Genetically engineered mouse models for studying inflammatory bowel disease.

PubMed

Mizoguchi, Atsushi; Takeuchi, Takahito; Himuro, Hidetomo; Okada, Toshiyuki; Mizoguchi, Emiko

2016-01-01

Inflammatory bowel disease (IBD) is a chronic intestinal inflammatory condition that is mediated by very complex mechanisms controlled by genetic, immune, and environmental factors. More than 74 kinds of genetically engineered mouse strains have been established since 1993 for studying IBD. Although mouse models cannot fully reflect human IBD, they have provided significant contributions for not only understanding the mechanism, but also developing new therapeutic means for IBD. Indeed, 20 kinds of genetically engineered mouse models carry the susceptibility genes identified in human IBD, and the functions of some other IBD susceptibility genes have also been dissected out using mouse models. Cutting-edge technologies such as cell-specific and inducible knockout systems, which were recently employed to mouse IBD models, have further enhanced the ability of investigators to provide important and unexpected rationales for developing new therapeutic strategies for IBD. In this review article, we briefly introduce 74 kinds of genetically engineered mouse models that spontaneously develop intestinal inflammation. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

Murine genetically engineered and human xenograft models of chronic lymphocytic leukemia.

PubMed

Chen, Shih-Shih; Chiorazzi, Nicholas

2014-07-01

Chronic lymphocytic leukemia (CLL) is a genetically complex disease, with multiple factors having an impact on onset, progression, and response to therapy. Genetic differences/abnormalities have been found in hematopoietic stem cells from patients, as well as in B lymphocytes of individuals with monoclonal B-cell lymphocytosis who may develop the disease. Furthermore, after the onset of CLL, additional genetic alterations occur over time, often causing disease worsening and altering patient outcomes. Therefore, being able to genetically engineer mouse models that mimic CLL or at least certain aspects of the disease will help us understand disease mechanisms and improve treatments. This notwithstanding, because neither the genetic aberrations responsible for leukemogenesis and progression nor the promoting factors that support these are likely identical in character or influences for all patients, genetically engineered mouse models will only completely mimic CLL when all of these factors are precisely defined. In addition, multiple genetically engineered models may be required because of the heterogeneity in susceptibility genes among patients that can have an effect on genetic and environmental characteristics influencing disease development and outcome. For these reasons, we review the major murine genetically engineered and human xenograft models in use at the present time, aiming to report the advantages and disadvantages of each. Copyright © 2014 Elsevier Inc. All rights reserved.

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

Effect of genetic polymorphisms on development of gout.

PubMed

Urano, Wako; Taniguchi, Atsuo; Inoue, Eisuke; Sekita, Chieko; Ichikawa, Naomi; Koseki, Yumi; Kamatani, Naoyuki; Yamanaka, Hisashi

2013-08-01

To validate the association between genetic polymorphisms and gout in Japanese patients, and to investigate the cumulative effects of multiple genetic factors on the development of gout. Subjects were 153 Japanese male patients with gout and 532 male controls. The genotypes of 11 polymorphisms in the 10 genes that have been indicated to be associated with serum uric acid levels or gout were determined. The cumulative effects of the genetic polymorphisms were investigated using a weighted genotype risk score (wGRS) based on the number of risk alleles and the OR for gout. A model to discriminate between patients with gout and controls was constructed by incorporating the wGRS and clinical factors. C statistics method was applied to evaluate the capability of the model to discriminate gout patients from controls. Seven polymorphisms were shown to be associated with gout. The mean wGRS was significantly higher in patients with gout (15.2 ± 2.01) compared to controls (13.4 ± 2.10; p < 0.0001). The C statistic for the model using genetic information alone was 0.72, while the C statistic was 0.81 for the full model that incorporated all genetic and clinical factors. Accumulation of multiple genetic factors is associated with the development of gout. A prediction model for gout that incorporates genetic and clinical factors may be useful for identifying individuals who are at risk of gout.

Developing Pedagogical Tools to Improve Teaching Multiple Models of the Gene in High School

ERIC Educational Resources Information Center

Auckaraaree, Nantaya

2013-01-01

Multiple models of the gene are used to explore genetic phenomena in scientific practices and in the classroom. In genetics curricula, the classical and molecular models are presented in disconnected domains. Research demonstrates that, without explicit connections, students have difficulty developing an understanding of the gene that spans…

From Novice to Seasoned Practitioner: a Qualitative Investigation of Genetic Counselor Professional Development.

PubMed

Zahm, Kimberly Wehner; Veach, Patricia McCarthy; Martyr, Meredith A; LeRoy, Bonnie S

2016-08-01

Research on genetic counselor professional development would characterize typical developmental processes, inform training and supervision, and promote life-long development opportunities. To date, however no studies have comprehensively examined this phenomenon. The aims of this study were to investigate the nature of professional development for genetic counselors (processes, influences, and outcomes) and whether professional development varies across experience levels. Thirty-four genetic counselors participated in semi-structured telephone interviews exploring their perspectives on their professional development. Participants were sampled from three levels of post-degree genetic counseling experience: novice (0-5 years), experienced (6-14 years), and seasoned (>15 years). Using modified Consensual Qualitative Research and grounded theory methods, themes, domains, and categories were extracted from the data. The themes reflect genetic counselors' evolving perceptions of their professional development and its relationship to: (a) being a clinician, (b) their professional identity, and (c) the field itself. Across experience levels, prevalent influences on professional development were interpersonal (e.g., experiences with patients, genetic counseling colleagues) and involved professional and personal life events. Common developmental experiences included greater confidence and less anxiety over time, being less information-driven and more emotion-focused with patients, delivering "bad news" to patients remains challenging, and individuals' professional development experiences parallel genetic counseling's development as a field. With a few noteworthy exceptions, professional development was similar across experience levels. A preliminary model of genetic counselor professional development is proposed suggesting development occurs in a non-linear fashion throughout the professional lifespan. Each component of the model mutually influences the others, and there are positive and negative avenues of development.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models.

PubMed

Mulder, Han A; Rönnegård, Lars; Fikse, W Freddy; Veerkamp, Roel F; Strandberg, Erling

2013-07-04

Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike's information criterion using h-likelihood to select the best fitting model. We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike's information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike's information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Genetically engineered mouse models of melanoma.

PubMed

Pérez-Guijarro, Eva; Day, Chi-Ping; Merlino, Glenn; Zaidi, M Raza

2017-06-01

Melanoma is a complex disease that exhibits highly heterogeneous etiological, histopathological, and genetic features, as well as therapeutic responses. Genetically engineered mouse (GEM) models provide powerful tools to unravel the molecular mechanisms critical for melanoma development and drug resistance. Here, we expound briefly the basis of the mouse modeling design, the available technology for genetic engineering, and the aspects influencing the use of GEMs to model melanoma. Furthermore, we describe in detail the currently available GEM models of melanoma. Cancer 2017;123:2089-103. © 2017 American Cancer Society. © 2017 American Cancer Society.

Development of a tiered and binned genetic counseling model for informed consent in the era of multiplex testing for cancer susceptibility.

PubMed

Bradbury, Angela R; Patrick-Miller, Linda; Long, Jessica; Powers, Jacquelyn; Stopfer, Jill; Forman, Andrea; Rybak, Christina; Mattie, Kristin; Brandt, Amanda; Chambers, Rachelle; Chung, Wendy K; Churpek, Jane; Daly, Mary B; Digiovanni, Laura; Farengo-Clark, Dana; Fetzer, Dominique; Ganschow, Pamela; Grana, Generosa; Gulden, Cassandra; Hall, Michael; Kohler, Lynne; Maxwell, Kara; Merrill, Shana; Montgomery, Susan; Mueller, Rebecca; Nielsen, Sarah; Olopade, Olufunmilayo; Rainey, Kimberly; Seelaus, Christina; Nathanson, Katherine L; Domchek, Susan M

2015-06-01

Multiplex genetic testing, including both moderate- and high-penetrance genes for cancer susceptibility, is associated with greater uncertainty than traditional testing, presenting challenges to informed consent and genetic counseling. We sought to develop a new model for informed consent and genetic counseling for four ongoing studies. Drawing from professional guidelines, literature, conceptual frameworks, and clinical experience, a multidisciplinary group developed a tiered-binned genetic counseling approach proposed to facilitate informed consent and improve outcomes of cancer susceptibility multiplex testing. In this model, tier 1 "indispensable" information is presented to all patients. More specific tier 2 information is provided to support variable informational needs among diverse patient populations. Clinically relevant information is "binned" into groups to minimize information overload, support informed decision making, and facilitate adaptive responses to testing. Seven essential elements of informed consent are provided to address the unique limitations, risks, and uncertainties of multiplex testing. A tiered-binned model for informed consent and genetic counseling has the potential to address the challenges of multiplex testing for cancer susceptibility and to support informed decision making and adaptive responses to testing. Future prospective studies including patient-reported outcomes are needed to inform how to best incorporate multiplex testing for cancer susceptibility into clinical practice.Genet Med 17 6, 485-492.

Boiler-turbine control system design using a genetic algorithm

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

Dimeo, R.; Lee, K.Y.

1995-12-01

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

Are we there yet? Tracking the development of new model systems

Treesearch

A. Abzhanov; C. Extavour; A. Groover; S. Hodges; H. Hoekstra; E. Kramer; A. Monteiro

2008-01-01

It is increasingly clear that additional ‘model’ systems are needed to elucidate the genetic and developmental basis of organismal diversity. Whereas model system development previously required enormous investment, recent advances including the decreasing cost of DNA sequencing and the power of reverse genetics to study gene function are greatly facilitating...

Using an Adoption Design to Separate Genetic, Prenatal, and Temperament Influences on Toddler Executive Function

PubMed Central

Leve, Leslie D.; DeGarmo, David S.; Bridgett, David J.; Neiderhiser, Jenae M.; Shaw, Daniel S.; Harold, Gordon T.; Natsuaki, Misaki N.; Reiss, David

2012-01-01

Poor executive functioning has been implicated in children’s concurrent and future behavioral difficulties, making work aimed at understanding processes related to the development of early executive function (EF) critical for models of developmental psychopathology. Deficits in EF have been associated with adverse prenatal experiences, genetic influences, and temperament characteristics. However, our ability to disentangle the predictive and independent effects of these influences has been limited by a dearth of genetically-informed research designs that also consider prenatal influences. The present study examined EF and language development in a sample of 361 toddlers who were adopted at birth and reared in non-relative adoptive families. Predictors included genetic influences (as inherited from birth mothers), prenatal risk, and growth in child negative emotionality. Structural equation modeling indicated that the effect of prenatal risk on toddler effortful attention at age 27 months became nonsignificant once genetic influences were considered in the model. In addition, genetic influences had unique effects on toddler effortful attention. Latent growth modeling indicated that increases in toddler negative emotionality from 9 to 27 months were associated with poorer delay of gratification and poorer language development. Similar results were obtained in models incorporating birth father data. Mechanisms of intergenerational transmission of EF deficits are discussed. PMID:22799580

Using an adoption design to separate genetic, prenatal, and temperament influences on toddler executive function.

PubMed

Leve, Leslie D; DeGarmo, David S; Bridgett, David J; Neiderhiser, Jenae M; Shaw, Daniel S; Harold, Gordon T; Natsuaki, Misaki N; Reiss, David

2013-06-01

Poor executive functioning has been implicated in children's concurrent and future behavioral difficulties, making work aimed at understanding processes related to the development of early executive function (EF) critical for models of developmental psychopathology. Deficits in EF have been associated with adverse prenatal experiences, genetic influences, and temperament characteristics. However, our ability to disentangle the predictive and independent effects of these influences has been limited by a dearth of genetically informed research designs that also consider prenatal influences. The present study examined EF and language development in a sample of 361 toddlers who were adopted at birth and reared in nonrelative adoptive families. Predictors included genetic influences (as inherited from birth mothers), prenatal risk, and growth in child negative emotionality. Structural equation modeling indicated that the effect of prenatal risk on toddler effortful attention at age 27 months became nonsignificant once genetic influences were considered in the model. In addition, genetic influences had unique effects on toddler effortful attention. Latent growth modeling indicated that increases in toddler negative emotionality from 9 to 27 months were associated with poorer delay of gratification and poorer language development. Similar results were obtained in models incorporating birth father data. Mechanisms of intergenerational transmission of EF deficits are discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved

Evolving hard problems: Generating human genetics datasets with a complex etiology.

PubMed

Himmelstein, Daniel S; Greene, Casey S; Moore, Jason H

2011-07-07

A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models. Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate eight-hundred Pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth-order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects. This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire Pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 76,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/.

Terminology, concepts, and models in genetic epidemiology.

PubMed

Teare, M Dawn; Koref, Mauro F Santibàñez

2011-01-01

Genetic epidemiology brings together approaches and techniques developed in mathematical genetics and statistics, medical genetics, quantitative genetics, and epidemiology. In the 1980s, the focus was on the mapping and identification of genes where defects had large effects at the individual level. More recently, statistical and experimental advances have made possible to identify and characterise genes associated with small effects at the individual level. In this chapter, we provide a brief outline of the models, concepts, and terminology used in genetic epidemiology.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

PubMed

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y; Chen, Wei

2016-02-01

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. © 2016 WILEY PERIODICALS, INC.

Gene-based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

PubMed Central

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E.; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y.; Chen, Wei

2015-01-01

Summary Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, we develop here Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT) which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. PMID:26782979

Advanced technologies for genetically manipulating the silkworm Bombyx mori, a model Lepidopteran insect

PubMed Central

Xu, Hanfu; O'Brochta, David A.

2015-01-01

Genetic technologies based on transposon-mediated transgenesis along with several recently developed genome-editing technologies have become the preferred methods of choice for genetically manipulating many organisms. The silkworm, Bombyx mori, is a Lepidopteran insect of great economic importance because of its use in silk production and because it is a valuable model insect that has greatly enhanced our understanding of the biology of insects, including many agricultural pests. In the past 10 years, great advances have been achieved in the development of genetic technologies in B. mori, including transposon-based technologies that rely on piggyBac-mediated transgenesis and genome-editing technologies that rely on protein- or RNA-guided modification of chromosomes. The successful development and application of these technologies has not only facilitated a better understanding of B. mori and its use as a silk production system, but also provided valuable experiences that have contributed to the development of similar technologies in non-model insects. This review summarizes the technologies currently available for use in B. mori, their application to the study of gene function and their use in genetically modifying B. mori for biotechnology applications. The challenges, solutions and future prospects associated with the development and application of genetic technologies in B. mori are also discussed. PMID:26108630

Genetic Stability of Cognitive Development from Childhood to Adulthood.

ERIC Educational Resources Information Center

DeFries, J. C.; And Others

1987-01-01

A path model of genetic and family environmental transmission was fitted to published twin correlations and to general cognitive ability data from adoptive and nonadoptive families in which children were tested yearly through the fourth year. Longitudinal genetic correlations from infancy to adulthood were modeled explicitly, as were effects of…

A Study of Two Instructional Sequences Informed by Alternative Learning Progressions in Genetics

NASA Astrophysics Data System (ADS)

Duncan, Ravit Golan; Choi, Jinnie; Castro-Faix, Moraima; Cavera, Veronica L.

2017-12-01

Learning progressions (LPs) are hypothetical models of how learning in a domain develops over time with appropriate instruction. In the domain of genetics, there are two independently developed alternative LPs. The main difference between the two progressions hinges on their assumptions regarding the accessibility of classical (Mendelian) versus molecular genetics and the order in which they should be taught. In order to determine the relative difficulty of the different genetic ideas included in the two progressions, and to test which one is a better fit with students' actual learning, we developed two modules in classical and molecular genetics and alternated their sequence in an implementation study with 11th grade students studying biology. We developed a set of 56 ordered multiple-choice items that collectively assessed both molecular and classical genetic ideas. We found significant gains in students' learning in both molecular and classical genetics, with the largest gain relating to understanding the informational content of genes and the smallest gain in understanding modes of inheritance. Using multidimensional item response modeling, we found no statistically significant differences between the two instructional sequences. However, there was a trend of slightly higher gains for the molecular-first sequence for all genetic ideas.

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

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

PubMed Central

2013-01-01

Background Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring. PMID:23827014

Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing

PubMed Central

Yan, Qiang; Fong, Stephen S.

2017-01-01

Metabolic diversity in microorganisms can provide the basis for creating novel biochemical products. However, most metabolic engineering projects utilize a handful of established model organisms and thus, a challenge for harnessing the potential of novel microbial functions is the ability to either heterologously express novel genes or directly utilize non-model organisms. Genetic manipulation of non-model microorganisms is still challenging due to organism-specific nuances that hinder universal molecular genetic tools and translatable knowledge of intracellular biochemical pathways and regulatory mechanisms. However, in the past several years, unprecedented progress has been made in synthetic biology, molecular genetics tools development, applications of omics data techniques, and computational tools that can aid in developing non-model hosts in a systematic manner. In this review, we focus on concerns and approaches related to working with non-model microorganisms including developing molecular genetics tools such as shuttle vectors, selectable markers, and expression systems. In addition, we will discuss: (1) current techniques in controlling gene expression (transcriptional/translational level), (2) advances in site-specific genome engineering tools [homologous recombination (HR) and clustered regularly interspaced short palindromic repeats (CRISPR)], and (3) advances in genome-scale metabolic models (GSMMs) in guiding design of non-model species. Application of these principles to metabolic engineering strategies for consolidated bioprocessing (CBP) will be discussed along with some brief comments on foreseeable future prospects. PMID:29123506

Genetically Engineered Humanized Mouse Models for Preclinical Antibody Studies

PubMed Central

Proetzel, Gabriele; Wiles, Michael V.; Roopenian, Derry C.

2015-01-01

The use of genetic engineering has vastly improved our capabilities to create animal models relevant in preclinical research. With the recent advances in gene-editing technologies, it is now possible to very rapidly create highly tunable mouse models as needs arise. Here, we provide an overview of genetic engineering methods, as well as the development of humanized neonatal Fc receptor (FcRn) models and their use for monoclonal antibody in vivo studies. PMID:24150980

Manipulations in Maternal Environment Reverse Periodontitis in Genetically Predisposed Rats

PubMed Central

Sluyter, Frans; Breivik, Torbjørn; Cools, Alexander

2002-01-01

The predisposition to develop periodontitis is partly genetically determined in humans as well as in animals. Here we demonstrate, however, that early manipulations in the maternal environment of an animal (rat) model of periodontitis can fully reverse the genetic predisposition to develop periodontitis at adult age. PMID:12093700

Population Genetics of Three Dimensional Range Expansions

NASA Astrophysics Data System (ADS)

Lavrentovich, Maxim; Nelson, David

2014-03-01

We develop a simple model of genetic diversity in growing spherical cell clusters, where the growth is confined to the cluster surface. This kind of growth occurs in cells growing in soft agar, and can also serve as a simple model of avascular tumors. Mutation-selection balance in these radial expansions is strongly influenced by scaling near a neutral, voter model critical point and by the inflating frontier. We develop a scaling theory to describe how the dynamics of mutation-selection balance is cut off by inflation. Genetic drift, i.e., local fluctuations in the genetic diversity, also plays an important role, and can lead to the extinction even of selectively advantageous strains. We calculate this extinction probability, taking into account the effect of rough population frontiers.

Inferring Causalities in Landscape Genetics: An Extension of Wright's Causal Modeling to Distance Matrices.

PubMed

Fourtune, Lisa; Prunier, Jérôme G; Paz-Vinas, Ivan; Loot, Géraldine; Veyssière, Charlotte; Blanchet, Simon

2018-04-01

Identifying landscape features that affect functional connectivity among populations is a major challenge in fundamental and applied sciences. Landscape genetics combines landscape and genetic data to address this issue, with the main objective of disentangling direct and indirect relationships among an intricate set of variables. Causal modeling has strong potential to address the complex nature of landscape genetic data sets. However, this statistical approach was not initially developed to address the pairwise distance matrices commonly used in landscape genetics. Here, we aimed to extend the applicability of two causal modeling methods-that is, maximum-likelihood path analysis and the directional separation test-by developing statistical approaches aimed at handling distance matrices and improving functional connectivity inference. Using simulations, we showed that these approaches greatly improved the robustness of the absolute (using a frequentist approach) and relative (using an information-theoretic approach) fits of the tested models. We used an empirical data set combining genetic information on a freshwater fish species (Gobio occitaniae) and detailed landscape descriptors to demonstrate the usefulness of causal modeling to identify functional connectivity in wild populations. Specifically, we demonstrated how direct and indirect relationships involving altitude, temperature, and oxygen concentration influenced within- and between-population genetic diversity of G. occitaniae.

Emerging Technologies to Create Inducible and Genetically Defined Porcine Cancer Models.

PubMed

Schook, Lawrence B; Rund, Laurie; Begnini, Karine R; Remião, Mariana H; Seixas, Fabiana K; Collares, Tiago

2016-01-01

There is an emerging need for new animal models that address unmet translational cancer research requirements. Transgenic porcine models provide an exceptional opportunity due to their genetic, anatomic, and physiological similarities with humans. Due to recent advances in the sequencing of domestic animal genomes and the development of new organism cloning technologies, it is now very feasible to utilize pigs as a malleable species, with similar anatomic and physiological features with humans, in which to develop cancer models. In this review, we discuss genetic modification technologies successfully used to produce porcine biomedical models, in particular the Cre-loxP System as well as major advances and perspectives the CRISPR/Cas9 System. Recent advancements in porcine tumor modeling and genome editing will bring porcine models to the forefront of translational cancer research.

Learning to Fish with Genetics: A Primer on the Vertebrate Model Danio rerio

PubMed Central

Holtzman, Nathalia G.; Iovine, M. Kathryn; Liang, Jennifer O.; Morris, Jacqueline

2016-01-01

In the last 30 years, the zebrafish has become a widely used model organism for research on vertebrate development and disease. Through a powerful combination of genetics and experimental embryology, significant inroads have been made into the regulation of embryonic axis formation, organogenesis, and the development of neural networks. Research with this model has also expanded into other areas, including the genetic regulation of aging, regeneration, and animal behavior. Zebrafish are a popular model because of the ease with which they can be maintained, their small size and low cost, the ability to obtain hundreds of embryos on a daily basis, and the accessibility, translucency, and rapidity of early developmental stages. This primer describes the swift progress of genetic approaches in zebrafish and highlights recent advances that have led to new insights into vertebrate biology. PMID:27384027

Initial assessment of a model relating intratumoral genetic heterogeneity to radiological morphology

PubMed Central

Noterdaeme, O; Kelly, M; Friend, P; Soonowalla, Z; Steers, G; Brady, M

2010-01-01

Tumour heterogeneity has major implications for tumour development and response to therapy. Tumour heterogeneity results from mutations in the genes responsible for mismatch repair or maintenance of chromosomal stability. Cells with different genetic properties may grow at different rates and exhibit different resistance to therapeutic interventions. To date, there exists no approach to non-invasively assess tumour heterogeneity. Here we present a biologically inspired model of tumour growth, which relates intratumoral genetic heterogeneity to gross morphology visible on radiological images. The model represents the development of a tumour as a set of expanding spheres, each sphere representing a distinct clonal centre, with the sprouting of new spheres corresponding to new clonal centres. Each clonal centre may possess different characteristics relating to genetic composition, growth rate and response to treatment. We present a clinical example for which the model accurately tracks tumour growth and shows the correspondence to genetic variation (as determined by array comparative genomic hybridisation). One clinical implication of our work is that the assessment of heterogeneous tumours using Response Evaluation Criteria In Solid Tumours (RECIST) or volume measurements may not accurately reflect tumour growth, stability or the response to treatment. We believe that this is the first model linking the macro-scale appearance of tumours to their genetic composition. We anticipate that our model will provide a more informative way to assess the response of heterogeneous tumours to treatment, which is of increasing importance with the development of novel targeted anti-cancer treatments. PMID:19690073

CRISPR: a Versatile Tool for Both Forward and Reverse Genetics Research

PubMed Central

Gurumurthy, Channabasavaiah B.; Grati, M'hamed; Ohtsuka, Masato; Schilit, Samantha L.P.; Quadros, Rolen M.; Liu, Xue Zhong

2016-01-01

Human genetics research employs the two opposing approaches of forward and reverse genetics. While forward genetics identifies and links a mutation to an observed disease etiology, reverse genetics induces mutations in model organisms to study their role in disease. In most cases, causality for mutations identified by forward genetics is confirmed by reverse genetics through the development of genetically engineered animal models and an assessment of whether the model can recapitulate the disease. While many technological advances have helped improve these approaches, some gaps still remain. CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated) system, which has emerged as a revolutionary genetic engineering tool, holds great promise for closing such gaps. By combining the benefits of forward and reverse genetics, it has dramatically expedited human genetics research. We provide a perspective on the power of CRISPR-based forward and reverse genetics tools in human genetics and discuss its applications using some disease examples. PMID:27384229

Identifying future models for delivering genetic services: a nominal group study in primary care

PubMed Central

Elwyn, Glyn; Edwards, Adrian; Iredale, Rachel; Davies, Peter; Gray, Jonathon

2005-01-01

Background To enable primary care medical practitioners to generate a range of possible service delivery models for genetic counselling services and critically assess their suitability. Methods Modified nominal group technique using in primary care professional development workshops. Results 37 general practitioners in Wales, United Kingdom too part in the nominal group process. The practitioners who attended did not believe current systems were sufficient to meet anticipated demand for genetic services. A wide range of different service models was proposed, although no single option emerged as a clear preference. No argument was put forward for genetic assessment and counselling being central to family practice, neither was there a voice for the view that the family doctor should become skilled at advising patients about predictive genetic testing and be able to counsel patients about the wider implications of genetic testing for patients and their family members, even for areas such as common cancers. Nevertheless, all the preferred models put a high priority on providing the service in the community, and often co-located in primary care, by clinicians who had developed expertise. Conclusion There is a need for a wider debate about how healthcare systems address individual concerns about genetic concerns and risk, especially given the increasing commercial marketing of genetic tests. PMID:15831099

Genetics of Attention Deficit Hyperactivity Disorder: A Current Review and Future Prospects

ERIC Educational Resources Information Center

Levy, Florence; Hay, David A.; Bennett, Kellie S.

2006-01-01

While there have been significant advances in both the behaviour genetics and molecular genetics of Attention Deficit Hyperactivity Disorder (ADHD), researchers are now beginning to develop hypotheses about relationships between phenotypes and genetic mechanisms. Twin studies are able to model genetic, shared environmental and non-shared…

Exploring Middle School Students' Understanding of Three Conceptual Models in Genetics

NASA Astrophysics Data System (ADS)

Bresler Freidenreich, Hava; Golan Duncan, Ravit; Shea, Nicole

2011-11-01

Genetics is the cornerstone of modern biology and a critical aspect of scientific literacy. Research has shown, however, that many high school graduates lack fundamental understandings in genetics necessary to make informed decisions about issues and emerging technologies in this domain, such as genetic screening, genetically modified foods, etc. Genetic literacy entails understanding three interrelated models: a genetic model that describes patterns of genetic inheritance, a meiotic model that describes the process by which genes are segregated into sex cells, and a molecular model that describes the mechanisms that link genotypes to phenotypes within an individual. Currently, much of genetics instruction, especially in terms of the molecular model, occurs at the high school level, and we know little about the ways in which middle school students can reason about these models. Furthermore, we do not know the extent to which carefully designed instruction can help younger students develop coherent and interrelated understandings in genetics. In this paper, we discuss a research study aimed at elucidating middle school students' abilities to reason about the three genetic models. As part of our research, we designed an eight-week inquiry unit that was implemented in a combined sixth- to eighth-grade science classroom. We describe our instructional design and report results based on an analysis of written assessments, clinical interviews, and artifacts of the unit. Our findings suggest that middle school students are able to successfully reason about all three genetic models.

Increased prediction accuracy in wheat breeding trials using a marker x environment interaction genomic selection model

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates for selection. Originally these models were developed without considering genotype ' environment interaction (GE). Several authors have proposed extensions of the cannonical GS model that accomm...

Modeling the Etiology of Individual Differences in Early Reading Development: Evidence for Strong Genetic Influences

PubMed Central

Christopher, Micaela E.; Hulslander, Jacqueline; Byrne, Brian; Samuelsson, Stefan; Keenan, Janice M.; Pennington, Bruce; DeFries, John C.; Wadsworth, Sally J.; Willcutt, Erik; Olson, Richard K.

2012-01-01

We explored the etiology of individual differences in reading development from post-kindergarten to post-4th grade by analyzing data from 487 twin pairs tested in Colorado. Data from three reading measures and one spelling measure were fit to biometric latent growth curve models, allowing us to extend previous behavioral genetic studies of the etiology of early reading development at specific time points. We found primarily genetic influences on individual differences at post-1st grade for all measures. Genetic influences on variance in growth rates were also found, with evidence of small, nonsignificant, shared environmental influences for two measures. We discuss our results, including their implications for educational policy. PMID:24489459

Meganucleases Revolutionize the Production of Genetically Engineered Pigs for the Study of Human Diseases.

PubMed

Redel, Bethany K; Prather, Randall S

2016-04-01

Animal models of human diseases are critically necessary for developing an in-depth knowledge of disease development and progression. In addition, animal models are vital to the development of potential treatments or even cures for human diseases. Pigs are exceptional models as their size, physiology, and genetics are closer to that of humans than rodents. In this review, we discuss the use of pigs in human translational research and the evolving technology that has increased the efficiency of genetically engineering pigs. With the emergence of the clustered, regularly interspaced, short palindromic repeat (CRISPR)/CRISPR-associated (Cas) protein 9 system technology, the cost and time it takes to genetically engineer pigs has markedly decreased. We will also discuss the use of another meganuclease, the transcription activator-like effector nucleases , to produce pigs with severe combined immunodeficiency by developing targeted modifications of the recombination activating gene 2 (RAG2).RAG2mutant pigs may become excellent animals to facilitate the development of xenotransplantation, regenerative medicine, and tumor biology. The use of pig biomedical models is vital for furthering the knowledge of, and for treating human, diseases. © The Author(s) 2015.

Mining disease fingerprints from within genetic pathways.

PubMed

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components ('fingerprints') of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ~77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways.

Mining Disease Fingerprints From Within Genetic Pathways

PubMed Central

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components (‘fingerprints’) of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ∼77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways. PMID:23304411

The Genomic and Genetic Toolbox of the Teleost Medaka (Oryzias latipes)

PubMed Central

Kirchmaier, Stephan; Naruse, Kiyoshi; Wittbrodt, Joachim; Loosli, Felix

2015-01-01

The Japanese medaka, Oryzias latipes, is a vertebrate teleost model with a long history of genetic research. A number of unique features and established resources distinguish medaka from other vertebrate model systems. A large number of laboratory strains from different locations are available. Due to a high tolerance to inbreeding, many highly inbred strains have been established, thus providing a rich resource for genetic studies. Furthermore, closely related species native to different habitats in Southeast Asia permit comparative evolutionary studies. The transparency of embryos, larvae, and juveniles allows a detailed in vivo analysis of development. New tools to study diverse aspects of medaka biology are constantly being generated. Thus, medaka has become an important vertebrate model organism to study development, behavior, and physiology. In this review, we provide a comprehensive overview of established genetic and molecular-genetic tools that render medaka fish a full-fledged vertebrate system. PMID:25855651

Goethe and the ABC model of flower development.

PubMed

Coen, E

2001-06-01

About 10 years ago, the ABC model for the genetic control of flower development was proposed. This model was initially based on the analysis of mutant flowers but has subsequently been confirmed by molecular analysis. This paper describes the 200-year history behind this model, from the late 18th century when Goethe arrived at his idea of plant metamorphosis, to the genetic studies on flower mutants carried out on Arabidopsis and Antirrhinum in the late 20th century.

A Genetically Engineered Mouse Model of Sporadic Colorectal Cancer.

PubMed

Betzler, Alexander M; Kochall, Susan; Blickensdörfer, Linda; Garcia, Sebastian A; Thepkaysone, May-Linn; Nanduri, Lahiri K; Muders, Michael H; Weitz, Jürgen; Reissfelder, Christoph; Schölch, Sebastian

2017-07-06

Despite the advantages of easy applicability and cost-effectiveness, colorectal cancer mouse models based on tumor cell injection have severe limitations and do not accurately simulate tumor biology and tumor cell dissemination. Genetically engineered mouse models have been introduced to overcome these limitations; however, such models are technically demanding, especially in large organs such as the colon in which only a single tumor is desired. As a result, an immunocompetent, genetically engineered mouse model of colorectal cancer was developed which develops highly uniform tumors and can be used for tumor biology studies as well as therapeutic trials. Tumor development is initiated by surgical, segmental infection of the distal colon with adeno-cre virus in compound conditionally mutant mice. The tumors can be easily detected and monitored via colonoscopy. We here describe the surgical technique of segmental adeno-cre infection of the colon, the surveillance of the tumor via high-resolution colonoscopy and present the resulting colorectal tumors.

[Genetics of congenital heart diseases].

PubMed

Bonnet, Damien

2017-06-01

Developmental genetics of congenital heart diseases has evolved from analysis of serial slices in embryos towards molecular genetics of cardiac morphogenesis with a dynamic view of cardiac development. Genetics of congenital heart diseases has also changed from formal genetic analysis of familial recurrences or population-based analysis to screening for mutations in candidates genes identified in animal models. Close cooperation between molecular embryologists, pathologists involved in heart development and pediatric cardiologists is crucial for further increase of knowledge in the field of cardiac morphogenesis and genetics of cardiac defects. The genetic model for congenital heart disease has to be revised to favor a polygenic origin rather than a monogenic one. The main mechanism is altered genic dosage that can account for heart diseases in chromosomal anomalies as well as in point mutations in syndromic and isolated congenital heart diseases. The use of big data grouping information from cardiac development, interactions between genes and proteins, epigenetic factors such as chromatin remodeling or DNA methylation is the current source for improving our knowledge in the field and to give clues for future therapies. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

PubMed

Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

Definition of Historical Models of Gene Function and Their Relation to Students' Understanding of Genetics

ERIC Educational Resources Information Center

Gericke, Niklas Markus; Hagberg, Mariana

2007-01-01

Models are often used when teaching science. In this paper historical models and students' ideas about genetics are compared. The historical development of the scientific idea of the gene and its function is described and categorized into five historical models of gene function. Differences and similarities between these historical models are made…

Emerging Technologies to Create Inducible and Genetically Defined Porcine Cancer Models

PubMed Central

Schook, Lawrence B.; Rund, Laurie; Begnini, Karine R.; Remião, Mariana H.; Seixas, Fabiana K.; Collares, Tiago

2016-01-01

There is an emerging need for new animal models that address unmet translational cancer research requirements. Transgenic porcine models provide an exceptional opportunity due to their genetic, anatomic, and physiological similarities with humans. Due to recent advances in the sequencing of domestic animal genomes and the development of new organism cloning technologies, it is now very feasible to utilize pigs as a malleable species, with similar anatomic and physiological features with humans, in which to develop cancer models. In this review, we discuss genetic modification technologies successfully used to produce porcine biomedical models, in particular the Cre-loxP System as well as major advances and perspectives the CRISPR/Cas9 System. Recent advancements in porcine tumor modeling and genome editing will bring porcine models to the forefront of translational cancer research. PMID:26973698

Genetically Engineered Pig Models for Human Diseases

PubMed Central

Prather, Randall S.; Lorson, Monique; Ross, Jason W.; Whyte, Jeffrey J.; Walters, Eric

2015-01-01

Although pigs are used widely as models of human disease, their utility as models has been enhanced by genetic engineering. Initially, transgenes were added randomly to the genome, but with the application of homologous recombination, zinc finger nucleases, and transcription activator-like effector nuclease (TALEN) technologies, now most any genetic change that can be envisioned can be completed. To date these genetic modifications have resulted in animals that have the potential to provide new insights into human diseases for which a good animal model did not exist previously. These new animal models should provide the preclinical data for treatments that are developed for diseases such as Alzheimer's disease, cystic fibrosis, retinitis pigmentosa, spinal muscular atrophy, diabetes, and organ failure. These new models will help to uncover aspects and treatments of these diseases that were otherwise unattainable. The focus of this review is to describe genetically engineered pigs that have resulted in models of human diseases. PMID:25387017

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Convergence Properties of a Class of Probabilistic Adaptive Schemes Called Sequential Reproductive Plans. Psychology and Education Series, Technical Report No. 210.

