverine systems is less clear. Because rivers are dendritic networks, there is only one dispersal route from any given location to another. Thus, range shifts are only possible if branches are connected by suitable habitat, and stream-dwelling organisms can disperse through these branches. We used Cumberlandia monodonta (Bivalvia: Unionoida: Margaritiferidae) as a model species to investigate the effects of climate change on population connectivity because a majority of contemporary populations are panmictic. We combined ecological niche models (ENMs) with population genetic simulations to investigate the effects of climate change on population connectivity and genetic diversity of C. monodonta. The ENMs were constructed using bioclimatic and landscape data to project shifts in suitable habitat under future climate scenarios. We then used forward-time simulations to project potential changes in genetic diversity and population connectivity based on these range shifts. ENM results under current conditions indicated long stretches of highly suitable habitat in rivers where C. monodonta persists; populations in the upper Mississippi River remain connected by suitable habitat that does not impede gene flow. Future climate scenarios projected northward and headwater-ward range contraction and drastic declines in habitat suitability for most extant populations throughout the Mississippi River Basin. Simulations indicated that climate change would greatly reduce genetic diversity and connectivity across populations. Results suggest that a single, large population of C. monodonta will become further fragmented into smaller populations, each of which will be isolated and begin to differentiate genetically. Because C. monodonta is a widely distributed species and purely aquatic, our results suggest that persistence and connectivity of stream-dwelling organisms will be significantly altered in response to future climate change. © 2016 John Wiley & Sons Ltd.

Production scheduling and rescheduling with genetic algorithms.

PubMed

Bierwirth, C; Mattfeld, D C

1999-01-01

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.

Studying human disease genes in Caenorhabditis elegans: a molecular genetics laboratory project.

PubMed

Cox-Paulson, Elisabeth A; Grana, Theresa M; Harris, Michelle A; Batzli, Janet M

2012-01-01

Scientists routinely integrate information from various channels to explore topics under study. We designed a 4-wk undergraduate laboratory module that used a multifaceted approach to study a question in molecular genetics. Specifically, students investigated whether Caenorhabditis elegans can be a useful model system for studying genes associated with human disease. In a large-enrollment, sophomore-level laboratory course, groups of three to four students were assigned a gene associated with either breast cancer (brc-1), Wilson disease (cua-1), ovarian dysgenesis (fshr-1), or colon cancer (mlh-1). Students compared observable phenotypes of wild-type C. elegans and C. elegans with a homozygous deletion in the assigned gene. They confirmed the genetic deletion with nested polymerase chain reaction and performed a bioinformatics analysis to predict how the deletion would affect the encoded mRNA and protein. Students also performed RNA interference (RNAi) against their assigned gene and evaluated whether RNAi caused a phenotype similar to that of the genetic deletion. As a capstone activity, students prepared scientific posters in which they presented their data, evaluated whether C. elegans was a useful model system for studying their assigned genes, and proposed future directions. Assessment showed gains in understanding genotype versus phenotype, RNAi, common bioinformatics tools, and the utility of model organisms.

Studying Human Disease Genes in Caenorhabditis elegans: A Molecular Genetics Laboratory Project

PubMed Central

Cox-Paulson, Elisabeth A.; Grana, Theresa M.; Harris, Michelle A.; Batzli, Janet M.

2012-01-01

Scientists routinely integrate information from various channels to explore topics under study. We designed a 4-wk undergraduate laboratory module that used a multifaceted approach to study a question in molecular genetics. Specifically, students investigated whether Caenorhabditis elegans can be a useful model system for studying genes associated with human disease. In a large-enrollment, sophomore-level laboratory course, groups of three to four students were assigned a gene associated with either breast cancer (brc-1), Wilson disease (cua-1), ovarian dysgenesis (fshr-1), or colon cancer (mlh-1). Students compared observable phenotypes of wild-type C. elegans and C. elegans with a homozygous deletion in the assigned gene. They confirmed the genetic deletion with nested polymerase chain reaction and performed a bioinformatics analysis to predict how the deletion would affect the encoded mRNA and protein. Students also performed RNA interference (RNAi) against their assigned gene and evaluated whether RNAi caused a phenotype similar to that of the genetic deletion. As a capstone activity, students prepared scientific posters in which they presented their data, evaluated whether C. elegans was a useful model system for studying their assigned genes, and proposed future directions. Assessment showed gains in understanding genotype versus phenotype, RNAi, common bioinformatics tools, and the utility of model organisms. PMID:22665589

Applying landscape genomic tools to forest management and restoration of Hawaiian koa (Acacia koa) in a changing environment.

PubMed

Gugger, Paul F; Liang, Christina T; Sork, Victoria L; Hodgskiss, Paul; Wright, Jessica W

2018-02-01

Identifying and quantifying the importance of environmental variables in structuring population genetic variation can help inform management decisions for conservation, restoration, or reforestation purposes, in both current and future environmental conditions. Landscape genomics offers a powerful approach for understanding the environmental factors that currently associate with genetic variation, and given those associations, where populations may be most vulnerable under future environmental change. Here, we applied genotyping by sequencing to generate over 11,000 single nucleotide polymorphisms from 311 trees and then used nonlinear, multivariate environmental association methods to examine spatial genetic structure and its association with environmental variation in an ecologically and economically important tree species endemic to Hawaii, Acacia koa . Admixture and principal components analyses showed that trees from different islands are genetically distinct in general, with the exception of some genotypes that match other islands, likely as the result of recent translocations. Gradient forest and generalized dissimilarity models both revealed a strong association between genetic structure and mean annual rainfall. Utilizing a model for projected future climate on the island of Hawaii, we show that predicted changes in rainfall patterns may result in genetic offset, such that trees no longer may be genetically matched to their environment. These findings indicate that knowledge of current and future rainfall gradients can provide valuable information for the conservation of existing populations and also help refine seed transfer guidelines for reforestation or replanting of koa throughout the state.

The threshold hypothesis: solving the equation of nurture vs nature in type 1 diabetes.

PubMed

Wasserfall, C; Nead, K; Mathews, C; Atkinson, M A

2011-09-01

For more than 40 years, the contributions of nurture (i.e. the environment) and nature (i.e. genetics) have been touted for their aetiological importance in type 1 diabetes. Disappointingly, knowledge gains in these areas, while individually successful, have to a large extent occurred in isolation from each other. One reason underlying this divide is the lack of a testable model that simultaneously considers the contributions of genetic and environmental determinants in the formation of this and potentially other disorders that are subject to these variables. To address this void, we have designed a model based on the hypothesis that the aetiological influences of genetics and environment, when evaluated as intersecting and reciprocal trend lines based on odds ratios, result in a method of concurrently evaluating both facets and defining the attributable risk of clinical onset of type 1 diabetes. The model, which we have elected to term the 'threshold hypothesis', also provides a novel means of conceptualising the complex interactions of nurture with nature in type 1 diabetes across various geographical populations.

Generation of Mouse Lung Epithelial Cells.

PubMed

Kasinski, Andrea L; Slack, Frank J

2013-08-05

Although in vivo models are excellent for assessing various facets of whole organism physiology, pathology, and overall response to treatments, evaluating basic cellular functions, and molecular events in mammalian model systems is challenging. It is therefore advantageous to perform these studies in a refined and less costly setting. One approach involves utilizing cells derived from the model under evaluation. The approach to generate such cells varies based on the cell of origin and often the genetics of the cell. Here we describe the steps involved in generating epithelial cells from the lungs of Kras LSL-G12D/+ ; p53 LSL-R172/+ mice (Kasinski and Slack, 2012). These mice develop aggressive lung adenocarcinoma following cre-recombinase dependent removal of a stop cassette in the transgenes and subsequent expression of Kra -G12D and p53 R172 . While this protocol may be useful for the generation of epithelial lines from other genetic backgrounds, it should be noted that the Kras; p53 cell line generated here is capable of proliferating in culture without any additional genetic manipulation that is often needed for less aggressive backgrounds.

Pick Your Poisson: An Educational Primer for Luria and Delbrück's Classic Paper.