ERIC Educational Resources Information Center

Martin, Nancy

Presented is a technical report concerning the use of a mathematical model describing certain aspects of the duplication and selection processes in natural genetic adaptation. This reproductive plan/model occurs in artificial genetics (the use of ideas from genetics to develop general problem solving techniques for computers). The reproductive…

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

A model of genetic variation for Pinus ponderosa in the Inland Northwest (U.S.A.): applications in gene resource management

Treesearch

Gerald Rehfeldt

1991-01-01

Models were developed to describe genetic variation among 201 seedling populations of Pinus ponderosa var. ponderosa in the Inland Northwest of the United States. Common-garden studies provided three variables Jhat reflected growth and development in field environments and three principal components of six variables that reflected patterns of shoot elongation....

Genetic Influences on Peer and Family Relationships Across Adolescent Development: Introduction to the Special Issue.

PubMed

Mullineaux, Paula Y; DiLalla, Lisabeth Fisher

2015-07-01

Nearly all aspects of human development are influenced by genetic and environmental factors, which conjointly shape development through several gene-environment interplay mechanisms. More recently, researchers have begun to examine the influence of genetic factors on peer and family relationships across the pre-adolescent and adolescent time periods. This article introduces the special issue by providing a critical overview of behavior genetic methodology and existing research demonstrating gene-environment processes operating on the link between peer and family relationships and adolescent adjustment. The overview is followed by a summary of new research studies, which use genetically informed samples to examine how peer and family environment work together with genetic factors to influence behavioral outcomes across adolescence. The studies in this special issue provide further evidence of gene-environment interplay through innovative behavior genetic methodological approaches across international samples. Results from the quantitative models indicate environmental moderation of genetic risk for coercive adolescent-parent relationships and deviant peer affiliation. The molecular genetics studies provide support for a gene-environment interaction differential susceptibility model for dopamine regulation genes across positive and negative peer and family environments. Overall, the findings from the studies in this special issue demonstrate the importance of considering how genes and environments work in concert to shape developmental outcomes during adolescence.

Conserved genetic pathways associated with microphthalmia, anophthalmia, and coloboma

PubMed Central

Reis, Linda M.; Semina, Elena V.

2016-01-01

The human eye is a complex organ whose development requires extraordinary coordination of developmental processes. The conservation of ocular developmental steps in vertebrates suggests possible common genetic mechanisms. Genetic diseases involving the eye represent a leading cause of blindness in children and adults. During the last decades, there has been an exponential increase in genetic studies of ocular disorders. In this review, we summarize current success in identification of genes responsible for microphthalmia, anophthalmia and coloboma (MAC) phenotypes, which are associated with early defects in embryonic eye development. Studies in animal models for the orthologous genes identified overlapping phenotypes for most factors confirming the conservation of their function in vertebrate development. These animal models allow for further investigation of the mechanisms of MAC, integration of various identified genes into common developmental pathways and, finally, provide an avenue for the development and testing of therapeutic interventions. PMID:26046913

Conserved genetic pathways associated with microphthalmia, anophthalmia, and coloboma.

PubMed

Reis, Linda M; Semina, Elena V

2015-06-01

The human eye is a complex organ whose development requires extraordinary coordination of developmental processes. The conservation of ocular developmental steps in vertebrates suggests possible common genetic mechanisms. Genetic diseases involving the eye represent a leading cause of blindness in children and adults. During the last decades, there has been an exponential increase in genetic studies of ocular disorders. In this review, we summarize current success in identification of genes responsible for microphthalmia, anophthalmia, and coloboma (MAC) phenotypes, which are associated with early defects in embryonic eye development. Studies in animal models for the orthologous genes identified overlapping phenotypes for most factors, confirming the conservation of their function in vertebrate development. These animal models allow for further investigation of the mechanisms of MAC, integration of various identified genes into common developmental pathways and finally, provide an avenue for the development and testing of therapeutic interventions. © 2015 Wiley Periodicals, Inc.

Defining the role of polyamines in colon carcinogenesis using mouse models

PubMed Central

Ignatenko, Natalia A.; Gerner, Eugene W.; Besselsen, David G.

2011-01-01

Genetics and diet are both considered important risk determinants for colorectal cancer, a leading cause of death in the US and worldwide. Genetically engineered mouse (GEM) models have made a significant contribution to the characterization of colorectal cancer risk factors. Reliable, reproducible, and clinically relevant animal models help in the identification of the molecular events associated with disease progression and in the development of effictive treatment strategies. This review is focused on the use of mouse models for studying the role of polyamines in colon carcinogenesis. We describe how the available mouse models of colon cancer such as the multiple intestinal neoplasia (Min) mice and knockout genetic models facilitate understanding of the role of polyamines in colon carcinogenesis and help in the development of a rational strategy for colon cancer chemoprevention. PMID:21712957

A Model Program for Translational Medicine in Epilepsy Genetics

PubMed Central

Smith, Lacey A.; Ullmann, Jeremy F. P.; Olson, Heather E.; El Achkar, Christelle M.; Truglio, Gessica; Kelly, McKenna; Rosen-Sheidley, Beth; Poduri, Annapurna

2017-01-01

Recent technological advances in gene sequencing have led to a rapid increase in gene discovery in epilepsy. However, the ability to assess pathogenicity of variants, provide functional analysis, and develop targeted therapies has not kept pace with rapid advances in sequencing technology. Thus, although clinical genetic testing may lead to a specific molecular diagnosis for some patients, test results often lead to more questions than answers. As the field begins to focus on therapeutic applications of genetic diagnoses using precision medicine, developing processes that offer more than equivocal test results is essential. The success of precision medicine in epilepsy relies on establishing a correct genetic diagnosis, analyzing functional consequences of genetic variants, screening potential therapeutics in the preclinical laboratory setting, and initiating targeted therapy trials for patients. We describe the structure of a comprehensive, pediatric Epilepsy Genetics Program that can serve as a model for translational medicine in epilepsy. PMID:28056630

Cycles of Exploration, Reflection, and Consolidation in Model-Based Learning of Genetics

ERIC Educational Resources Information Center

Kim, Beaumie; Pathak, Suneeta A.; Jacobson, Michael J.; Zhang, Baohui; Gobert, Janice D.

2015-01-01

Model-based reasoning has been introduced as an authentic way of learning science, and many researchers have developed technological tools for learning with models. This paper describes how a model-based tool, "BioLogica"™, was used to facilitate genetics learning in secondary 3-level biology in Singapore. The research team co-designed…

Genetic diversity and population structure among six cattle breeds in South Africa using a whole genome SNP panel

PubMed Central

Makina, Sithembile O.; Muchadeyi, Farai C.; van Marle-Köster, Este; MacNeil, Michael D.; Maiwashe, Azwihangwisi

2014-01-01

Information about genetic diversity and population structure among cattle breeds is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of cattle breeds. This study investigated genetic diversity and the population structure among six cattle breeds in South African (SA) including Afrikaner (n = 44), Nguni (n = 54), Drakensberger (n = 47), Bonsmara (n = 44), Angus (n = 31), and Holstein (n = 29). Genetic diversity within cattle breeds was analyzed using three measures of genetic diversity namely allelic richness (AR), expected heterozygosity (He) and inbreeding coefficient (f). Genetic distances between breed pairs were evaluated using Nei's genetic distance. Population structure was assessed using model-based clustering (ADMIXTURE). Results of this study revealed that the allelic richness ranged from 1.88 (Afrikaner) to 1.73 (Nguni). Afrikaner cattle had the lowest level of genetic diversity (He = 0.24) and the Drakensberger cattle (He = 0.30) had the highest level of genetic variation among indigenous and locally-developed cattle breeds. The level of inbreeding was lower across the studied cattle breeds. As expected the average genetic distance was the greatest between indigenous cattle breeds and Bos taurus cattle breeds but the lowest among indigenous and locally-developed breeds. Model-based clustering revealed some level of admixture among indigenous and locally-developed breeds and supported the clustering of the breeds according to their history of origin. The results of this study provided useful insight regarding genetic structure of SA cattle breeds. PMID:25295053

Genetic diversity and population structure among six cattle breeds in South Africa using a whole genome SNP panel.

PubMed

Makina, Sithembile O; Muchadeyi, Farai C; van Marle-Köster, Este; MacNeil, Michael D; Maiwashe, Azwihangwisi

2014-01-01

Information about genetic diversity and population structure among cattle breeds is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of cattle breeds. This study investigated genetic diversity and the population structure among six cattle breeds in South African (SA) including Afrikaner (n = 44), Nguni (n = 54), Drakensberger (n = 47), Bonsmara (n = 44), Angus (n = 31), and Holstein (n = 29). Genetic diversity within cattle breeds was analyzed using three measures of genetic diversity namely allelic richness (AR), expected heterozygosity (He) and inbreeding coefficient (f). Genetic distances between breed pairs were evaluated using Nei's genetic distance. Population structure was assessed using model-based clustering (ADMIXTURE). Results of this study revealed that the allelic richness ranged from 1.88 (Afrikaner) to 1.73 (Nguni). Afrikaner cattle had the lowest level of genetic diversity (He = 0.24) and the Drakensberger cattle (He = 0.30) had the highest level of genetic variation among indigenous and locally-developed cattle breeds. The level of inbreeding was lower across the studied cattle breeds. As expected the average genetic distance was the greatest between indigenous cattle breeds and Bos taurus cattle breeds but the lowest among indigenous and locally-developed breeds. Model-based clustering revealed some level of admixture among indigenous and locally-developed breeds and supported the clustering of the breeds according to their history of origin. The results of this study provided useful insight regarding genetic structure of SA cattle breeds.

Risk assessment model for development of advanced age-related macular degeneration.

PubMed

Klein, Michael L; Francis, Peter J; Ferris, Frederick L; Hamon, Sara C; Clemons, Traci E

2011-12-01

To design a risk assessment model for development of advanced age-related macular degeneration (AMD) incorporating phenotypic, demographic, environmental, and genetic risk factors. We evaluated longitudinal data from 2846 participants in the Age-Related Eye Disease Study. At baseline, these individuals had all levels of AMD, ranging from none to unilateral advanced AMD (neovascular or geographic atrophy). Follow-up averaged 9.3 years. We performed a Cox proportional hazards analysis with demographic, environmental, phenotypic, and genetic covariates and constructed a risk assessment model for development of advanced AMD. Performance of the model was evaluated using the C statistic and the Brier score and externally validated in participants in the Complications of Age-Related Macular Degeneration Prevention Trial. The final model included the following independent variables: age, smoking history, family history of AMD (first-degree member), phenotype based on a modified Age-Related Eye Disease Study simple scale score, and genetic variants CFH Y402H and ARMS2 A69S. The model did well on performance measures, with very good discrimination (C statistic = 0.872) and excellent calibration and overall performance (Brier score at 5 years = 0.08). Successful external validation was performed, and a risk assessment tool was designed for use with or without the genetic component. We constructed a risk assessment model for development of advanced AMD. The model performed well on measures of discrimination, calibration, and overall performance and was successfully externally validated. This risk assessment tool is available for online use.

[The discussion of the infiltrative model of mathematical knowledge to genetics teaching].

PubMed

Liu, Jun; Luo, Pei-Gao

2011-11-01

Genetics, the core course of biological field, is an importance major-basic course in curriculum of many majors related with biology. Due to strong theoretical and practical as well as abstract of genetics, it is too difficult to study on genetics for many students. At the same time, mathematics is one of the basic courses in curriculum of the major related natural science, which has close relationship with the establishment, development and modification of genetics. In this paper, to establish the intrinsic logistic relationship and construct the integral knowledge network and to help students improving the analytic, comprehensive and logistic abilities, we applied some mathematical infiltrative model genetic knowledge in genetics teaching, which could help students more deeply learn and understand genetic knowledge.

Paul Polani and the development of medical genetics

PubMed Central

Harper, Peter S.

2007-01-01

Paul Polani (1914-2006) was one of the key figures internationally in the beginnings and development of medical genetics. Best remembered scientifically for his highly original work on the basis of human sex chromosome disorders, notably Turner syndrome, he pioneered the application of basic biological research to clinical genetic problems. The unit that he founded in 1960, at Guys Hospital, London, provided an unparalleled model for combined research and service in medical genetics across a wide range of laboratory areas and helped to establish medical genetics as a specific discipline. PMID:17066298

Form Follows Function: A Model for Clinical Supervision of Genetic Counseling Students.

PubMed

Wherley, Colleen; Veach, Patricia McCarthy; Martyr, Meredith A; LeRoy, Bonnie S

2015-10-01

Supervision plays a vital role in genetic counselor training, yet models describing genetic counseling supervision processes and outcomes are lacking. This paper describes a proposed supervision model intended to provide a framework to promote comprehensive and consistent clinical supervision training for genetic counseling students. Based on the principle "form follows function," the model reflects and reinforces McCarthy Veach et al.'s empirically derived model of genetic counseling practice - the "Reciprocal Engagement Model" (REM). The REM consists of mutually interactive educational, relational, and psychosocial components. The Reciprocal Engagement Model of Supervision (REM-S) has similar components and corresponding tenets, goals, and outcomes. The 5 REM-S tenets are: Learning and applying genetic information are key; Relationship is integral to genetic counseling supervision; Student autonomy must be supported; Students are capable; and Student emotions matter. The REM-S outcomes are: Student understands and applies information to independently provide effective services, develop professionally, and engage in self-reflective practice. The 16 REM-S goals are informed by the REM of genetic counseling practice and supported by prior literature. A review of models in medicine and psychology confirms the REM-S contains supervision elements common in healthcare fields, while remaining unique to genetic counseling. The REM-S shows promise for enhancing genetic counselor supervision training and practice and for promoting research on clinical supervision. The REM-S is presented in detail along with specific examples and training and research suggestions.

Building a Genome Engineering Toolbox in Non-Model Prokaryotic Microbes

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

Eckert, Carrie A; Freed, Emily; Smolinski, Sharon

The realization of a sustainable bioeconomy requires our ability to understand and engineer complex design principles for the development of platform organisms capable of efficient conversion of cheap and sustainable feedstocks (e.g. sunlight, CO2, non-food biomass) to biofuels and bioproducts at sufficient titers and costs. For model microbes such as E. coli, advances in DNA reading and writing technologies are driving adoption of new paradigms for engineering biological systems. Unfortunately, microbes with properties of interest for the utilization of cheap and renewable feedstocks such as photosynthesis, autotrophic growth, and cellulose degradation have very few, if any, genetic tools for metabolicmore » engineering. Therefore, it is important to begin to develop 'design rules' for building a genetic toolbox for novel microbes. Here, we present an overview of our current understanding of these rules for the genetic manipulation of prokaryotic microbes and available genetic tools to expand our ability to genetically engineer non-model systems.« less

Current Progress of Genetically Engineered Pig Models for Biomedical Research

PubMed Central

Gün, Gökhan

2014-01-01

Abstract The first transgenic pigs were generated for agricultural purposes about three decades ago. Since then, the micromanipulation techniques of pig oocytes and embryos expanded from pronuclear injection of foreign DNA to somatic cell nuclear transfer, intracytoplasmic sperm injection-mediated gene transfer, lentiviral transduction, and cytoplasmic injection. Mechanistically, the passive transgenesis approach based on random integration of foreign DNA was developed to active genetic engineering techniques based on the transient activity of ectopic enzymes, such as transposases, recombinases, and programmable nucleases. Whole-genome sequencing and annotation of advanced genome maps of the pig complemented these developments. The full implementation of these tools promises to immensely increase the efficiency and, in parallel, to reduce the costs for the generation of genetically engineered pigs. Today, the major application of genetically engineered pigs is found in the field of biomedical disease modeling. It is anticipated that genetically engineered pigs will increasingly be used in biomedical research, since this model shows several similarities to humans with regard to physiology, metabolism, genome organization, pathology, and aging. PMID:25469311

A Comparison of Reasoning Processes in a Collaborative Modelling Environment: Learning about genetics problems using virtual chat

NASA Astrophysics Data System (ADS)

Pata, Kai; Sarapuu, Tago

2006-09-01

This study investigated the possible activation of different types of model-based reasoning processes in two learning settings, and the influence of various terms of reasoning on the learners’ problem representation development. Changes in 53 students’ problem representations about genetic issue were analysed while they worked with different modelling tools in a synchronous network-based environment. The discussion log-files were used for the “microgenetic” analysis of reasoning types. For studying the stages of students’ problem representation development, individual pre-essays and post-essays and their utterances during two reasoning phases were used. An approach for mapping problem representations was developed. Characterizing the elements of mental models and their reasoning level enabled the description of five hierarchical categories of problem representations. Learning in exploratory and experimental settings was registered as the shift towards more complex stages of problem representations in genetics. The effect of different types of reasoning could be observed as the divergent development of problem representations within hierarchical categories.

[Analysis of genetico-demographic structure of rural populations living near the Semipalatinsk nuclear test site].

PubMed

Sviatova, G S; Berezina, G M; Abil'dinova, G Zh

2001-12-01

Rural populations neighboring the Semipalatinsk nuclear test site were used as a model to develop and test an integrated population-genetic approach to analysis of the medical genetic situation and environmental conditions in the areas studied. The contributions of individual factors of population dynamics into the formation of the genetic load were also assessed. The informative values of some genetic markers were estimated. Based on these estimates, a mathematical model was constructed that makes it possible to calculate numerical scores for analysis of the genetic loads in populations differing in environmental exposure.

A Systems Genetic Approach to Identify Low Dose Radiation-Induced Lymphoma Susceptibility/DOE2013FinalReport

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

Balmain, Allan; Song, Ihn Young

2013-05-15

The ultimate goal of this project is to identify the combinations of genetic variants that confer an individual's susceptibility to the effects of low dose (0.1 Gy) gamma-radiation, in particular with regard to tumor development. In contrast to the known effects of high dose radiation in cancer induction, the responses to low dose radiation (defined as 0.1 Gy or less) are much less well understood, and have been proposed to involve a protective anti-tumor effect in some in vivo scientific models. These conflicting results confound attempts to develop predictive models of the risk of exposure to low dose radiation, particularlymore » when combined with the strong effects of inherited genetic variants on both radiation effects and cancer susceptibility. We have used a Systems Genetics approach in mice that combines genetic background analysis with responses to low and high dose radiation, in order to develop insights that will allow us to reconcile these disparate observations. Using this comprehensive approach we have analyzed normal tissue gene expression (in this case the skin and thymus), together with the changes that take place in this gene expression architecture a) in response to low or high- dose radiation and b) during tumor development. Additionally, we have demonstrated that using our expression analysis approach in our genetically heterogeneous/defined radiation-induced tumor mouse models can uniquely identify genes and pathways relevant to human T-ALL, and uncover interactions between common genetic variants of genes which may lead to tumor susceptibility.« less

Addressing the Complexity of Tourette's Syndrome through the Use of Animal Models

PubMed Central

Nespoli, Ester; Rizzo, Francesca; Boeckers, Tobias M.; Hengerer, Bastian; Ludolph, Andrea G.

2016-01-01

Tourette's syndrome (TS) is a neurodevelopmental disorder characterized by fluctuating motor and vocal tics, usually preceded by sensory premonitions, called premonitory urges. Besides tics, the vast majority—up to 90%—of TS patients suffer from psychiatric comorbidities, mainly attention deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). The etiology of TS remains elusive. Genetics is believed to play an important role, but it is clear that other factors contribute to TS, possibly altering brain functioning and architecture during a sensitive phase of neural development. Clinical brain imaging and genetic studies have contributed to elucidate TS pathophysiology and disease mechanisms; however, TS disease etiology still is poorly understood. Findings from genetic studies led to the development of genetic animal models, but they poorly reflect the pathophysiology of TS. Addressing the role of neurotransmission, brain regions, and brain circuits in TS disease pathomechanisms is another focus area for preclinical TS model development. We are now in an interesting moment in time when numerous innovative animal models are continuously brought to the attention of the public. Due to the diverse and largely unknown etiology of TS, there is no single preclinical model featuring all different aspects of TS symptomatology. TS has been dissected into its key symptomst hat have been investigated separately, in line with the Research Domain Criteria concept. The different rationales used to develop the respective animal models are critically reviewed, to discuss the potential of the contribution of animal models to elucidate TS disease mechanisms. PMID:27092043

Advances in the Study of Heart Development and Disease Using Zebrafish

PubMed Central

Brown, Daniel R.; Samsa, Leigh Ann; Qian, Li; Liu, Jiandong

2016-01-01

Animal models of cardiovascular disease are key players in the translational medicine pipeline used to define the conserved genetic and molecular basis of disease. Congenital heart diseases (CHDs) are the most common type of human birth defect and feature structural abnormalities that arise during cardiac development and maturation. The zebrafish, Danio rerio, is a valuable vertebrate model organism, offering advantages over traditional mammalian models. These advantages include the rapid, stereotyped and external development of transparent embryos produced in large numbers from inexpensively housed adults, vast capacity for genetic manipulation, and amenability to high-throughput screening. With the help of modern genetics and a sequenced genome, zebrafish have led to insights in cardiovascular diseases ranging from CHDs to arrhythmia and cardiomyopathy. Here, we discuss the utility of zebrafish as a model system and summarize zebrafish cardiac morphogenesis with emphasis on parallels to human heart diseases. Additionally, we discuss the specific tools and experimental platforms utilized in the zebrafish model including forward screens, functional characterization of candidate genes, and high throughput applications. PMID:27335817

Overview of genetically engineered mouse models of colorectal carcinoma to enable translational biology and drug development.

PubMed

Roper, Jatin; Martin, Eric S; Hung, Kenneth E

2014-06-16

Preclinical models for colorectal cancer (CRC) are critical for translational biology and drug development studies to characterize and treat this condition. Mouse models of human cancer are particularly popular because of their relatively low cost, short life span, and ease of use. Genetically engineered mouse models (GEMMs) of CRC are engineered from germline or somatic modification of critical tumor suppressor genes and/or oncogenes that drive mutations in human disease. Detailed in this overview are the salient features of several useful colorectal cancer GEMMs and their value as tools for translational biology and preclinical drug development. Copyright © 2014 John Wiley & Sons, Inc.

Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations

USGS Publications Warehouse

Row, Jeffrey R.; Knick, Steven T.; Oyler-McCance, Sara J.; Lougheed, Stephen C.; Fedy, Bradley C.

2017-01-01

Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing genetic structure, yet there are currently no consensus for the best protocols. Here, we develop landscape-directed simulations and test a series of replicates that emulate independent empirical datasets of two species with different life history characteristics (greater sage-grouse; eastern foxsnake). We determined that in our simulated scenarios, AIC and BIC were the best model selection indices and that marginal R2 values were biased toward more complex models. The model coefficients for landscape variables generally reflected the underlying dispersal model with confidence intervals that did not overlap with zero across the entire model set. When we controlled for geographic distance, variables not in the underlying dispersal models (i.e., nontrue) typically overlapped zero. Our study helps establish methods for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.

Sleep and Development in Genetically Tractable Model Organisms.

PubMed

Kayser, Matthew S; Biron, David

2016-05-01

Sleep is widely recognized as essential, but without a clear singular function. Inadequate sleep impairs cognition, metabolism, immune function, and many other processes. Work in genetic model systems has greatly expanded our understanding of basic sleep neurobiology as well as introduced new concepts for why we sleep. Among these is an idea with its roots in human work nearly 50 years old: sleep in early life is crucial for normal brain maturation. Nearly all known species that sleep do so more while immature, and this increased sleep coincides with a period of exuberant synaptogenesis and massive neural circuit remodeling. Adequate sleep also appears critical for normal neurodevelopmental progression. This article describes recent findings regarding molecular and circuit mechanisms of sleep, with a focus on development and the insights garnered from models amenable to detailed genetic analyses. Copyright © 2016 by the Genetics Society of America.

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

Environment, genes, and experience: lessons from behavior genetics.

PubMed

Barsky, Philipp I

2010-11-01

The article reviews the theoretical analysis of the problems inherent in studying the environment within behavior genetics across several periods in the development of environmental studies in behavior genetics and proposes some possible alternatives to traditional approaches to studying the environment in behavior genetics. The first period (from the end of the 1920s to the end of the 1970s), when the environment was not actually studied, is called pre-environmental; during this time, the basic principles and theoretical models of understanding environmental effects in behavior genetics were developed. The second period is characterized by the development of studies on environmental influences within the traditional behavior genetics paradigm; several approaches to studying the environment emerged in behavior genetics during this period, from the beginning of the 1980s until today. At the present time, the field is undergoing paradigmatic changes, concerned with methodology, theory, and mathematical models of genotype-environment interplay; this might be the beginning of a third period of development of environmental studies in behavior genetics. In another part, the methodological problems related to environmental studies in behavior genetics are discussed. Although the methodology used in differential psychology is applicable for assessment of differences between individuals, it is insufficient to explain the sources of these differences. In addition, we stress that psychoanalytic studies of twins and their experiences, initiated in the 1930s and continued episodically until the 1980s, could bring an interesting methodology and contribute to the explanation of puzzling findings from environmental studies of behavior genetics. Finally, we will conclude with implications from the results of environmental studies in behavior genetics, including methodological issues. Copyright © 2010 Elsevier Ltd. All rights reserved.

Molecular Population Genetics

PubMed Central

Casillas, Sònia; Barbadilla, Antonio

2017-01-01

Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data. PMID:28270526

Molecular Population Genetics.

PubMed

Casillas, Sònia; Barbadilla, Antonio

2017-03-01

Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data. Copyright © 2017 Casillas and Barbadilla.

Modelling soil water retention using support vector machines with genetic algorithm optimisation.

PubMed

Lamorski, Krzysztof; Sławiński, Cezary; Moreno, Felix; Barna, Gyöngyi; Skierucha, Wojciech; Arrue, José L

2014-01-01

This work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: -0.98, -3.10, -9.81, -31.02, -491.66, and -1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the ν-SVM method was used for model development and the results were compared with the formerly used the C-SVM method. For the purpose of models' parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67-0.92. Studies demonstrated usability of ν-SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.

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.

Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics.

PubMed

Moore, Jason H; Boczko, Erik M; Summar, Marshall L

2005-02-01

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two or more DNA sequence variations. We review here this approach and then discuss how it can be used to model biochemical and metabolic data in the context of genetic studies of human disease susceptibility.

Near infrared spectrometric technique for testing fruit quality: optimisation of regression models using genetic algorithms

NASA Astrophysics Data System (ADS)

Isingizwe Nturambirwe, J. Frédéric; Perold, Willem J.; Opara, Umezuruike L.

2016-02-01

Near infrared (NIR) spectroscopy has gained extensive use in quality evaluation. It is arguably one of the most advanced spectroscopic tools in non-destructive quality testing of food stuff, from measurement to data analysis and interpretation. NIR spectral data are interpreted through means often involving multivariate statistical analysis, sometimes associated with optimisation techniques for model improvement. The objective of this research was to explore the extent to which genetic algorithms (GA) can be used to enhance model development, for predicting fruit quality. Apple fruits were used, and NIR spectra in the range from 12000 to 4000 cm-1 were acquired on both bruised and healthy tissues, with different degrees of mechanical damage. GAs were used in combination with partial least squares regression methods to develop bruise severity prediction models, and compared to PLS models developed using the full NIR spectrum. A classification model was developed, which clearly separated bruised from unbruised apple tissue. GAs helped improve prediction models by over 10%, in comparison with full spectrum-based models, as evaluated in terms of error of prediction (Root Mean Square Error of Cross-validation). PLS models to predict internal quality, such as sugar content and acidity were developed and compared to the versions optimized by genetic algorithm. Overall, the results highlighted the potential use of GA method to improve speed and accuracy of fruit quality prediction.

Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

NASA Astrophysics Data System (ADS)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.

Interating the History of Mathematics in Educational Praxis

ERIC Educational Resources Information Center

Farmaki, Vassiliki; Klaudatos, Nikos; Paschos, Theodorus

2004-01-01

The integration of History in the educational practice can lead to the development of a series of activities exploiting genetic "moments" of the history of Mathematics. Utilizing genetic ideas that developed during the 14th century (Merton College, N. Oresme), activities are developed and mathematical models for solving problems related to uniform…

[The discussion of the infiltrative model of chemical knowledge stepping into genetics teaching in agricultural institute or university].

PubMed

Zou, Ping; Luo, Pei-Gao

2010-05-01

Chemistry is an important group of basic courses, while genetics is one of the important major-basic courses in curriculum of many majors in agricultural institutes or universities. In order to establish the linkage between the major course and the basic course, the ability of application of the chemical knowledge previously learned in understanding genetic knowledge in genetics teaching is worthy of discussion for genetics teachers. In this paper, the authors advocate to apply some chemical knowledge previously learned to understand genetic knowledge in genetics teaching with infiltrative model, which could help students learn and understand genetic knowledge more deeply. Analysis of the intrinsic logistic relationship among the knowledge of different courses and construction of the integral knowledge network are useful for students to improve their analytic, comprehensive and logistic abilities. By this way, we could explore a new teaching model to develop the talents with new ideas and comprehensive competence in agricultural fields.

Barriers to the use of genetic information for the development of new epilepsy treatments.

PubMed

Ferraro, Thomas N

2016-01-01

Genetic analysis is providing new information on the biological basis of epilepsy at a rapid pace; this article identifies factors acting as major barriers to use of these data for therapy development. Disease heterogeneity is a primary obstacle since so many genes can cause or predispose to epilepsy and the clinical presentation of epilepsy is so diverse, thus making it difficult to define the most therapeutically relevant targets. Further, many epilepsy genes affect brain development, an observation that represents a barrier unto itself given the challenge of reversing or preventing genetically mediated alterations of brain pathway formation. Finally, the lack of appropriate models for testing new therapies is also recognized as a fundamental limitation. Overcoming these barriers will be aided by full characterization of the genetic landscape of epilepsy, elucidation of key pathway points for therapeutic intervention and creation of unique experimental models to validate results.

Genetic thinking in the study of social relationships: Five points of entry

PubMed Central

Reiss, David

2014-01-01

For nearly a generation, researchers studying human behavioral development have combined genetically informed research designs with careful measures of social relationships: parenting, sibling relationships, peer relationships, marital processes, social class stratifications and patterns of social engagement in the elderly. In what way have these genetically informed studies altered the construction and testing of social theories of human development? We consider five points where genetic thinking is taking hold. First, genetic findings suggest an alternative scenario for explaining social data. Associations between measures of the social environment and human development may be due to genes that influence both. Second, genetic studies add to other prompts to study the early developmental origins of current social phenomena in mid-life and beyond. Third, genetic analyses promise to bring to the surface understudied social systems, such as sibling relationships, that have an impact on human development independent of genotype. Fourth, genetic analyses anchor in neurobiology individual differences in resilience and sensitivity to both adverse and favorable social environments. Finally, genetic analyses increase the utility of laboratory simulations of human social processes and of animal models. PMID:25419225

Experimental Population Genetics in the Introductory Genetics Laboratory Using "Drosophila" as a Model Organism

ERIC Educational Resources Information Center

Johnson, Ronald; Kennon, Tillman

2009-01-01

Hypotheses of population genetics are derived and tested by students in the introductory genetics laboratory classroom as they explore the effects of biotic variables (physical traits of fruit flies) and abiotic variables (island size and distance) on fruit fly populations. In addition to this hypothesis-driven experiment, the development of…

Genetic coding and gene expression - new Quadruplet genetic coding model

NASA Astrophysics Data System (ADS)

Shankar Singh, Rama

2012-07-01

Successful demonstration of human genome project has opened the door not only for developing personalized medicine and cure for genetic diseases, but it may also answer the complex and difficult question of the origin of life. It may lead to making 21st century, a century of Biological Sciences as well. Based on the central dogma of Biology, genetic codons in conjunction with tRNA play a key role in translating the RNA bases forming sequence of amino acids leading to a synthesized protein. This is the most critical step in synthesizing the right protein needed for personalized medicine and curing genetic diseases. So far, only triplet codons involving three bases of RNA, transcribed from DNA bases, have been used. Since this approach has several inconsistencies and limitations, even the promise of personalized medicine has not been realized. The new Quadruplet genetic coding model proposed and developed here involves all four RNA bases which in conjunction with tRNA will synthesize the right protein. The transcription and translation process used will be the same, but the Quadruplet codons will help overcome most of the inconsistencies and limitations of the triplet codes. Details of this new Quadruplet genetic coding model and its subsequent potential applications including relevance to the origin of life will be presented.

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia.

PubMed

Vallat, Laurent; Kemper, Corey A; Jung, Nicolas; Maumy-Bertrand, Myriam; Bertrand, Frédéric; Meyer, Nicolas; Pocheville, Arnaud; Fisher, John W; Gribben, John G; Bahram, Seiamak

2013-01-08

Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions--notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverse-engineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method integrates the temporal dimension of biological rewiring of genetic programs and enables the prediction of the effect of targeted gene disruption at the system level. We tested the performance accuracy of this model on synthetic data before reverse-engineering the response of primary cancer cells to a proliferative (protumorigenic) stimulation in a multistate leukemia biological model (i.e., chronic lymphocytic leukemia). To validate the ability of our method to predict the effects of gene modulation on the global program, we performed an intervention experiment on a targeted gene. Comparison of the predicted and observed gene expression changes demonstrates the possibility of predicting the effects of a perturbation in a gene regulatory network, a first step toward an orientated intervention in a cancer cell genetic program.

Genetics, development and composition of the insect head--a beetle's view.

PubMed

Posnien, Nico; Schinko, Johannes B; Kittelmann, Sebastian; Bucher, Gregor

2010-11-01

Many questions regarding evolution and ontogeny of the insect head remain open. Likewise, the genetic basis of insect head development is poorly understood. Recently, the investigation of gene expression data and the analysis of patterning gene function have revived interest in insect head development. Here, we argue that the red flour beetle Tribolium castaneum is a well suited model organism to spearhead research with respect to the genetic control of insect head development. We review recent molecular data and discuss its bearing on early development and morphogenesis of the head. We present a novel hypothesis on the ontogenetic origin of insect head sutures and review recent insights into the question on the origin of the labrum. Further, we argue that the study of developmental genes may identify the elusive anterior non-segmental region and present some evidence in favor of its existence. With respect to the question of evolution of patterning we show that the head Anlagen of the fruit fly Drosophila melanogaster and Tribolium differ considerably and we review profound differences of their genetic regulation. Finally, we discuss which insect model species might help us to answer the open questions concerning the genetic regulation of head development and its evolution. Copyright © 2010 Elsevier Ltd. All rights reserved.

Population genetics of Setaria viridis, a new model system

USDA-ARS?s Scientific Manuscript database

An extensive survey of the standing genetic variation in natural populations is among the priority steps in developing a species into a model system. In recent years, green foxtail (Setaria viridis), along with its domesticated form foxtail millet (S. italica), has rapidly become a promising new mod...

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:

Quantifying and predicting Drosophila larvae crawling phenotypes

NASA Astrophysics Data System (ADS)

Günther, Maximilian N.; Nettesheim, Guilherme; Shubeita, George T.

2016-06-01

The fruit fly Drosophila melanogaster is a widely used model for cell biology, development, disease, and neuroscience. The fly’s power as a genetic model for disease and neuroscience can be augmented by a quantitative description of its behavior. Here we show that we can accurately account for the complex and unique crawling patterns exhibited by individual Drosophila larvae using a small set of four parameters obtained from the trajectories of a few crawling larvae. The values of these parameters change for larvae from different genetic mutants, as we demonstrate for fly models of Alzheimer’s disease and the Fragile X syndrome, allowing applications such as genetic or drug screens. Using the quantitative model of larval crawling developed here we use the mutant-specific parameters to robustly simulate larval crawling, which allows estimating the feasibility of laborious experimental assays and aids in their design.

[From stone-craved genes to Michelangelo: significance and different aspects of gene-environment interaction].

PubMed

Lazary, Judit

2017-12-01

Although genetic studies have improved a lot in recent years, without clinical relevance sometimes their significance is devalued. Reviewing the major milestones of psychogenomics it can be seen that break-through success is just a question of time. Investigations of direct effect of genetic variants on phenotypes have not yielded positive findings. However, an important step was taken by adapting the gene-environment interaction model. In this model genetic vulnerability stepped into the place of "stone craved" pathology. Further progress happened when studies of environmental factors were combined with genetic function (epigenetics). This model provided the possibility for investigation of therapeutic interventions as environmental factors and it was proven that effective treatments exert a modifying effect on gene expression. Moreover, recent developments focus on therapeutic manipulation of gene function (e.g. chemogenetics). Instead of "stone craved" genes up-to-date dynamically interacting gene function became the basis of psychogenomics in which correction of the expression is a potential therapeutic tool. Keeping in mind these trends and developments, there is no doubt that genetics will be a fundamental part of daily clinical routine in the future.

Translating Discovery in Zebrafish Pancreatic Development to Human Pancreatic Cancer: Biomarkers, Targets, Pathogenesis, and Therapeutics

PubMed Central

Kazi, Abid A.; Yee, Rosemary K.

2013-01-01

Abstract Experimental studies in the zebrafish have greatly facilitated understanding of genetic regulation of the early developmental events in the pancreas. Various approaches using forward and reverse genetics, chemical genetics, and transgenesis in zebrafish have demonstrated generally conserved regulatory roles of mammalian genes and discovered novel genetic pathways in exocrine pancreatic development. Accumulating evidence has supported the use of zebrafish as a model of human malignant diseases, including pancreatic cancer. Studies have shown that the genetic regulators of exocrine pancreatic development in zebrafish can be translated into potential clinical biomarkers and therapeutic targets in human pancreatic adenocarcinoma. Transgenic zebrafish expressing oncogenic K-ras and zebrafish tumor xenograft model have emerged as valuable tools for dissecting the pathogenetic mechanisms of pancreatic cancer and for drug discovery and toxicology. Future analysis of the pancreas in zebrafish will continue to advance understanding of the genetic regulation and biological mechanisms during organogenesis. Results of those studies are expected to provide new insights into how aberrant developmental pathways contribute to formation and growth of pancreatic neoplasia, and hopefully generate valid biomarkers and targets as well as effective and safe therapeutics in pancreatic cancer. PMID:23682805

Translating discovery in zebrafish pancreatic development to human pancreatic cancer: biomarkers, targets, pathogenesis, and therapeutics.

PubMed

Yee, Nelson S; Kazi, Abid A; Yee, Rosemary K

2013-06-01

Abstract Experimental studies in the zebrafish have greatly facilitated understanding of genetic regulation of the early developmental events in the pancreas. Various approaches using forward and reverse genetics, chemical genetics, and transgenesis in zebrafish have demonstrated generally conserved regulatory roles of mammalian genes and discovered novel genetic pathways in exocrine pancreatic development. Accumulating evidence has supported the use of zebrafish as a model of human malignant diseases, including pancreatic cancer. Studies have shown that the genetic regulators of exocrine pancreatic development in zebrafish can be translated into potential clinical biomarkers and therapeutic targets in human pancreatic adenocarcinoma. Transgenic zebrafish expressing oncogenic K-ras and zebrafish tumor xenograft model have emerged as valuable tools for dissecting the pathogenetic mechanisms of pancreatic cancer and for drug discovery and toxicology. Future analysis of the pancreas in zebrafish will continue to advance understanding of the genetic regulation and biological mechanisms during organogenesis. Results of those studies are expected to provide new insights into how aberrant developmental pathways contribute to formation and growth of pancreatic neoplasia, and hopefully generate valid biomarkers and targets as well as effective and safe therapeutics in pancreatic cancer.

INVOLVEMENT OF MULTIPLE MOLECULAR PATHWAYS IN THE GENETICS OF OCULAR REFRACTION AND MYOPIA.

PubMed

Wojciechowski, Robert; Cheng, Ching-Yu

2018-01-01

The prevalence of myopia has increased dramatically worldwide within the last three decades. Recent studies have shown that refractive development is influenced by environmental, behavioral, and inherited factors. This review aims to analyze recent progress in the genetics of refractive error and myopia. A comprehensive literature search of PubMed and OMIM was conducted to identify relevant articles in the genetics of refractive error. Genome-wide association and sequencing studies have increased our understanding of the genetics involved in refractive error. These studies have identified interesting candidate genes. All genetic loci discovered to date indicate that refractive development is a heterogeneous process mediated by a number of overlapping biological processes. The exact mechanisms by which these biological networks regulate eye growth are poorly understood. Although several individual genes and/or molecular pathways have been investigated in animal models, a systematic network-based approach in modeling human refractive development is necessary to understand the complex interplay between genes and environment in refractive error. New biomedical technologies and better-designed studies will continue to refine our understanding of the genetics and molecular pathways of refractive error, and may lead to preventative and therapeutic measures to combat the myopia epidemic.

Applying Biometric Growth Curve Models to Developmental Synchronies in Cognitive Development: The Louisville Twin Study.

PubMed

Finkel, Deborah; Davis, Deborah Winders; Turkheimer, Eric; Dickens, William T

2015-11-01

Biometric latent growth curve models were applied to data from the LTS in order to replicate and extend Wilson's (Child Dev 54:298-316, 1983) findings. Assessments of cognitive development were available from 8 measurement occasions covering the period 4-15 years for 1032 individuals. Latent growth curve models were fit to percent correct for 7 subscales: information, similarities, arithmetic, vocabulary, comprehension, picture completion, and block design. Models were fit separately to WPPSI (ages 4-6 years) and WISC-R (ages 7-15). Results indicated the expected increases in heritability in younger childhood, and plateaus in heritability as children reached age 10 years. Heritability of change, per se (slope estimates), varied dramatically across domains. Significant genetic influences on slope parameters that were independent of initial levels of performance were found for only information and picture completion subscales. Thus evidence for both genetic continuity and genetic innovation in the development of cognitive abilities in childhood were found.

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

Aggressive Behavior between Siblings and the Development of Externalizing Problems: Evidence from a Genetically Sensitive Study

ERIC Educational Resources Information Center

Natsuaki, Misaki N.; Ge, Xiaojia; Reiss, David; Neiderhiser, Jenae M.

2009-01-01

This study investigated the prospective links between sibling aggression and the development of externalizing problems using a multilevel modeling approach with a genetically sensitive design. The sample consisted of 780 adolescents (390 sibling pairs) who participated in 2 waves of the Nonshared Environment in Adolescent Development project.…

[The genetics of thrombosis in cancer].

PubMed

Soria, José Manuel; López, Sonia

2015-01-01

Venous thromboembolism (VTE) is a multifactorial and complex disease in which the interaction of genetic factors (estimated at 60%) and environmental factors (e.g., the use of oral contraceptives, pregnancy, immobility and cancer) determine the risk of thrombosis for each individual. In particular, the association between thrombosis and cancer is well established. Approximately 20% of patients with cancer develop a thromboembolic event over the course of the natural history of the tumor process, with thrombosis being the second leading cause of death for these patients. One of the greatest challenges currently facing the field of oncology is the identification of patients at high risk of VTE who can benefit from thromboprophylaxis. Currently, there is a VTE risk prediction model for patients with cancer (the Khorana risk score); however, its ability to identify patients at high risk is very low. It is important to note that this score, which is based on five clinical parameters, ignores the genetic variability associated with VTE risk. In this article, we present the preliminary results of the Oncothromb study, whose objective is to develop an individual VTE risk prediction model for patients with cancer who are treated with outpatient chemotherapy. Our model includes the clinical and genetic data on each patient (Thrombo inCode(®) genetic profile). Only by integrating multiple layers of biological information (clinical, plasmatic and genetic) we could obtain models that provide accurate information as to which patients are at high risk of developing a thromboembolic event associated with cancer so as to take appropriate prophylactic measures. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

Genetically engineered mouse models for epithelial ovarian cancer: are we there yet?

PubMed

Howell, Viive M

2014-03-01

The development of preclinical spontaneous genetically engineered mouse models (GEMMs) requires an understanding of the genetic basis of the human disease. Such robust models have proven invaluable for increasing understanding of human malignancies as well as identifying new biomarkers and testing new therapies for these diseases. While GEMMs have been reported for ovarian cancer, the majority have proven disappointing overall in their recapitulation of paired genetic and histological features especially for serous ovarian epithelial cancer. This review describes GEMMs for ovarian cancer, in particular, high grade serous ovarian cancer and assesses these in light of recent changes in our understanding of the human malignancy. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

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 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

Are Australasian Genetic Counselors Interested in Private Practice at the Primary Care Level of Health Service?

PubMed

Sane, Vrunda; Humphreys, Linda; Peterson, Madelyn

2015-10-01

This study explored the perceived interest in development of private genetic counseling services in collaboration with primary care physicians in the Australasian setting by online survey of members of the Australasian Society of Genetic Counselors. Four hypothetical private practice models of professional collaboration between genetic counselors and primary care physicians or clinical geneticists were proposed to gauge interest and enthusiasm of ASGC members for this type of professional development. Perceived barriers and facilitators were also evaluated. 78 completed responses were included for analysis. The majority of participants (84.6 %) showed a positive degree of interest and enthusiasm towards potential for clinical work in private practice. All proposed practice models yielded a positive degree of interest from participants. Model 4 (the only model of collaboration with a clinical geneticist rather than primary care physician) was the clearly preferred option (mean = 4.26/5), followed by Model 2 (collaboration with a single primary care practice) (mean = 4.09/5), Model 3 (collaboration with multiple primary care clinics, multidisciplinary clinic or specialty clinic) (mean = 3.77/5) and finally, Model 1 (mean = 3.61/5), which was the most independent model of practice. When participants ranked the options in the order of preference, Model 4 remained the most popular first preference (44.6 %), followed by model 2 (21.6 %), model 3 (18.9 %) and model 1 was again least popular (10.8 %). There was no significant statistical correlation between demographic characteristics (age bracket, years of work experience, current level of work autonomy) and participants' preference for private practice models. Support from clinical genetics colleagues and the professional society was highly rated as a facilitator and, conversely, lack of such support as a significant barrier.

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

Myeloproliferative Neoplasm Animal Models

PubMed Central

Mullally, Ann; Lane, Steven W.; Brumme, Kristina; Ebert, Benjamin L.

2012-01-01

Synopsis Myeloproliferative neoplasm (MPN) animal models accurately re-capitulate human disease in mice and have been an important tool for the study of MPN biology and therapy. Transplantation of BCR-ABL transduced bone marrow cells into irradiated syngeneic mice established the field of MPN animal modeling and the retroviral bone marrow transplantation (BMT) assay has been used extensively since. Genetically engineered MPN animal models have enabled detailed characterization of the effects of specific MPN associated genetic abnormalities on the hematopoietic stem and progenitor cell (HSPC) compartment and xenograft models have allowed the study of primary human MPN-propagating cells in vivo. All models have facilitated the pre-clinical development of MPN therapies. JAK2V617F, the most common molecular abnormality in BCR-ABL negative MPN, has been extensively studied using retroviral, transgenic, knock-in and xenograft models. MPN animal models have also been used to investigate additional genetic lesions found in human MPN and to evaluate the bone marrow microenvironment in these diseases. Finally, several genetic lesions, although not common, somatically mutated drivers of MPN in humans induce a MPN phenotype in mice. Future uses for MPN animal models will include modeling compound genetic lesions in MPN and studying myelofibrotic transformation. PMID:23009938

Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model.

PubMed

Emura, Takeshi; Nakatochi, Masahiro; Matsui, Shigeyuki; Michimae, Hirofumi; Rondeau, Virginie

2017-01-01

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.

[Studying the association between genetic polymorphism of growth factors and the development of primary open-angle glaucoma].

PubMed

Kirilenko, M Yu; Tikunova, E V; Sirotina, S S; Polonikov, A V; Bushueva, O Yu; Churnosov, M I

Primary open-angle glaucoma (POAG) is a multifactorial disease, etiopathogenesis of which largely depends on growth factors. Possessing a variety of medical and biological effects, these cytokines may influence the development and progression of POAG. to reveal the role of genetic polymorphisms of growth factors in predisposition to developing POAG that is refractory to local hypotensive therapy. The object of the study were 162 patients with stage II-III POAG, in whom local hypotensive therapy was inefficient, 90 patients with stage II-III POAG well controlled on local hypotensive therapy, and 191 controls. The material for the study was venous blood taken from the cubital vein of a proband. Isolation of genomic DNA was performed by phenol-chloroform extraction. Analysis of genetic polymorphisms of growth factors was performed through allelic discrimination. For that, synthesis of DNA was carried out via polymerase chain reaction (PCR). It is found that the T IGFR-1 genetic variant (OR=1.34) and a combination of the C VEGF-A and T IGFR-1 genetic variants (OR=1.90) are risk factors of developing POAG that is refractory to local hypotensive therapy. A statistical model for predicting such a risk has been proposed that includes: VEGF-A с.-958C>T genetic marker (rs 833,061), age, concomitant non-inflammatory ocular diseases, microvascular changes in the conjunctiva, the degree of pigmentation of the angle of the anterior chamber, and pseudoexfoliative syndrome. Recognition accuracy of the model is 90.42%. The T IGFR-1 genetic variant and a combination of the C VEGF-A and T IGFR-1 genetic variants increase the risk of developing POAG that is refractory to local hypotensive therapy.

Optimization of Land Use Suitability for Agriculture Using Integrated Geospatial Model and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Mansor, S. B.; Pormanafi, S.; Mahmud, A. R. B.; Pirasteh, S.

2012-08-01

In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

Methods for cost estimation in software project management

NASA Astrophysics Data System (ADS)

Briciu, C. V.; Filip, I.; Indries, I. I.

2016-02-01

The speed in which the processes used in software development field have changed makes it very difficult the task of forecasting the overall costs for a software project. By many researchers, this task has been considered unachievable, but there is a group of scientist for which this task can be solved using the already known mathematical methods (e.g. multiple linear regressions) and the new techniques as genetic programming and neural networks. The paper presents a solution for building a model for the cost estimation models in the software project management using genetic algorithms starting from the PROMISE datasets related COCOMO 81 model. In the first part of the paper, a summary of the major achievements in the research area of finding a model for estimating the overall project costs is presented together with the description of the existing software development process models. In the last part, a basic proposal of a mathematical model of a genetic programming is proposed including here the description of the chosen fitness function and chromosome representation. The perspective of model described it linked with the current reality of the software development considering as basis the software product life cycle and the current challenges and innovations in the software development area. Based on the author's experiences and the analysis of the existing models and product lifecycle it was concluded that estimation models should be adapted with the new technologies and emerging systems and they depend largely by the chosen software development method.

Models of ovarian cancer metastasis: Murine models

PubMed Central

Šale, Sanja; Orsulic, Sandra

2008-01-01

Mice have mainly been used in ovarian cancer research as immunodeficient hosts for cell lines derived from the primary tumors and ascites of ovarian cancer patients. These xenograft models have provided a valuable system for pre-clinical trials, however, the genetic complexity of human tumors has precluded the understanding of key events that drive metastatic dissemination. Recently developed immunocompetent, genetically defined mouse models of epithelial ovarian cancer represent significant improvements in the modeling of metastatic disease. PMID:19337569

Advancing the understanding of autism disease mechanisms through genetics

PubMed Central

de la Torre-Ubieta, Luis; Won, Hyejung; Stein, Jason L; Geschwind, Daniel H

2016-01-01

Progress in understanding the genetic etiology of autism spectrum disorders (ASD) has fueled remarkable advances in our understanding of its potential neurobiological mechanisms. Yet, at the same time, these findings highlight extraordinary causal diversity and complexity at many levels ranging from molecules to circuits and emphasize the gaps in our current knowledge. Here we review current understanding of the genetic architecture of ASD and integrate genetic evidence, neuropathology and studies in model systems with how they inform mechanistic models of ASD pathophysiology. Despite the challenges, these advances provide a solid foundation for the development of rational, targeted molecular therapies. PMID:27050589

Measurement and modeling of intrinsic transcription terminators

PubMed Central

Cambray, Guillaume; Guimaraes, Joao C.; Mutalik, Vivek K.; Lam, Colin; Mai, Quynh-Anh; Thimmaiah, Tim; Carothers, James M.; Arkin, Adam P.; Endy, Drew

2013-01-01

The reliable forward engineering of genetic systems remains limited by the ad hoc reuse of many types of basic genetic elements. Although a few intrinsic prokaryotic transcription terminators are used routinely, termination efficiencies have not been studied systematically. Here, we developed and validated a genetic architecture that enables reliable measurement of termination efficiencies. We then assembled a collection of 61 natural and synthetic terminators that collectively encode termination efficiencies across an ∼800-fold dynamic range within Escherichia coli. We simulated co-transcriptional RNA folding dynamics to identify competing secondary structures that might interfere with terminator folding kinetics or impact termination activity. We found that structures extending beyond the core terminator stem are likely to increase terminator activity. By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54). The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination. PMID:23511967

PredictABEL: an R package for the assessment of risk prediction models.

PubMed

Kundu, Suman; Aulchenko, Yurii S; van Duijn, Cornelia M; Janssens, A Cecile J W

2011-04-01

The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL ( http://www.genabel.org ) and CRAN ( http://cran.r-project.org/).

Genetic Thinking in the Study of Social Relationships: Five Points of Entry.

PubMed

Reiss, David

2010-09-01

For nearly a generation, researchers studying human behavioral development have combined genetically informed research designs with careful measures of social relationships such as parenting, sibling relationships, peer relationships, marital processes, social class stratifications, and patterns of social engagement in the elderly. In what way have these genetically informed studies altered the construction and testing of social theories of human development? We consider five points of entry where genetic thinking is taking hold. First, genetic findings suggest an alternative scenario for explaining social data. Associations between measures of the social environment and human development may be due to genes that influence both. Second, genetic studies add to other prompts to study the early developmental origins of current social phenomena in midlife and beyond. Third, genetic analyses promise to shed light on understudied social systems, such as sibling relationships, that have an impact on human development independent of genotype. Fourth, genetic analyses anchor in neurobiology individual differences in resilience and sensitivity to both adverse and favorable social environments. Finally, genetic analyses increase the utility of laboratory simulations of human social processes and of animal models. © The Author(s) 2010.

Stochastic models for inferring genetic regulation from microarray gene expression data.

PubMed

Tian, Tianhai

2010-03-01

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information. 2009 Elsevier Ireland Ltd. All rights reserved.

Dr. Lewis Kitchener Dahl, the Dahl Rats and the ‘Inconvenient truth’ abou the Genetics of Hypertension

PubMed Central

Joe, Bina

2014-01-01

Synopsis Lewis K. Dahl is regarded as an iconic figure in the field of hypertension research. During the 1960s and 1970s he published several seminal articles in the field that shed light on the relationship between salt and hypertension. Further, the Dahl rat models of hypertension that he developed by a selective breeding strategy are among the most widely used models for hypertension research. To this day, genetic studies using this model are ongoing in our laboratory. While Dr. Dahl is known for his contributions to the field of hypertension, very little, if any, of his personal history is documented. This article details a short biography of Dr. Lewis Dahl, the history behind the development of the Dahl rats and presents an overview of the results obtained through the genetic analysis of the Dahl rat as an experimental model to study the inheritance of hypertension. PMID:25646295

Dynamic traffic assignment : genetic algorithms approach

DOT National Transportation Integrated Search

1997-01-01

Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

Animal models of neoplastic development.

PubMed

Pitot, H C

2001-01-01

The basic animal model for neoplastic development used by regulatory agencies is the two-year chronic bioassay developed more than 30 years ago and based on the presumed mechanism of action of a few potential chemical carcinogens. Since that time, a variety of other model carcinogenic systems have been developed, usually involving shorter duration, single organ endpoints, multistage models, and those in genetically-engineered mice. The chronic bioassay is still the "gold standard" of regulatory agencies despite a number of deficiencies, while in this country the use of shorter term assays based on single organ endpoints has not been popular. The multistage model of carcinogenesis in mouse epidermis actually preceded the development of the chronic two-year bioassay, but it was not until multistage models in other organ systems were developed that the usefulness of such systems became apparent. Recently, several genetically-engineered mouse lines involving mutations in proto-oncogenes and tumour suppressor genes have been proposed as additional model systems for use in regulatory decisions. It is likely that a combination of several of these model systems may be most useful in both practical and basic applications of cancer prevention and therapy.

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.

Association of education & lifestyle factors with the perception of genetic knowledge on the development of lung cancer.

PubMed

Wang, Liang; Wang, Kesheng; Liu, Xuefeng; He, Yi

2016-05-01

The perception of genetic knowledge is useful for improving the heath behaviour change against developing cancers. However, no studies have investigated the perception of genetic knowledge on the development of lung cancer. The aim of this study was to examine demographic and lifestyle factors of the perception of genetic knowledge on the development of lung cancer. Data on 2,295 US adults (739 had the perception of genetic knowledge) were taken from the 2003 Health Information National Trends Survey. Multiple logistic regression models were used to evaluate potential factors of the perception of genetic knowledge of lung cancer. Participants aged ≥65 yr were more likely to have the perception of genetic knowledge than those aged 18-44 yr (OR=1.77, 95% CI=1.27-2.46). Higher education was associated with a greater perception of genetic knowledge (OR=1.47, 95% CI=1.16-1.87). Subjects with correct smoking attitude were more than three times more likely to have the perception of genetic knowledge (OR=3.15, 95% CI=2.10-4.72). Subjects with exercise were at an increased likelihood of having the perception of genetic knowledge than those without exercise (OR=1.63, 95% CI=1.24-2.13). Positive associations were observed between education and lifestyle factors and the perception of genetic knowledge on the development of lung cancer among US adults. Strategies developed to improve the perception of genetic knowledge of lung cancer may target on individuals who are young, less educated, and lack correct smoking attitude or exercise.

Genome-wide single nucleotide polymorphisms (SNPs) for a model invasive ascidian Botryllus schlosseri.

PubMed

Gao, Yangchun; Li, Shiguo; Zhan, Aibin

2018-04-01

Invasive species cause huge damages to ecology, environment and economy globally. The comprehensive understanding of invasion mechanisms, particularly genetic bases of micro-evolutionary processes responsible for invasion success, is essential for reducing potential damages caused by invasive species. The golden star tunicate, Botryllus schlosseri, has become a model species in invasion biology, mainly owing to its high invasiveness nature and small well-sequenced genome. However, the genome-wide genetic markers have not been well developed in this highly invasive species, thus limiting the comprehensive understanding of genetic mechanisms of invasion success. Using restriction site-associated DNA (RAD) tag sequencing, here we developed a high-quality resource of 14,119 out of 158,821 SNPs for B. schlosseri. These SNPs were relatively evenly distributed at each chromosome. SNP annotations showed that the majority of SNPs (63.20%) were located at intergenic regions, and 21.51% and 14.58% were located at introns and exons, respectively. In addition, the potential use of the developed SNPs for population genomics studies was primarily assessed, such as the estimate of observed heterozygosity (H O ), expected heterozygosity (H E ), nucleotide diversity (π), Wright's inbreeding coefficient (F IS ) and effective population size (Ne). Our developed SNP resource would provide future studies the genome-wide genetic markers for genetic and genomic investigations, such as genetic bases of micro-evolutionary processes responsible for invasion success.

Are genetically robust regulatory networks dynamically different from random ones?

NASA Astrophysics Data System (ADS)

Sevim, Volkan; Rikvold, Per Arne

We study a genetic regulatory network model developed to demonstrate that genetic robustness can evolve through stabilizing selection for optimal phenotypes. We report preliminary results on whether such selection could result in a reorganization of the state space of the system. For the chosen parameters, the evolution moves the system slightly toward the more ordered part of the phase diagram. We also find that strong memory effects cause the Derrida annealed approximation to give erroneous predictions about the model's phase diagram.

Eye growth and myopia development: Unifying theory and Matlab model.

PubMed

Hung, George K; Mahadas, Kausalendra; Mohammad, Faisal

2016-03-01

The aim of this article is to present an updated unifying theory of the mechanisms underlying eye growth and myopia development. A series of model simulation programs were developed to illustrate the mechanism of eye growth regulation and myopia development. Two fundamental processes are presumed to govern the relationship between physiological optics and eye growth: genetically pre-programmed signaling and blur feedback. Cornea/lens is considered to have only a genetically pre-programmed component, whereas eye growth is considered to have both a genetically pre-programmed and a blur feedback component. Moreover, based on the Incremental Retinal-Defocus Theory (IRDT), the rate of change of blur size provides the direction for blur-driven regulation. The various factors affecting eye growth are shown in 5 simulations: (1 - unregulated eye growth): blur feedback is rendered ineffective, as in the case of form deprivation, so there is only genetically pre-programmed eye growth, generally resulting in myopia; (2 - regulated eye growth): blur feedback regulation demonstrates the emmetropization process, with abnormally excessive or reduced eye growth leading to myopia and hyperopia, respectively; (3 - repeated near-far viewing): simulation of large-to-small change in blur size as seen in the accommodative stimulus/response function, and via IRDT as well as nearwork-induced transient myopia (NITM), leading to the development of myopia; (4 - neurochemical bulk flow and diffusion): release of dopamine from the inner plexiform layer of the retina, and the subsequent diffusion and relay of neurochemical cascade show that a decrease in dopamine results in a reduction of proteoglycan synthesis rate, which leads to myopia; (5 - Simulink model): model of genetically pre-programmed signaling and blur feedback components that allows for different input functions to simulate experimental manipulations that result in hyperopia, emmetropia, and myopia. These model simulation programs (available upon request) can provide a useful tutorial for the general scientist and serve as a quantitative tool for researchers in eye growth and myopia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pathogenesis of Pancreatic Cancer: Lessons from Animal Models

PubMed Central

Murtaugh, L. Charles

2014-01-01

The past several decades have seen great effort devoted to mimicking the key features of pancreatic ductal adenocarcinoma (PDAC) in animals, and have produced two robust models of this deadly cancer. Carcinogen-treated Syrian hamsters develop PDAC with genetic lesions that reproduce those of human, including activation of the Kras oncogene, and early studies in this species validated non-genetic risk factors for PDAC including pancreatitis, obesity and diabetes. More recently, PDAC research has been invigorated by the development of genetically-engineered mouse models based on tissue-specific Kras activation and deletion of tumor suppressor genes. Surprisingly, mouse PDAC appears to arise from exocrine acinar rather than ductal cells, via a process of phenotypic reprogramming that is accelerated by inflammation. Studies in both models have uncovered molecular mechanisms by which inflammation promotes and sustains PDAC, and identified targets for chemoprevention to suppress PDAC in high-risk individuals. The mouse model, in particular, has also been instrumental in developing new approaches to early detection as well as treatment of advanced disease. Together, animal models enable diverse approaches to basic and preclinical research on pancreatic cancer, the results of which will accelerate progress against this currently intractable cancer. PMID:24178582

Reading Development in Young Children: Genetic and Environmental Influences

PubMed Central

Logan, Jessica A. R.; Hart, Sara A.; Cutting, Laurie; Deater-Deckard, Kirby; Schatschneider, Chris; Petrill, Stephen

2013-01-01

The development of reading skills in typical students is commonly described as a rapid growth across early grades of active reading education, with a slowing down of growth as active instruction tapers. This study examined the extent to which genetics and environments influence these growth rates. Participants were 371 twin pairs, aged approximately 6 through 12, from the Western Reserve Reading Project. Development of word-level reading, reading comprehension, and rapid naming was examined using genetically sensitive latent quadratic growth curve modeling. Results confirmed the developmental trajectory described in the phenotypic literature. Furthermore, the same shared environmental influences were related to early reading skills and subsequent growth, but genetic influences on these factors were unique. PMID:23574275

The filamentous fungus Sordaria macrospora as a genetic model to study fruiting body development.

PubMed

Teichert, Ines; Nowrousian, Minou; Pöggeler, Stefanie; Kück, Ulrich

2014-01-01

Filamentous fungi are excellent experimental systems due to their short life cycles as well as easy and safe manipulation in the laboratory. They form three-dimensional structures with numerous different cell types and have a long tradition as genetic model organisms used to unravel basic mechanisms underlying eukaryotic cell differentiation. The filamentous ascomycete Sordaria macrospora is a model system for sexual fruiting body (perithecia) formation. S. macrospora is homothallic, i.e., self-fertile, easily genetically tractable, and well suited for large-scale genomics, transcriptomics, and proteomics studies. Specific features of its life cycle and the availability of a developmental mutant library make it an excellent system for studying cellular differentiation at the molecular level. In this review, we focus on recent developments in identifying gene and protein regulatory networks governing perithecia formation. A number of tools have been developed to genetically analyze developmental mutants and dissect transcriptional profiles at different developmental stages. Protein interaction studies allowed us to identify a highly conserved eukaryotic multisubunit protein complex, the striatin-interacting phosphatase and kinase complex and its role in sexual development. We have further identified a number of proteins involved in chromatin remodeling and transcriptional regulation of fruiting body development. Furthermore, we review the involvement of metabolic processes from both primary and secondary metabolism, and the role of nutrient recycling by autophagy in perithecia formation. Our research has uncovered numerous players regulating multicellular development in S. macrospora. Future research will focus on mechanistically understanding how these players are orchestrated in this fungal model system. Copyright © 2014 Elsevier Inc. All rights reserved.

Aggressive Behavior between Siblings and the Development of Externalizing Problems: Evidence from a Genetically Sensitive Study

PubMed Central

Natsuaki, Misaki N.; Ge, Xiaojia; Reiss, David; Neiderhiser, Jenae M.

2011-01-01

This study investigated the prospective links between sibling aggression and the development of externalizing problems using a multilevel modeling approach with a genetically sensitive design. The sample consisted of 780 adolescents (390 sibling pairs) who participated in two waves of the Nonshared Environment for Adolescent Development (NEAD) project. Sibling pairs with varying degree of genetic relatedness, including monozygotic twins, dizygotic twins, full siblings, half siblings, and genetically unrelated siblings, were included. The results showed that sibling aggression at Time 1 was significantly associated with the focal child’s externalizing problems at Time 2 after accounting for the intra-class correlations between siblings. Sibling aggression remained significant in predicting subsequent externalizing problems even after controlling for the levels of pre-existing externalizing problems and mothers’ punitive parenting. This pattern of results was fairly robust across models using different informants. The findings provide converging evidence for the unique contribution of sibling aggression in understanding changes in externalizing problems during adolescence. PMID:19586176

Algebraic, geometric, and stochastic aspects of genetic operators

NASA Technical Reports Server (NTRS)

Foo, N. Y.; Bosworth, J. L.

1972-01-01

Genetic algorithms for function optimization employ genetic operators patterned after those observed in search strategies employed in natural adaptation. Two of these operators, crossover and inversion, are interpreted in terms of their algebraic and geometric properties. Stochastic models of the operators are developed which are employed in Monte Carlo simulations of their behavior.

Genetic and Environmental Contributions to General Cognitive Ability through the First 16 Years of Life

ERIC Educational Resources Information Center

Petrill, Stephen A.; Lipton, Paul A.; Hewitt, John K.; Plomin, Robert; Cherny, Stacey S.; Corley, Robin; DeFries, John C.

2004-01-01

The genetic and environmental contributions to the development of general cognitive ability throughout the first 16 years of life were examined using sibling data from the Colorado Adoption Project. Correlations were analyzed along with structural equation models to characterize the genetic and environmental influences on longitudinal stability…

Using "Fremyella Diplosiphon" as a Model Organism for Genetics-Based Laboratory Exercises

ERIC Educational Resources Information Center

Montgomery, Beronda L.

2011-01-01

In this pilot study, a genetics-based laboratory exercise using the cyanobacterium Fremyella diplosiphon was developed and trialled with thirteen Natural Sciences undergraduates. Despite most students only having limited prior exposure to molecular genetics laboratory methods, this cohort confirmed that they were able to follow the protocol and…

Alternate Service Delivery Models in Cancer Genetic Counseling: A Mini-Review.

PubMed

Buchanan, Adam Hudson; Rahm, Alanna Kulchak; Williams, Janet L

2016-01-01

Demand for cancer genetic counseling has grown rapidly in recent years as germline genomic information has become increasingly incorporated into cancer care, and the field has entered the public consciousness through high-profile celebrity publications. Increased demand and existing variability in the availability of trained cancer genetics clinicians place a priority on developing and evaluating alternate service delivery models for genetic counseling. This mini-review summarizes the state of science regarding service delivery models, such as telephone counseling, telegenetics, and group counseling. Research on comparative effectiveness of these models in traditional individual, in-person genetic counseling has been promising for improving access to care in a manner acceptable to patients. Yet, it has not fully evaluated the short- and long-term patient- and system-level outcomes that will help answer the question of whether these models achieve the same beneficial psychosocial and behavioral outcomes as traditional cancer genetic counseling. We propose a research agenda focused on comparative effectiveness of available service delivery models and how to match models to patients and practice settings. Only through this rigorous research can clinicians and systems find the optimal balance of clinical quality, ready and secure access to care, and financial sustainability. Such research will be integral to achieving the promise of genomic medicine in oncology.