PubMed

Meneely, Philip M

2016-02-01

The origin of beneficial mutations is fundamentally important in understanding the processes by which natural selection works. Using phage-resistant mutants in Escherichia coli as their model for identifying the origin of beneficial mutations, Luria and Delbrück distinguished between two different hypotheses. Under the first hypothesis, which they termed "acquired immunity," the phages induced bacteria to mutate to immunity; this predicts that none of the resistant mutants were present before infection by the phages. Under the second hypothesis, termed "mutation to immunity," resistant bacteria arose from random mutations independent of the presence of the phages; this predicts that resistant bacteria were present in the population before infection by the phages. These two hypotheses could be distinguished by calculating the frequencies at which resistant mutants arose in separate cultures infected at the same time and comparing these frequencies to the theoretical results under each model. The data clearly show that mutations arise at a frequency that is independent of the presence of the phages. By inference, natural selection reveals the genetic variation that is present in a population rather than inducing or causing this variation. Copyright © 2016 by the Genetics Society of America.

Empirical complexities in the genetic foundations of lethal mutagenesis.

PubMed

Bull, James J; Joyce, Paul; Gladstone, Eric; Molineux, Ian J

2013-10-01

From population genetics theory, elevating the mutation rate of a large population should progressively reduce average fitness. If the fitness decline is large enough, the population will go extinct in a process known as lethal mutagenesis. Lethal mutagenesis has been endorsed in the virology literature as a promising approach to viral treatment, and several in vitro studies have forced viral extinction with high doses of mutagenic drugs. Yet only one empirical study has tested the genetic models underlying lethal mutagenesis, and the theory failed on even a qualitative level. Here we provide a new level of analysis of lethal mutagenesis by developing and evaluating models specifically tailored to empirical systems that may be used to test the theory. We first quantify a bias in the estimation of a critical parameter and consider whether that bias underlies the previously observed lack of concordance between theory and experiment. We then consider a seemingly ideal protocol that avoids this bias-mutagenesis of virions-but find that it is hampered by other problems. Finally, results that reveal difficulties in the mere interpretation of mutations assayed from double-strand genomes are derived. Our analyses expose unanticipated complexities in testing the theory. Nevertheless, the previous failure of the theory to predict experimental outcomes appears to reside in evolutionary mechanisms neglected by the theory (e.g., beneficial mutations) rather than from a mismatch between the empirical setup and model assumptions. This interpretation raises the specter that naive attempts at lethal mutagenesis may augment adaptation rather than retard it.

Shared and Unique Genetic and Environmental Influences on Aging-Related Changes in Multiple Cognitive Abilities

PubMed Central

Tucker-Drob, Elliot M.; Reynolds, Chandra A.; Finkel, Deborah; Pedersen, Nancy L.

2013-01-01

Aging-related declines occur in many different domains of cognitive function during later adulthood. However, whether a global dimension underlies individual differences in changes in different domains of cognition, and whether global genetic influences on cognitive changes exist, is less clear. We addressed these issues by applying multivariate growth curve models to longitudinal data from 857 individuals from the Swedish Adoption/Twin Study of Aging, who had been measured on 11 cognitive variables representative of verbal, spatial, memory, and processing speed abilities up to 5 times over up to 16 years between ages 50 and 96 years. Between ages 50 and 65 years scores on different tests changed relatively independently of one another, and there was little evidence for strong underlying dimensions of change. In contrast, over the period between 65 and 96 years of age, there were strong interrelations among rates of change both within and across domains. During this age period, variability in rates of change were, on average, 52% domain-general, 8% domain-specific, and 39% test specific. Quantitative genetic decomposition indicated that 29% of individual differences in a global domain-general dimension of cognitive changes from 65 to 96 years were attributable to genetic influences, but some domain-specific genetic influences were also evident, even after accounting for domain-general contributions. These findings are consistent with a balanced global and domain-specific account of the genetics of cognitive aging. PMID:23586942

Shared and unique genetic and environmental influences on aging-related changes in multiple cognitive abilities.

PubMed

Tucker-Drob, Elliot M; Reynolds, Chandra A; Finkel, Deborah; Pedersen, Nancy L

2014-01-01

Aging-related declines occur in many different domains of cognitive function during middle and late adulthood. However, whether a global dimension underlies individual differences in changes in different domains of cognition and whether global genetic influences on cognitive changes exist is less clear. We addressed these issues by applying multivariate growth curve models to longitudinal data from 857 individuals from the Swedish Adoption/Twin Study of Aging, who had been measured on 11 cognitive variables representative of verbal, spatial, memory, and processing speed abilities up to 5 times over up to 16 years between ages 50 and 96 years. Between ages 50 and 65 years scores on different tests changed relatively independently of one another, and there was little evidence for strong underlying dimensions of change. In contrast, over the period between 65 and 96 years of age, there were strong interrelations among rates of change both within and across domains. During this age period, variability in rates of change were, on average, 52% domain-general, 8% domain-specific, and 39% test-specific. Quantitative genetic decomposition indicated that 29% of individual differences in a global domain-general dimension of cognitive changes during this age period were attributable to genetic influences, but some domain-specific genetic influences were also evident, even after accounting for domain-general contributions. These findings are consistent with a balanced global and domain-specific account of the genetics of cognitive aging. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Genetic benefits of a female mating preference in gray tree frogs are context-dependent.

PubMed

Welch, Allison M

2003-04-01

"Good genes" models of sexual selection predict that male courtship displays can advertise genetic quality and that, by mating with males with extreme displays, females can obtain genetic benefits for their offspring. However, because the relative performance of different genotypes can vary across environments, these genetic benefits may depend on the environmental context; in which case, static mating preferences may not be adaptive. To better understand how selection acts on the preference that female gray tree frogs (Hyla versicolor) express for long advertisement calls, I tested for genetic benefits in two realistic natural environments, by comparing the performance of half-sibling offspring sired by males with long versus short calls. Tadpoles from twelve such maternal half-sibships were raised in enclosures in their natal pond at two densities. In the low-density treatment, offspring of long-call males were larger at metamorphosis than were offspring of short-call males, whereas in the high-density treatment, offspring of males with long calls tended to metamorphose later than offspring of males with short calls. Thus, although the genes indicated by long calls were advantageous under low-density conditions, they were not beneficial under all conditions, suggesting that a static preference for long calls may not be adaptive in all environments. Such a genotype-by-environment interaction in the genetic consequences of mate choice predicts that when the environment is variable, selection may favor plasticity in female preferences or female selectivity among environments to control the conditions experienced by the offspring.

The developmental association between eating disorders symptoms and symptoms of depression and anxiety in juvenile twin girls.

PubMed

Silberg, Judy L; Bulik, Cynthia M

2005-12-01

We investigated the role of genetic and environmental factors in the developmental association among symptoms of eating disorders, depression, and anxiety syndromes in 8-13-year-old and 14-17-year-old twin girls. Multivariate genetic models were fitted to child-reported longitudinal symptom data gathered from clinical interview on 408 MZ and 198 DZ female twin pairs from the Virginia Twin Study of Adolescent Behavioural Development (VTSABD). Model-fitting revealed distinct etiological patterns underlying the association among symptoms of eating disorders, depression, overanxious disorder (OAD), and separation anxiety disorder (SAD) during the course of development: 1) a common genetic factor influencing liability to all symptoms - of early and later OAD, depression, SAD, and eating symptoms; 2) a distinct genetic factor specifically indexing liability to early eating disorders symptoms; 3) a shared environmental factor specifically influencing early depression and early eating disorders symptoms; and 4) a common environmental factor affecting liability to symptoms of later eating disorders and both early and later separation anxiety. These results suggest a pervasive genetic effect that influences liability to symptoms of over-anxiety, separation anxiety, depression, and eating disorder throughout development, a shared environmental influence on later adolescent eating problems and persistent separation anxiety, genetic influences specific to early eating disorders symptoms, and a shared environmental factor influencing symptoms of early eating and depression.