High school students' understanding and problem solving in population genetics

NASA Astrophysics Data System (ADS)

Soderberg, Patti D.

This study is an investigation of student understanding of population genetics and how students developed, used and revised conceptual models to solve problems. The students in this study participated in three rounds of problem solving. The first round involved the use of a population genetics model to predict the number of carriers in a population. The second round required them to revise their model of simple dominance population genetics to make inferences about populations containing three phenotype variations. The third round of problem solving required the students to revise their model of population genetics to explain anomalous data where the proportions of males and females with a trait varied significantly. As the students solved problems, they were involved in basic scientific processes as they observed population phenomena, constructed explanatory models to explain the data they observed, and attempted to persuade their peers as to the adequacy of their models. In this study, the students produced new knowledge about the genetics of a trait in a population through the revision and use of explanatory population genetics models using reasoning that was similar to what scientists do. The students learned, used and revised a model of Hardy-Weinberg equilibrium to generate and test hypotheses about the genetics of phenotypes given only population data. Students were also interviewed prior to and following instruction. This study suggests that a commonly held intuitive belief about the predominance of a dominant variation in populations is resistant to change, despite instruction and interferes with a student's ability to understand Hardy-Weinberg equilibrium and microevolution.

Evolving Ideas on the Origin and Evolution of Flowers: New Perspectives in the Genomic Era

PubMed Central

Chanderbali, Andre S.; Berger, Brent A.; Howarth, Dianella G.; Soltis, Pamela S.; Soltis, Douglas E.

2016-01-01

The origin of the flower was a key innovation in the history of complex organisms, dramatically altering Earth’s biota. Advances in phylogenetics, developmental genetics, and genomics during the past 25 years have substantially advanced our understanding of the evolution of flowers, yet crucial aspects of floral evolution remain, such as the series of genetic and morphological changes that gave rise to the first flowers; the factors enabling the origin of the pentamerous eudicot flower, which characterizes ∼70% of all extant angiosperm species; and the role of gene and genome duplications in facilitating floral innovations. A key early concept was the ABC model of floral organ specification, developed by Elliott Meyerowitz and Enrico Coen and based on two model systems, Arabidopsis thaliana and Antirrhinum majus. Yet it is now clear that these model systems are highly derived species, whose molecular genetic-developmental organization must be very different from that of ancestral, as well as early, angiosperms. In this article, we will discuss how new research approaches are illuminating the early events in floral evolution and the prospects for further progress. In particular, advancing the next generation of research in floral evolution will require the development of one or more functional model systems from among the basal angiosperms and basal eudicots. More broadly, we urge the development of “model clades” for genomic and evolutionary-developmental analyses, instead of the primary use of single “model organisms.” We predict that new evolutionary models will soon emerge as genetic/genomic models, providing unprecedented new insights into floral evolution. PMID:27053123

A Study of Two Instructional Sequences Informed by Alternative Learning Progressions in Genetics

ERIC Educational Resources Information Center

Duncan, Ravit Golan; Choi, Jinnie; Castro-Faix, Moraima; Cavera, Veronica L.

2017-01-01

Learning progressions (LPs) are hypothetical models of how learning in a domain develops over time with appropriate instruction. In the domain of genetics, there are two independently developed alternative LPs. The main difference between the two progressions hinges on their assumptions regarding the accessibility of classical (Mendelian) versus…

The accuracies of DNA-based estimates of genetic merit derived from Angus or multibreed beef cattle training populations

USDA-ARS?s Scientific Manuscript database

Several organizations have developed prediction models for molecular breeding values (MBV) for quantitative growth and carcass traits in beef cattle using BovineSNP50 genotypes and phenotypic or EBV data. MBV for Angus cattle have been developed by IGENITY, Pfizer Animal Genetics, and a collaboratio...

Genetically engineered mouse models of craniopharyngioma: an opportunity for therapy development and understanding of tumor biology

PubMed Central

Martinez‐Barbera, Juan Pedro

2017-01-01

Abstract Adamantinomatous craniopharyngioma (ACP) is the commonest tumor of the sellar region in childhood. Two genetically engineered mouse models have been developed and are giving valuable insights into ACP biology. These models have identified novel pathways activated in tumors, revealed an important function of paracrine signalling and extended conventional theories about the role of organ‐specific stem cells in tumorigenesis. In this review, we summarize these mouse models, what has been learnt, their limitations and open questions for future research. We then discussed how these mouse models may be used to test novel therapeutics against potentially targetable pathways recently identified in human ACP. PMID:28414891

The Genetics of Biofuel Traits in Panicum Grasses: Developing a Model System with Diploid Panicum Hallii

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

Juenger, Thomas; Wolfrum, Ed

Our DOE funded project focused on characterizing natural variation in C4 perennial grasses including switchgrass (Panicum virgatum) and Hall’s panicgrass (Panicum hallii). The main theme of our project was to better understand traits linked with plant performance and that impact the utility of plant biomass as a biofuel feedstock. In addition, our project developed tools and resources for studying genetic variation in Panicum hallii. Our project successfully screened both Panicum virgatum and Panicum hallii diverse natural collections for a host of phenotypes, developed genetic mapping populations for both species, completed genetic mapping for biofuel related traits, and helped in themore » development of genomic resources of Panicum hallii. Together, these studies have improved our understanding of the role of genetic and environmental factors in impacting plant performance. This information, along with new tools, will help foster the improvement of perennial grasses for feedstock applications.« less

Genetic and environmental influences on thin-ideal internalization across puberty and preadolescent, adolescent, and young adult development.

PubMed

Suisman, Jessica L; Thompson, J Kevin; Keel, Pamela K; Burt, S Alexandra; Neale, Michael; Boker, Steven; Sisk, Cheryl; Klump, Kelly L

2014-11-01

Mean-levels of thin-ideal internalization increase during adolescence and pubertal development, but it is unknown whether these phenotypic changes correspond to developmental changes in etiological (i.e., genetic and environmental) risk. Given the limited knowledge on risk for thin-ideal internalization, research is needed to guide the identification of specific types of risk factors during critical developmental periods. The present twin study examined genetic and environmental influences on thin-ideal internalization across adolescent and pubertal development. Participants were 1,064 female twins (ages 8-25 years) from the Michigan State University Twin Registry. Thin-ideal internalization and pubertal development were assessed using self-report questionnaires. Twin moderation models were used to examine if age and/or pubertal development moderate genetic and environmental influences on thin-ideal internalization. Phenotypic analyses indicated significant increases in thin-ideal internalization across age and pubertal development. Twin models suggested no significant differences in etiologic effects across development. Nonshared environmental influences were most important in the etiology of thin-ideal internalization, with genetic, shared environmental, and nonshared environmental accounting for approximately 8%, 15%, and 72%, respectively, of the total variance. Despite mean-level increases in thin-ideal internalization across development, the relative influence of genetic versus environmental risk did not differ significantly across age or pubertal groups. The majority of variance in thin-ideal internalization was accounted for by environmental factors, suggesting that mean-level increases in thin-ideal internalization may reflect increases in the magnitude/strength of environmental risk across this period. Replication is needed, particularly with longitudinal designs that assess thin-ideal internalization across key developmental phases. © 2014 Wiley Periodicals, Inc.

Genetic and Environmental Influences on Thin-Ideal Internalization across Puberty and Pre-Adolescent, Adolescent, and Young Adult Development

PubMed Central

Suisman, Jessica L.; Thompson, J. Kevin; Keel, Pamela K.; Burt, S. Alexandra; Neale, Michael; Boker, Steven; Sisk, Cheryl; Klump, Kelly L.

2014-01-01

Objective Mean-levels of thin-ideal internalization increase during adolescence and pubertal development, but it is unknown whether these phenotypic changes correspond to developmental changes in etiological (i.e., genetic and environmental) risk. Given the limited knowledge on risk for thin-ideal internalization, research is needed to guide the identification of specific types of risk factors during critical developmental periods. The present twin study examined genetic and environmental influences on thin-ideal internalization across adolescent and pubertal development. Method Participants were 1,064 female twins (ages 8–25 years) from the Michigan State University Twin Registry. Thin-ideal internalization and pubertal development were assessed using self-report questionnaires. Twin moderation models were used to examine if age and/or pubertal development moderate genetic and environmental influences on thin-ideal internalization. Results Phenotypic analyses indicated significant increases in thin-ideal internalization across age and pubertal development. Twin models suggested no significant differences in etiologic effects across development. Nonshared environmental influences were most important in the etiology of thin-ideal internalization, with genetic, shared environmental, and nonshared environmental accounting for approximately 8%, 15%, and 72%, respectively, of the total variance. Discussion Despite mean-level increases in thin-ideal internalization across development, the relative influence of genetic versus environmental risk did not differ significantly across age or pubertal groups. The majority of variance in thin-ideal internalization was accounted for by environmental factors, suggesting that mean-level increases in thin-ideal internalization may reflect increases in the magnitude/strength of environmental risk across this period. Replication is needed, particularly with longitudinal designs that assess thin-ideal internalization across key developmental phases. PMID:24962440

Speciation genetics: current status and evolving approaches

PubMed Central

Wolf, Jochen B. W.; Lindell, Johan; Backström, Niclas

2010-01-01

The view of species as entities subjected to natural selection and amenable to change put forth by Charles Darwin and Alfred Wallace laid the conceptual foundation for understanding speciation. Initially marred by a rudimental understanding of hereditary principles, evolutionists gained appreciation of the mechanistic underpinnings of speciation following the merger of Mendelian genetic principles with Darwinian evolution. Only recently have we entered an era where deciphering the molecular basis of speciation is within reach. Much focus has been devoted to the genetic basis of intrinsic postzygotic isolation in model organisms and several hybrid incompatibility genes have been successfully identified. However, concomitant with the recent technological advancements in genome analysis and a newfound interest in the role of ecology in the differentiation process, speciation genetic research is becoming increasingly open to non-model organisms. This development will expand speciation research beyond the traditional boundaries and unveil the genetic basis of speciation from manifold perspectives and at various stages of the splitting process. This review aims at providing an extensive overview of speciation genetics. Starting from key historical developments and core concepts of speciation genetics, we focus much of our attention on evolving approaches and introduce promising methodological approaches for future research venues. PMID:20439277

Gene silencing in non-model insects: Overcoming hurdles using symbiotic bacteria for trauma-free sustainable delivery of RNA interference: Sustained RNA interference in insects mediated by symbiotic bacteria: Applications as a genetic tool and as a biocide.

PubMed

Whitten, Miranda; Dyson, Paul

2017-03-01

Insight into animal biology and development provided by classical genetic analysis of the model organism Drosophila melanogaster was an incentive to develop advanced genetic tools for this insect. But genetic systems for the over one million other known insect species are largely undeveloped. With increasing information about insect genomes resulting from next generation sequencing, RNA interference is now the method of choice for reverse genetics, although it is constrained by the means of delivery of interfering RNA. A recent advance to ensure sustained delivery with minimal experimental intervention or trauma to the insect is to exploit commensal bacteria for symbiont-mediated RNA interference. This technology not only offers an efficient means for RNA interference in insects in laboratory conditions, but also has potential for use in the control of human disease vectors, agricultural pests and pathogens of beneficial insects. © 2017 WILEY Periodicals, Inc.

NETWORK ASSISTED ANALYSIS TO REVEAL THE GENETIC BASIS OF AUTISM1

PubMed Central

Liu, Li; Lei, Jing; Roeder, Kathryn

2016-01-01

While studies show that autism is highly heritable, the nature of the genetic basis of this disorder remains illusive. Based on the idea that highly correlated genes are functionally interrelated and more likely to affect risk, we develop a novel statistical tool to find more potentially autism risk genes by combining the genetic association scores with gene co-expression in specific brain regions and periods of development. The gene dependence network is estimated using a novel partial neighborhood selection (PNS) algorithm, where node specific properties are incorporated into network estimation for improved statistical and computational efficiency. Then we adopt a hidden Markov random field (HMRF) model to combine the estimated network and the genetic association scores in a systematic manner. The proposed modeling framework can be naturally extended to incorporate additional structural information concerning the dependence between genes. Using currently available genetic association data from whole exome sequencing studies and brain gene expression levels, the proposed algorithm successfully identified 333 genes that plausibly affect autism risk. PMID:27134692

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.

Genetic and non-genetic animal models for autism spectrum disorders (ASD).

PubMed

Ergaz, Zivanit; Weinstein-Fudim, Liza; Ornoy, Asher

2016-09-01

Autism spectrum disorder (ASD) is associated, in addition to complex genetic factors, with a variety of prenatal, perinatal and postnatal etiologies. We discuss the known animal models, mostly in mice and rats, of ASD that helps us to understand the etiology, pathogenesis and treatment of human ASD. We describe only models where behavioral testing has shown autistic like behaviors. Some genetic models mimic known human syndromes like fragile X where ASD is part of the clinical picture, and others are without defined human syndromes. Among the environmentally induced ASD models in rodents, the most common model is the one induced by valproic acid (VPA) either prenatally or early postnatally. VPA induces autism-like behaviors following single exposure during different phases of brain development, implying that the mechanism of action is via a general biological mechanism like epigenetic changes. Maternal infection and inflammation are also associated with ASD in man and animal models. Copyright © 2016 Elsevier Inc. All rights reserved.

Nonhuman Primate Models in the Genomic Era: A Paradigm Shift

PubMed Central

Vallender, Eric J.; Miller, Gregory M.

2013-01-01

Because of their strong similarities to humans across physiologic, developmental, behavioral, immunologic, and genetic levels, nonhuman primates are essential models for a wide spectrum of biomedical research. But unlike other animal models, nonhuman primates possess substantial outbred genetic variation, reducing statistical power and potentially confounding interpretation of results in research studies. Although unknown genetic variation is a hindrance in studies that allocate animals randomly, taking genetic variation into account in study design affords an opportunity to transform the way that nonhuman primates are used in biomedical research. New understandings of how the function of individual genes in rhesus macaques mimics that seen in humans are greatly advancing the rhesus macaques utility as research models, but epistatic interaction, epigenetic regulatory mechanisms, and the intricacies of gene networks limit model development. We are now entering a new era of nonhuman primate research, brought on by the proliferation and rapid expansion of genomic data. Already the cost of a rhesus macaque genome is dwarfed by its purchase and husbandry costs, and complete genomic datasets will inevitably encompass each rhesus macaque used in biomedical research. Advancing this outcome is paramount. It represents an opportunity to transform the way animals are assigned and used in biomedical research and to develop new models of human disease. The genetic and genomic revolution brings with it a paradigm shift for nonhuman primates and new mandates on how nonhuman primates are used in biomedical research. PMID:24174439

Genetic models of homosexuality: generating testable predictions

PubMed Central

Gavrilets, Sergey; Rice, William R

2006-01-01

Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

A Theory of Information Genetics: How Four Subforces Generate Information and the Implications for Total Quality Knowledge Management.

ERIC Educational Resources Information Center

Tsai, Bor-sheng

2002-01-01

Proposes a model called information genetics to elaborate on the origin of information generating. Explains conceptual and data models; and describes a software program that was developed for citation data mining, infomapping, and information repackaging for total quality knowledge management in Web representation. (Contains 112 references.)…

Sleep and Development in Genetically Tractable Model Organisms

PubMed Central

Kayser, Matthew S.; Biron, David

2016-01-01

Sleep is widely recognized as essential, but without a clear singular function. Inadequate sleep impairs cognition, metabolism, immune function, and many other processes. Work in genetic model systems has greatly expanded our understanding of basic sleep neurobiology as well as introduced new concepts for why we sleep. Among these is an idea with its roots in human work nearly 50 years old: sleep in early life is crucial for normal brain maturation. Nearly all known species that sleep do so more while immature, and this increased sleep coincides with a period of exuberant synaptogenesis and massive neural circuit remodeling. Adequate sleep also appears critical for normal neurodevelopmental progression. This article describes recent findings regarding molecular and circuit mechanisms of sleep, with a focus on development and the insights garnered from models amenable to detailed genetic analyses. PMID:27183564

An introduction to genetic quality in the context of sexual selection.

PubMed

Pitcher, Trevor E; Mays, Herman L

2008-09-01

This special issue of Genetica brings together empirical researchers and theoreticians to present the latest on the evolutionary ecology of genetic quality in the context of sexual selection. The work comes from different fields of study including behavioral ecology, quantitative genetics and molecular genetics on a diversity of organisms using different approaches from comparative studies, mathematical modeling, field studies and laboratory experiments. The papers presented in this special issue primarily focus on genetic quality in relation to (1) sources of genetic variation, (2) polyandry, (3) new theoretical developments and (4) comprehensive reviews.

Landscape genetics of a pollinator longhorn beetle [Typocerus v. velutinus (Olivier)] on a continuous habitat surface.

PubMed

Abdel Moniem, H E M; Schemerhorn, B J; DeWoody, J A; Holland, J D

2016-10-01

Landscape connectivity, the degree to which the landscape structure facilitates or impedes organismal movement and gene flow, is increasingly important to conservationists and land managers. Metrics for describing the undulating shape of continuous habitat surfaces can expand the usefulness of continuous gradient surfaces that describe habitat and predict the flow of organisms and genes. We adopted a landscape gradient model of habitat and used surface metrics of connectivity to model the genetic continuity between populations of the banded longhorn beetle [Typocerus v. velutinus (Olivier)] collected at 17 sites across a fragmentation gradient in Indiana, USA. We tested the hypothesis that greater habitat connectivity facilitates gene flow between beetle populations against a null model of isolation by distance (IBD). We used next-generation sequencing to develop 10 polymorphic microsatellite loci and genotype the individual beetles to assess the population genetic structure. Isolation by distance did not explain the population genetic structure. The surface metrics model of habitat connectivity explained the variance in genetic dissimilarities 30 times better than the IBD model. We conclude that surface metrology of habitat maps is a powerful extension of landscape genetics in heterogeneous landscapes. © 2016 John Wiley & Sons Ltd.

Genetic variance of tolerance and the toxicant threshold model.

PubMed

Tanaka, Yoshinari; Mano, Hiroyuki; Tatsuta, Haruki

2012-04-01

A statistical genetics method is presented for estimating the genetic variance (heritability) of tolerance to pollutants on the basis of a standard acute toxicity test conducted on several isofemale lines of cladoceran species. To analyze the genetic variance of tolerance in the case when the response is measured as a few discrete states (quantal endpoints), the authors attempted to apply the threshold character model in quantitative genetics to the threshold model separately developed in ecotoxicology. The integrated threshold model (toxicant threshold model) assumes that the response of a particular individual occurs at a threshold toxicant concentration and that the individual tolerance characterized by the individual's threshold value is determined by genetic and environmental factors. As a case study, the heritability of tolerance to p-nonylphenol in the cladoceran species Daphnia galeata was estimated by using the maximum likelihood method and nested analysis of variance (ANOVA). Broad-sense heritability was estimated to be 0.199 ± 0.112 by the maximum likelihood method and 0.184 ± 0.089 by ANOVA; both results implied that the species examined had the potential to acquire tolerance to this substance by evolutionary change. Copyright © 2012 SETAC.

Genetically engineered mouse models and human osteosarcoma

PubMed Central

2012-01-01

Osteosarcoma is the most common form of bone cancer. Pivotal insight into the genes involved in human osteosarcoma has been provided by the study of rare familial cancer predisposition syndromes. Three kindreds stand out as predisposing to the development of osteosarcoma: Li-Fraumeni syndrome, familial retinoblastoma and RecQ helicase disorders, which include Rothmund-Thomson Syndrome in particular. These disorders have highlighted the important roles of P53 and RB respectively, in the development of osteosarcoma. The association of OS with RECQL4 mutations is apparent but the relevance of this to OS is uncertain as mutations in RECQL4 are not found in sporadic OS. Application of the knowledge or mutations of P53 and RB in familial and sporadic OS has enabled the development of tractable, highly penetrant murine models of OS. These models share many of the cardinal features associated with human osteosarcoma including, importantly, a high incidence of spontaneous metastasis. The recent development of these models has been a significant advance for efforts to improve our understanding of the genetics of human OS and, more critically, to provide a high-throughput genetically modifiable platform for preclinical evaluation of new therapeutics. PMID:23036272

Mouse Models for Investigating the Developmental Bases of Human Birth Defects

PubMed Central

MOON, ANNE M.

2006-01-01

Clinicians and basic scientists share an interest in discovering how genetic or environmental factors interact to perturb normal development and cause birth defects and human disease. Given the complexity of such interactions, it is not surprising that 4% of human infants are born with a congenital malformation, and cardiovascular defects occur in nearly 1%. Our research is based on the fundamental hypothesis that an understanding of normal and abnormal development will permit us to generate effective strategies for both prevention and treatment of human birth defects. Animal models are invaluable in these efforts because they allow one to interrogate the genetic, molecular and cellular events that distinguish normal from abnormal development. Several features of the mouse make it a particularly powerful experimental model: it is a mammalian system with similar embryology, anatomy and physiology to humans; genes, proteins and regulatory programs are largely conserved between human and mouse; and finally, gene targeting in murine embryonic stem cells has made the mouse genome amenable to sophisticated genetic manipulation currently unavailable in any other model organism. PMID:16641221

Automated design of genetic toggle switches with predetermined bistability.

PubMed

Chen, Shuobing; Zhang, Haoqian; Shi, Handuo; Ji, Weiyue; Feng, Jingchen; Gong, Yan; Yang, Zhenglin; Ouyang, Qi

2012-07-20

Synthetic biology aims to rationally construct biological devices with required functionalities. Methods that automate the design of genetic devices without post-hoc adjustment are therefore highly desired. Here we provide a method to predictably design genetic toggle switches with predetermined bistability. To accomplish this task, a biophysical model that links ribosome binding site (RBS) DNA sequence to toggle switch bistability was first developed by integrating a stochastic model with RBS design method. Then, to parametrize the model, a library of genetic toggle switch mutants was experimentally built, followed by establishing the equivalence between RBS DNA sequences and switch bistability. To test this equivalence, RBS nucleotide sequences for different specified bistabilities were in silico designed and experimentally verified. Results show that the deciphered equivalence is highly predictive for the toggle switch design with predetermined bistability. This method can be generalized to quantitative design of other probabilistic genetic devices in synthetic biology.

Genetic mapping in the presence of genotyping errors.

PubMed

Cartwright, Dustin A; Troggio, Michela; Velasco, Riccardo; Gutin, Alexander

2007-08-01

Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders.

Genetic Mapping in the Presence of Genotyping Errors

PubMed Central

Cartwright, Dustin A.; Troggio, Michela; Velasco, Riccardo; Gutin, Alexander

2007-01-01

Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders. PMID:17277374

A fully humanized transgenic mouse model of Huntington disease

PubMed Central

Southwell, Amber L.; Warby, Simon C.; Carroll, Jeffrey B.; Doty, Crystal N.; Skotte, Niels H.; Zhang, Weining; Villanueva, Erika B.; Kovalik, Vlad; Xie, Yuanyun; Pouladi, Mahmoud A.; Collins, Jennifer A.; Yang, X. William; Franciosi, Sonia; Hayden, Michael R.

2013-01-01

Silencing the mutant huntingtin gene (muHTT) is a direct and simple therapeutic strategy for the treatment of Huntington disease (HD) in principle. However, targeting the HD mutation presents challenges because it is an expansion of a common genetic element (a CAG tract) that is found throughout the genome. Moreover, the HTT protein is important for neuronal health throughout life, and silencing strategies that also reduce the wild-type HTT allele may not be well tolerated during the long-term treatment of HD. Several HTT silencing strategies are in development that target genetic sites in HTT that are outside of the CAG expansion, including HD mutation-linked single-nucleotide polymorphisms and the HTT promoter. Preclinical testing of these genetic therapies has required the development of a new mouse model of HD that carries these human-specific genetic targets. To generate a fully humanized mouse model of HD, we have cross-bred BACHD and YAC18 on the Hdh−/− background. The resulting line, Hu97/18, is the first murine model of HD that fully genetically recapitulates human HD having two human HTT genes, no mouse Hdh genes and heterozygosity of the HD mutation. We find that Hu97/18 mice display many of the behavioral changes associated with HD including motor, psychiatric and cognitive deficits, as well as canonical neuropathological abnormalities. This mouse line will be useful for gaining additional insights into the disease mechanisms of HD as well as for testing genetic therapies targeting human HTT. PMID:23001568

Unified reduction principle for the evolution of mutation, migration, and recombination

PubMed Central

Altenberg, Lee; Liberman, Uri; Feldman, Marcus W.

2017-01-01

Modifier-gene models for the evolution of genetic information transmission between generations of organisms exhibit the reduction principle: Selection favors reduction in the rate of variation production in populations near equilibrium under a balance of constant viability selection and variation production. Whereas this outcome has been proven for a variety of genetic models, it has not been proven in general for multiallelic genetic models of mutation, migration, and recombination modification with arbitrary linkage between the modifier and major genes under viability selection. We show that the reduction principle holds for all of these cases by developing a unifying mathematical framework that characterizes all of these evolutionary models. PMID:28265103

Simulating Drosophila Genetics with the Computer.

ERIC Educational Resources Information Center

Small, James W., Jr.; Edwards, Kathryn L.

1979-01-01

Presents some techniques developed to help improve student understanding of Mendelian principles through the use of a computer simulation model by the genetic system of the fruit fly. Includes discussion and evaluation of this computer assisted program. (MA)

Genetic and Environmental Contributions to the Development of Childhood Aggression

ERIC Educational Resources Information Center

Lubke, Gitta H.; McArtor, Daniel B.; Boomsma, Dorret I.; Bartels, Meike

2018-01-01

Longitudinal data from a large sample of twins participating in the Netherlands Twin Register (n = 42,827, age range 3-16) were analyzed to investigate the genetic and environmental contributions to childhood aggression. Genetic auto-regressive (simplex) models were used to assess whether the same genes are involved or whether new genes come into…

Using Genetic Mouse Models to Gain Insight into Glaucoma: Past Results and Future Possibilities

PubMed Central

Fernandes, Kimberly A.; Harder, Jeffrey M.; Williams, Pete A.; Rausch, Rebecca L.; Kiernan, Amy E.; Nair, K. Saidas; Anderson, Michael G.; John, Simon W.; Howell, Gareth R.; Libby, Richard T.

2015-01-01

While all forms of glaucoma are characterized by a specific pattern of retinal ganglion cell death, they are clinically divided into several distinct subclasses, including normal tension glaucoma, primary open angle glaucoma, congenital glaucoma, and secondary glaucoma. For each type of glaucoma there are likely numerous molecular pathways that control susceptibility to the disease. Given this complexity, a single animal model will never precisely model all aspects of all the different types of human glaucoma. Therefore, multiple animal models have been utilized to study glaucoma but more are needed. Because of the powerful genetic tools available to use in the laboratory mouse, it has proven to be a highly useful mammalian system for studying the pathophysiology of human disease. The similarity between human and mouse eyes coupled with the ability to use a combination of advanced cell biological and genetic tools in mice have led to a large increase in the number of studies using mice to model specific glaucoma phenotypes. Over the last decade, numerous new mouse models and genetic tools have emerged, providing important insight into the cell biology and genetics of glaucoma. In this review, we describe available mouse genetic models that can be used to study glaucoma-relevant disease/pathobiology. Furthermore, we discuss how these models have been used to gain insights into ocular hypertension (a major risk factor for glaucoma) and glaucomatous retinal ganglion cell death. Finally, the potential for developing new mouse models and using advanced genetic tools and resources for studying glaucoma are discussed. PMID:26116903

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

Anomalies as a Catalyst for Middle School Students' Knowledge Construction and Scientific Reasoning during Science Inquiry.

ERIC Educational Resources Information Center

Echevarria, Marissa

2003-01-01

Knowledge construction and scientific reasoning were examined during a unit in genetics, in which anomalies were used as a catalyst for student learning. Students used genetics simulation software to develop hypotheses and run tests of fruit fly crosses to develop mental models of simple dominance trait transmission. Instruction was intended to…

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

[Sporulation or competence development? A genetic regulatory network model of cell-fate determination in Bacillus subtilis].

PubMed

Lu, Zhenghui; Zhou, Yuling; Zhang, Xiaozhou; Zhang, Guimin

2015-11-01

Bacillus subtilis is a generally recognized as safe (GRAS) strain that has been widely used in industries including fodder, food, and biological control. In addition, B. subtilis expression system also plays a significant role in the production of industrial enzymes. However, its application is limited by its low sporulation frequency and transformation efficiency. Immense studies have been done on interpreting the molecular mechanisms of sporulation and competence development, whereas only few of them were focused on improving sporulation frequency and transformation efficiency of B. subtilis by genetic modification. The main challenge is that sporulation and competence development, as the two major developmental events in the stationary phase of B. subtilis, are regulated by the complicated intracellular genetic regulatory systems. In addition, mutual regulatory mechanisms also exist in these two developmental events. With the development of genetic and metabolic engineering, constructing genetic regulatory networks is currently one of the most attractive research fields, together with the genetic information of cell growth, metabolism, and development, to guide the industrial application. In this review, the mechanisms of sporulation and competence development of B. subtilis, their interactions, and the genetic regulation of cell growth were interpreted. In addition, the roles of these regulatory networks in guiding basic and applied research of B. subtilis and its related species were discussed.

A One Health Approach to Hypertrophic Cardiomyopathy

PubMed Central

Ueda, Yu; Stern, Joshua A.

2017-01-01

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease in humans and results in significant morbidity and mortality. Research over the past 25 years has contributed enormous insight into this inherited disease particularly in the areas of genetics, molecular mechanisms, and pathophysiology. Our understanding continues to be limited by the heterogeneity of clinical presentations with various genetic mutations associated with HCM. Transgenic mouse models have been utilized especially studying the genotypic and phenotypic interactions. However, mice possess intrinsic cardiac and hemodynamic differences compared to humans and have limitations preventing their direct translation. Other animal models of HCM have been studied or generated in part to overcome these limitations. HCM in cats shows strikingly similar molecular, histopathological, and genetic similarities to human HCM, and offers an important translational opportunity for the study of this disease. Recently, inherited left ventricular hypertrophy in rhesus macaques was identified and collaborative investigations have been conducted to begin to develop a non-human primate HCM model. These naturally-occurring large-animal models may aid in advancing our understanding of HCM and developing novel therapeutic approaches to this disease. This review will highlight the features of HCM in humans and the relevant available and developing animal models of this condition. PMID:28955182

Brain-derived neurotrophic factor as a model system for examining gene by environment interactions across development.

PubMed

Casey, B J; Glatt, C E; Tottenham, N; Soliman, F; Bath, K; Amso, D; Altemus, M; Pattwell, S; Jones, R; Levita, L; McEwen, B; Magariños, A M; Gunnar, M; Thomas, K M; Mezey, J; Clark, A G; Hempstead, B L; Lee, F S

2009-11-24

There has been a dramatic rise in gene x environment studies of human behavior over the past decade that have moved the field beyond simple nature versus nurture debates. These studies offer promise in accounting for more variability in behavioral and biological phenotypes than studies that focus on genetic or experiential factors alone. They also provide clues into mechanisms of modifying genetic risk or resilience in neurodevelopmental disorders. Yet, it is rare that these studies consider how these interactions change over the course of development. In this paper, we describe research that focuses on the impact of a polymorphism in a brain-derived neurotrophic factor (BDNF) gene, known to be involved in learning and development. Specifically we present findings that assess the effects of genotypic and environmental loadings on neuroanatomic and behavioral phenotypes across development. The findings illustrate the use of a genetic mouse model that mimics the human polymorphism, to constrain the interpretation of gene-environment interactions across development in humans.

Brachypodium distachyon as a Genetic Model System.

PubMed

Kellogg, Elizabeth A

2015-01-01

Brachypodium distachyon has emerged as a powerful model system for studying the genetics of flowering plants. Originally chosen for its phylogenetic proximity to the large-genome cereal crops wheat and barley, it is proving to be useful for more than simply providing markers for comparative mapping. Studies in B. distachyon have provided new insight into the structure and physiology of plant cell walls, the development and chemical composition of endosperm, and the genetic basis for cold tolerance. Recent work on auxin transport has uncovered mechanisms that apply to all angiosperms other than Arabidopsis. In addition to the areas in which it is currently used, B. distachyon is uniquely suited for studies of floral development, vein patterning, the controls of the perennial versus annual habit, and genome organization.

Comparing Mammography Abnormality Features and Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

PubMed Central

Burnside, Elizabeth S.; Liu, Jie; Wu, Yirong; Onitilo, Adedayo A.; McCarty, Catherine; Page, C. David; Peissig, Peggy; Trentham-Dietz, Amy; Kitchner, Terrie; Fan, Jun; Yuan, Ming

2015-01-01

Rationale and Objectives The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors. Materials and Methods Our IRB-approved, HIPAA-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature including: demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to BI-RADS. We developed predictive models using logistic regression to determine the predictive ability of: 1) demographic variables, 2) 10 selected genetic variants, or 3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross validation; used this risk estimate to construct ROC curves; and compared the AUC of each using the DeLong method. Results The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (p=0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; p<0.001) and the genetic model (AUC = .601; p<0.001). Conclusion BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy. PMID:26514439

Ciona as a Simple Chordate Model for Heart Development and Regeneration

PubMed Central

Evans Anderson, Heather; Christiaen, Lionel

2016-01-01

Cardiac cell specification and the genetic determinants that govern this process are highly conserved among Chordates. Recent studies have established the importance of evolutionarily-conserved mechanisms in the study of congenital heart defects and disease, as well as cardiac regeneration. As a basal Chordate, the Ciona model system presents a simple scaffold that recapitulates the basic blueprint of cardiac development in Chordates. Here we will focus on the development and cellular structure of the heart of the ascidian Ciona as compared to other Chordates, principally vertebrates. Comparison of the Ciona model system to heart development in other Chordates presents great potential for dissecting the genetic mechanisms that underlie congenital heart defects and disease at the cellular level and might provide additional insight into potential pathways for therapeutic cardiac regeneration. PMID:27642586

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

Learning Gene Expression Through Modelling and Argumentation. A Case Study Exploring the Connections Between the Worlds of Knowledge

NASA Astrophysics Data System (ADS)

Puig, Blanca; Ageitos, Noa; Jiménez-Aleixandre, María Pilar

2017-12-01

There is emerging interest on the interactions between modelling and argumentation in specific contexts, such as genetics learning. It has been suggested that modelling might help students understand and argue on genetics. We propose modelling gene expression as a way to learn molecular genetics and diseases with a genetic component. The study is framed in Tiberghien's (2000) two worlds of knowledge, the world of "theories & models" and the world of "objects & events", adding a third component, the world of representations. We seek to examine how modelling and argumentation interact and connect the three worlds of knowledge while modelling gene expression. It is a case study of 10th graders learning about diseases with a genetic component. The research questions are as follows: (1) What argumentative and modelling operations do students enact in the process of modelling gene expression? Specifically, which operations allow connecting the three worlds of knowledge? (2) What are the interactions between modelling and argumentation in modelling gene expression? To what extent do these interactions help students connect the three worlds of knowledge and modelling gene expression? The argumentative operation of using evidence helps students to relate the three worlds of knowledge, enacted in all the connections. It seems to be a relationship among the number of interactions between modelling and argumentation, the connections between world of knowledge and students' capacity to develop a more sophisticated representation. Despite this is a case study, this approach of analysis reveals potentialities for a deeper understanding of learning genetics though scientific practices.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

2013-01-01

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

A Road Map for 21st Century Genetic Restoration: Gene Pool Enrichment of the Black-Footed Ferret.