Pauci- and Multibacillary Leprosy: Two Distinct, Genetically Neglected Diseases

PubMed Central

Gaschignard, Jean; Grant, Audrey Virginia; Thuc, Nguyen Van; Orlova, Marianna; Cobat, Aurélie; Huong, Nguyen Thu; Ba, Nguyen Ngoc; Thai, Vu Hong; Abel, Laurent; Schurr, Erwin; Alcaïs, Alexandre

2016-01-01

After sustained exposure to Mycobacterium leprae, only a subset of exposed individuals develops clinical leprosy. Moreover, leprosy patients show a wide spectrum of clinical manifestations that extend from the paucibacillary (PB) to the multibacillary (MB) form of the disease. This “polarization” of leprosy has long been a major focus of investigation for immunologists because of the different immune response in these two forms. But while leprosy per se has been shown to be under tight human genetic control, few epidemiological or genetic studies have focused on leprosy subtypes. Using PubMed, we collected available data in English on the epidemiology of leprosy polarization and the possible role of human genetics in its pathophysiology until September 2015. At the genetic level, we assembled a list of 28 genes from the literature that are associated with leprosy subtypes or implicated in the polarization process. Our bibliographical search revealed that improved study designs are needed to identify genes associated with leprosy polarization. Future investigations should not be restricted to a subanalysis of leprosy per se studies but should instead contrast MB to PB individuals. We show the latter approach to be the most powerful design for the identification of genetic polarization determinants. Finally, we bring to light the important resource represented by the nine-banded armadillo model, a unique animal model for leprosy. PMID:27219008

Novel polyglutamine model uncouples proteotoxicity from aging.

PubMed

Christie, Nakeirah T M; Lee, Amy L; Fay, Hannah G; Gray, Amelia A; Kikis, Elise A

2014-01-01

Polyglutamine expansions in certain proteins are the genetic determinants for nine distinct progressive neurodegenerative disorders and resultant age-related dementia. In these cases, neurodegeneration is due to the aggregation propensity and resultant toxic properties of the polyglutamine-containing proteins. We are interested in elucidating the underlying mechanisms of toxicity of the protein ataxin-3, in which a polyglutamine expansion is the genetic determinant for Machado-Joseph Disease (MJD), also referred to as spinocerebellar ataxia 3 (SCA3). To this end, we have developed a novel model for ataxin-3 protein aggregation, by expressing a disease-related polyglutamine-containing fragment of ataxin-3 in the genetically tractable body wall muscle cells of the model system C. elegans. Here, we demonstrate that this ataxin-3 fragment aggregates in a polyQ length-dependent manner in C. elegans muscle cells and that this aggregation is associated with cellular dysfunction. However, surprisingly, this aggregation and resultant toxicity was not influenced by aging. This is in contrast to polyglutamine peptides alone whose aggregation/toxicity is highly dependent on age. Thus, the data presented here not only describe a new polyglutamine model, but also suggest that protein context likely influences the cellular interactions of the polyglutamine-containing protein and thereby modulates its toxic properties.

A statistical framework for genetic association studies of power curves in bird flight

PubMed Central

Lin, Min; Zhao, Wei

2006-01-01

How the power required for bird flight varies as a function of forward speed can be used to predict the flight style and behavioral strategy of a bird for feeding and migration. A U-shaped curve was observed between the power and flight velocity in many birds, which is consistent to the theoretical prediction by aerodynamic models. In this article, we present a general genetic model for fine mapping of quantitative trait loci (QTL) responsible for power curves in a sample of birds drawn from a natural population. This model is developed within the maximum likelihood context, implemented with the EM algorithm for estimating the population genetic parameters of QTL and the simplex algorithm for estimating the QTL genotype-specific parameters of power curves. Using Monte Carlo simulation derived from empirical observations of power curves in the European starling (Sturnus vulgaris), we demonstrate how the underlying QTL for power curves can be detected from molecular markers and how the QTL detected affect the most appropriate flight speeds used to design an optimal migration strategy. The results from our model can be directly integrated into a conceptual framework for understanding flight origin and evolution. PMID:17066123

Understanding crop genetic diversity under modern plant breeding.

PubMed

Fu, Yong-Bi

2015-11-01

Maximizing crop yield while at the same time minimizing crop failure for sustainable agriculture requires a better understanding of the impacts of plant breeding on crop genetic diversity. This review identifies knowledge gaps and shows the need for more research into genetic diversity changes under plant breeding. Modern plant breeding has made a profound impact on food production and will continue to play a vital role in world food security. For sustainable agriculture, a compromise should be sought between maximizing crop yield under changing climate and minimizing crop failure under unfavorable conditions. Such a compromise requires better understanding of the impacts of plant breeding on crop genetic diversity. Efforts have been made over the last three decades to assess crop genetic diversity using molecular marker technologies. However, these assessments have revealed some temporal diversity patterns that are largely inconsistent with our perception that modern plant breeding reduces crop genetic diversity. An attempt was made in this review to explain such discrepancies by examining empirical assessments of crop genetic diversity and theoretical investigations of genetic diversity changes over time under artificial selection. It was found that many crop genetic diversity assessments were not designed to assess diversity impacts from specific plant breeding programs, while others were experimentally inadequate and contained technical biases from the sampling of cultivars and genomes. Little attention has been paid to theoretical investigations on crop genetic diversity changes from plant breeding. A computer simulation of five simplified breeding schemes showed the substantial effects of plant breeding on the retention of heterozygosity over generations. It is clear that more efforts are needed to investigate crop genetic diversity in space and time under plant breeding to achieve sustainable crop production.

Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions

USDA-ARS?s Scientific Manuscript database

Information about genetic parameters is essential for selection decisions and genetic evaluation. Those estimates are population specific, but few studies are available for dairy cattle populations reared under tropical and subtropical conditions. Heritability and genetic correlations for milk yield...

A genetic test of the natal homing versus social facilitation models for green turtle migration.

PubMed

Meylan, A B; Bowen, B W; Avise, J C

1990-05-11

Female green turtles exhibit strong nest-site fidelity as adults, but whether the nesting beach is the natal site is not known. Under the natal homing hypothesis, females return to their natal beach to nest, whereas under the social facilitation model, virgin females follow experienced breeders to nesting beaches and after a "favorable" nesting experience, fix on that site for future nestings. Differences shown in mitochondrial DNA genotype frequency among green turtle colonies in the Caribbean Sea and Atlantic Ocean are consistent with natal homing expectations and indicate that social facilitation to nonnatal sites is rare.

Associations of ACE I/D, AGT M235T gene polymorphisms with pregnancy induced hypertension in Chinese population: a meta-analysis.

PubMed

Zhu, Ming; Zhang, Jie; Nie, Shaofa; Yan, Weirong

2012-09-01

There have been many studies concerning the associations of angiotensin-converting enzyme (ACE) I/D, angiotensinogen (AGT) M235T polymorphisms with pregnancy induced hypertension (PIH) among Chinese populations. However, the results were inconsistent, prompting the necessity of meta-analysis. Studies published in English and Chinese were mainly searched in EMbase, PubMed and CBM up to January 2012. Twenty-three studies with 3,551 subjects for ACE I/D and seven studies with 1,296 subjects for AGT M235T were included. Significant associations were found between ACE I/D and PIH under dominant, recessive and allelic models. A separate analysis confined to preeclampsia suggested that ACE I/D was associated with preeclampsia under recessive model and allelic model, but not dominant model. Stratified analyses were conducted as meta-regression analysis indicated that the sample size of case group was a significant source of heterogeneity, which suggested no significant association between ACE I/D and PIH in the subgroup of more than 100 cases. Associations were found between AGT M235T and PIH under dominant genetic model (OR = 1.59; 95 %CI: 1.04-2.42), recessive genetic model (OR = 1.60; 95 %CI: 1.07-2.40), and allelic model (OR = 1.40; 95 %CI: 1.17-1.68). No publication bias was found in either meta-analysis. The present meta-analysis suggested significant associations between ACE I/D, AGT M235T and PIH in Chinese populations. However, no significant association was found between ACE I/D and PIH in the subgroup of more than 100 cases. Studies with larger sample sizes are necessary to investigate the associations between gene polymorphisms and PIH in Chinese populations.