PubMed

Wisely, Samantha M; Ryder, Oliver A; Santymire, Rachel M; Engelhardt, John F; Novak, Ben J

2015-01-01

Interspecies somatic cell nuclear transfer (iSCNT) could benefit recovery programs of critically endangered species but must be weighed with the risks of failure. To weigh the risks and benefits, a decision-making process that evaluates progress is needed. Experiments that evaluate the efficiency and efficacy of blastocyst, fetal, and post-parturition development are necessary to determine the success or failure or species-specific iSCNT programs. Here, we use the black-footed ferret (Mustela nigripes) as a case study for evaluating this emerging biomedical technology as a tool for genetic restoration. The black-footed ferret has depleted genetic variation yet genome resource banks contain genetic material of individuals not currently represented in the extant lineage. Thus, genetic restoration of the species is in theory possible and could help reduce the persistent erosion of genetic diversity from drift. Extensive genetic, genomic, and reproductive science tools have previously been developed in black-footed ferrets and would aid in the process of developing an iSCNT protocol for this species. Nonetheless, developing reproductive cloning will require years of experiments and a coordinated effort among recovery partners. The information gained from a well-planned research effort with the goal of genetic restoration via reproductive cloning could establish a 21st century model for evaluating and implementing conservation breeding that would be applicable to other genetically impoverished species. © The American Genetic Association. 2015.

Genetics-based control of a mimo boiler-turbine plant

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

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome

PubMed Central

Jacquez, Geoffrey M.; Sabel, Clive E.; Shi, Chen

2015-01-01

The exposome, defined as the totality of an individual’s exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic geographic information science (Genetic GISc) that is founded on the exposome, genome+ and behavome. It provides an improved understanding of human health in relation to biology (the genome+), environmental exposures (the exposome), and their social, societal and behavioral determinants (the behavome). Genetic GISc poses three key needs: First, a mathematical foundation for emergent theory; Second, process-based models that bridge biological and geographic scales; Third, biologically plausible estimates of space-time disease lags. Compartmental models are a possible solution; this article develops two models using pancreatic cancer as an exemplar. The first models carcinogenesis based on the cascade of mutations and cellular changes that lead to metastatic cancer. The second models cancer stages by diagnostic criteria. These provide empirical estimates of the distribution of latencies in cellular states and disease stages, and maps of the burden of yet to be diagnosed disease. This approach links our emerging knowledge of genomics to cancer progression at the cellular level, to individuals and their cancer stage at diagnosis, to geographic distributions of cancer in extant populations. These methodological developments and exemplar provide the basis for a new synthesis in health geography: genetic geographic information science. PMID:26339073

D-VASim: an interactive virtual laboratory environment for the simulation and analysis of genetic circuits.

PubMed

Baig, Hasan; Madsen, Jan

2017-01-15

Simulation and behavioral analysis of genetic circuits is a standard approach of functional verification prior to their physical implementation. Many software tools have been developed to perform in silico analysis for this purpose, but none of them allow users to interact with the model during runtime. The runtime interaction gives the user a feeling of being in the lab performing a real world experiment. In this work, we present a user-friendly software tool named D-VASim (Dynamic Virtual Analyzer and Simulator), which provides a virtual laboratory environment to simulate and analyze the behavior of genetic logic circuit models represented in an SBML (Systems Biology Markup Language). Hence, SBML models developed in other software environments can be analyzed and simulated in D-VASim. D-VASim offers deterministic as well as stochastic simulation; and differs from other software tools by being able to extract and validate the Boolean logic from the SBML model. D-VASim is also capable of analyzing the threshold value and propagation delay of a genetic circuit model. D-VASim is available for Windows and Mac OS and can be downloaded from bda.compute.dtu.dk/downloads/. haba@dtu.dk, jama@dtu.dk. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use.

PubMed

Edwards, Alexis C; Maes, Hermine H; Prescott, Carol A; Kendler, Kenneth S

2015-02-01

Alcohol consumption is typically correlated with the alcohol use behaviors of one's peers. Previous research has suggested that this positive relationship could be due to social selection, social influence, or a combination of both processes. However, few studies have considered the role of shared genetic and environmental influences in conjunction with causal processes. This study uses data from a sample of male twins (N = 1,790) who provided retrospective reports of their own alcohol consumption and their peers' alcohol-related behaviors, from adolescence into young adulthood (ages 12 to 25). Structural equation modeling was employed to compare 3 plausible models of genetic and environmental influences on the relationship between phenotypes over time. Model fitting indicated that one's own alcohol consumption and the alcohol use of one's peers are related through both genetic and shared environmental factors and through unique environmental causal influences. The relative magnitude of these factors, and their contribution to covariation, changed over time, with genetic factors becoming more meaningful later in development. Peers' alcohol use behaviors and one's own alcohol consumption are related through a complex combination of genetic and environmental factors that act via correlated factors and the complementary causal mechanisms of social selection and influence. Understanding these processes can inform risk assessment as well as improve our ability to model the development of alcohol use. Copyright © 2015 by the Research Society on Alcoholism.

Multiple mechanisms influencing the relationship between alcohol consumption and peer alcohol use

PubMed Central

Edwards, Alexis C.; Maesr, Hermine H.; Prescott, Carol A.; Kendler, Kenneth S.

2014-01-01

Background Alcohol consumption is typically correlated with the alcohol use behaviors of one’s peers. Previous research has suggested that this positive relationship could be due to social selection, social influence, or a combination of both processes. However, few studies have considered the role of shared genetic and environmental influences in conjunction with causal processes. Methods The current study uses data from a sample of male twins (N=1790) who provided retrospective reports of their own alcohol consumption and their peers’ alcohol related behaviors, from adolescence into young adulthood (ages 12–25). Structural equation modeling was employed to compare three plausible models of genetic and environmental influences on the relationship between phenotypes over time. Results Model fitting indicated that one’s own alcohol consumption and the alcohol use of one’s peers are related through both genetic and shared environmental factors and through unique environmental causal influences. The relative magnitude of these factors, and their contribution to covariation, changed over time, with genetic factors becoming more meaningful later in development. Conclusions Peers’ alcohol use behaviors and one’s own alcohol consumption are related through a complex combination of genetic and environmental factors that act via correlated factors and the complementary causal mechanisms of social selection and influence. Understanding these processes can inform risk assessment as well as improve our ability to model the development of alcohol use. PMID:25597346

Exploring how nature and nurture affect the development of reading: An analysis of the Florida Twin Project on Reading

PubMed Central

Hart, Sara A.; Logan, Jessica A.R.; Soden-Hensler, Brooke; Kershaw, Sarah; Taylor, Jeanette; Schatschneider, Christopher

2013-01-01

Research on the development of reading skills through the primary school years has pointed to the importance of individual differences in initial ability as well as the growth of those skills. Additionally, it has been theorized that reading skills develop incrementally. The present study examined the genetic and environmental influences on two developmental models representing these parallel ideas, generalizing the findings to explore the processes of reading development. Participants were drawn from the Florida Twin Project on Reading, with a total of 2370 pairs of twins’ representative of the state of Florida. Twins’ oral reading fluency scores from school progress monitoring records collected in the fall of grades 1 through 5 were used to model development. Results suggested that genetic influences on the development of reading are general, shared across the early school years, as well as novel, with new genetic influences introduced at each of the first three years of school. The shared environment estimates suggest a pattern of general influences only, suggesting environmental effects which are moderate and stable across development. PMID:23294149

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.

Fashion sketch design by interactive genetic algorithms

NASA Astrophysics Data System (ADS)

Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.

2012-11-01

Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.

Determining Risk of Barrett's Esophagus and Esophageal Adenocarcinoma Based on Epidemiologic Factors and Genetic Variants.

PubMed

Dong, Jing; Buas, Matthew F; Gharahkhani, Puya; Kendall, Bradley J; Onstad, Lynn; Zhao, Shanshan; Anderson, Lesley A; Wu, Anna H; Ye, Weimin; Bird, Nigel C; Bernstein, Leslie; Chow, Wong-Ho; Gammon, Marilie D; Liu, Geoffrey; Caldas, Carlos; Pharoah, Paul D; Risch, Harvey A; Iyer, Prasad G; Reid, Brian J; Hardie, Laura J; Lagergren, Jesper; Shaheen, Nicholas J; Corley, Douglas A; Fitzgerald, Rebecca C; Whiteman, David C; Vaughan, Thomas L; Thrift, Aaron P

2018-04-01

We developed comprehensive models to determine risk of Barrett's esophagus (BE) or esophageal adenocarcinoma (EAC) based on genetic and non-genetic factors. We used pooled data from 3288 patients with BE, 2511 patients with EAC, and 2177 individuals without either (controls) from participants in the international Barrett's and EAC consortium as well as the United Kingdom's BE gene study and stomach and esophageal cancer study. We collected data on 23 genetic variants associated with risk for BE or EAC, and constructed a polygenic risk score (PRS) for cases and controls by summing the risk allele counts for the variants weighted by their natural log-transformed effect estimates (odds ratios) extracted from genome-wide association studies. We also collected data on demographic and lifestyle factors (age, sex, smoking, body mass index, use of nonsteroidal anti-inflammatory drugs) and symptoms of gastroesophageal reflux disease (GERD). Risk models with various combinations of non-genetic factors and the PRS were compared for their accuracy in identifying patients with BE or EAC using the area under the receiver operating characteristic curve (AUC) analysis. Individuals in the highest quartile of risk, based on genetic factors (PRS), had a 2-fold higher risk of BE (odds ratio, 2.22; 95% confidence interval, 1.89-2.60) or EAC (odds ratio, 2.46; 95% confidence interval, 2.07-2.92) than individual in the lowest quartile of risk based on PRS. Risk models developed based on only demographic or lifestyle factors or GERD symptoms identified patients with BE or EAC with AUC values ranging from 0.637 to 0.667. Combining data on demographic or lifestyle factors with data on GERD symptoms identified patients with BE with an AUC of 0.793 and patients with EAC with an AUC of 0.745. Including PRSs with these data only minimally increased the AUC values for BE (to 0.799) and EAC (to 0.754). Including the PRSs in the model developed based on non-genetic factors resulted in a net reclassification improvement for BE of 3.0% and for EAC of 5.6%. We used data from 3 large databases of patients from studies of BE or EAC to develop a risk prediction model based on genetic, clinical, and demographic/lifestyle factors. We identified a PRS that increases discrimination and net reclassification of individuals with vs without BE and EAC. However, the absolute magnitude of improvement is not sufficient to justify its clinical use. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Ontology driven modeling for the knowledge of genetic susceptibility to disease.

PubMed

Lin, Yu; Sakamoto, Norihiro

2009-05-12

For the machine helped exploring the relationships between genetic factors and complex diseases, a well-structured conceptual framework of the background knowledge is needed. However, because of the complexity of determining a genetic susceptibility factor, there is no formalization for the knowledge of genetic susceptibility to disease, which makes the interoperability between systems impossible. Thus, the ontology modeling language OWL was used for formalization in this paper. After introducing the Semantic Web and OWL language propagated by W3C, we applied text mining technology combined with competency questions to specify the classes of the ontology. Then, an N-ary pattern was adopted to describe the relationships among these defined classes. Based on the former work of OGSF-DM (Ontology of Genetic Susceptibility Factors to Diabetes Mellitus), we formalized the definition of "Genetic Susceptibility", "Genetic Susceptibility Factor" and other classes by using OWL-DL modeling language; and a reasoner automatically performed the classification of the class "Genetic Susceptibility Factor". The ontology driven modeling is used for formalization the knowledge of genetic susceptibility to complex diseases. More importantly, when a class has been completely formalized in an ontology, the OWL reasoning can automatically compute the classification of the class, in our case, the class of "Genetic Susceptibility Factors". With more types of genetic susceptibility factors obtained from the laboratory research, our ontologies always needs to be refined, and many new classes must be taken into account to harmonize with the ontologies. Using the ontologies to develop the semantic web needs to be applied in the future.

Arms race between selfishness and policing: two-trait quantitative genetic model for caste fate conflict in eusocial Hymenoptera.

PubMed

Dobata, Shigeto

2012-12-01

Policing against selfishness is now regarded as the main force maintaining cooperation, by reducing costly conflict in complex social systems. Although policing has been studied extensively in social insect colonies, its coevolution against selfishness has not been fully captured by previous theories. In this study, I developed a two-trait quantitative genetic model of the conflict between selfish immature females (usually larvae) and policing workers in eusocial Hymenoptera over the immatures' propensity to develop into new queens. This model allows for the analysis of coevolution between genomes expressed in immatures and workers that collectively determine the immatures' queen caste fate. The main prediction of the model is that a higher level of polyandry leads to a smaller fraction of queens produced among new females through caste fate policing. The other main prediction of the present model is that, as a result of arms race, caste fate policing by workers coevolves with exaggerated selfishness of the immatures achieving maximum potential to develop into queens. Moreover, the model can incorporate genetic correlation between traits, which has been largely unexplored in social evolution theory. This study highlights the importance of understanding social traits as influenced by the coevolution of conflicting genomes. © 2012 The Author. Evolution© 2012 The Society for the Study of Evolution.

Developmental changes in genetic and environmental influences on rule-breaking and aggression: age and pubertal development.

PubMed

Harden, K Paige; Patterson, Megan W; Briley, Daniel A; Engelhardt, Laura E; Kretsch, Natalie; Mann, Frank D; Tackett, Jennifer L; Tucker-Drob, Elliot M

2015-12-01

Antisocial behavior (ASB) can be meaningfully divided into nonaggressive rule-breaking versus aggressive dimensions, which differ in developmental course and etiology. Previous research has found that genetic influences on rule-breaking, but not aggression, increase from late childhood to mid-adolescence. This study tested the extent to which the developmental increase in genetic influence on rule-breaking was associated with pubertal development compared to chronological age. Child and adolescent twins (n = 1,031), ranging in age from 8 to 20 years (M age = 13.5 years), were recruited from public schools as part of the Texas Twin Project. Participants reported on their pubertal development using the Pubertal Development Scale and on their involvement in ASB on items from the Child Behavior Checklist. Measurement invariance of ASB subtypes across age groups (≤12 years vs. >12 years old) was tested using confirmatory factor analyses. Quantitative genetic modeling was used to test whether the genetic and environmental influences on aggression and rule-breaking were moderated by age, pubertal status, or both. Quantitative genetic modeling indicated that genetic influences specific to rule-breaking increased as a function of pubertal development controlling for age (a gene × puberty interaction), but did not vary as a function of age controlling for pubertal status. There were no developmental differences in the genetic etiology of aggression. Family-level environmental influences common to aggression and rule-breaking decreased with age, further contributing to the differentiation between these subtypes of ASB from childhood to adolescence. Future research should discriminate between alternative possible mechanisms underlying gene × puberty interactions on rule-breaking forms of antisocial behavior, including possible effects of pubertal hormones on gene expression. © 2015 Association for Child and Adolescent Mental Health.

Population genetics of Setaria viridis, a new model system.

PubMed

Huang, Pu; Feldman, Maximilian; Schroder, Stephan; Bahri, Bochra A; Diao, Xianmin; Zhi, Hui; Estep, Matt; Baxter, Ivan; Devos, Katrien M; Kellogg, Elizabeth A

2014-10-01

An extensive survey of the standing genetic variation in natural populations is among the priority steps in developing a species into a model system. In recent years, green foxtail (Setaria viridis), along with its domesticated form foxtail millet (S. italica), has rapidly become a promising new model system for C4 grasses and bioenergy crops, due to its rapid life cycle, large amount of seed production and small diploid genome, among other characters. However, remarkably little is known about the genetic diversity in natural populations of this species. In this study, we survey the genetic diversity of a worldwide sample of more than 200 S. viridis accessions, using the genotyping-by-sequencing technique. Two distinct genetic groups in S. viridis and a third group resembling S. italica were identified, with considerable admixture among the three groups. We find the genetic variation of North American S. viridis correlates with both geography and climate and is representative of the total genetic diversity in this species. This pattern may reflect several introduction/dispersal events of S. viridis into North America. We also modelled demographic history and show signal of recent population decline in one subgroup. Finally, we show linkage disequilibrium decay is rapid (<45 kb) in our total sample and slow in genetic subgroups. These results together provide an in-depth understanding of the pattern of genetic diversity of this new model species on a broad geographic scale. They also provide key guidelines for on-going and future work including germplasm preservation, local adaptation, crossing designs and genomewide association studies. © 2014 John Wiley & Sons Ltd.

Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape.

PubMed

Baker, Robert L; Leong, Wen Fung; Brock, Marcus T; Markelz, R J Cody; Covington, Michael F; Devisetty, Upendra K; Edwards, Christine E; Maloof, Julin; Welch, Stephen; Weinig, Cynthia

2015-10-01

Improved predictions of fitness and yield may be obtained by characterizing the genetic controls and environmental dependencies of organismal ontogeny. Elucidating the shape of growth curves may reveal novel genetic controls that single-time-point (STP) analyses do not because, in theory, infinite numbers of growth curves can result in the same final measurement. We measured leaf lengths and widths in Brassica rapa recombinant inbred lines (RILs) throughout ontogeny. We modeled leaf growth and allometry as function valued traits (FVT), and examined genetic correlations between these traits and aspects of phenology, physiology, circadian rhythms and fitness. We used RNA-seq to construct a SNP linkage map and mapped trait quantitative trait loci (QTL). We found genetic trade-offs between leaf size and growth rate FVT and uncovered differences in genotypic and QTL correlations involving FVT vs STPs. We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development. Leaf shape is associated with venation features that affect desiccation resistance. The genetic independence of leaf shape from other leaf traits may therefore enable crop optimization in leaf shape without negative effects on traits such as size, growth rate, duration or gas exchange. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

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.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Characterization of Planctomyces limnophilus and development of genetic tools for its manipulation establish it as a model species for the phylum Planctomycetes.

PubMed

Jogler, Christian; Glöckner, Frank Oliver; Kolter, Roberto

2011-08-15

Planctomycetes represent a remarkable clade in the domain Bacteria because they play crucial roles in global carbon and nitrogen cycles and display cellular structures that closely parallel those of eukaryotic cells. Studies on Planctomycetes have been hampered by the lack of genetic tools, which we developed for Planctomyces limnophilus.

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.

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.

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

Identification of landscape features influencing gene flow: How useful are habitat selection models?

USGS Publications Warehouse

Roffler, Gretchen H.; Schwartz, Michael K.; Pilgrim, Kristy L.; Talbot, Sandra L.; Sage, Kevin; Adams, Layne G.; Luikart, Gordon

2016-01-01

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider explicit assumptions and spatial scales of measurement. We calculated pairwise genetic distance among 301 Dall's sheep (Ovis dalli dalli) in southcentral Alaska using an intensive noninvasive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important during summer (rugged terrain, mid-range elevation) were not influential to effective dispersal. This work underscores that consideration of both habitat selection and landscape genetics models may be useful in developing management strategies to both meet the immediate survival of a species and allow for long-term genetic connectivity.

Genetic data simulators and their applications: an overview

PubMed Central

Peng, Bo; Chen, Huann-Sheng; Mechanic, Leah E.; Racine, Ben; Clarke, John; Gillanders, Elizabeth; Feuer, Eric J.

2016-01-01

Computer simulations have played an indispensable role in the development and application of statistical models and methods for genetic studies across multiple disciplines. The need to simulate complex evolutionary scenarios and pseudo-datasets for various studies has fueled the development of dozens of computer programs with varying reliability, performance, and application areas. To help researchers compare and choose the most appropriate simulators for their studies, we have created the Genetic Simulation Resources (GSR) website, which allows authors of simulation software to register their applications and describe them with more than 160 defined attributes. This article summarizes the properties of 93 simulators currently registered at GSR and provides an overview of the development and applications of genetic simulators. Unlike other review articles that address technical issues or compare simulators for particular application areas, we focus on software development, maintenance, and features of simulators, often from a historical perspective. Publications that cite these simulators are used to summarize both the applications of genetic simulations and the utilization of simulators. PMID:25504286

Using the Communication in Science Inquiry Project Professional Development Model to Facilitate Learning Middle School Genetics Concepts

ERIC Educational Resources Information Center

Baker, Dale R.; Lewis, Elizabeth B.; Uysal, Sibel; Purzer, Senay; Lang, Michael; Baker, Perry

2011-01-01

This study describes the effect of embedding content in the Communication in Inquiry Science Project professional development model for science and language arts teachers. The model uses four components of successful professional development (content focus, active learning, extended duration, participation by teams of teachers from the same school…

"Wrecks of Ancient Life": Genetic Variants Vetted by Natural Selection.

PubMed

Postlethwait, John H

2015-07-01

The Genetics Society of America's George W. Beadle Award honors individuals who have made outstanding contributions to the community of genetics researchers and who exemplify the qualities of its namesake as a respected academic, administrator, and public servant. The 2015 recipient is John Postlethwait. He has made groundbreaking contributions in developing the zebrafish as a molecular genetic model and in understanding the evolution of new gene functions in vertebrates. He built the first zebrafish genetic map and showed that its genome, along with that of distantly related teleost fish, had been duplicated. Postlethwait played an integral role in the zebrafish genome-sequencing project and elucidated the genomic organization of several fish species. Postlethwait is also honored for his active involvement with the zebrafish community, advocacy for zebrafish as a model system, and commitment to driving the field forward. Copyright © 2015 by the Genetics Society of America.

Can mutation-mediated effects occurring early in development cause long-term seizure susceptibility in genetic generalized epilepsies?

PubMed

Reid, Christopher Alan; Rollo, Ben; Petrou, Steven; Berkovic, Samuel F

2018-05-01

Epilepsy has a strong genetic component, with an ever-increasing number of disease-causing genes being discovered. Most epilepsy-causing mutations are germ line and thus present from conception. These mutations are therefore well positioned to have a deleterious impact during early development. Here we review studies that investigate the role of genetic lesions within the early developmental window, specifically focusing on genetic generalized epilepsy (GGE). Literature on the potential pathogenic role of sub-mesoscopic structural changes in GGE is also reviewed. Evidence from rodent models of genetic epilepsy support the idea that functional and structural changes can occur in early development, leading to altered seizure susceptibility into adulthood. Both animal and human studies suggest that sub-mesoscopic structural changes occur in GGE. The existence of sub-mesoscopic structural changes prior to seizure onset may act as biomarkers of excitability in genetic epilepsies. We also propose that presymptomatic treatment may be essential for limiting the long-term consequences of disease-causing mutations in genetic epilepsies. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

PubMed

Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks. The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

Mouse Models for Drug Discovery. Can New Tools and Technology Improve Translational Power?

PubMed Central

Zuberi, Aamir; Lutz, Cathleen

2016-01-01

Abstract The use of mouse models in biomedical research and preclinical drug evaluation is on the rise. The advent of new molecular genome-altering technologies such as CRISPR/Cas9 allows for genetic mutations to be introduced into the germ line of a mouse faster and less expensively than previous methods. In addition, the rapid progress in the development and use of somatic transgenesis using viral vectors, as well as manipulations of gene expression with siRNAs and antisense oligonucleotides, allow for even greater exploration into genomics and systems biology. These technological advances come at a time when cost reductions in genome sequencing have led to the identification of pathogenic mutations in patient populations, providing unprecedented opportunities in the use of mice to model human disease. The ease of genetic engineering in mice also offers a potential paradigm shift in resource sharing and the speed by which models are made available in the public domain. Predictively, the knowledge alone that a model can be quickly remade will provide relief to resources encumbered by licensing and Material Transfer Agreements. For decades, mouse strains have provided an exquisite experimental tool to study the pathophysiology of the disease and assess therapeutic options in a genetically defined system. However, a major limitation of the mouse has been the limited genetic diversity associated with common laboratory mice. This has been overcome with the recent development of the Collaborative Cross and Diversity Outbred mice. These strains provide new tools capable of replicating genetic diversity to that approaching the diversity found in human populations. The Collaborative Cross and Diversity Outbred strains thus provide a means to observe and characterize toxicity or efficacy of new therapeutic drugs for a given population. The combination of traditional and contemporary mouse genome editing tools, along with the addition of genetic diversity in new modeling systems, are synergistic and serve to make the mouse a better model for biomedical research, enhancing the potential for preclinical drug discovery and personalized medicine. PMID:28053071

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

Monte-Carlo model development for evaluation of current clinical target volume definition for heterogeneous and hypoxic glioblastoma.

PubMed

Moghaddasi, L; Bezak, E; Harriss-Phillips, W

2016-05-07

Clinical target volume (CTV) determination may be complex and subjective. In this work a microscopic-scale tumour model was developed to evaluate current CTV practices in glioblastoma multiforme (GBM) external radiotherapy. Previously, a Geant4 cell-based dosimetry model was developed to calculate the dose deposited in individual GBM cells. Microscopic extension probability (MEP) models were then developed using Matlab-2012a. The results of the cell-based dosimetry model and MEP models were combined to calculate survival fractions (SF) for CTV margins of 2.0 and 2.5 cm. In the current work, oxygenation and heterogeneous radiosensitivity profiles were incorporated into the GBM model. The genetic heterogeneity was modelled using a range of α/β values (linear-quadratic model parameters) associated with different GBM cell lines. These values were distributed among the cells randomly, taken from a Gaussian-weighted sample of α/β values. Cellular oxygen pressure was distributed randomly taken from a sample weighted to profiles obtained from literature. Three types of GBM models were analysed: homogeneous-normoxic, heterogeneous-normoxic, and heterogeneous-hypoxic. The SF in different regions of the tumour model and the effect of the CTV margin extension from 2.0-2.5 cm on SFs were investigated for three MEP models. The SF within the beam was increased by up to three and two orders of magnitude following incorporation of heterogeneous radiosensitivities and hypoxia, respectively, in the GBM model. However, the total SF was shown to be overdominated by the presence of tumour cells in the penumbra region and to a lesser extent by genetic heterogeneity and hypoxia. CTV extension by 0.5 cm reduced the SF by a maximum of 78.6  ±  3.3%, 78.5  ±  3.3%, and 77.7  ±  3.1% for homogeneous and heterogeneous-normoxic, and heterogeneous hypoxic GBMs, respectively. Monte-Carlo model was developed to quantitatively evaluate SF for genetically heterogeneous and hypoxic GBM with two CTV margins and three MEP distributions. The results suggest that photon therapy may not provide cure for hypoxic and genetically heterogeneous GBM. However, the extension of the CTV margin by 0.5 cm could be beneficial to delay the recurrence time for this tumour type due to significant increase in tumour cell irradiation.

Deriving estimates of individual variability in genetic potentials of performance traits for 3 dairy breeds, using a model of lifetime nutrient partitioning.

PubMed

Phuong, H N; Martin, O; de Boer, I J M; Ingvartsen, K L; Schmidely, Ph; Friggens, N C

2015-01-01

This study explored the ability of an existing lifetime nutrient partitioning model for simulating individual variability in genetic potentials of dairy cows. Generally, the model assumes a universal trajectory of dynamic partitioning of priority between life functions and genetic scaling parameters are then incorporated to simulate individual difference in performance. Data of 102 cows including 180 lactations of 3 breeds: Danish Red, Danish Holstein, and Jersey, which were completely independent from those used previously for model development, were used. Individual cow performance records through sequential lactations were used to derive genetic scaling parameters for each animal by calibrating the model to achieve best fit, cow by cow. The model was able to fit individual curves of body weight, and milk fat, milk protein, and milk lactose concentrations with a high degree of accuracy. Daily milk yield and dry matter intake were satisfactorily predicted in early and mid lactation, but underpredictions were found in late lactation. Breeds and parities did not significantly affect the prediction accuracy. The means of genetic scaling parameters between Danish Red and Danish Holstein were similar but significantly different from those of Jersey. The extent of correlations between the genetic scaling parameters was consistent with that reported in the literature. In conclusion, this model is of value as a tool to derive estimates of genetic potentials of milk yield, milk composition, body reserve usage, and growth for different genotypes of cow. Moreover, it can be used to separate genetic variability in performance between individual cows from environmental noise. The model enables simulation of the effects of a genetic selection strategy on lifetime efficiency of individual cows, which has a main advantage of including the rearing costs, and thus, can be used to explore the impact of future selection on animal performance and efficiency. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Family history in primary care: understanding GPs' resistance to clinical genetics--qualitative study.

PubMed

Mathers, Jonathan; Greenfield, Sheila; Metcalfe, Alison; Cole, Trevor; Flanagan, Sarah; Wilson, Sue

2010-05-01

National and local evaluations of clinical genetics service pilots have experienced difficulty in engaging with GPs. To understand GPs' reluctance to engage with clinical genetics service developments, via an examination of the role of family history in general practice. Qualitative study using semi-structured one-to-one interviews. The West Midlands, UK. Interviews with 21 GPs working in 15 practices, based on a stratified random sample from the Midlands Research Practices Consortium database. Thematic analysis proceeded alongside data generation. Framework grids were constructed for comparative analytical questioning. Interpretation was framed by two explanatory models: a knowledge deficit model, and practice and professional identity model. There is a clear distinction between the routine use and function of family history in GPs' clinical decision making, and contrasting conceptualisations of genetics and 'genetic conditions'. Although genetics is clearly a part of current GP practice, with acknowledgement of genetic components to multifactorial disease, this is distinguished from 'genetic conditions' which are seen as rare, complex single-gene disorders. Importantly, family history takes its place within a broader notion of the 'family doctor' that interviewees identified as a key aspect of their role. In contrast, clinical genetics was not identified as a core component of generalist practice. The likely effectiveness of educational policy interventions aimed at GPs that focus solely on knowledge deficit models, is questionable. There is a need to acknowledge how appropriate practice is constructed by GPs, within the context of accepted generalist roles and related identities.

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.

The regulation of agricultural biotechnology: science shows a better way.

PubMed

Miller, Henry I

2010-11-30

National and international regulation of recombinant DNA-modified, or 'genetically engineered' (also referred to as 'genetically modified' or GM), organisms is unscientific and illogical, a lamentable illustration of the maxim that bad science makes bad law. Instead of regulatory scrutiny that is proportional to risk, the degree of oversight is actually inversely proportional to risk. The current approach to regulation, which captures for case-by-case review organisms to be field tested or commercialized according to the techniques used to construct them rather than their properties, flies in the face of scientific consensus. This approach has been costly in terms of economic losses and human suffering. The poorest of the poor have suffered the most because of hugely inflated development costs of genetically engineered plants and food. A model for regulation of field trials known as the 'Stanford Model' is designed to assess risks of new agricultural introductions - whether or not the organisms are genetically engineered, and independent of the genetic modification techniques employed. It offers a scientific, rational, risk-based basis for field trial regulations. Using this sort of model for regulatory review would not only better protect human health and the environment, but would also permit more expeditious development and more widespread use of new plants and seeds. Copyright © 2010 Elsevier B.V. All rights reserved.

Developmental vitamin D deficiency and schizophrenia: the role of animal models.

PubMed

Schoenrock, S A; Tarantino, L M

2016-01-01

Schizophrenia is a debilitating neuropsychiatric disorder that affects 1% of the US population. Based on twin and genome-wide association studies, it is clear that both genetics and environmental factors increase the risk for developing schizophrenia. Moreover, there is evidence that conditions in utero, either alone or in concert with genetic factors, may alter neurodevelopment and lead to an increased risk for schizophrenia. There has been progress in identifying genetic loci and environmental exposures that increase risk, but there are still considerable gaps in our knowledge. Furthermore, very little is known about the specific neurodevelopmental mechanisms upon which genetics and the environment act to increase disposition to developing schizophrenia in adulthood. Vitamin D deficiency during the perinatal period has been hypothesized to increase risk for schizophrenia in humans. The developmental vitamin D (DVD) deficiency hypothesis of schizophrenia arises from the observation that disease risk is increased in individuals who are born in winter or spring, live further from the equator or live in urban vs. rural settings. These environments result in less exposure to sunlight, thereby reducing the initial steps in the production of vitamin D. Rodent models have been developed to characterize the behavioral and developmental effects of DVD deficiency. This review focuses on these animal models and discusses the current knowledge of the role of DVD deficiency in altering behavior and neurobiology relevant to schizophrenia. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Engineering Large Animal Species to Model Human Diseases.

PubMed

Rogers, Christopher S

2016-07-01

Animal models are an important resource for studying human diseases. Genetically engineered mice are the most commonly used species and have made significant contributions to our understanding of basic biology, disease mechanisms, and drug development. However, they often fail to recreate important aspects of human diseases and thus can have limited utility as translational research tools. Developing disease models in species more similar to humans may provide a better setting in which to study disease pathogenesis and test new treatments. This unit provides an overview of the history of genetically engineered large animals and the techniques that have made their development possible. Factors to consider when planning a large animal model, including choice of species, type of modification and methodology, characterization, production methods, and regulatory compliance, are also covered. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

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.

A Road Map for 21st Century Genetic Restoration: Gene Pool Enrichment of the Black-Footed Ferret

PubMed Central

Ryder, Oliver A.; Santymire, Rachel M.; Engelhardt, John F.; Novak, Ben J.