Mouse models for the study of colon carcinogenesis

PubMed Central

Rosenberg, Daniel W.; Giardina, Charles; Tanaka, Takuji

2009-01-01

The study of experimental colon carcinogenesis in rodents has a long history, dating back almost 80 years. There are many advantages to studying the pathogenesis of carcinogen-induced colon cancer in mouse models, including rapid and reproducible tumor induction and the recapitulation of the adenoma–carcinoma sequence that occurs in humans. The availability of recombinant inbred mouse panels and the existence of transgenic, knock-out and knock-in genetic models further increase the value of these studies. In this review, we discuss the general mechanisms of tumor initiation elicited by commonly used chemical carcinogens and how genetic background influences the extent of disease. We will also describe the general features of lesions formed in response to carcinogen treatment, including the underlying molecular aberrations and how these changes may relate to the pathogenesis of human colorectal cancer. PMID:19037092

Genetic structure of the threatened Dipterocarpus costatus populations in lowland tropical rainforests of southern Vietnam.

PubMed

Duc, N M; Duy, V D; Xuan, B T T; Thang, B V; Ha, N T H; Tam, N M

2016-10-24

Dipterocarpus costatus is an endangered species restricted to the lowland forests of southern Vietnam. Habitat loss and over-exploitation of D. costatus wood are the major threats to this species. We investigated the level of genetic variability within and among populations of D. costatus in order to provide guidelines for the conservation, management, and restoration of this species to the Forest Protection Department, Vietnam. Nine microsatellite markers were used to analyze 114 samples from four populations representing the natural range of D. costatus in southeast Vietnam. We indicated the low allelic diversity (N A = 2.3) and low genetic diversities with an average observed and expected heterozygosity of 0.130 and 0.151, respectively, in the lowland forests of southeast Vietnam. The low genetic diversity might be a consequence of inbreeding within the small and isolated populations of D. costatus owing to its habitat loss and over-exploitation. All populations deviated from Hardy-Weinberg equilibrium showing reduced heterozygosity. Alleles were lost from the populations by genetic drift. Genetic differentiation among populations was high (average pairwise F ST = 0.405), indicating low gene flow (<1) and isolated populations due to its destructed habitat and large geographical distances (P < 0.05) among populations. Heterozygosity excess tests (except of Bu Gia Map only under infinite allele model) were negative. The high genetic variation (62.7%) was found within populations. The STRUCTURE and neighbor joining tree results suggest strong differentiation among D. costatus populations, with the three genetic clusters, Phu Quoc, Tan Phu and Bu Gia Map, and Lo Go-Xa Mat due to habitat fragmentation and isolation. The threatened status of D. costatus was related to a lack of genetic diversity, with all its populations isolated in small forest patches. We recommend the establishment of an ex situ conservation site for D. costatus with a new big population comprising all genetic groups in order to enhance its survival under different environmental stresses.

Genetic divergence and signatures of natural selection in marginal populations of a keystone, long-lived conifer, Eastern White Pine (Pinus strobus) from Northern Ontario.

PubMed

Chhatre, Vikram E; Rajora, Om P

2014-01-01

Marginal populations are expected to provide the frontiers for adaptation, evolution and range shifts of plant species under the anticipated climate change conditions. Marginal populations are predicted to show genetic divergence from central populations due to their isolation, and divergent natural selection and genetic drift operating therein. Marginal populations are also expected to have lower genetic diversity and effective population size (Ne) and higher genetic differentiation than central populations. We tested these hypotheses using eastern white pine (Pinus strobus) as a model for keystone, long-lived widely-distributed plants. All 614 eastern white pine trees, in a complete census of two populations each of marginal old-growth, central old-growth, and central second-growth, were genotyped at 11 microsatellite loci. The central populations had significantly higher allelic and genotypic diversity, latent genetic potential (LGP) and Ne than the marginal populations. However, heterozygosity and fixation index were similar between them. The marginal populations were genetically diverged from the central populations. Model testing suggested predominant north to south gene flow in the study area with curtailed gene flow to northern marginal populations. Signatures of natural selection were detected at three loci in the marginal populations; two showing divergent selection with directional change in allele frequencies, and one balancing selection. Contrary to the general belief, no significant differences were observed in genetic diversity, differentiation, LGP, and Ne between old-growth and second-growth populations. Our study provides information on the dynamics of migration, genetic drift and selection in central versus marginal populations of a keystone long-lived plant species and has broad evolutionary, conservation and adaptation significance.

Genetic Divergence and Signatures of Natural Selection in Marginal Populations of a Keystone, Long-Lived Conifer, Eastern White Pine (Pinus strobus) from Northern Ontario

PubMed Central

Chhatre, Vikram E.; Rajora, Om P.

2014-01-01

Marginal populations are expected to provide the frontiers for adaptation, evolution and range shifts of plant species under the anticipated climate change conditions. Marginal populations are predicted to show genetic divergence from central populations due to their isolation, and divergent natural selection and genetic drift operating therein. Marginal populations are also expected to have lower genetic diversity and effective population size (N e) and higher genetic differentiation than central populations. We tested these hypotheses using eastern white pine (Pinus strobus) as a model for keystone, long-lived widely-distributed plants. All 614 eastern white pine trees, in a complete census of two populations each of marginal old-growth, central old-growth, and central second-growth, were genotyped at 11 microsatellite loci. The central populations had significantly higher allelic and genotypic diversity, latent genetic potential (LGP) and N e than the marginal populations. However, heterozygosity and fixation index were similar between them. The marginal populations were genetically diverged from the central populations. Model testing suggested predominant north to south gene flow in the study area with curtailed gene flow to northern marginal populations. Signatures of natural selection were detected at three loci in the marginal populations; two showing divergent selection with directional change in allele frequencies, and one balancing selection. Contrary to the general belief, no significant differences were observed in genetic diversity, differentiation, LGP, and N e between old-growth and second-growth populations. Our study provides information on the dynamics of migration, genetic drift and selection in central versus marginal populations of a keystone long-lived plant species and has broad evolutionary, conservation and adaptation significance. PMID:24859159

Genetic tests obtainable through pharmacies: the good, the bad, and the ugly.

PubMed

Patrinos, George P; Baker, Darrol J; Al-Mulla, Fahd; Vasiliou, Vasilis; Cooper, David N

2013-07-08

Genomic medicine seeks to exploit an individual's genomic information in the context of guiding the clinical decision-making process. In the post-genomic era, a range of novel molecular genetic testing methodologies have emerged, allowing the genetic testing industry to grow at a very rapid pace. As a consequence, a considerable number of different private diagnostic testing laboratories now provide a wide variety of genetic testing services, often employing a direct-to-consumer (DTC) business model to identify mutations underlying (or associated with) common Mendelian disorders, to individualize drug response, to attempt to determine an individual's risk of a multitude of complex (multifactorial) diseases, or even to determine a person's identity. Recently, we have noted a novel trend in the provision of private molecular genetic testing services, namely saliva and buccal swab collection kits (for deoxyribonucleic acid (DNA) isolation) being offered for sale over the counter by pharmacies. This situation is somewhat different from the standard DTC genetic testing model, since pharmacists are healthcare professionals who are supposedly qualified to give appropriate advice to their clients. There are, however, a number of issues to be addressed in relation to the marketing of DNA collection kits for genetic testing through pharmacies, namely a requirement for regulatory clearance, the comparative lack of appropriate genetics education of the healthcare professionals involved, and most importantly, the lack of awareness on the part of both the patients and the general public with respect to the potential benefits or otherwise of the various types of genetic test offered, which may result in confusion as to which test could be beneficial in their own particular case. We believe that some form of genetic counseling should ideally be integrated into, and made inseparable from, the genetic testing process, while pharmacists should be obliged to receive some basic training about the genetic tests that they offer for sale.

The bridging integrator 1 Gene rs7561528 polymorphism contributes to Alzheimer's disease susceptibility in East Asian and Caucasian populations.

PubMed

Zhou, Futao; Haina, Dong

2017-06-01

Genetic variants of the bridging integrator 1 (BIN1) at the rs7561528 single nucleotide polymorphism were implicated in increased risk of Alzheimer's disease in several case-control association studies. However, the studies have reported apparently conflicting results. Here, we searched the PubMed and Google Scholar databases. In total, 17,179 AD patients and 17,448 healthy controls (HCs) from 18 studies are included in the current study to examine the association between this polymorphism and AD risk. Significant associations of the SNP rs242557 with AD are found under allelic [A vs. G: odds ratio (OR)=0.86, 95% confidence interval (CI)=0.78, 0.96, P=0.006], dominant (AA+AG vs. GG: OR=0.87, 95% CI=0.77, 0.97, P=0.01), recessive (AA vs. AG+GG: OR=0.86, 95% CI=0.76, 0.98, P=0.21), homozygous (AA vs. GG: OR=0.86, 95% CI=0.76, 0.99, P=0.03) and heterozygous (AG vs. GG: OR=0.87, 95% CI=0.83, 0.92, P<0.00001) models in the pooled populations, under allelic (OR=0.77, 95% CI=0.65, 0.91, P=0.002), dominant (OR=0.75, 95% CI=0.63, 0.90, P=0.001) and heterozygous (OR =0.79, 95% CI=0.70, 0.88, P<0.0001) models in East Asian population, under heterozygous (OR=0.89, 95% CI=0.84, 0.94, P<0.0001) model in Caucasian population. The results of the current meta-analysis suggest that the rs7561528 A allele carriers may be a protective factor against susceptibility to AD under all the genetic models in the pooled populations and under allelic and dominant model in East Asian population, and individuals with A/G heterozygous genotype are not prone to suffer from AD in both Asians and Caucasians. Copyright © 2017 Elsevier B.V. All rights reserved.

A non-classical phase diagram for virus-bacterial co-evolution mediated by CRISPR

NASA Astrophysics Data System (ADS)

Han, Pu; Deem, Michael

CRISPR is a newly discovered prokaryotic immune system. Bacteria and archaea with this system incorporate genetic material from invading viruses into their genomes, providing protection against future infection by similar viruses. Due to the cost of CRISPR, bacteria can lose the acquired immunity. We will show an intriguing phase diagram of the virus extinction probability, which when the rate of losing the acquired immunity is small, is more complex than that of the classic predator-prey model. As the CRISPR incorporates genetic material, viruses are under pressure to evolve to escape the recognition by CRISPR, and this co-evolution leads to a non-trivial phase structure that cannot be explained by the classical predator-prey model.

Autophagy and Human Neurodegenerative Diseases-A Fly's Perspective.

PubMed

Kim, Myungjin; Ho, Allison; Lee, Jun Hee

2017-07-23

Neurodegenerative diseases in humans are frequently associated with prominent accumulation of toxic protein inclusions and defective organelles. Autophagy is a process of bulk lysosomal degradation that eliminates these harmful substances and maintains the subcellular environmental quality. In support of autophagy's importance in neuronal homeostasis, several genetic mutations that interfere with autophagic processes were found to be associated with familial neurodegenerative disorders. In addition, genetic mutations in autophagy-regulating genes provoked neurodegenerative phenotypes in animal models. The Drosophila model significantly contributed to these recent developments, which led to the theory that autophagy dysregulation is one of the major underlying causes of human neurodegenerative disorders. In the current review, we discuss how studies using Drosophila enhanced our understanding of the relationship between autophagy and neurodegenerative processes.

Neural Tube Defects

PubMed Central

Greene, Nicholas D.E.; Copp, Andrew J.

2015-01-01

Neural tube defects (NTDs), including spina bifida and anencephaly, are severe birth defects of the central nervous system that originate during embryonic development when the neural tube fails to close completely. Human NTDs are multifactorial, with contributions from both genetic and environmental factors. The genetic basis is not yet well understood, but several nongenetic risk factors have been identified as have possibilities for prevention by maternal folic acid supplementation. Mechanisms underlying neural tube closure and NTDs may be informed by experimental models, which have revealed numerous genes whose abnormal function causes NTDs and have provided details of critical cellular and morphological events whose regulation is essential for closure. Such models also provide an opportunity to investigate potential risk factors and to develop novel preventive therapies. PMID:25032496

Genetic Variability Under the Seedbank Coalescent.

PubMed

Blath, Jochen; González Casanova, Adrián; Eldon, Bjarki; Kurt, Noemi; Wilke-Berenguer, Maite

2015-07-01

We analyze patterns of genetic variability of populations in the presence of a large seedbank with the help of a new coalescent structure called the seedbank coalescent. This ancestral process appears naturally as a scaling limit of the genealogy of large populations that sustain seedbanks, if the seedbank size and individual dormancy times are of the same order as those of the active population. Mutations appear as Poisson processes on the active lineages and potentially at reduced rate also on the dormant lineages. The presence of "dormant" lineages leads to qualitatively altered times to the most recent common ancestor and nonclassical patterns of genetic diversity. To illustrate this we provide a Wright-Fisher model with a seedbank component and mutation, motivated from recent models of microbial dormancy, whose genealogy can be described by the seedbank coalescent. Based on our coalescent model, we derive recursions for the expectation and variance of the time to most recent common ancestor, number of segregating sites, pairwise differences, and singletons. Estimates (obtained by simulations) of the distributions of commonly employed distance statistics, in the presence and absence of a seedbank, are compared. The effect of a seedbank on the expected site-frequency spectrum is also investigated using simulations. Our results indicate that the presence of a large seedbank considerably alters the distribution of some distance statistics, as well as the site-frequency spectrum. Thus, one should be able to detect from genetic data the presence of a large seedbank in natural populations. Copyright © 2015 by the Genetics Society of America.

Training to Provide Psychiatric Genetic Counseling: How Does It Impact Recent Graduates' and Current Students' Readiness to Provide Genetic Counseling for Individuals with Psychiatric Illness and Attitudes towards this Population?

PubMed

Low, Ashley; Dixon, Shannan; Higgs, Amanda; Joines, Jessica; Hippman, Catriona

2018-02-01

Mental illness is extremely common and genetic counselors frequently see patients with mental illness. Genetic counselors report discomfort in providing psychiatric genetic counseling (GC), suggesting the need to look critically at training for psychiatric GC. This study aimed to investigate psychiatric GC training and its impact on perceived preparedness to provide psychiatric GC (preparedness). Current students and recent graduates were invited to complete an anonymous survey evaluating psychiatric GC training and outcomes. Bivariate correlations (p<.10) identified variables for inclusion in a logistic regression model to predict preparedness. Data were checked for assumptions underlying logistic regression. The logistic regression model for the 286 respondents [χ 2 (8)=84.87, p<.001] explained between 37.1% (Cox & Snell R 2 =.371) and 49.7% (Nagelkerke R 2 =.497) of the variance in preparedness scores. More frequent psychiatric GC instruction (OR=5.13), more active methods for practicing risk assessment (OR=4.43), and education on providing resources for mental illness (OR=4.99) made uniquely significant contributions to the model (p<.001). Responses to open-ended questions revealed interest in further psychiatric GC training, particularly enabling "hands on" experience. This exploratory study suggests that enriching GC training through more frequent psychiatric GC instruction and more active opportunities to practice psychiatric GC skills will support students in feeling more prepared to provide psychiatric GC after graduation.

Leber congenital amaurosis/early-onset severe retinal dystrophy: clinical features, molecular genetics and therapeutic interventions.