2015-01-01

Interspecies somatic cell nuclear transfer (iSCNT) could benefit recovery programs of critically endangered species but must be weighed with the risks of failure. To weigh the risks and benefits, a decision-making process that evaluates progress is needed. Experiments that evaluate the efficiency and efficacy of blastocyst, fetal, and post-parturition development are necessary to determine the success or failure or species-specific iSCNT programs. Here, we use the black-footed ferret (Mustela nigripes) as a case study for evaluating this emerging biomedical technology as a tool for genetic restoration. The black-footed ferret has depleted genetic variation yet genome resource banks contain genetic material of individuals not currently represented in the extant lineage. Thus, genetic restoration of the species is in theory possible and could help reduce the persistent erosion of genetic diversity from drift. Extensive genetic, genomic, and reproductive science tools have previously been developed in black-footed ferrets and would aid in the process of developing an iSCNT protocol for this species. Nonetheless, developing reproductive cloning will require years of experiments and a coordinated effort among recovery partners. The information gained from a well-planned research effort with the goal of genetic restoration via reproductive cloning could establish a 21st century model for evaluating and implementing conservation breeding that would be applicable to other genetically impoverished species. PMID:26304983

The Role of Abcb5 Alleles in Susceptibility to Haloperidol-Induced Toxicity in Mice and Humans

PubMed Central

Zheng, Ming; Zhang, Haili; Dill, David L.; Clark, J. David; Tu, Susan; Yablonovitch, Arielle L.; Tan, Meng How; Zhang, Rui; Rujescu, Dan; Wu, Manhong; Tessarollo, Lino; Vieira, Wilfred; Gottesman, Michael M.; Deng, Suhua; Eberlin, Livia S.; Zare, Richard N.; Billard, Jean-Martin; Gillet, Jean-Pierre; Li, Jin Billy; Peltz, Gary

2015-01-01

Background We know very little about the genetic factors affecting susceptibility to drug-induced central nervous system (CNS) toxicities, and this has limited our ability to optimally utilize existing drugs or to develop new drugs for CNS disorders. For example, haloperidol is a potent dopamine antagonist that is used to treat psychotic disorders, but 50% of treated patients develop characteristic extrapyramidal symptoms caused by haloperidol-induced toxicity (HIT), which limits its clinical utility. We do not have any information about the genetic factors affecting this drug-induced toxicity. HIT in humans is directly mirrored in a murine genetic model, where inbred mouse strains are differentially susceptible to HIT. Therefore, we genetically analyzed this murine model and performed a translational human genetic association study. Methods and Findings A whole genome SNP database and computational genetic mapping were used to analyze the murine genetic model of HIT. Guided by the mouse genetic analysis, we demonstrate that genetic variation within an ABC-drug efflux transporter (Abcb5) affected susceptibility to HIT. In situ hybridization results reveal that Abcb5 is expressed in brain capillaries, and by cerebellar Purkinje cells. We also analyzed chromosome substitution strains, imaged haloperidol abundance in brain tissue sections and directly measured haloperidol (and its metabolite) levels in brain, and characterized Abcb5 knockout mice. Our results demonstrate that Abcb5 is part of the blood-brain barrier; it affects susceptibility to HIT by altering the brain concentration of haloperidol. Moreover, a genetic association study in a haloperidol-treated human cohort indicates that human ABCB5 alleles had a time-dependent effect on susceptibility to individual and combined measures of HIT. Abcb5 alleles are pharmacogenetic factors that affect susceptibility to HIT, but it is likely that additional pharmacogenetic susceptibility factors will be discovered. Conclusions ABCB5 alleles alter susceptibility to HIT in mouse and humans. This discovery leads to a new model that (at least in part) explains inter-individual differences in susceptibility to a drug-induced CNS toxicity. PMID:25647612

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.

Path analysis of the genetic integration of traits in the sand cricket: a novel use of BLUPs.

PubMed

Roff, D A; Fairbairn, D J

2011-09-01

This study combines path analysis with quantitative genetics to analyse a key life history trade-off in the cricket, Gryllus firmus. We develop a path model connecting five traits associated with the trade-off between flight capability and reproduction and test this model using phenotypic data and estimates of breeding values (best linear unbiased predictors) from a half-sibling experiment. Strong support by both types of data validates our causal model and indicates concordance between the phenotypic and genetic expression of the trade-off. Comparisons of the trade-off between sexes and wing morphs reveal that these discrete phenotypes are not genetically independent and that the evolutionary trajectories of the two wing morphs are more tightly constrained to covary than those of the two sexes. Our results illustrate the benefits of combining a quantitative genetic analysis, which examines statistical correlations between traits, with a path model that focuses upon the causal components of variation. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

NASA Astrophysics Data System (ADS)

Muduli, Pradyut; Das, Sarat

2014-06-01

This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

A longitudinal twin study of callous-unemotional traits during childhood.

PubMed

Henry, Jeffrey; Dionne, Ginette; Viding, Essi; Petitclerc, Amélie; Feng, Bei; Vitaro, Frank; Brendgen, Mara; Tremblay, Richard E; Boivin, Michel

2018-05-01

Previous research indicates that genetic factors largely account for the stability of callous-unemotional (CU) traits in adolescence. However, the genetic-environmental etiology of the development of CU traits has not been extensively investigated in childhood, despite work showing the reliable measurement and stability of CU traits from a young age. The aim of this study was to investigate the temporal pattern of genetic and environmental etiology of CU traits across primary school, from school entry (7 years) to middle (9 and 10 years) and late childhood (12 years). Data were collected in a population sample of twins composed of 662 twin pairs (Quebec Newborn Twin Study). CU traits were reported by teachers and analyzed using a biometric latent growth curve model and a Cholesky decomposition model. Latent growth curve analyses revealed that genetic factors explain most of the variance in the intercept of CU traits. Individual differences in change over time were not significant. The Cholesky model revealed that genetic factors at 7 years had enduring contributions to CU traits at 9, 10, and 12 years. New, modest genetic contributions appeared at 9 and 10 years. Nonshared environmental contributions were generally age-specific. No shared environmental contributions were detected. In sum, both modeling approaches showed that genetic factors underlie CU traits during childhood. Initial and new genetic contributions arise during this period. Environments have substantial contributions, over and above genetic factors. Future research should investigate the source of genetic risk associated with CU traits. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Teaching Human Genetics with Mustard: Rapid Cycling Brassica rapa (Fast Plants Type) as a Model for Human Genetics in the Classroom Laboratory

PubMed Central

Pickard, Dawn

2007-01-01

We have developed experiments and materials to model human genetics using rapid cycling Brassica rapa, also known as Fast Plants. Because of their self-incompatibility for pollination and the genetic diversity within strains, B. rapa can serve as a relevant model for human genetics in teaching laboratory experiments. The experiment presented here is a paternity exclusion project in which a child is born with a known mother but two possible alleged fathers. Students use DNA markers (microsatellites) to perform paternity exclusion on these subjects. Realistic DNA marker analysis can be challenging to implement within the limitations of an instructional lab, but we have optimized the experimental methods to work in a teaching lab environment and to maximize the “hands-on” experience for the students. The genetic individuality of each B. rapa plant, revealed by analysis of polymorphic microsatellite markers, means that each time students perform this project, they obtain unique results that foster independent thinking in the process of data interpretation. PMID:17548880

The evolutionary history of steelhead (Oncorhynchus mykiss) along the US Pacific Coast: Developing a conservation strategy using genetic diversity

USGS Publications Warehouse

Nielsen, J.L.

1999-01-01

Changes in genetic variation across a species range may indicate patterns of population structure resulting from past ecological and demographic events that are otherwise difficult to infer and thus provide insight into evolutionary development. Genetic data is used, drawn from 11 microsatellite loci amplified from anadromous steelhead (Oncorhynchus mykiss) sampled throughout its range in the eastern Pacific Ocean, to explore population structure at the southern edge in California. Steelhead populations in this region represent less than 10% of their reported historic abundance and survive in very small populations found in fragmented habitats. Genetic data derived from three independent molecular systems (allozymes, mtDNA, and microsatellites) have shown that the southernmost populations are characterized by a relatively high genetic diversity. Two hypothetical models supporting genetic population substructure such as observed were considered: (1) range expansion with founder-flush effects and subsequent population decline; (2) a second Pleistocene radiation from the Gulf of California. Using genetic and climatic data, a second Pleistocene refugium contributing to a southern ecotone seems more feasible. These data support strong conservation measures based on genetic diversity be developed to ensure the survival of this uniquely diverse gene pool.

Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics

NASA Astrophysics Data System (ADS)

Greene, Casey S.; Hill, Douglas P.; Moore, Jason H.

The relationship between interindividual variation in our genomes and variation in our susceptibility to common diseases is expected to be complex with multiple interacting genetic factors. A central goal of human genetics is to identify which DNA sequence variations predict disease risk in human populations. Our success in this endeavour will depend critically on the development and implementation of computational intelligence methods that are able to embrace, rather than ignore, the complexity of the genotype to phenotype relationship. To this end, we have developed a computational evolution system (CES) to discover genetic models of disease susceptibility involving complex relationships between DNA sequence variations. The CES approach is hierarchically organized and is capable of evolving operators of any arbitrary complexity. The ability to evolve operators distinguishes this approach from artificial evolution approaches using fixed operators such as mutation and recombination. Our previous studies have shown that a CES that can utilize expert knowledge about the problem in evolved operators significantly outperforms a CES unable to use this knowledge. This environmental sensing of external sources of biological or statistical knowledge is important when the search space is both rugged and large as in the genetic analysis of complex diseases. We show here that the CES is also capable of evolving operators which exploit one of several sources of expert knowledge to solve the problem. This is important for both the discovery of highly fit genetic models and because the particular source of expert knowledge used by evolved operators may provide additional information about the problem itself. This study brings us a step closer to a CES that can solve complex problems in human genetics in addition to discovering genetic models of disease.

Oceanography and life history predict contrasting genetic population structure in two Antarctic fish species.

PubMed

Young, Emma F; Belchier, Mark; Hauser, Lorenz; Horsburgh, Gavin J; Meredith, Michael P; Murphy, Eugene J; Pascoal, Sonia; Rock, Jennifer; Tysklind, Niklas; Carvalho, Gary R

2015-06-01

Understanding the key drivers of population connectivity in the marine environment is essential for the effective management of natural resources. Although several different approaches to evaluating connectivity have been used, they are rarely integrated quantitatively. Here, we use a 'seascape genetics' approach, by combining oceanographic modelling and microsatellite analyses, to understand the dominant influences on the population genetic structure of two Antarctic fishes with contrasting life histories, Champsocephalus gunnari and Notothenia rossii. The close accord between the model projections and empirical genetic structure demonstrated that passive dispersal during the planktonic early life stages is the dominant influence on patterns and extent of genetic structuring in both species. The shorter planktonic phase of C. gunnari restricts direct transport of larvae between distant populations, leading to stronger regional differentiation. By contrast, geographic distance did not affect differentiation in N. rossii, whose longer larval period promotes long-distance dispersal. Interannual variability in oceanographic flows strongly influenced the projected genetic structure, suggesting that shifts in circulation patterns due to climate change are likely to impact future genetic connectivity and opportunities for local adaptation, resilience and recovery from perturbations. Further development of realistic climate models is required to fully assess such potential impacts.

Genetic evaluations for growth heat tolerance in Angus cattle.

PubMed

Bradford, H L; Fragomeni, B O; Bertrand, J K; Lourenco, D A L; Misztal, I

2016-10-01

The objectives were to assess the impact of heat stress and to develop a model for genetic evaluation of growth heat tolerance in Angus cattle. The American Angus Association provided weaning weight (WW) and yearling weight (YW) data, and records from the Upper South region were used because of the hot climatic conditions. Heat stress was characterized by a weaning (yearling) heat load function defined as the mean temperature-humidity index (THI) units greater than 75 (70) for 30 (150) d prior to the weigh date. Therefore, a weaning (yearling) heat load of 5 units corresponded to 80 (75) for the corresponding period prior to the weigh date. For all analyses, 82,669 WW and 69,040 YW were used with 3 ancestral generations in the pedigree. Univariate models were a proxy for the Angus growth evaluation, and reaction norms using 2 B-splines for heat load were fit separately for weaning and yearling heat loads. For both models, random effects included direct genetic, maternal genetic, maternal permanent environment (WW only), and residual. Fixed effects included a linear age covariate, age-of-dam class (WW only), and contemporary group for both models and fixed regressions on the B-splines in the reaction norm. Direct genetic correlations for WW were strong for modest heat load differences but decreased to less than 0.50 for large differences. Reranking of proven sires occurred for only WW direct effects for the reaction norms with extreme heat load differences. Conversely, YW results indicated little effect of heat stress on genetic merit. Therefore, weaning heat tolerance was a better candidate for developing selection tools. Maternal heritabilities were consistent across heat loads, and maternal genetic correlations were greater than 0.90 for nearly all heat load combinations. No evidence existed for a genotype × environment interaction for the maternal component of growth. Overall, some evidence exists for phenotypic plasticity for the direct genetic effects of WW, but traditional national cattle evaluations are likely adequately ranking sires for nonextreme environmental conditions.

Genetically engineered mouse models of craniopharyngioma: an opportunity for therapy development and understanding of tumor biology.

PubMed

Apps, John Richard; Martinez-Barbera, Juan Pedro

2017-05-01

Adamantinomatous craniopharyngioma (ACP) is the commonest tumor of the sellar region in childhood. Two genetically engineered mouse models have been developed and are giving valuable insights into ACP biology. These models have identified novel pathways activated in tumors, revealed an important function of paracrine signalling and extended conventional theories about the role of organ-specific stem cells in tumorigenesis. In this review, we summarize these mouse models, what has been learnt, their limitations and open questions for future research. We then discussed how these mouse models may be used to test novel therapeutics against potentially targetable pathways recently identified in human ACP. © 2017 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.

Transgenic animal models of neurodegeneration based on human genetic studies

PubMed Central

Richie, Christopher T.; Hoffer, Barry J.; Airavaara, Mikko

2011-01-01

The identification of genes linked to neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD) has led to the development of animal models for studying mechanism and evaluating potential therapies. None of the transgenic models developed based on disease-associated genes have been able to fully recapitulate the behavioral and pathological features of the corresponding disease. However, there has been enormous progress made in identifying potential therapeutic targets and understanding some of the common mechanisms of neurodegeneration. In this review, we will discuss transgenic animal models for AD, ALS, HD and PD that are based on human genetic studies. All of the diseases discussed have active or complete clinical trials for experimental treatments that benefited from transgenic models of the disease. PMID:20931247

Modeling Viral Infectious Diseases and Development of Antiviral Therapies Using Human Induced Pluripotent Stem Cell-Derived Systems.

PubMed

Trevisan, Marta; Sinigaglia, Alessandro; Desole, Giovanna; Berto, Alessandro; Pacenti, Monia; Palù, Giorgio; Barzon, Luisa

2015-07-13

The recent biotechnology breakthrough of cell reprogramming and generation of induced pluripotent stem cells (iPSCs), which has revolutionized the approaches to study the mechanisms of human diseases and to test new drugs, can be exploited to generate patient-specific models for the investigation of host-pathogen interactions and to develop new antimicrobial and antiviral therapies. Applications of iPSC technology to the study of viral infections in humans have included in vitro modeling of viral infections of neural, liver, and cardiac cells; modeling of human genetic susceptibility to severe viral infectious diseases, such as encephalitis and severe influenza; genetic engineering and genome editing of patient-specific iPSC-derived cells to confer antiviral resistance.

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

Revising explanatory models to accommodate anomalous genetic phenomena: Problem solving in the context of discovery

NASA Astrophysics Data System (ADS)

Hafner, Robert; Stewart, Jim

Past problem-solving research has provided a basis for helping students structure their knowledge and apply appropriate problem-solving strategies to solve problems for which their knowledge (or mental models) of scientific phenomena is adequate (model-using problem solving). This research examines how problem solving in the domain of Mendelian genetics proceeds in situations where solvers' mental models are insufficient to solve problems at hand (model-revising problem solving). Such situations require solvers to use existing models to recognize anomalous data and to revise those models to accommodate the data. The study was conducted in the context of 9-week high school genetics course and addressed: the heuristics charactenstic of successful model-revising problem solving: the nature of the model revisions, made by students as well as the nature of model development across problem types; and the basis upon which solvers decide that a revised model is sufficient (that t has both predictive and explanatory power).

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.

Developing Novel Therapeutic Approaches in Small Cell Lung Carcinoma Using Genetically Engineered Mouse Models and Human Circulating Tumor Cells

DTIC Science & Technology

2015-10-01

xenograft models . 12-36 Dr. Engelman Subtask 3: Analyze CTCs for P-4EBP1, P-S6, BIM , Bcl-2, Bcl-xL, and Mcl-1 using ISH and IHC We propose...Using Genetically Engineered Mouse Models and Human Circulating Tumor Cells PRINCIPAL INVESTIGATOR: Jeffrey Engelman MD PhD CONTRACTING...reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions

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

Genetically modified mouse models to investigate thyroid development, function and growth.

PubMed

Löf, C; Patyra, K; Kero, A; Kero, J

2018-06-01

The thyroid gland produces thyroid hormones (TH), which are essential regulators for growth, development and metabolism. The thyroid is mainly controlled by the thyroid-stimulating hormone (TSH) that binds to its receptor (TSHR) on thyrocytes and mediates its action via different G protein-mediated signaling pathways. TSH primarily activates the G s -pathway, and at higher concentrations also the G q/11 -pathway, leading to an increase of intracellular cAMP and Ca 2+ , respectively. To date, the physiological importance of other G protein-mediated signaling pathways in thyrocytes is unclear. Congenital hypothyroidism (CH) is defined as the lack of TH at birth. In familial cases, high-throughput sequencing methods have facilitated the identification of novel mutations. Nevertheless, the precise etiology of CH yet remains unraveled in a proportion of cases. Genetically modified mouse models can reveal new pathophysiological mechanisms of thyroid diseases. Here, we will present an overview of genetic mouse models for thyroid diseases, which have provided crucial insights into thyroid gland development, function, and growth with a special focus on TSHR and microRNA signaling. Copyright © 2018 Elsevier Ltd. All rights reserved.

Canine and Feline Models of Human Genetic Diseases and Their Contributions to Advancing Clinical Therapies
.

PubMed

Gurda, Brittney L; Bradbury, Allison M; Vite, Charles H

2017-09-01

For many lethal or debilitating genetic disorders in patients there are no satisfactory therapies. Several barriers exist that hinder the developments of effective therapies including the limited availability of clinically relevant animal models that faithfully recapitulate human genetic disease. In 1974, the Referral Center for Animal Models of Human Genetic Disease (RCAM) was established by Dr. Donald F. Patterson and continued by Dr. Mark E. Haskins at the University of Pennsylvania with the mission to discover, understand, treat, and maintain breeding colonies of naturally occurring hereditary disorders in dogs and cats that are orthologous to those found in human patients. Although non-human primates, sheep, and pig models are also available within the medical community, naturally occurring diseases are rarely identified in non-human primates, and the vast behavioral, clinicopathological, physiological, and anatomical knowledge available regarding dogs and cats far surpasses what is available in ovine and porcine species. The canine and feline models that are maintained at RCAM are presented here with a focus on preclinical therapy data. Clinical studies that have been generated from preclinical work in these models are also presented.

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

The Etiology of Variation in Language Skills Changes with Development: A Longitudinal Twin Study of Language from 2 to 12 Years

ERIC Educational Resources Information Center

Hayiou-Thomas, Marianna E.; Dale, Philip S.; Plomin, Robert

2012-01-01

The present study is the first long-term longitudinal examination of the etiology of individual differences in language from early childhood through to adolescence. We applied a multivariate latent factor genetic model to longitudinal data from the Twins Early Development Study in order to (a) compare the magnitude of genetic and environmental…

The Life Course Health Development Model: A Guide to Children's Health Care Policy and Practice

ERIC Educational Resources Information Center

Halfon, Neal; Russ, Shirley; Regalado, Michael

2005-01-01

As medical knowledge and treatments improve, pediatricians' role in promoting children's health continues to change. Genetics and early experiences may have long-term effects on health and development. Theoretical models that influence providers' decisions about the use of health-care resources are: the disease model, the neuromaturational model,…

MHC class II is an important genetic risk factor for canine systemic lupus erythematosus (SLE)-related disease: implications for reproductive success.

PubMed

Wilbe, M; Andersson, G

2012-01-01

Major histocompatibility complex (MHC) class II genes are important genetic risk factors for development of immune-mediated diseases in mammals. Recently, the dog (Canis lupus familiaris) has emerged as a useful model organism to identify critical MHC class II genotypes that contribute to development of these diseases. Therefore, a study aimed to evaluate a potential genetic association between the dog leukocyte antigen (DLA) class II region and an immune-mediated disease complex in dogs of the Nova Scotia duck tolling retriever breed was performed. We show that DLA is one of several genetic risk factors for this disease complex and that homozygosity of the risk haplotype is disadvantageous. Importantly, the disease is complex and has many genetic risk factors and therefore we cannot provide recommendations for breeders exclusively on the basis of genetic testing for DLA class II genotype. © 2012 Blackwell Verlag GmbH.

High Drinking in the Dark Mice: A genetic model of drinking to intoxication

PubMed Central

Barkley-Levenson, Amanda M.; Crabbe, John C.

2014-01-01

Drinking to intoxication is a critical component of risky drinking behaviors in humans, such as binge drinking. Previous rodent models of alcohol consumption largely failed to demonstrate that animals were patterning drinking in such a way as to experience intoxication. Therefore, few rodent models of binge-like drinking and no specifically genetic models were available to study possible predisposing genes. The High Drinking in the Dark (HDID) selective breeding project was started to help fill this void, with HDID mice selected for reaching high blood alcohol levels in a limited access procedure. HDID mice now represent a genetic model of drinking to intoxication and can be used to help answer questions regarding predisposition toward this trait as well as potential correlated responses. They should also prove useful for the eventual development of better therapeutic strategies. PMID:24360287

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

The potential of large studies for building genetic risk prediction models

Cancer.gov

NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

Studies of threespine stickleback developmental evolution: progress and promise.

PubMed

Cresko, William A; McGuigan, Katrina L; Phillips, Patrick C; Postlethwait, John H

2007-01-01

A promising route for understanding the origin and diversification of organismal form is through studies at the intersection of evolution and development (evo-devo). While much has been learned over the last two decades concerning macroevolutionary patterns of developmental change, a fundamental gap in the evo-devo synthesis is the integration of mathematical population and quantitative genetics with studies of how genetic variation in natural populations affects developmental processes. This micro-evo-devo synthesis requires model organisms with which to ask empirical questions. Threespine stickleback fish (Gasterosteus aculeatus), long a model for studying behavior, ecology and evolution, is emerging as a prominent model micro-evo-devo system. Research on stickleback over the last decade has begun to address the genetic basis of morphological variation and sex determination, and much of this work has important implications for understanding the genetics of speciation. In this paper we review recent threespine stickleback micro-evo-devo results, and outline the resources that have been developed to make this synthesis possible. The prospects for stickleback research to speed the micro-(and macro-) evo-devo syntheses are great, and this workhorse model system is well situated to continue contributing to our understanding of the generation of diversity in organismal form for many more decades.

How lay people respond to messages about genetics, health, and race.

PubMed

Condit, C; Bates, B

2005-08-01

There is a growing movement in medical genetics to develop, implement, and promote a model of race-based medicine. Although race-based medicine may become a widely disseminated standard of care, messages that advocate race-based selection for diagnosing, screening and prescribing drugs may exacerbate health disparities. These messages are present in clinical genetic counseling sessions, mass media, and everyday talk. Messages promoting linkages among genes, race, and health and messages emphasizing genetic causation may promote both general racism and genetically based racism. This mini-review examines research in three areas: studies that address the effects of these messages about genetics on levels of genetic determinism and genetic discrimination; studies that address the effects of these messages on attitudes about race; and, studies of the impacts of race-specific genetic messages on recipients. Following an integration of this research, this mini-review suggests that the current literature appears fragmented because of methodological and measurement issues and offers strategies for future research. Finally, the authors offer a path model to help organize future research examining the effects of messages about genetics on socioculturally based racism, genetically based racism, and unaccounted for racism. Research in this area is needed to understand and mitigate the negative attitudinal effects of messages that link genes, race, and health and/or emphasize genetic causation.

Ceratopteris richardii: a productive model for revealing secrets of signaling and development

NASA Technical Reports Server (NTRS)

Chatterjee, A.; Roux, S. J.

2000-01-01

Ceratopteris richardii is an aquatic fern grown in tropical and subtropical regions of the world. It is proven to be a productive model system for studies in the genetics, biochemistry, and cell biology of basic biologic processes that occur in early gametophytic development. It provides several advantages to biologists, especially those interested in gravitational biology, polarity development, and in the genetics of sexual development. It is easy to culture, has a relatively short life cycle, and offers an array of attractive features that facilitate genetic studies. The germination and early development of large populations of genetically identical spores are easy to synchronize, and both the direction of polarity development and cell-level gravity responses can be measured and readily manipulated within the first 24 h of spore development. Although there is no reliable transformation system available yet in Ceratopteris, recent studies suggest that the technique of RNA interference can be used to block translation of specific genes in a related fern, Marsilea, and current studies will soon reveal the applicability of this approach, as well as of other transformation approaches, in Ceratopteris. A recently completed expressed sequence tag (EST) sequencing project makes available the partial sequence of more than 2000 cDNAs, representing a significant percentage of the genes being expressed during the first 24 h of spore germination, when many developmentally interesting processes are occurring. A microarray of these ESTs is being constructed, so especially for those scientists interested in basic cellular phenomena that occur early in spore germination, the availability of the ESTs and of the microarray will make Ceratopteris an even more attractive model system.

Ceratopteris richardii: a productive model for revealing secrets of signaling and development.

PubMed

Chatterjee, A; Roux, S J

2000-09-01

Ceratopteris richardii is an aquatic fern grown in tropical and subtropical regions of the world. It is proven to be a productive model system for studies in the genetics, biochemistry, and cell biology of basic biologic processes that occur in early gametophytic development. It provides several advantages to biologists, especially those interested in gravitational biology, polarity development, and in the genetics of sexual development. It is easy to culture, has a relatively short life cycle, and offers an array of attractive features that facilitate genetic studies. The germination and early development of large populations of genetically identical spores are easy to synchronize, and both the direction of polarity development and cell-level gravity responses can be measured and readily manipulated within the first 24 h of spore development. Although there is no reliable transformation system available yet in Ceratopteris, recent studies suggest that the technique of RNA interference can be used to block translation of specific genes in a related fern, Marsilea, and current studies will soon reveal the applicability of this approach, as well as of other transformation approaches, in Ceratopteris. A recently completed expressed sequence tag (EST) sequencing project makes available the partial sequence of more than 2000 cDNAs, representing a significant percentage of the genes being expressed during the first 24 h of spore germination, when many developmentally interesting processes are occurring. A microarray of these ESTs is being constructed, so especially for those scientists interested in basic cellular phenomena that occur early in spore germination, the availability of the ESTs and of the microarray will make Ceratopteris an even more attractive model system.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

In Vitro Tissue-Engineered Skeletal Muscle Models for Studying Muscle Physiology and Disease.

PubMed

Khodabukus, Alastair; Prabhu, Neel; Wang, Jason; Bursac, Nenad

2018-04-25

Healthy skeletal muscle possesses the extraordinary ability to regenerate in response to small-scale injuries; however, this self-repair capacity becomes overwhelmed with aging, genetic myopathies, and large muscle loss. The failure of small animal models to accurately replicate human muscle disease, injury and to predict clinically-relevant drug responses has driven the development of high fidelity in vitro skeletal muscle models. Herein, the progress made and challenges ahead in engineering biomimetic human skeletal muscle tissues that can recapitulate muscle development, genetic diseases, regeneration, and drug response is discussed. Bioengineering approaches used to improve engineered muscle structure and function as well as the functionality of satellite cells to allow modeling muscle regeneration in vitro are also highlighted. Next, a historical overview on the generation of skeletal muscle cells and tissues from human pluripotent stem cells, and a discussion on the potential of these approaches to model and treat genetic diseases such as Duchenne muscular dystrophy, is provided. Finally, the need to integrate multiorgan microphysiological systems to generate improved drug discovery technologies with the potential to complement or supersede current preclinical animal models of muscle disease is described. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Human pluripotent stem cell models of autism spectrum disorder: emerging frontiers, opportunities, and challenges towards neuronal networks in a dish.

PubMed

Aigner, Stefan; Heckel, Tobias; Zhang, Jitao D; Andreae, Laura C; Jagasia, Ravi

2014-03-01

Autism spectrum disorder (ASD) is characterized by deficits in language development and social cognition and the manifestation of repetitive and restrictive behaviors. Despite recent major advances, our understanding of the pathophysiological mechanisms leading to ASD is limited. Although most ASD cases have unknown genetic underpinnings, animal and human cellular models of several rare, genetically defined syndromic forms of ASD have provided evidence for shared pathophysiological mechanisms that may extend to idiopathic cases. Here, we review our current knowledge of the genetic basis and molecular etiology of ASD and highlight how human pluripotent stem cell-based disease models have the potential to advance our understanding of molecular dysfunction. We summarize landmark studies in which neuronal cell populations generated from human embryonic stem cells and patient-derived induced pluripotent stem cells have served to model disease mechanisms, and we discuss recent technological advances that may ultimately allow in vitro modeling of specific human neuronal circuitry dysfunction in ASD. We propose that these advances now offer an unprecedented opportunity to help better understand ASD pathophysiology. This should ultimately enable the development of cellular models for ASD, allowing drug screening and the identification of molecular biomarkers for patient stratification.

Beneficial bacteria stimulate host immune cells to counteract dietary and genetic predisposition to mammary cancer in mice.

PubMed

Lakritz, Jessica R; Poutahidis, Theofilos; Levkovich, Tatiana; Varian, Bernard J; Ibrahim, Yassin M; Chatzigiagkos, Antonis; Mirabal, Sheyla; Alm, Eric J; Erdman, Susan E

2014-08-01

Recent studies suggest health benefits including protection from cancer after eating fermented foods such as probiotic yogurt, though the mechanisms are not well understood. Here we tested mechanistic hypotheses using two different animal models: the first model studied development of mammary cancer when eating a Westernized diet, and the second studied animals with a genetic predilection to breast cancer. For the first model, outbred Swiss mice were fed a Westernized chow putting them at increased risk for development of mammary tumors. In this Westernized diet model, mammary carcinogenesis was inhibited by routine exposure to Lactobacillus reuteri ATCC-PTA-6475 in drinking water. The second model was FVB strain erbB2 (HER2) mutant mice, genetically susceptible to mammary tumors mimicking breast cancers in humans, being fed a regular (non-Westernized) chow diet. We found that oral supplement with these purified lactic acid bacteria alone was sufficient to inhibit features of mammary neoplasia in both models. The protective mechanism was determined to be microbially-triggered CD4+CD25+ lymphocytes. When isolated and transplanted into other subjects, these L. reuteri-stimulated lymphocytes were sufficient to convey transplantable anti-cancer protection in the cell recipient animals. These data demonstrate that host immune responses to environmental microbes significantly impact and inhibit cancer progression in distal tissues such as mammary glands, even in genetically susceptible mice. This leads us to conclude that consuming fermentative microbes such as L. reuteri may offer a tractable public health approach to help counteract the accumulated dietary and genetic carcinogenic events integral in the Westernized diet and lifestyle. © 2013 The Authors. Published by Wiley Periodicals, Inc. on behalf of UICC.

A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

PubMed

Xu, Lifeng; Henke, Michael; Zhu, Jun; Kurth, Winfried; Buck-Sorlin, Gerhard

2011-04-01

Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype-phenotype model, we present here a three-dimensional functional-structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed.

A functional–structural model of rice linking quantitative genetic information with morphological development and physiological processes

PubMed Central

Xu, Lifeng; Henke, Michael; Zhu, Jun; Kurth, Winfried; Buck-Sorlin, Gerhard

2011-01-01

Background and Aims Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype–phenotype model, we present here a three-dimensional functional–structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. Methods The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Key Results Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. Conclusions We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed. PMID:21247905

Metabolomics and In-Silico Analysis Reveal Critical Energy Deregulations in Animal Models of Parkinson’s Disease

PubMed Central

Poliquin, Pierre O.; Chen, Jingkui; Cloutier, Mathieu; Trudeau, Louis-Éric; Jolicoeur, Mario

2013-01-01

Parkinson’s disease (PD) is a multifactorial disease known to result from a variety of factors. Although age is the principal risk factor, other etiological mechanisms have been identified, including gene mutations and exposure to toxins. Deregulation of energy metabolism, mostly through the loss of complex I efficiency, is involved in disease progression in both the genetic and sporadic forms of the disease. In this study, we investigated energy deregulation in the cerebral tissue of animal models (genetic and toxin induced) of PD using an approach that combines metabolomics and mathematical modelling. In a first step, quantitative measurements of energy-related metabolites in mouse brain slices revealed most affected pathways. A genetic model of PD, the Park2 knockout, was compared to the effect of CCCP, a complex I blocker. Model simulated and experimental results revealed a significant and sustained decrease in ATP after CCCP exposure, but not in the genetic mice model. In support to data analysis, a mathematical model of the relevant metabolic pathways was developed and calibrated onto experimental data. In this work, we show that a short-term stress response in nucleotide scavenging is most probably induced by the toxin exposure. In turn, the robustness of energy-related pathways in the model explains how genetic perturbations, at least in young animals, are not sufficient to induce significant changes at the metabolite level. PMID:23935941

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.

Orthotopic xenografts of RCC retain histological, immunophenotypic and genetic features of tumors in patients

PubMed Central

Grisanzio, Chiara; Seeley, Apryle; Chang, Michelle; Collins, Michael; Di Napoli, Arianna; Cheng, Su-Chun; Percy, Andrew; Beroukhim, Rameen; Signoretti, Sabina

2013-01-01

Renal cell carcinoma (RCC) is an aggressive malignancy with limited responsiveness to existing treatments. In vivo models of human cancer, including RCC, are critical for developing more effective therapies. Unfortunately, current RCC models do not accurately represent relevant properties of the human disease. The goal of this study was to develop clinically relevant animal models of RCC for preclinical investigations. We transplanted intact human tumor tissue fragments orthotopically in immunodeficient mice. The xenografts were validated by comparing the morphologic, phenotypic, and genetic characteristics of the kidney tumor tissues before and after implantation. Twenty kidney tumors were transplanted into mice. Successful tumor growth was detected in 19 cases (95%). The histopathologic and immunophenotypic features of the xenografts and those of the original tumors largely overlapped in all the cases. Evaluation of genetic alterations in a subset of 10 cases demonstrated that the grafts largely retained the genetic features of the pre-implantation RCC tissues. Indeed, primary tumors and corresponding grafts displayed identical VHL mutations. Moreover, an identical pattern of DNA copy amplification or loss was observed in 6 of 10 cases (60%). In summary, orthotopic engrafting of RCC tissue fragments can be successfully used to generate animal models that closely resemble RCC in patients. These models will be invaluable for in vivo preclinical drug testing, and for deeper understanding of kidney carcinogenesis. PMID:21710693

Unraveling additive from nonadditive effects using genomic relationship matrices.

PubMed

Muñoz, Patricio R; Resende, Marcio F R; Gezan, Salvador A; Resende, Marcos Deon Vilela; de Los Campos, Gustavo; Kirst, Matias; Huber, Dudley; Peter, Gary F

2014-12-01

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies. Copyright © 2014 by the Genetics Society of America.

Coherent spectroscopic methods for monitoring pathogens, genetically modified products and nanostructured materials in colloidal solution

NASA Astrophysics Data System (ADS)

Moguilnaya, T.; Suminov, Y.; Botikov, A.; Ignatov, S.; Kononenko, A.; Agibalov, A.

2017-01-01

We developed the new automatic method that combines the method of forced luminescence and stimulated Brillouin scattering. This method is used for monitoring pathogens, genetically modified products and nanostructured materials in colloidal solution. We carried out the statistical spectral analysis of pathogens, genetically modified soy and nano-particles of silver in water from different regions in order to determine the statistical errors of the method. We studied spectral characteristics of these objects in water to perform the initial identification with 95% probability. These results were used for creation of the model of the device for monitor of pathogenic organisms and working model of the device to determine the genetically modified soy in meat.

Rule-Based Models of the Interplay between Genetic and Environmental Factors in Childhood Allergy

PubMed Central

Melén, Erik; Bergström, Anna; Torabi Moghadam, Behrooz; Pulkkinen, Ville; Acevedo, Nathalie; Orsmark Pietras, Christina; Ege, Markus; Braun-Fahrländer, Charlotte; Riedler, Josef; Doekes, Gert; Kabesch, Michael; van Hage, Marianne; Kere, Juha; Scheynius, Annika; Söderhäll, Cilla; Pershagen, Göran; Komorowski, Jan

2013-01-01

Both genetic and environmental factors are important for the development of allergic diseases. However, a detailed understanding of how such factors act together is lacking. To elucidate the interplay between genetic and environmental factors in allergic diseases, we used a novel bioinformatics approach that combines feature selection and machine learning. In two materials, PARSIFAL (a European cross-sectional study of 3113 children) and BAMSE (a Swedish birth-cohort including 2033 children), genetic variants as well as environmental and lifestyle factors were evaluated for their contribution to allergic phenotypes. Monte Carlo feature selection and rule based models were used to identify and rank rules describing how combinations of genetic and environmental factors affect the risk of allergic diseases. Novel interactions between genes were suggested and replicated, such as between ORMDL3 and RORA, where certain genotype combinations gave odds ratios for current asthma of 2.1 (95% CI 1.2-3.6) and 3.2 (95% CI 2.0-5.0) in the BAMSE and PARSIFAL children, respectively. Several combinations of environmental factors appeared to be important for the development of allergic disease in children. For example, use of baby formula and antibiotics early in life was associated with an odds ratio of 7.4 (95% CI 4.5-12.0) of developing asthma. Furthermore, genetic variants together with environmental factors seemed to play a role for allergic diseases, such as the use of antibiotics early in life and COL29A1 variants for asthma, and farm living and NPSR1 variants for allergic eczema. Overall, combinations of environmental and life style factors appeared more frequently in the models than combinations solely involving genes. In conclusion, a new bioinformatics approach is described for analyzing complex data, including extensive genetic and environmental information. Interactions identified with this approach could provide useful hints for further in-depth studies of etiological mechanisms and may also strengthen the basis for risk assessment and prevention. PMID:24260339

Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

PubMed

Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

2014-05-01

Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

Effectiveness of students worksheet based on mastery learning in genetics subject

NASA Astrophysics Data System (ADS)

Megahati, R. R. P.; Yanti, F.; Susanti, D.

2018-05-01

Genetics is one of the subjects that must be followed by students in Biology education department. Generally, students do not like the genetics subject because of genetics concepts difficult to understand and the unavailability of a practical students worksheet. Consequently, the complete learning process (mastery learning) is not fulfilled and low students learning outcomes. The aim of this study develops student worksheet based on mastery learning that practical in genetics subject. This research is a research and development using 4-D models. The data analysis technique used is the descriptive analysis that describes the results of the practicalities of students worksheets based on mastery learning by students and lecturer of the genetic subject. The result is the student worksheet based on mastery learning on genetics subject are to the criteria of 80,33% and 80,14%, which means that the students worksheet practical used by lecturer and students. Student’s worksheet based on mastery learning effective because it can increase the activity and student learning outcomes.

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.

Genetics in medical school curriculum: A look at the University of Rochester School of Medicine and Dentistry

PubMed Central

Robinson, Deanne M.; Fong, Chin-To

2008-01-01

Genetics is assuming an increasingly important role in medicine. As a result, the teaching of genetics should also be increased proportionally to ensure that future physicians will be able to take advantage of the new genetic technology, and to understand the associated ethical, legal and social issues. At the University of Rochester School of Medicine and Dentistry, we have been able to incorporate genetic education into a four-year medical curriculum in a fully integrated fashion. This model may serve as a template for other medical curriculum still in development. PMID:18196607

Recent advances in genetic modification systems for Actinobacteria.

PubMed

Deng, Yu; Zhang, Xi; Zhang, Xiaojuan

2017-03-01

Actinobacteria are extremely important to human health, agriculture, and forests. Because of the vast differences of the characteristics of Actinobacteria, a lot of genetic tools have been developed for efficiently manipulating the genetics. Although there are a lot of successful examples of engineering Actinobacteria, they are still more difficult to be genetically manipulated than other model microorganisms such as Saccharomyces cerevisiae, Escherichia coli, and Bacillus subtilis etc. due to the diverse genomics and biochemical machinery. Here, we review the methods to introduce heterologous DNA into Actinobacteria and the available genetic modification tools. The trends and problems existing in engineering Actinobacteria are also covered.

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.

Drosophila Melanogaster as an Emerging Translational Model of Human Nephrolithiasis

PubMed Central

Miller, Joe; Chi, Thomas; Kapahi, Pankaj; Kahn, Arnold J.; Kim, Man Su; Hirata, Taku; Romero, Michael F.; Dow, Julian A.T.; Stoller, Marshall L.

2013-01-01

Purpose The limitations imposed by human clinical studies and mammalian models of nephrolithiasis have hampered the development of effective medical treatments and preventative measures for decades. The simple but elegant Drosophila melanogaster is emerging as a powerful translational model of human disease, including nephrolithiasis and may provide important information essential to our understanding of stone formation. We present the current state of research using D. melanogaster as a model of human nephrolithiasis. Materials and Methods A comprehensive review of the English language literature was performed using PUBMED. When necessary, authoritative texts on relevant subtopics were consulted. Results The genetic composition, anatomic structure and physiologic function of Drosophila Malpighian tubules are remarkably similar to those of the human nephron. The direct effects of dietary manipulation, environmental alteration, and genetic variation on stone formation can be observed and quantified in a matter of days. Several Drosophila models of human nephrolithiasis, including genetically linked and environmentally induced stones, have been developed. A model of calcium oxalate stone formation is among the most recent fly models of human nephrolithiasis. Conclusions The ability to readily manipulate and quantify stone formation in D. melanogaster models of human nephrolithiasis presents the urologic community with a unique opportunity to increase our understanding of this enigmatic disease. PMID:23500641

Creation of a National, At-home Model for Ashkenazi Jewish Carrier Screening.

PubMed

Grinzaid, Karen Arnovitz; Page, Patricia Zartman; Denton, Jessica Johnson; Ginsberg, Jessica

2015-06-01

Ethnicity-based carrier screening for the Ashkenazi Jewish population has been available and encouraged by advocacy and community groups since the early 1970's. Both the American College of Medical Genetics and the American Congress of Obstetricians and Gynecologists recommend carrier screening for this population (Obstetrics and Gynecology, 114(4), 950-953, 2009; Genetics in Medicine, 10(1), 55-56, 2008). While many physicians inquire about ethnic background and offer appropriate carrier screening, studies show that a gap remains in implementing recommendations (Genetic testing and molecular biomarkers, 2011). In addition, education and outreach efforts targeting Jewish communities have had limited success in reaching this at-risk population. Despite efforts by the medical and Jewish communities, many Jews of reproductive age are not aware of screening, and remain at risk for having children with preventable diseases. Reaching this population, preferably pre-conception, and facilitating access to screening is critically important. To address this need, genetic counselors at Emory University developed JScreen, a national Jewish genetic disease screening program. The program includes a national marketing and PR campaign, online education, at-home saliva-based screening, post-test genetic counseling via telephone or secure video conferencing, and referrals for face-to-face genetic counseling as needed. Our goals are to create a successful education and screening program for this population and to develop a model that could potentially be used for other at-risk populations.

Distinct developmental genetic mechanisms underlie convergently evolved tooth gain in sticklebacks

PubMed Central

Ellis, Nicholas A.; Glazer, Andrew M.; Donde, Nikunj N.; Cleves, Phillip A.; Agoglia, Rachel M.; Miller, Craig T.

2015-01-01

Teeth are a classic model system of organogenesis, as repeated and reciprocal epithelial and mesenchymal interactions pattern placode formation and outgrowth. Less is known about the developmental and genetic bases of tooth formation and replacement in polyphyodonts, which are vertebrates with continual tooth replacement. Here, we leverage natural variation in the threespine stickleback fish Gasterosteus aculeatus to investigate the genetic basis of tooth development and replacement. We find that two derived freshwater stickleback populations have both convergently evolved more ventral pharyngeal teeth through heritable genetic changes. In both populations, evolved tooth gain manifests late in development. Using pulse-chase vital dye labeling to mark newly forming teeth in adult fish, we find that both high-toothed freshwater populations have accelerated tooth replacement rates relative to low-toothed ancestral marine fish. Despite the similar evolved phenotype of more teeth and an accelerated adult replacement rate, the timing of tooth number divergence and the spatial patterns of newly formed adult teeth are different in the two populations, suggesting distinct developmental mechanisms. Using genome-wide linkage mapping in marine-freshwater F2 genetic crosses, we find that the genetic basis of evolved tooth gain in the two freshwater populations is largely distinct. Together, our results support a model whereby increased tooth number and an accelerated tooth replacement rate have evolved convergently in two independently derived freshwater stickleback populations using largely distinct developmental and genetic mechanisms. PMID:26062935

A 100-Year Review: Methods and impact of genetic selection in dairy cattle-From daughter-dam comparisons to deep learning algorithms.

PubMed

Weigel, K A; VanRaden, P M; Norman, H D; Grosu, H

2017-12-01

In the early 1900s, breed society herdbooks had been established and milk-recording programs were in their infancy. Farmers wanted to improve the productivity of their cattle, but the foundations of population genetics, quantitative genetics, and animal breeding had not been laid. Early animal breeders struggled to identify genetically superior families using performance records that were influenced by local environmental conditions and herd-specific management practices. Daughter-dam comparisons were used for more than 30 yr and, although genetic progress was minimal, the attention given to performance recording, genetic theory, and statistical methods paid off in future years. Contemporary (herdmate) comparison methods allowed more accurate accounting for environmental factors and genetic progress began to accelerate when these methods were coupled with artificial insemination and progeny testing. Advances in computing facilitated the implementation of mixed linear models that used pedigree and performance data optimally and enabled accurate selection decisions. Sequencing of the bovine genome led to a revolution in dairy cattle breeding, and the pace of scientific discovery and genetic progress accelerated rapidly. Pedigree-based models have given way to whole-genome prediction, and Bayesian regression models and machine learning algorithms have joined mixed linear models in the toolbox of modern animal breeders. Future developments will likely include elucidation of the mechanisms of genetic inheritance and epigenetic modification in key biological pathways, and genomic data will be used with data from on-farm sensors to facilitate precision management on modern dairy farms. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

To grow or not to grow: Hair morphogenesis and human genetic hair disorders

PubMed Central

Duverger, Olivier; Morasso, Maria I.

2014-01-01

Mouse models have greatly helped in elucidating the molecular mechanisms involved in hair formation and regeneration. Recent publications have reviewed the genes involved in mouse hair development based on the phenotype of transgenic, knockout and mutant animal models. While much of this information has been instrumental in determining molecular aspects of human hair development and cycling, mice exhibit a specific pattern of hair morphogenesis and hair distribution throughout the body that cannot be directly correlated to human hair. In this mini-review, we discuss specific aspects of human hair follicle development and present an up-to-date summary of human genetic disorders associated with abnormalities in hair follicle morphogenesis, structure or regeneration. PMID:24361867

DEVELOPMENT OF AQUATIC MODELS FOR TESTING THE RELATIONSHIP BETWEEN GENETIC DIVERSITY AND POPULATION EXTINCTION RISK

EPA Science Inventory

The relationship between population adaptive potential and extinction risk in a changing environment is not well understood. Although the expectation is that genetic diversity is directly related to the capacity of populations to adapt, the statistical and predictive aspects of ...

Genetic analysis across differential spatial scales reveals multiple dispersal mechanisms for the invasive hydrozoan Cordylophora in the Great Lakes

EPA Science Inventory

Understanding patterns of post-establishment spread by invasive species is critically important for the design of effective management strategies and the development of appropriate theoretical models predicting spatial expansion of introduced populations. Here we explore genetic ...

Incorporation of genomic information into genetic evaluation: U. S. beef industry as a model

USDA-ARS?s Scientific Manuscript database

In his presentation, Dr. Kuehn described approaches for using information garnered through developments in genomics to improve the accuracy of genetic evaluation. He considered the history of these molecular-based techniques, including their strengths and potential weaknesses, and his experiences wi...

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Disease Modeling via Large-Scale Network Analysis

DTIC Science & Technology

2015-05-20

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

Genetic service delivery: infrastructure, assessment and information.

PubMed

Kaye, C I

2012-01-01

Identification of genomic determinants of complex disorders such as cancer, diabetes and cardiovascular disease has prompted public health systems to focus on genetic service delivery for prevention of these disorders, adding to their previous efforts in birth defects prevention and newborn screening. This focus is consistent with previously identified obligations of the public health system as well as the core functions of public health identified by the Institute of Medicine. Models of service delivery include provision of services by the primary care provider in conjunction with subspecialists, provision of services through the medical home with co-management by genetics providers, provision of services in conjunction with disorder-specific treatment centers, and provision of services through a network of genetics clinics linked to medical homes. Whatever the model for provision of genetic services, tools to assist providers include facilities for outreach and telemedicine, information technology, just-in-time management plans, and emergency management tools. Assessment tools to determine which care is best are critical for quality improvement and development of best practices. Because the workforce of genetics providers is not keeping pace with the need for services, an understanding of the factors contributing to this lag is important, as is the development of an improved knowledge base in genomics for primary care providers. Copyright © 2012 S. Karger AG, Basel.

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.

Accelerating Genetic Gains in Legumes for the Development of Prosperous Smallholder Agriculture: Integrating Genomics, Phenotyping, Systems Modelling and Agronomy.

PubMed

Varshney, Rajeev K; Thudi, Mahendar; Pandey, Manish K; Tardieu, Francois; Ojiewo, Chris; Vadez, Vincent; Whitbread, Anthony M; Siddique, Kadambot H M; Nguyen, Henry T; Carberry, Peter S; Bergvinson, David

2018-03-05

Grain legumes form an important component of the human diet, feed for livestock and replenish soil fertility through biological nitrogen fixation. Globally, the demand for food legumes is increasing as they complement cereals in protein requirements and possess a high percentage of digestible protein. Climate change has enhanced the frequency and intensity of drought stress that is posing serious production constraints, especially in rainfed regions where most legumes are produced. Genetic improvement of legumes, like other crops, is mostly based on pedigree and performance-based selection over the last half century. For achieving faster genetic gains in legumes in rainfed conditions, this review article proposes the integration of modern genomics approaches, high throughput phenomics and simulation modelling as support for crop improvement that leads to improved varieties that perform with appropriate agronomy. Selection intensity, generation interval and improved operational efficiencies in breeding are expected to further enhance the genetic gain in experiment plots. Improved seed access to farmers, combined with appropriate agronomic packages in farmers' fields, will deliver higher genetic gains. Enhanced genetic gains including not only productivity but also nutritional and market traits will increase the profitability of farmers and the availability of affordable nutritious food especially in developing countries.

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.

Revising and assessing explanatory models in a high school genetics class: A comparison of unsuccessful and successful performance

NASA Astrophysics Data System (ADS)

Johnson, Susan K.; Stewart, Jim

2002-07-01

In this paper we describe the model-revising problem-solving strategies of two groups of students (one successful, one unsuccessful) as they worked (in a genetics course we developed) to revise Mendel's simple dominance model to explain the inheritance of a trait expressed in any of four variations. The two groups described in this paper were chosen with the intent that the strategies that they employed be used to inform the design of model-based instruction. Differences were found in the groups' abilities to recognize anomalous data, use existing models as templates for revisions, and assess revised models.

Optimization of multi-environment trials for genomic selection based on crop models.

PubMed

Rincent, R; Kuhn, E; Monod, H; Oury, F-X; Rousset, M; Allard, V; Le Gouis, J

2017-08-01

We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

Diverse data supports the transition of filamentous fungal model organisms into the post-genomics era

DOE PAGES

McCluskey, Kevin; Baker, Scott E.

2017-02-17

As model organisms filamentous fungi have been important since the beginning of modern biological inquiry and have benefitted from open data since the earliest genetic maps were shared. From early origins in simple Mendelian genetics of mating types, parasexual genetics of colony colour, and the foundational demonstration of the segregation of a nutritional requirement, the contribution of research systems utilising filamentous fungi has spanned the biochemical genetics era, through the molecular genetics era, and now are at the very foundation of diverse omics approaches to research and development. Fungal model organisms have come from most major taxonomic groups although Ascomycetemore » filamentous fungi have seen the most major sustained effort. In addition to the published material about filamentous fungi, shared molecular tools have found application in every area of fungal biology. Likewise, shared data has contributed to the success of model systems. Furthermore, the scale of data supporting research with filamentous fungi has grown by 10 to 12 orders of magnitude. From genetic to molecular maps, expression databases, and finally genome resources, the open and collaborative nature of the research communities has assured that the rising tide of data has lifted all of the research systems together.« less

Diverse data supports the transition of filamentous fungal model organisms into the post-genomics era

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

McCluskey, Kevin; Baker, Scott E.

As model organisms filamentous fungi have been important since the beginning of modern biological inquiry and have benefitted from open data since the earliest genetic maps were shared. From early origins in simple Mendelian genetics of mating types, parasexual genetics of colony colour, and the foundational demonstration of the segregation of a nutritional requirement, the contribution of research systems utilising filamentous fungi has spanned the biochemical genetics era, through the molecular genetics era, and now are at the very foundation of diverse omics approaches to research and development. Fungal model organisms have come from most major taxonomic groups although Ascomycetemore » filamentous fungi have seen the most major sustained effort. In addition to the published material about filamentous fungi, shared molecular tools have found application in every area of fungal biology. Likewise, shared data has contributed to the success of model systems. Furthermore, the scale of data supporting research with filamentous fungi has grown by 10 to 12 orders of magnitude. From genetic to molecular maps, expression databases, and finally genome resources, the open and collaborative nature of the research communities has assured that the rising tide of data has lifted all of the research systems together.« less

The Plant Genetic Engineering Laboratory For Desert Adaptation

NASA Astrophysics Data System (ADS)

Kemp, John D.; Phillips, Gregory C.

1985-11-01

The Plant Genetic Engineering Laboratory for Desert Adaptation (PGEL) is one of five Centers of Technical Excellence established as a part of the state of New Mexico's Rio Grande Research Corridor (RGRC). The scientific mission of PGEL is to bring innovative advances in plant biotechnology to bear on agricultural productivity in arid and semi-arid regions. Research activities focus on molecular and cellular genetics technology development in model systems, but also include stress physiology investigations and development of desert plant resources. PGEL interacts with the Los Alamos National Laboratory (LANL), a national laboratory participating in the RGRC. PGEL also has an economic development mission, which is being pursued through technology transfer activities to private companies and public agencies.

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.

The current social, political, and medical role of genetic testing in familial breast and ovarian carcinomas.

PubMed

Weitzel, J N

1999-02-01

Few advances in medical science have yielded as much publicity and controversy as discoveries in genetics. Moving quickly from the bench to the bedside, genetic testing for inherited susceptibility to breast and ovarian cancer has had a significant impact on our paradigms for decisions about the treatment and prevention of disease. Assessment of cancer risk is developing into a distinct discipline, with rapidly evolving genetic technologies and models for estimating an individual's risk of cancer. Exciting developments in chemoprevention of breast cancer demonstrate the potential to offer a broader range of options for decreasing cancer risk. This article will consider recent advances in the understanding of cancer genetics, and describe the state-of-the-art in terms of management of individuals with inherited susceptibility to breast and ovarian cancer.

A classical twin study of PTSD symptoms and resilience: Evidence for a single spectrum of vulnerability to traumatic stress.

PubMed

Wolf, Erika J; Miller, Mark W; Sullivan, Danielle R; Amstadter, Ananda B; Mitchell, Karen S; Goldberg, Jack; Magruder, Kathryn M

2018-02-01

To examine shared genetic and environmental risk factors across PTSD symptoms and resilience. Classical twin study of 2010-2012 survey data conducted among 3,318 male twin pairs in the Vietnam Era Twin Registry. Analyses included: (a) estimates of genetic and environmental influences on PTSD symptom severity (as measured by the PTSD Checklist) and resilience (assessed with the Connor-Davidson Resilience Scale-10); (b) development of a latent model of traumatic stress, spanning both PTSD and resilience; and (c) estimates of genetic and environmental influences on this spectrum. The heritability of PTSD was 49% and of resilience was 25%. PTSD and resilience were correlated at r = -.59, and 59% of this correlation was attributable to a single genetic factor, whereas the remainder was due to a single non-shared environment factor. Resilience was also influenced by common and unique environmental factors not shared with PTSD, but there was no genetic factor specific to resilience. Confirmatory factor analysis supported the Development of a revised phenotype reflecting the broader dimension of traumatic stress, with biometric models suggesting increased heritability (66%) of this spectrum compared to PTSD or resilience individually. Genetic factors contribute to a single spectrum of traumatic stress reflecting resilience at one end and high symptom severity at the other. This carries implications for phenotype refinement in the search for molecular genetic markers of trauma-related psychopathology. Rather than focusing only on genetic risk for PTSD, molecular genetics research may benefit from evaluation of the broader spectrum of traumatic stress. © 2017 Wiley Periodicals, Inc.

Tissue Engineering and Cellular Regeneration at NASA Report to Regenetech SAB

NASA Technical Reports Server (NTRS)

Goodwin, Thomas J.

2004-01-01

A project overview describing three dimensional tissue models is shown. The topics include: 1) cellular regeneration; 2) haemopoietic replacement; 3) novel vaccine development; 4) pharmacology and toxicology interventions; 5) development of synthetic viruses; and 6) molecular genetics and proteomics of recapitulated models.

How animal models of leukaemias have already benefited patients.

PubMed

Ablain, Julien; Nasr, Rihab; Zhu, Jun; Bazarbachi, Ali; Lallemand-Breittenbach, Valérie; de Thé, Hugues

2013-04-01

The relative genetic simplicity of leukaemias, the development of which likely relies on a limited number of initiating events has made them ideal for disease modelling, particularly in the mouse. Animal models provide incomparable insights into the mechanisms of leukaemia development and allow exploration of the molecular pillars of disease maintenance, an aspect often biased in cell lines or ex vivo systems. Several of these models, which faithfully recapitulate the characteristics of the human disease, have been used for pre-clinical purposes and have been instrumental in predicting therapy response in patients. We plea for a wider use of genetically defined animal models in the design of clinical trials, with a particular focus on reassessment of existing cancer or non-cancer drugs, alone or in combination. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

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.

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.

Developmental vitamin D deficiency and schizophrenia: the role of animal models

PubMed Central

Schoenrock, S. A.; Tarantino, L. M.

2016-01-01

Schizophrenia is a debilitating neuropsychiatric disorder that affects 1% of the US population. Based on twin and genome-wide association studies, it is clear that both genetics and environmental factors increase the risk for developing schizophrenia. Moreover, there is evidence that conditions in utero, either alone or in concert with genetic factors, may alter neurodevelopment and lead to an increased risk for schizophrenia. There has been progress in identifying genetic loci and environmental exposures that increase risk, but there are still considerable gaps in our knowledge. Furthermore, very little is known about the specific neurodevelopmental mechanisms upon which genetics and the environment act to increase disposition to developing schizophrenia in adulthood. Vitamin D deficiency during the perinatal period has been hypothesized to increase risk for schizophrenia in humans. The developmental vitamin D (DVD) deficiency hypothesis of schizophrenia arises from the observation that disease risk is increased in individuals who are born in winter or spring, live further from the equator or live in urban vs. rural settings. These environments result in less exposure to sunlight, thereby reducing the initial steps in the production of vitamin D. Rodent models have been developed to characterize the behavioral and developmental effects of DVD deficiency. This review focuses on these animal models and discusses the current knowledge of the role of DVD deficiency in altering behavior and neurobiology relevant to schizophrenia. PMID:26560996

Genetically engineered livestock for biomedical models.

PubMed

Rogers, Christopher S

2016-06-01

To commemorate Transgenic Animal Research Conference X, this review summarizes the recent progress in developing genetically engineered livestock species as biomedical models. The first of these conferences was held in 1997, which turned out to be a watershed year for the field, with two significant events occurring. One was the publication of the first transgenic livestock animal disease model, a pig with retinitis pigmentosa. Before that, the use of livestock species in biomedical research had been limited to wild-type animals or disease models that had been induced or were naturally occurring. The second event was the report of Dolly, a cloned sheep produced by somatic cell nuclear transfer. Cloning subsequently became an essential part of the process for most of the models developed in the last 18 years and is stilled used prominently today. This review is intended to highlight the biomedical modeling achievements that followed those key events, many of which were first reported at one of the previous nine Transgenic Animal Research Conferences. Also discussed are the practical challenges of utilizing livestock disease models now that the technical hurdles of model development have been largely overcome.

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.

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 versatile strategy for gene trapping and trap conversion in emerging model organisms.

PubMed

Kontarakis, Zacharias; Pavlopoulos, Anastasios; Kiupakis, Alexandros; Konstantinides, Nikolaos; Douris, Vassilis; Averof, Michalis

2011-06-01

Genetic model organisms such as Drosophila, C. elegans and the mouse provide formidable tools for studying mechanisms of development, physiology and behaviour. Established models alone, however, allow us to survey only a tiny fraction of the morphological and functional diversity present in the animal kingdom. Here, we present iTRAC, a versatile gene-trapping approach that combines the implementation of unbiased genetic screens with the generation of sophisticated genetic tools both in established and emerging model organisms. The approach utilises an exon-trapping transposon vector that carries an integrase docking site, allowing the targeted integration of new constructs into trapped loci. We provide proof of principle for iTRAC in the emerging model crustacean Parhyale hawaiensis: we generate traps that allow specific developmental and physiological processes to be visualised in unparalleled detail, we show that trapped genes can be easily cloned from an unsequenced genome, and we demonstrate targeting of new constructs into a trapped locus. Using this approach, gene traps can serve as platforms for generating diverse reporters, drivers for tissue-specific expression, gene knockdown and other genetic tools not yet imagined.

Hereditary gynaecologic cancers in Nepal: a proposed model of care to serve high risk populations in developing countries.

PubMed

Pokharel, Hanoon P; Hacker, Neville F; Andrews, Lesley

2017-01-01

Endometrial, ovarian and breast cancers are paradigms for global health disparity. Women living in the developing world continue to present in later stages of disease and have fewer options for treatment than those in developed countries. Risk reducing surgery is of proven benefit for women at high risk of gynaecological cancer. There is no specific model for identification and management of such women in the developing world. We have integrated data from our published audit of a major gynaecological oncology centre at Royal Hospital for Women in Australia, with data from our survey and a focus group discussion of Nepalese gynaecological health care professionals regarding genetic testing, and findings from the literature. These data have been used to identify current barriers to multidisciplinary gynaecological oncology care in developing nations, and to develop a model to integrate hereditary cancer services into cancer care in Nepal, as a paradigm for other developing nations. The ability to identify women with hereditary gynaecological cancer in developing nations is influenced by their late presentation (if active management is declined or not appropriate), limited access to specialised services and cultural and financial barriers. In order to include genetic assessment in multidisciplinary gynaecological cancer care, education needs to be provided to all levels of health care providers to enable reporting of family history, and appropriate ordering of investigations. Training of genetic counsellors is needed to assist in the interpretation of results and extending care to unaffected at-risk relatives. Novel approaches will be required to overcome geographic and financial barriers, including mainstreaming of genetic testing, telephone counselling, use of mouth swabs and utilisation of international laboratories. Women in Nepal have yet to receive benefits from the advances in early cancer diagnosis and management. There is a potential of extending the benefits of hereditary cancer diagnosis in Nepal due to the rapid fall in the cost of genetic testing and the ability to collect DNA from a buccal swab through appropriate training of the gynaecological carers.

Americamysis bahia: A New Laboratory Model for Conservation Genetics in a Changing Environment (AGA09)

EPA Science Inventory

Levels of population genetic diversity are expected to play an important role in species persistence during periods of environmental change, yet our understanding of how to quantify relevant aspects of this diversity is not well developed. We are conducting a long-term study wit...

Physical Aggression and Expressive Vocabulary in 19-Month-Old Twins.

ERIC Educational Resources Information Center

Dionne, Ginette; Tremblay, Richard; Boivin, Michel; Laplante, David; Perusse, Daniel

2003-01-01

Used a genetic design to investigate association between physical aggression and language development in 19-month-old twins. Found a modest but significant correlation between aggression and expressive vocabulary. Substantial heritability was found for physical aggression. Quantitative genetic modeling suggested that the correlation could not be…

Deviant Peer Affiliation and Antisocial Behavior: Interaction with Monoamine Oxidase A (MAOA) Genotype

ERIC Educational Resources Information Center

Lee, Steve S.

2011-01-01

Although genetic and environmental factors are separately implicated in the development of antisocial behavior (ASB), interactive models have emerged relatively recently, particularly those incorporating molecular genetic data. Using a large sample of male Caucasian adolescents and young adults from the National Longitudinal Study of Adolescent…

Synthesizing genetic divergence and climate modeling to inform conservation planning for ponderosa pine

Treesearch

Kevin M. Potter; Douglas J. Shinneman; Robert E. Means; Valerie D. Hipkins; Mary Frances Mahalovich

2017-01-01

Geological, climatological and ecological processes partially or entirely isolate evolutionary lineages within tree species. These lineages may develop adaptations to different local environmental conditions, and may eventually evolve into distinct forms or species. Isolation also can reduce adaptive genetic variation within populations of a species, potentially...

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida (Published Proceedings)

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes

PubMed Central

Reinagel, Adam; Bray Speth, Elena

2016-01-01

In an introductory biology course, we implemented a learner-centered, model-based pedagogy that frequently engaged students in building conceptual models to explain how genes determine phenotypes. Model-building tasks were incorporated within case studies and aimed at eliciting students’ understanding of 1) the origin of variation in a population and 2) how genes/alleles determine phenotypes. Guided by theory on hierarchical development of systems-thinking skills, we scaffolded instruction and assessment so that students would first focus on articulating isolated relationships between pairs of molecular genetics structures and then integrate these relationships into an explanatory network. We analyzed models students generated on two exams to assess whether students’ learning of molecular genetics progressed along the theoretical hierarchical sequence of systems-thinking skills acquisition. With repeated practice, peer discussion, and instructor feedback over the course of the semester, students’ models became more accurate, better contextualized, and more meaningful. At the end of the semester, however, more than 25% of students still struggled to describe phenotype as an output of protein function. We therefore recommend that 1) practices like modeling, which require connecting genes to phenotypes; and 2) well-developed case studies highlighting proteins and their functions, take center stage in molecular genetics instruction. PMID:26903496

Accuracies of univariate and multivariate genomic prediction models in African cassava.

PubMed

Okeke, Uche Godfrey; Akdemir, Deniz; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2017-12-04

Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a single-environment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies. In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

A Population Genetics Model of Marker-Assisted Selection

PubMed Central

Luo, Z. W.; Thompson, R.; Woolliams, J. A.

1997-01-01

A deterministic two-loci model was developed to predict genetic response to marker-assisted selection (MAS) in one generation and in multiple generations. Formulas were derived to relate linkage disequilibrium in a population to the proportion of additive genetic variance used by MAS, and in turn to an extra improvement in genetic response over phenotypic selection. Predictions of the response were compared to those predicted by using an infinite-loci model and the factors affecting efficiency of MAS were examined. Theoretical analyses of the present study revealed the nonlinearity between the selection intensity and genetic response in MAS. In addition to the heritability of the trait and the proportion of the marker-associated genetic variance, the frequencies of the selectively favorable alleles at the two loci, one marker and one quantitative trait locus, were found to play an important role in determining both the short- and long-term efficiencies of MAS. The evolution of linkage disequilibrium and thus the genetic response over several generations were predicted theoretically and examined by simulation. MAS dissipated the disequilibrium more quickly than drift alone. In some cases studied, the rate of dissipation was as large as that to be expected in the circumstance where the true recombination fraction was increased by three times and selection was absent. PMID:9215918

Zebrafish for the Study of the Biological Effects of Nicotine

PubMed Central

Klee, Eric W.; Schneider, Henning; Hurt, Richard D.; Ekker, Stephen C.

2011-01-01

Introduction: Zebrafish are emerging as a powerful animal model for studying the molecular and physiological effects of nicotine exposure. The zebrafish have many advantageous physical characteristics, including small size, high fecundity rates, and externally developing transparent embryos. When combined with a battery of molecular–genetic tools and behavioral assays, these attributes enable studies to be conducted that are not practical using traditional animal models. Methods: We reviewed the literature on the application of the zebrafish model as a preclinical model to study the biological effects of nicotine exposure. Results: The identified studies used zebrafish to examine the effects of nicotine exposure on early development, addiction, anxiety, and learning. The methods used included green fluorescent protein–labeled proteins to track in vivo nicotine-altered neuron development, nicotine-conditioned place preference, and locomotive sensitization linked with high-throughput molecular and genetic screens and behavioral models of learning and stress response to nicotine. Data are presented on the complete homology of all known human neural nicotinic acetylcholine receptors in zebrafish and on the biological similarity of human and zebrafish dopaminergic signaling. Conclusions: Tobacco dependence remains a major health problem worldwide. Further understanding of the molecular effects of nicotine exposure and genetic contributions to dependence may lead to improvement in patient treatment strategies. While there are limitations to the use of zebrafish as a preclinical model, it should provide a valuable tool to complement existing model systems. The reviewed studies demonstrate the enormous opportunity zebrafish have to advance the science of nicotine and tobacco research. PMID:21385906

Genetic and phylogenetic consequences of island biogeography.

PubMed

Johnson, K P; Adler, F R; Cherry, J L

2000-04-01

Island biogeography theory predicts that the number of species on an island should increase with island size and decrease with island distance to the mainland. These predictions are generally well supported in comparative and experimental studies. These ecological, equilibrium predictions arise as a result of colonization and extinction processes. Because colonization and extinction are also important processes in evolution, we develop methods to test evolutionary predictions of island biogeography. We derive a population genetic model of island biogeography that incorporates island colonization, migration of individuals from the mainland, and extinction of island populations. The model provides a means of estimating the rates of migration and extinction from population genetic data. This model predicts that within an island population the distribution of genetic divergences with respect to the mainland source population should be bimodal, with much of the divergence dating to the colonization event. Across islands, this model predicts that populations on large islands should be on average more genetically divergent from mainland source populations than those on small islands. Likewise, populations on distant islands should be more divergent than those on close islands. Published observations of a larger proportion of endemic species on large and distant islands support these predictions.