PubMed

Kumaran, Neruban; Moore, Anthony T; Weleber, Richard G; Michaelides, Michel

2017-09-01

Leber congenital amaurosis (LCA) and early-onset severe retinal dystrophy (EOSRD) are both genetically and phenotypically heterogeneous, and characterised clinically by severe congenital/early infancy visual loss, nystagmus, amaurotic pupils and markedly reduced/absent full-field electroretinograms. The vast genetic heterogeneity of inherited retinal disease has been established over the last 10 - 20 years, with disease-causing variants identified in 25 genes to date associated with LCA/EOSRD, accounting for 70-80% of cases, with thereby more genes yet to be identified. There is now far greater understanding of the structural and functional associations seen in the various LCA/EOSRD genotypes. Subsequent development/characterisation of LCA/EOSRD animal models has shed light on the underlying pathogenesis and allowed the demonstration of successful rescue with gene replacement therapy and pharmacological intervention in multiple models. These advancements have culminated in more than 12 completed, ongoing and anticipated phase I/II and phase III gene therapy and pharmacological human clinical trials. This review describes the clinical and genetic characteristics of LCA/EOSRD and the differential diagnoses to be considered. We discuss in further detail the diagnostic clinical features, pathophysiology, animal models and human treatment studies and trials, in the more common genetic subtypes and/or those closest to intervention. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle

PubMed Central

Fang, Lingzhao; Sahana, Goutam; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

2017-01-01

Connecting genome-wide association study (GWAS) to biological mechanisms underlying complex traits is a major challenge. Mastitis resistance and milk production are complex traits of economic importance in the dairy sector and are associated with intra-mammary infection (IMI). Here, we integrated IMI-relevant RNA-Seq data from Holstein cattle and sequence-based GWAS data from three dairy cattle breeds (i.e., Holstein, Nordic red cattle, and Jersey) to explore the genetic basis of mastitis resistance and milk production using post-GWAS analyses and a genomic feature linear mixed model. At 24 h post-IMI, genes responsive to IMI in the mammary gland were preferentially enriched for genetic variants associated with mastitis resistance rather than milk production. Response genes in the liver were mainly enriched for variants associated with mastitis resistance at an early time point (3 h) post-IMI, whereas responsive genes at later stages were enriched for associated variants with milk production. The up- and down-regulated genes were enriched for associated variants with mastitis resistance and milk production, respectively. The patterns were consistent across breeds, indicating that different breeds shared similarities in the genetic basis of these traits. Our approaches provide a framework for integrating multiple layers of data to understand the genetic architecture underlying complex traits. PMID:28358110

Small Heat Shock Proteins Are Novel Common Determinants of Alcohol and Nicotine Sensitivity in Caenorhabditis elegans

PubMed Central

Johnson, James R.; Rajamanoharan, Dayani; McCue, Hannah V.; Rankin, Kim

2016-01-01

Addiction to drugs is strongly determined by multiple genetic factors. Alcohol and nicotine produce distinct pharmacological effects within the nervous system through discrete molecular targets; yet, data from family and twin analyses support the existence of common genetic factors for addiction in general. The mechanisms underlying addiction, however, are poorly described and common genetic factors for alcohol and nicotine remain unidentified. We investigated the role that the heat shock transcription factor, HSF-1, and its downstream effectors played as common genetic modulators of sensitivity to addictive substances. Using Caenorhabditis elegans, an exemplary model organism with substance dose-dependent responses similar to mammals, we demonstrate that HSF-1 altered sensitivity to both alcohol and nicotine. Using a combination of a targeted RNAi screen of downstream factors and transgenic approaches we identified that these effects were contingent upon the constitutive neuronal expression of HSP-16.48, a small heat shock protein (HSP) homolog of human α-crystallin. Furthermore we demonstrated that the function of HSP-16.48 in drug sensitivity surprisingly was independent of chaperone activity during the heat shock stress response. Instead we identified a distinct domain within the N-terminal region of the HSP-16.48 protein that specified its function in comparison to related small HSPs. Our findings establish and characterize a novel genetic determinant underlying sensitivity to diverse addictive substances. PMID:26773049

Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture

PubMed Central

Pauli, Duke; Ziegler, Greg; Ren, Min; Jenks, Matthew A.; Hunsaker, Douglas J.; Zhang, Min; Baxter, Ivan; Gore, Michael A.

2018-01-01

To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions. PMID:29437829

Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies.

PubMed

Spielman, Stephanie J; Wilke, Claus O

2015-01-01

We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny using continuous-time Markov models of sequence evolution. Easily incorporated into Python bioinformatics pipelines, Pyvolve can simulate sequences according to most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Pyvolve is an open-source project under a FreeBSD license, and it is available for download, along with a detailed user-manual and example scripts, from http://github.com/sjspielman/pyvolve.

Yeast diversity and native vigor for flavor phenotypes.

PubMed

Carrau, Francisco; Gaggero, Carina; Aguilar, Pablo S

2015-03-01

Saccharomyces cerevisiae, the yeast used widely for beer, bread, cider, and wine production, is the most resourceful eukaryotic model used for genetic engineering. A typical concern about using engineered yeasts for food production might be negative consumer perception of genetically modified organisms. However, we believe the true pitfall of using genetically modified yeasts is their limited capacity to either refine or improve the sensory properties of fermented foods under real production conditions. Alternatively, yeast diversity screening to improve the aroma and flavors could offer groundbreaking opportunities in food biotechnology. We propose a 'Yeast Flavor Diversity Screening' strategy which integrates knowledge from sensory analysis and natural whole-genome evolution with information about flavor metabolic networks and their regulation. Copyright © 2015 Elsevier Ltd. All rights reserved.

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

Revisiting the case for genetically engineered mouse models in human myelodysplastic syndrome research.

PubMed

Zhou, Ting; Kinney, Marsha C; Scott, Linda M; Zinkel, Sandra S; Rebel, Vivienne I

2015-08-27

Much-needed attention has been given of late to diseases specifically associated with an expanding elderly population. Myelodysplastic syndrome (MDS), a hematopoietic stem cell-based blood disease, is one of these. The lack of clear understanding of the molecular mechanisms underlying the pathogenesis of this disease has hampered the development of efficacious therapies, especially in the presence of comorbidities. Mouse models could potentially provide new insights into this disease, although primary human MDS cells grow poorly in xenografted mice. This makes genetically engineered murine models a more attractive proposition, although this approach is not without complications. In particular, it is unclear if or how myelodysplasia (abnormal blood cell morphology), a key MDS feature in humans, presents in murine cells. Here, we evaluate the histopathologic features of wild-type mice and 23 mouse models with verified myelodysplasia. We find that certain features indicative of myelodysplasia in humans, such as Howell-Jolly bodies and low neutrophilic granularity, are commonplace in healthy mice, whereas other features are similarly abnormal in humans and mice. Quantitative hematopoietic parameters, such as blood cell counts, are required to distinguish between MDS and related diseases. We provide data that mouse models of MDS can be genetically engineered and faithfully recapitulate human disease. © 2015 by The American Society of Hematology.

A general population-genetic model for the production by population structure of spurious genotype-phenotype associations in discrete, admixed or spatially distributed populations.

PubMed

Rosenberg, Noah A; Nordborg, Magnus

2006-07-01

In linkage disequilibrium mapping of genetic variants causally associated with phenotypes, spurious associations can potentially be generated by any of a variety of types of population structure. However, mathematical theory of the production of spurious associations has largely been restricted to population structure models that involve the sampling of individuals from a collection of discrete subpopulations. Here, we introduce a general model of spurious association in structured populations, appropriate whether the population structure involves discrete groups, admixture among such groups, or continuous variation across space. Under the assumptions of the model, we find that a single common principle--applicable to both the discrete and admixed settings as well as to spatial populations--gives a necessary and sufficient condition for the occurrence of spurious associations. Using a mathematical connection between the discrete and admixed cases, we show that in admixed populations, spurious associations are less severe than in corresponding mixtures of discrete subpopulations, especially when the variance of admixture across individuals is small. This observation, together with the results of simulations that examine the relative influences of various model parameters, has important implications for the design and analysis of genetic association studies in structured populations.

A Unified Framework Integrating Parent-of-Origin Effects for Association Study

PubMed Central

Xiao, Feifei; Ma, Jianzhong; Amos, Christopher I.

2013-01-01

Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting is related to several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we generalize the natural and orthogonal interactions (NOIA) framework to allow for estimation of both main allelic effects and POEs. We develop a statistical (Stat-POE) model that has the orthogonal estimates of parameters including the POEs. We conducted simulation studies for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits. PMID:23991061

Association between stressful life events and psychotic experiences in adolescence: evidence for gene-environment correlations.

PubMed

Shakoor, Sania; Zavos, Helena M S; Haworth, Claire M A; McGuire, Phillip; Cardno, Alastair G; Freeman, Daniel; Ronald, Angelica

2016-06-01

Stressful life events (SLEs) are associated with psychotic experiences. SLEs might act as an environmental risk factor, but may also share a genetic propensity with psychotic experiences. To estimate the extent to which genetic and environmental factors influence the relationship between SLEs and psychotic experiences. Self- and parent reports from a community-based twin sample (4830 16-year-old pairs) were analysed using structural equation model fitting. SLEs correlated with positive psychotic experiences (r = 0.12-0.14, all P<0.001). Modest heritability was shown for psychotic experiences (25-57%) and dependent SLEs (32%). Genetic influences explained the majority of the modest covariation between dependent SLEs and paranoia and cognitive disorganisation (bivariate heritabilities 74-86%). The relationship between SLEs and hallucinations and grandiosity was explained by both genetic and common environmental effects. Further to dependent SLEs being an environmental risk factor, individuals may have an underlying genetic propensity increasing their risk of dependent SLEs and positive psychotic experiences. © The Royal College of Psychiatrists 2016.

Polygamy and an absence of fine-scale structure in Dendroctonus ponderosae (Hopk.) (Coleoptera: Curcilionidae) confirmed using molecular markers

PubMed Central

Janes, J K; Roe, A D; Rice, A V; Gorrell, J C; Coltman, D W; Langor, D W; Sperling, F A H

2016-01-01

An understanding of mating systems and fine-scale spatial genetic structure is required to effectively manage forest pest species such as Dendroctonus ponderosae (mountain pine beetle). Here we used genome-wide single-nucleotide polymorphisms to assess the fine-scale genetic structure and mating system of D. ponderosae collected from a single stand in Alberta, Canada. Fine-scale spatial genetic structure was absent within the stand and the majority of genetic variation was best explained at the individual level. Relatedness estimates support previous reports of pre-emergence mating. Parentage assignment tests indicate that a polygamous mating system better explains the relationships among individuals within a gallery than the previously reported female monogamous/male polygynous system. Furthermore, there is some evidence to suggest that females may exploit the galleries of other females, at least under epidemic conditions. Our results suggest that current management models are likely to be effective across large geographic areas based on the absence of fine-scale genetic structure. PMID:26286666

Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus

PubMed Central

Johnson, Michael R.; Rossetti, Tiziana; Speed, Doug; Srivastava, Prashant K.; Chadeau-Hyam, Marc; Hajji, Nabil; Dabrowska, Aleksandra; Rotival, Maxime; Razzaghi, Banafsheh; Kovac, Stjepana; Wanisch, Klaus; Grillo, Federico W.; Slaviero, Anna; Langley, Sarah R.; Shkura, Kirill; Roncon, Paolo; De, Tisham; Mattheisen, Manuel; Niehusmann, Pitt; O’Brien, Terence J.; Petrovski, Slave; von Lehe, Marec; Hoffmann, Per; Eriksson, Johan; Coffey, Alison J.; Cichon, Sven; Walker, Matthew; Simonato, Michele; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Schoch, Susanne; De Paola, Vincenzo; Kaminski, Rafal M.; Cunliffe, Vincent T.; Becker, Albert J.; Petretto, Enrico

2015-01-01

Gene-regulatory network analysis is a powerful approach to elucidate the molecular processes and pathways underlying complex disease. Here we employ systems genetics approaches to characterize the genetic regulation of pathophysiological pathways in human temporal lobe epilepsy (TLE). Using surgically acquired hippocampi from 129 TLE patients, we identify a gene-regulatory network genetically associated with epilepsy that contains a specialized, highly expressed transcriptional module encoding proconvulsive cytokines and Toll-like receptor signalling genes. RNA sequencing analysis in a mouse model of TLE using 100 epileptic and 100 control hippocampi shows the proconvulsive module is preserved across-species, specific to the epileptic hippocampus and upregulated in chronic epilepsy. In the TLE patients, we map the trans-acting genetic control of this proconvulsive module to Sestrin 3 (SESN3), and demonstrate that SESN3 positively regulates the module in macrophages, microglia and neurons. Morpholino-mediated Sesn3 knockdown in zebrafish confirms the regulation of the transcriptional module, and attenuates chemically induced behavioural seizures in vivo. PMID:25615886

Admixture, Population Structure, and F-Statistics.

PubMed

Peter, Benjamin M

2016-04-01

Many questions about human genetic history can be addressed by examining the patterns of shared genetic variation between sets of populations. A useful methodological framework for this purpose isF-statistics that measure shared genetic drift between sets of two, three, and four populations and can be used to test simple and complex hypotheses about admixture between populations. This article provides context from phylogenetic and population genetic theory. I review how F-statistics can be interpreted as branch lengths or paths and derive new interpretations, using coalescent theory. I further show that the admixture tests can be interpreted as testing general properties of phylogenies, allowing extension of some ideas applications to arbitrary phylogenetic trees. The new results are used to investigate the behavior of the statistics under different models of population structure and show how population substructure complicates inference. The results lead to simplified estimators in many cases, and I recommend to replace F3 with the average number of pairwise differences for estimating population divergence. Copyright © 2016 by the Genetics Society of America.

Applying Spatial-Temporal Model and Game Theory to Asymmetric Threat Prediction

DTIC Science & Technology

2007-06-01

Genshe Chen, Denis Garagic, Xiaohuan Tan, Dongxu Li, Dan Shen, Mo Wei, Xu Wang, “Team Dynamics and Tactics for Mission Planning,” Proceedings...Cruz, Jr., Genshe Chen, Dongxu Li, and Denis Garagic, “Target Selection in UAV Cooperative Control Under Uncertain Environment: Genetic Algorithm

Genetic research: who is at risk for alcoholism.

PubMed

Foroud, Tatiana; Edenberg, Howard J; Crabbe, John C

2010-01-01

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) was founded 40 years ago to help elucidate the biological underpinnings of alcohol dependence, including the potential contribution of genetic factors. Twin, adoption, and family studies conclusively demonstrated that genetic factors account for 50 to 60 percent of the variance in risk for developing alcoholism. Case-control studies and linkage analyses have helped identify DNA variants that contribute to increased risk, and the NIAAA-sponsored Collaborative Studies on Genetics of Alcoholism (COGA) has the expressed goal of identifying contributing genes using state-of-the-art genetic technologies. These efforts have ascertained several genes that may contribute to an increased risk of alcoholism, including certain variants encoding alcohol-metabolizing enzymes and neurotransmitter receptors. Genome-wide association studies allowing the analysis of millions of genetic markers located throughout the genome will enable discovery of further candidate genes. In addition to these human studies, genetic animal models of alcohol's effects and alcohol use have greatly advanced our understanding of the genetic basis of alcoholism, resulting in the identification of quantitative trait loci and allowing for targeted manipulation of candidate genes. Novel research approaches-for example, into epigenetic mechanisms of gene regulation-also are under way and undoubtedly will further clarify the genetic basis of alcoholism.

The Sensitivity of Genetic Connectivity Measures to Unsampled and Under-Sampled Sites

PubMed Central

Koen, Erin L.; Bowman, Jeff; Garroway, Colin J.; Wilson, Paul J.

2013-01-01

Landscape genetic analyses assess the influence of landscape structure on genetic differentiation. It is rarely possible to collect genetic samples from all individuals on the landscape and thus it is important to assess the sensitivity of landscape genetic analyses to the effects of unsampled and under-sampled sites. Network-based measures of genetic distance, such as conditional genetic distance (cGD), might be particularly sensitive to sampling intensity because pairwise estimates are relative to the entire network. We addressed this question by subsampling microsatellite data from two empirical datasets. We found that pairwise estimates of cGD were sensitive to both unsampled and under-sampled sites, and FST, Dest, and deucl were more sensitive to under-sampled than unsampled sites. We found that the rank order of cGD was also sensitive to unsampled and under-sampled sites, but not enough to affect the outcome of Mantel tests for isolation by distance. We simulated isolation by resistance and found that although cGD estimates were sensitive to unsampled sites, by increasing the number of sites sampled the accuracy of conclusions drawn from landscape genetic analyses increased, a feature that is not possible with pairwise estimates of genetic differentiation such as FST, Dest, and deucl. We suggest that users of cGD assess the sensitivity of this measure by subsampling within their own network and use caution when making extrapolations beyond their sampled network. PMID:23409155

A new method of linkage analysis using LOD scores for quantitative traits supports linkage of monoamine oxidase activity to D17S250 in the Collaborative Study on the Genetics of Alcoholism pedigrees.

PubMed

Curtis, David; Knight, Jo; Sham, Pak C

2005-09-01

Although LOD score methods have been applied to diseases with complex modes of inheritance, linkage analysis of quantitative traits has tended to rely on non-parametric methods based on regression or variance components analysis. Here, we describe a new method for LOD score analysis of quantitative traits which does not require specification of a mode of inheritance. The technique is derived from the MFLINK method for dichotomous traits. A range of plausible transmission models is constructed, constrained to yield the correct population mean and variance for the trait but differing with respect to the contribution to the variance due to the locus under consideration. Maximized LOD scores under homogeneity and admixture are calculated, as is a model-free LOD score which compares the maximized likelihoods under admixture assuming linkage and no linkage. These LOD scores have known asymptotic distributions and hence can be used to provide a statistical test for linkage. The method has been implemented in a program called QMFLINK. It was applied to data sets simulated using a variety of transmission models and to a measure of monoamine oxidase activity in 105 pedigrees from the Collaborative Study on the Genetics of Alcoholism. With the simulated data, the results showed that the new method could detect linkage well if the true allele frequency for the trait was close to that specified. However, it performed poorly on models in which the true allele frequency was much rarer. For the Collaborative Study on the Genetics of Alcoholism data set only a modest overlap was observed between the results obtained from the new method and those obtained when the same data were analysed previously using regression and variance components analysis. Of interest is that D17S250 produced a maximized LOD score under homogeneity and admixture of 2.6 but did not indicate linkage using the previous methods. However, this region did produce evidence for linkage in a separate data set, suggesting that QMFLINK may have been able to detect a true linkage which was not picked up by the other methods. The application of model-free LOD score analysis to quantitative traits is novel and deserves further evaluation of its merits and disadvantages relative to other methods.

Genome and Epigenome Editing in Mechanistic Studies of Human Aging and Aging-Related Disease

PubMed Central

Lau, Cia-Hin; Suh, Yousin

2016-01-01

The recent advent of genome and epigenome editing technologies has provided a new paradigm in which the landscape of the human genome and epigenome can be precisely manipulated in their native context. Genome and epigenome editing technologies can be applied to many aspects of aging research and offer the potential to development novel therapeutics against age-related diseases. Here, we discuss the latest technological advances in the CRISPR-based genome and epigenome editing toolbox, and provide an insight into how these synthetic biology tools could facilitate aging research by establishing in vitro cell- and in vivo animal-models to dissect genetic and epigenetic mechanisms underlying aging and age-related diseases. We discuss recent developments in the field with the aims to precisely modulate gene expression and dynamic epigenetic landscapes in a spatial- and temporal- manner in cellular and animal models, by complementing the CRISPR-based editing capability with conditional genetic manipulation tools, including chemically inducible expression system, optogenetics, logic gate genetic circuits, tissue-specific promoters, and serotype-specific adeno-associated virus. We also discuss how the combined use of genome and epigenome editing tools permits investigators to uncover novel molecular pathways involved in pathophysiology and etiology conferred by risk variants associated with aging and aging-related disease. A better understanding of the genetic and epigenetic regulatory mechanisms underlying human aging and age-related disease will significantly contribute to the developments of new therapeutic interventions for extending healthspan and lifespan, ultimately improving the quality of life in the elderly populations. PMID:27974723

Bayesian inference of selection in a heterogeneous environment from genetic time-series data.

PubMed

Gompert, Zachariah

2016-01-01

Evolutionary geneticists have sought to characterize the causes and molecular targets of selection in natural populations for many years. Although this research programme has been somewhat successful, most statistical methods employed were designed to detect consistent, weak to moderate selection. In contrast, phenotypic studies in nature show that selection varies in time and that individual bouts of selection can be strong. Measurements of the genomic consequences of such fluctuating selection could help test and refine hypotheses concerning the causes of ecological specialization and the maintenance of genetic variation in populations. Herein, I proposed a Bayesian nonhomogeneous hidden Markov model to estimate effective population sizes and quantify variable selection in heterogeneous environments from genetic time-series data. The model is described and then evaluated using a series of simulated data, including cases where selection occurs on a trait with a simple or polygenic molecular basis. The proposed method accurately distinguished neutral loci from non-neutral loci under strong selection, but not from those under weak selection. Selection coefficients were accurately estimated when selection was constant or when the fitness values of genotypes varied linearly with the environment, but these estimates were less accurate when fitness was polygenic or the relationship between the environment and the fitness of genotypes was nonlinear. Past studies of temporal evolutionary dynamics in laboratory populations have been remarkably successful. The proposed method makes similar analyses of genetic time-series data from natural populations more feasible and thereby could help answer fundamental questions about the causes and consequences of evolution in the wild. © 2015 John Wiley & Sons Ltd.

Genetic and developmental basis for parallel evolution and its significance for hominoid evolution.

PubMed

Reno, Philip L

2014-01-01

Greater understanding of ape comparative anatomy and evolutionary history has brought a general appreciation that the hominoid radiation is characterized by substantial homoplasy.(1-4) However, little consensus has been reached regarding which features result from repeated evolution. This has important implications for reconstructing ancestral states throughout hominoid evolution, including the nature of the Pan-Homo last common ancestor (LCA). Advances from evolutionary developmental biology (evo-devo) have expanded the diversity of model organisms available for uncovering the morphogenetic mechanisms underlying instances of repeated phenotypic change. Of particular relevance to hominoids are data from adaptive radiations of birds, fish, and even flies demonstrating that parallel phenotypic changes often use similar genetic and developmental mechanisms. The frequent reuse of a limited set of genes and pathways underlying phenotypic homoplasy suggests that the conserved nature of the genetic and developmental architecture of animals can influence evolutionary outcomes. Such biases are particularly likely to be shared by closely related taxa that reside in similar ecological niches and face common selective pressures. Consideration of these developmental and ecological factors provides a strong theoretical justification for the substantial homoplasy observed in the evolution of complex characters and the remarkable parallel similarities that can occur in closely related taxa. Thus, as in other branches of the hominoid radiation, repeated phenotypic evolution within African apes is also a distinct possibility. If so, the availability of complete genomes for each of the hominoid genera makes them another model to explore the genetic basis of repeated evolution. © 2014 Wiley Periodicals, Inc.

Genome and Epigenome Editing in Mechanistic Studies of Human Aging and Aging-Related Disease.

PubMed

Lau, Cia-Hin; Suh, Yousin

2017-01-01

The recent advent of genome and epigenome editing technologies has provided a new paradigm in which the landscape of the human genome and epigenome can be precisely manipulated in their native context. Genome and epigenome editing technologies can be applied to many aspects of aging research and offer the potential to develop novel therapeutics against age-related diseases. Here, we discuss the latest technological advances in the CRISPR-based genome and epigenome editing toolbox, and provide insight into how these synthetic biology tools could facilitate aging research by establishing in vitro cell and in vivo animal models to dissect genetic and epigenetic mechanisms underlying aging and age-related diseases. We discuss recent developments in the field with the aims to precisely modulate gene expression and dynamic epigenetic landscapes in a spatial and temporal manner in cellular and animal models, by complementing the CRISPR-based editing capability with conditional genetic manipulation tools including chemically inducible expression systems, optogenetics, logic gate genetic circuits, tissue-specific promoters, and the serotype-specific adeno-associated virus. We also discuss how the combined use of genome and epigenome editing tools permits investigators to uncover novel molecular pathways involved in the pathophysiology and etiology conferred by risk variants associated with aging and aging-related disease. A better understanding of the genetic and epigenetic regulatory mechanisms underlying human aging and age-related disease will significantly contribute to the developments of new therapeutic interventions for extending health span and life span, ultimately improving the quality of life in the elderly populations. © 2016 S. Karger AG, Basel.