The ecology and evolution of animal medication: genetically fixed response versus phenotypic plasticity.

PubMed

Choisy, Marc; de Roode, Jacobus C

2014-08-01

Animal medication against parasites can occur either as a genetically fixed (constitutive) or phenotypically plastic (induced) behavior. Taking the tritrophic interaction between the monarch butterfly Danaus plexippus, its protozoan parasite Ophryocystis elektroscirrha, and its food plant Asclepias spp. as a test case, we develop a game-theory model to identify the epidemiological (parasite prevalence and virulence) and environmental (plant toxicity and abundance) conditions that predict the evolution of genetically fixed versus phenotypically plastic forms of medication. Our model shows that the relative benefits (the antiparasitic properties of medicinal food) and costs (side effects of medicine, the costs of searching for medicine, and the costs of plasticity itself) crucially determine whether medication is genetically fixed or phenotypically plastic. Our model suggests that animals evolve phenotypic plasticity when parasite risk (a combination of virulence and prevalence and thus a measure of the strength of parasite-mediated selection) is relatively low to moderately high and genetically fixed medication when parasite risk becomes very high. The latter occurs because at high parasite risk, the costs of plasticity are outweighed by the benefits of medication. Our model provides a simple and general framework to study the conditions that drive the evolution of alternative forms of animal medication.

Toward Universal Forward Genetics: Using a Draft Genome Sequence of the Nematode Oscheius tipulae To Identify Mutations Affecting Vulva Development.

PubMed

Besnard, Fabrice; Koutsovoulos, Georgios; Dieudonné, Sana; Blaxter, Mark; Félix, Marie-Anne

2017-08-01

Mapping-by-sequencing has become a standard method to map and identify phenotype-causing mutations in model species. Here, we show that a fragmented draft assembly is sufficient to perform mapping-by-sequencing in nonmodel species. We generated a draft assembly and annotation of the genome of the free-living nematode Oscheius tipulae , a distant relative of the model Caenorhabditis elegans We used this draft to identify the likely causative mutations at the O. tipulae cov -3 locus, which affect vulval development. The cov-3 locus encodes the O. tipulae ortholog of C. elegans mig-13 , and we further show that Cel-mig-13 mutants also have an unsuspected vulval-development phenotype. In a virtuous circle, we were able to use the linkage information collected during mutant mapping to improve the genome assembly. These results showcase the promise of genome-enabled forward genetics in nonmodel species. Copyright © 2017 by the Genetics Society of America.

Temperature-dependent behaviours are genetically variable in the nematode Caenorhabditis briggsae.

PubMed

Stegeman, Gregory W; de Mesquita, Matthew Bueno; Ryu, William S; Cutter, Asher D

2013-03-01

Temperature-dependent behaviours in Caenorhabditis elegans, such as thermotaxis and isothermal tracking, are complex behavioural responses that integrate sensation, foraging and learning, and have driven investigations to discover many essential genetic and neural pathways. The ease of manipulation of the Caenorhabditis model system also has encouraged its application to comparative analyses of phenotypic evolution, particularly contrasts of the classic model C. elegans with C. briggsae. And yet few studies have investigated natural genetic variation in behaviour in any nematode. Here we measure thermotaxis and isothermal tracking behaviour in genetically distinct strains of C. briggsae, further motivated by the latitudinal differentiation in C. briggsae that is associated with temperature-dependent fitness differences in this species. We demonstrate that C. briggsae performs thermotaxis and isothermal tracking largely similar to that of C. elegans, with a tendency to prefer its rearing temperature. Comparisons of these behaviours among strains reveal substantial heritable natural variation within each species that corresponds to three general patterns of behavioural response. However, intraspecific genetic differences in thermal behaviour often exceed interspecific differences. These patterns of temperature-dependent behaviour motivate further development of C. briggsae as a model system for dissecting the genetic underpinnings of complex behavioural traits.

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

Needed: A Standard Information Processing Model of Learning and Learning Processes.

ERIC Educational Resources Information Center

Carifio, James

One strategy to prevent confusion as new paradigms emerge is to have professionals in the area develop and use a standard model of the phenomenon in question. The development and use of standard models in physics, genetics, archaeology, and cosmology have been very productive. The cognitive revolution in psychology and education has produced a…

Rodent models of diabetic nephropathy: their utility and limitations

PubMed Central

Kitada, Munehiro; Ogura, Yoshio; Koya, Daisuke

2016-01-01

Diabetic nephropathy is the most common cause of end-stage renal disease. Therefore, novel therapies for the suppression of diabetic nephropathy must be developed. Rodent models are useful for elucidating the pathogenesis of diseases and testing novel therapies, and many type 1 and type 2 diabetic rodent models have been established for the study of diabetes and diabetic complications. Streptozotocin (STZ)-induced diabetic animals are widely used as a model of type 1 diabetes. Akita diabetic mice that have an Ins2+/C96Y mutation and OVE26 mice that overexpress calmodulin in pancreatic β-cells serve as a genetic model of type 1 diabetes. In addition, db/db mice, KK-Ay mice, Zucker diabetic fatty rats, Wistar fatty rats, Otsuka Long-Evans Tokushima Fatty rats and Goto-Kakizaki rats serve as rodent models of type 2 diabetes. An animal model of diabetic nephropathy should exhibit progressive albuminuria and a decrease in renal function, as well as the characteristic histological changes in the glomeruli and the tubulointerstitial lesions that are observed in cases of human diabetic nephropathy. A rodent model that strongly exhibits all these features of human diabetic nephropathy has not yet been developed. However, the currently available rodent models of diabetes can be useful in the study of diabetic nephropathy by increasing our understanding of the features of each diabetic rodent model. Furthermore, the genetic background and strain of each mouse model result in differences in susceptibility to diabetic nephropathy with albuminuria and the development of glomerular and tubulointerstitial lesions. Therefore, the validation of an animal model reproducing human diabetic nephropathy will significantly facilitate our understanding of the underlying genetic mechanisms that contribute to the development of diabetic nephropathy. In this review, we focus on rodent models of diabetes and discuss the utility and limitations of these models for the study of diabetic nephropathy. PMID:27881924

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

Community-Based Dialogue: Engaging Communities of Color in the United States’ Genetics Policy Conversation

PubMed Central

Bonham, Vence L.; Citrin, Toby; Modell, Stephen M.; Franklin, Tené Hamilton; Bleicher, Esther W. B.; Fleck, Leonard M.

2009-01-01

Engaging communities of color in the genetics public policy conversation is important for the translation of genetics research into strategies aimed at improving the health of all. Implementing model public participation and consultation processes can be informed by the Communities of Color Genetics Policy Project, which engaged individuals from African American and Latino communities of diverse socioeconomic levels in the process of “rational democratic deliberation” on ethical and policy issues stretching from genome research to privacy and discrimination concerns to public education. The results of the study included the development of a participatory framework based on a combination of the theory of democratic deliberation and the community-based public health model which we describe as “community-based dialogue.” PMID:19451407

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Animal models of obsessive–compulsive disorder: utility and limitations

PubMed Central

Alonso, Pino; López-Solà, Clara; Real, Eva; Segalàs, Cinto; Menchón, José Manuel

2015-01-01

Obsessive–compulsive disorder (OCD) is a disabling and common neuropsychiatric condition of poorly known etiology. Many attempts have been made in the last few years to develop animal models of OCD with the aim of clarifying the genetic, neurochemical, and neuroanatomical basis of the disorder, as well as of developing novel pharmacological and neurosurgical treatments that may help to improve the prognosis of the illness. The latter goal is particularly important given that around 40% of patients with OCD do not respond to currently available therapies. This article summarizes strengths and limitations of the leading animal models of OCD including genetic, pharmacologically induced, behavioral manipulation-based, and neurodevelopmental models according to their face, construct, and predictive validity. On the basis of this evaluation, we discuss that currently labeled “animal models of OCD” should be regarded not as models of OCD but, rather, as animal models of different psychopathological processes, such as compulsivity, stereotypy, or perseverance, that are present not only in OCD but also in other psychiatric or neurological disorders. Animal models might constitute a challenging approach to study the neural and genetic mechanism of these phenomena from a trans-diagnostic perspective. Animal models are also of particular interest as tools for developing new therapeutic options for OCD, with the greatest convergence focusing on the glutamatergic system, the role of ovarian and related hormones, and the exploration of new potential targets for deep brain stimulation. Finally, future research on neurocognitive deficits associated with OCD through the use of analogous animal tasks could also provide a genuine opportunity to disentangle the complex etiology of the disorder. PMID:26346234

Overview of Animal Models of Obesity

PubMed Central

Lutz, Thomas A.; Woods, Stephen C.

2012-01-01

This is a review of animal models of obesity currently used in research. We have focused upon more commonly utilized models since there are far too many newly created models to consider, especially those caused by selective molecular genetic approaches modifying one or more genes in specific populations of cells. Further, we will not discuss the generation and use of inducible transgenic animals (induced knock-out or knock-in) even though they often bear significant advantages compared to traditional transgenic animals; influences of the genetic modification during the development of the animals can be minimized. The number of these animal models is simply too large to be covered in this chapter. PMID:22948848

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

PubMed

Akbar, Shahid; Hayat, Maqsood; Iqbal, Muhammad; Jan, Mian Ahmad

2017-06-01

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Inherited genetic variants associated with occurrence of multiple primary melanoma.

PubMed

Gibbs, David C; Orlow, Irene; Kanetsky, Peter A; Luo, Li; Kricker, Anne; Armstrong, Bruce K; Anton-Culver, Hoda; Gruber, Stephen B; Marrett, Loraine D; Gallagher, Richard P; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J; Cust, Anne E; Ollila, David W; Begg, Colin B; Berwick, Marianne; Thomas, Nancy E

2015-06-01

Recent studies, including genome-wide association studies, have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 SNPs from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% confidence intervals were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma, while NCOA6 rs4911442 approached significance (P = 0.06). The GEM Study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. ©2015 American Association for Cancer Research.

Inherited genetic variants associated with occurrence of multiple primary melanoma

PubMed Central

Gibbs, David C.; Orlow, Irene; Kanetsky, Peter A.; Luo, Li; Kricker, Anne; Armstrong, Bruce K.; Anton-Culver, Hoda; Gruber, Stephen B.; Marrett, Loraine D.; Gallagher, Richard P.; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J.; Cust, Anne E.; Ollila, David W.; Begg, Colin B.; Berwick, Marianne; Thomas, Nancy E.

2015-01-01

Recent studies including genome-wide association studies have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 single nucleotide polymorphisms (SNP) from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% CIs were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma while NCOA6 rs4911442 approached significance (P = 0.06). The GEM study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. PMID:25837821

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

PubMed Central

2013-01-01

Background Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. Results We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. Conclusions It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance. PMID:23763755

Using Genetically Engineered Animal Models in the Postgenomic Era to Understand Gene Function in Alcoholism

PubMed Central

Reilly, Matthew T.; Harris, R. Adron; Noronha, Antonio

2012-01-01

Over the last 50 years, researchers have made substantial progress in identifying genetic variations that underlie the complex phenotype of alcoholism. Not much is known, however, about how this genetic variation translates into altered biological function. Genetic animal models recapitulating specific characteristics of the human condition have helped elucidate gene function and the genetic basis of disease. In particular, major advances have come from the ability to manipulate genes through a variety of genetic technologies that provide an unprecedented capacity to determine gene function in the living organism and in alcohol-related behaviors. Even newer genetic-engineering technologies have given researchers the ability to control when and where a specific gene or mutation is activated or deleted, allowing investigators to narrow the role of the gene’s function to circumscribed neural pathways and across development. These technologies are important for all areas of neuroscience, and several public and private initiatives are making a new generation of genetic-engineering tools available to the scientific community at large. Finally, high-throughput “next-generation sequencing” technologies are set to rapidly increase knowledge of the genome, epigenome, and transcriptome, which, combined with genetically engineered mouse mutants, will enhance insight into biological function. All of these resources will provide deeper insight into the genetic basis of alcoholism. PMID:23134044

Using genetically engineered animal models in the postgenomic era to understand gene function in alcoholism.

PubMed

Reilly, Matthew T; Harris, R Adron; Noronha, Antonio

2012-01-01

Over the last 50 years, researchers have made substantial progress in identifying genetic variations that underlie the complex phenotype of alcoholism. Not much is known, however, about how this genetic variation translates into altered biological function. Genetic animal models recapitulating specific characteristics of the human condition have helped elucidate gene function and the genetic basis of disease. In particular, major advances have come from the ability to manipulate genes through a variety of genetic technologies that provide an unprecedented capacity to determine gene function in the living organism and in alcohol-related behaviors. Even newer genetic-engineering technologies have given researchers the ability to control when and where a specific gene or mutation is activated or deleted, allowing investigators to narrow the role of the gene's function to circumscribed neural pathways and across development. These technologies are important for all areas of neuroscience, and several public and private initiatives are making a new generation of genetic-engineering tools available to the scientific community at large. Finally, high-throughput "next-generation sequencing" technologies are set to rapidly increase knowledge of the genome, epigenome, and transcriptome, which, combined with genetically engineered mouse mutants, will enhance insight into biological function. All of these resources will provide deeper insight into the genetic basis of alcoholism.

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.

Identifying novel interventional strategies for psychiatric disorders: integrating genomics, 'enviromics' and gene-environment interactions in valid preclinical models.

PubMed

McOmish, Caitlin E; Burrows, Emma L; Hannan, Anthony J

2014-10-01

Psychiatric disorders affect a substantial proportion of the population worldwide. This high prevalence, combined with the chronicity of the disorders and the major social and economic impacts, creates a significant burden. As a result, an important priority is the development of novel and effective interventional strategies for reducing incidence rates and improving outcomes. This review explores the progress that has been made to date in establishing valid animal models of psychiatric disorders, while beginning to unravel the complex factors that may be contributing to the limitations of current methodological approaches. We propose some approaches for optimizing the validity of animal models and developing effective interventions. We use schizophrenia and autism spectrum disorders as examples of disorders for which development of valid preclinical models, and fully effective therapeutics, have proven particularly challenging. However, the conclusions have relevance to various other psychiatric conditions, including depression, anxiety and bipolar disorders. We address the key aspects of construct, face and predictive validity in animal models, incorporating genetic and environmental factors. Our understanding of psychiatric disorders is accelerating exponentially, revealing extraordinary levels of genetic complexity, heterogeneity and pleiotropy. The environmental factors contributing to individual, and multiple, disorders also exhibit breathtaking complexity, requiring systematic analysis to experimentally explore the environmental mediators and modulators which constitute the 'envirome' of each psychiatric disorder. Ultimately, genetic and environmental factors need to be integrated via animal models incorporating the spatiotemporal complexity of gene-environment interactions and experience-dependent plasticity, thus better recapitulating the dynamic nature of brain development, function and dysfunction. © 2014 The British Pharmacological Society.

Mouse Models for Drug Discovery. Can New Tools and Technology Improve Translational Power?

PubMed

Zuberi, Aamir; Lutz, Cathleen

2016-12-01

The use of mouse models in biomedical research and preclinical drug evaluation is on the rise. The advent of new molecular genome-altering technologies such as CRISPR/Cas9 allows for genetic mutations to be introduced into the germ line of a mouse faster and less expensively than previous methods. In addition, the rapid progress in the development and use of somatic transgenesis using viral vectors, as well as manipulations of gene expression with siRNAs and antisense oligonucleotides, allow for even greater exploration into genomics and systems biology. These technological advances come at a time when cost reductions in genome sequencing have led to the identification of pathogenic mutations in patient populations, providing unprecedented opportunities in the use of mice to model human disease. The ease of genetic engineering in mice also offers a potential paradigm shift in resource sharing and the speed by which models are made available in the public domain. Predictively, the knowledge alone that a model can be quickly remade will provide relief to resources encumbered by licensing and Material Transfer Agreements. For decades, mouse strains have provided an exquisite experimental tool to study the pathophysiology of the disease and assess therapeutic options in a genetically defined system. However, a major limitation of the mouse has been the limited genetic diversity associated with common laboratory mice. This has been overcome with the recent development of the Collaborative Cross and Diversity Outbred mice. These strains provide new tools capable of replicating genetic diversity to that approaching the diversity found in human populations. The Collaborative Cross and Diversity Outbred strains thus provide a means to observe and characterize toxicity or efficacy of new therapeutic drugs for a given population. The combination of traditional and contemporary mouse genome editing tools, along with the addition of genetic diversity in new modeling systems, are synergistic and serve to make the mouse a better model for biomedical research, enhancing the potential for preclinical drug discovery and personalized medicine. © The Author 2016. Published by Oxford University Press.

Autoimmunity in narcolepsy.

PubMed

Bonvalet, Melodie; Ollila, Hanna M; Ambati, Aditya; Mignot, Emmanuel

2017-11-01

Summarize the recent findings in narcolepsy focusing on the environmental and genetic risk factors in disease development. Both genetic and epidemiological evidence point towards an autoimmune mechanism in the destruction of orexin/hypocretin neurons. Recent studies suggest both humoral and cellular immune responses in the disease development. Narcolepsy is a severe sleep disorder, in which neurons producing orexin/hypocretin in the hypothalamus are destroyed. The core symptoms of narcolepsy are debilitating, extreme sleepiness, cataplexy, and abnormalities in the structure of sleep. Both genetic and epidemiological evidence point towards an autoimmune mechanism in the destruction of orexin/hypocretin neurons. Importantly, the highest environmental risk is seen with influenza-A infection and immunization. However, how the cells are destroyed is currently unknown. In this review we summarize the disease symptoms, and focus on the immunological findings in narcolepsy. We also discuss the environmental and genetic risk factors as well as propose a model for disease development.

Canine and Feline Models of Human Genetic Diseases and Their Contributions to Advancing Clinical Therapies


PubMed Central

Gurda, Brittney L.; Bradbury, Allison M.; Vite, Charles H.

2017-01-01

For many lethal or debilitating genetic disorders in patients there are no satisfactory therapies. Several barriers exist that hinder the developments of effective therapies including the limited availability of clinically relevant animal models that faithfully recapitulate human genetic disease. In 1974, the Referral Center for Animal Models of Human Genetic Disease (RCAM) was established by Dr. Donald F. Patterson and continued by Dr. Mark E. Haskins at the University of Pennsylvania with the mission to discover, understand, treat, and maintain breeding colonies of naturally occurring hereditary disorders in dogs and cats that are orthologous to those found in human patients. Although non-human primates, sheep, and pig models are also available within the medical community, naturally occurring diseases are rarely identified in non-human primates, and the vast behavioral, clinicopathological, physiological, and anatomical knowledge available regarding dogs and cats far surpasses what is available in ovine and porcine species. The canine and feline models that are maintained at RCAM are presented here with a focus on preclinical therapy data. Clinical studies that have been generated from preclinical work in these models are also presented. PMID:28955181

A Computational Approach From Gene to Structure Analysis of the Human ABCA4 Transporter Involved in Genetic Retinal Diseases.

PubMed

Trezza, Alfonso; Bernini, Andrea; Langella, Andrea; Ascher, David B; Pires, Douglas E V; Sodi, Andrea; Passerini, Ilaria; Pelo, Elisabetta; Rizzo, Stanislao; Niccolai, Neri; Spiga, Ottavia

2017-10-01

The aim of this article is to report the investigation of the structural features of ABCA4, a protein associated with a genetic retinal disease. A new database collecting knowledge of ABCA4 structure may facilitate predictions about the possible functional consequences of gene mutations observed in clinical practice. In order to correlate structural and functional effects of the observed mutations, the structure of mouse P-glycoprotein was used as a template for homology modeling. The obtained structural information and genetic data are the basis of our relational database (ABCA4Database). Sequence variability among all ABCA4-deposited entries was calculated and reported as Shannon entropy score at the residue level. The three-dimensional model of ABCA4 structure was used to locate the spatial distribution of the observed variable regions. Our predictions from structural in silico tools were able to accurately link the functional effects of mutations to phenotype. The development of the ABCA4Database gathers all the available genetic and structural information, yielding a global view of the molecular basis of some retinal diseases. ABCA4 modeled structure provides a molecular basis on which to analyze protein sequence mutations related to genetic retinal disease in order to predict the risk of retinal disease across all possible ABCA4 mutations. Additionally, our ABCA4 predicted structure is a good starting point for the creation of a new data analysis model, appropriate for precision medicine, in order to develop a deeper knowledge network of the disease and to improve the management of patients.

Childhood separation anxiety disorder and adult onset panic attacks share a common genetic diathesis.

PubMed

Roberson-Nay, Roxann; Eaves, Lindon J; Hettema, John M; Kendler, Kenneth S; Silberg, Judy L

2012-04-01

Childhood separation anxiety disorder (SAD) is hypothesized to share etiologic roots with panic disorder. The aim of this study was to estimate the genetic and environmental sources of covariance between childhood SAD and adult onset panic attacks (AOPA), with the primary goal to determine whether these two phenotypes share a common genetic diathesis. Participants included parents and their monozygotic or dizygotic twins (n = 1,437 twin pairs) participating in the Virginia Twin Study of Adolescent Behavioral Development and those twins who later completed the Young Adult Follow-Up (YAFU). The Child and Adolescent Psychiatric Assessment was completed at three waves during childhood/adolescence followed by the Structured Clinical Interview for DSM-III-R at the YAFU. Two separate, bivariate Cholesky models were fit to childhood diagnoses of SAD and overanxious disorder (OAD), respectively, and their relation with AOPA; a trivariate Cholesky model also examined the collective influence of childhood SAD and OAD on AOPA. In the best-fitting bivariate model, the covariation between SAD and AOPA was accounted for by genetic and unique environmental factors only, with the genetic factor associated with childhood SAD explaining significant variance in AOPA. Environmental risk factors were not significantly shared between SAD and AOPA. By contrast, the genetic factor associated with childhood OAD did not contribute significantly to AOPA. Results of the trivariate Cholesky reaffirmed outcomes of bivariate models. These data indicate that childhood SAD and AOPA share a common genetic diathesis that is not observed for childhood OAD, strongly supporting the hypothesis of a specific genetic etiologic link between the two phenotypes. © 2012 Wiley Periodicals, Inc.

tropiTree: An NGS-Based EST-SSR Resource for 24 Tropical Tree Species

PubMed Central

Russell, Joanne R.; Hedley, Peter E.; Cardle, Linda; Dancey, Siobhan; Morris, Jenny; Booth, Allan; Odee, David; Mwaura, Lucy; Omondi, William; Angaine, Peter; Machua, Joseph; Muchugi, Alice; Milne, Iain; Kindt, Roeland; Jamnadass, Ramni; Dawson, Ian K.

2014-01-01

The development of genetic tools for non-model organisms has been hampered by cost, but advances in next-generation sequencing (NGS) have created new opportunities. In ecological research, this raises the prospect for developing molecular markers to simultaneously study important genetic processes such as gene flow in multiple non-model plant species within complex natural and anthropogenic landscapes. Here, we report the use of bar-coded multiplexed paired-end Illumina NGS for the de novo development of expressed sequence tag-derived simple sequence repeat (EST-SSR) markers at low cost for a range of 24 tree species. Each chosen tree species is important in complex tropical agroforestry systems where little is currently known about many genetic processes. An average of more than 5,000 EST-SSRs was identified for each of the 24 sequenced species, whereas prior to analysis 20 of the species had fewer than 100 nucleotide sequence citations. To make results available to potential users in a suitable format, we have developed an open-access, interactive online database, tropiTree (http://bioinf.hutton.ac.uk/tropiTree), which has a range of visualisation and search facilities, and which is a model for the efficient presentation and application of NGS data. PMID:25025376

Weighted Genetic Risk Scores and Prediction of Weight Gain in Solid Organ Transplant Populations

PubMed Central

Saigi-Morgui, Núria; Quteineh, Lina; Bochud, Pierre-Yves; Crettol, Severine; Kutalik, Zoltán; Wojtowicz, Agnieszka; Bibert, Stéphanie; Beckmann, Sonja; Mueller, Nicolas J; Binet, Isabelle; van Delden, Christian; Steiger, Jürg; Mohacsi, Paul; Stirnimann, Guido; Soccal, Paola M.; Pascual, Manuel; Eap, Chin B

2016-01-01

Background Polygenic obesity in Solid Organ Transplant (SOT) populations is considered a risk factor for the development of metabolic abnormalities and graft survival. Few studies to date have studied the genetics of weight gain in SOT recipients. We aimed to determine whether weighted genetic risk scores (w-GRS) integrating genetic polymorphisms from GWAS studies (SNP group#1 and SNP group#2) and from Candidate Gene studies (SNP group#3) influence BMI in SOT populations and if they predict ≥10% weight gain (WG) one year after transplantation. To do so, two samples (nA = 995, nB = 156) were obtained from naturalistic studies and three w-GRS were constructed and tested for association with BMI over time. Prediction of 10% WG at one year after transplantation was assessed with models containing genetic and clinical factors. Results w-GRS were associated with BMI in sample A and B combined (BMI increased by 0.14 and 0.11 units per additional risk allele in SNP group#1 and #2, respectively, p-values<0.008). w-GRS of SNP group#3 showed an effect of 0.01 kg/m2 per additional risk allele when combining sample A and B (p-value 0.04). Models with genetic factors performed better than models without in predicting 10% WG at one year after transplantation. Conclusions This is the first study in SOT evaluating extensively the association of w-GRS with BMI and the influence of clinical and genetic factors on 10% of WG one year after transplantation, showing the importance of integrating genetic factors in the final model. Genetics of obesity among SOT recipients remains an important issue and can contribute to treatment personalization and prediction of WG after transplantation. PMID:27788139

Weighted Genetic Risk Scores and Prediction of Weight Gain in Solid Organ Transplant Populations.

PubMed

Saigi-Morgui, Núria; Quteineh, Lina; Bochud, Pierre-Yves; Crettol, Severine; Kutalik, Zoltán; Wojtowicz, Agnieszka; Bibert, Stéphanie; Beckmann, Sonja; Mueller, Nicolas J; Binet, Isabelle; van Delden, Christian; Steiger, Jürg; Mohacsi, Paul; Stirnimann, Guido; Soccal, Paola M; Pascual, Manuel; Eap, Chin B

2016-01-01

Polygenic obesity in Solid Organ Transplant (SOT) populations is considered a risk factor for the development of metabolic abnormalities and graft survival. Few studies to date have studied the genetics of weight gain in SOT recipients. We aimed to determine whether weighted genetic risk scores (w-GRS) integrating genetic polymorphisms from GWAS studies (SNP group#1 and SNP group#2) and from Candidate Gene studies (SNP group#3) influence BMI in SOT populations and if they predict ≥10% weight gain (WG) one year after transplantation. To do so, two samples (nA = 995, nB = 156) were obtained from naturalistic studies and three w-GRS were constructed and tested for association with BMI over time. Prediction of 10% WG at one year after transplantation was assessed with models containing genetic and clinical factors. w-GRS were associated with BMI in sample A and B combined (BMI increased by 0.14 and 0.11 units per additional risk allele in SNP group#1 and #2, respectively, p-values<0.008). w-GRS of SNP group#3 showed an effect of 0.01 kg/m2 per additional risk allele when combining sample A and B (p-value 0.04). Models with genetic factors performed better than models without in predicting 10% WG at one year after transplantation. This is the first study in SOT evaluating extensively the association of w-GRS with BMI and the influence of clinical and genetic factors on 10% of WG one year after transplantation, showing the importance of integrating genetic factors in the final model. Genetics of obesity among SOT recipients remains an important issue and can contribute to treatment personalization and prediction of WG after transplantation.

Artificial Intelligence-Based Models for the Optimal and Sustainable Use of Groundwater in Coastal Aquifers

NASA Astrophysics Data System (ADS)

Sreekanth, J.; Datta, Bithin

2011-07-01

Overexploitation of the coastal aquifers results in saltwater intrusion. Once saltwater intrusion occurs, it involves huge cost and long-term remediation measures to remediate these contaminated aquifers. Hence, it is important to have strategies for the sustainable use of coastal aquifers. This study develops a methodology for the optimal management of saltwater intrusion prone aquifers. A linked simulation-optimization-based management strategy is developed. The methodology uses genetic-programming-based models for simulating the aquifer processes, which is then linked to a multi-objective genetic algorithm to obtain optimal management strategies in terms of groundwater extraction from potential well locations in the aquifer.

Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations

PubMed Central

Bilton, Timothy P.; Schofield, Matthew R.; Black, Michael A.; Chagné, David; Wilcox, Phillip L.; Dodds, Ken G.

2018-01-01

Next-generation sequencing is an efficient method that allows for substantially more markers than previous technologies, providing opportunities for building high-density genetic linkage maps, which facilitate the development of nonmodel species’ genomic assemblies and the investigation of their genes. However, constructing genetic maps using data generated via high-throughput sequencing technology (e.g., genotyping-by-sequencing) is complicated by the presence of sequencing errors and genotyping errors resulting from missing parental alleles due to low sequencing depth. If unaccounted for, these errors lead to inflated genetic maps. In addition, map construction in many species is performed using full-sibling family populations derived from the outcrossing of two individuals, where unknown parental phase and varying segregation types further complicate construction. We present a new methodology for modeling low coverage sequencing data in the construction of genetic linkage maps using full-sibling populations of diploid species, implemented in a package called GUSMap. Our model is based on the Lander–Green hidden Markov model but extended to account for errors present in sequencing data. We were able to obtain accurate estimates of the recombination fractions and overall map distance using GUSMap, while most existing mapping packages produced inflated genetic maps in the presence of errors. Our results demonstrate the feasibility of using low coverage sequencing data to produce genetic maps without requiring extensive filtering of potentially erroneous genotypes, provided that the associated errors are correctly accounted for in the model. PMID:29487138

Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations.

PubMed

Bilton, Timothy P; Schofield, Matthew R; Black, Michael A; Chagné, David; Wilcox, Phillip L; Dodds, Ken G

2018-05-01

Next-generation sequencing is an efficient method that allows for substantially more markers than previous technologies, providing opportunities for building high-density genetic linkage maps, which facilitate the development of nonmodel species' genomic assemblies and the investigation of their genes. However, constructing genetic maps using data generated via high-throughput sequencing technology ( e.g. , genotyping-by-sequencing) is complicated by the presence of sequencing errors and genotyping errors resulting from missing parental alleles due to low sequencing depth. If unaccounted for, these errors lead to inflated genetic maps. In addition, map construction in many species is performed using full-sibling family populations derived from the outcrossing of two individuals, where unknown parental phase and varying segregation types further complicate construction. We present a new methodology for modeling low coverage sequencing data in the construction of genetic linkage maps using full-sibling populations of diploid species, implemented in a package called GUSMap. Our model is based on the Lander-Green hidden Markov model but extended to account for errors present in sequencing data. We were able to obtain accurate estimates of the recombination fractions and overall map distance using GUSMap, while most existing mapping packages produced inflated genetic maps in the presence of errors. Our results demonstrate the feasibility of using low coverage sequencing data to produce genetic maps without requiring extensive filtering of potentially erroneous genotypes, provided that the associated errors are correctly accounted for in the model. Copyright © 2018 Bilton et al.

The genetic correlation between height and IQ: shared genes or assortative mating?

PubMed

Keller, Matthew C; Garver-Apgar, Christine E; Wright, Margaret J; Martin, Nicholas G; Corley, Robin P; Stallings, Michael C; Hewitt, John K; Zietsch, Brendan P

2013-04-01

Traits that are attractive to the opposite sex are often positively correlated when scaled such that scores increase with attractiveness, and this correlation typically has a genetic component. Such traits can be genetically correlated due to genes that affect both traits ("pleiotropy") and/or because assortative mating causes statistical correlations to develop between selected alleles across the traits ("gametic phase disequilibrium"). In this study, we modeled the covariation between monozygotic and dizygotic twins, their siblings, and their parents (total N = 7,905) to elucidate the nature of the correlation between two potentially sexually selected traits in humans: height and IQ. Unlike previous designs used to investigate the nature of the height-IQ correlation, the present design accounts for the effects of assortative mating and provides much less biased estimates of additive genetic, non-additive genetic, and shared environmental influences. Both traits were highly heritable, although there was greater evidence for non-additive genetic effects in males. After accounting for assortative mating, the correlation between height and IQ was found to be almost entirely genetic in nature. Model fits indicate that both pleiotropy and assortative mating contribute significantly and about equally to this genetic correlation.

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.

To grow or not to grow: hair morphogenesis and human genetic hair disorders.

PubMed

Duverger, Olivier; Morasso, Maria I

2014-01-01

Mouse models have greatly helped in elucidating the molecular mechanisms involved in hair formation and regeneration. Recent publications have reviewed the genes involved in mouse hair development based on the phenotype of transgenic, knockout and mutant animal models. While much of this information has been instrumental in determining molecular aspects of human hair development and cycling, mice exhibit a specific pattern of hair morphogenesis and hair distribution throughout the body that cannot be directly correlated to human hair. In this mini-review, we discuss specific aspects of human hair follicle development and present an up-to-date summary of human genetic disorders associated with abnormalities in hair follicle morphogenesis, structure or regeneration. Published by Elsevier Ltd.

PAQ: Partition Analysis of Quasispecies.

PubMed

Baccam, P; Thompson, R J; Fedrigo, O; Carpenter, S; Cornette, J L

2001-01-01

The complexities of genetic data may not be accurately described by any single analytical tool. Phylogenetic analysis is often used to study the genetic relationship among different sequences. Evolutionary models and assumptions are invoked to reconstruct trees that describe the phylogenetic relationship among sequences. Genetic databases are rapidly accumulating large amounts of sequences. Newly acquired sequences, which have not yet been characterized, may require preliminary genetic exploration in order to build models describing the evolutionary relationship among sequences. There are clustering techniques that rely less on models of evolution, and thus may provide nice exploratory tools for identifying genetic similarities. Some of the more commonly used clustering methods perform better when data can be grouped into mutually exclusive groups. Genetic data from viral quasispecies, which consist of closely related variants that differ by small changes, however, may best be partitioned by overlapping groups. We have developed an intuitive exploratory program, Partition Analysis of Quasispecies (PAQ), which utilizes a non-hierarchical technique to partition sequences that are genetically similar. PAQ was used to analyze a data set of human immunodeficiency virus type 1 (HIV-1) envelope sequences isolated from different regions of the brain and another data set consisting of the equine infectious anemia virus (EIAV) regulatory gene rev. Analysis of the HIV-1 data set by PAQ was consistent with phylogenetic analysis of the same data, and the EIAV rev variants were partitioned into two overlapping groups. PAQ provides an additional tool which can be used to glean information from genetic data and can be used in conjunction with other tools to study genetic similarities and genetic evolution of viral quasispecies.

Patterns and Mechanisms of Evolutionary Transitions between Genetic Sex-Determining Systems

PubMed Central

Sander van Doorn, G.

2014-01-01

The diversity and patchy phylogenetic distribution of genetic sex-determining mechanisms observed in some taxa is thought to have arisen by the addition, modification, or replacement of regulators at the upstream end of the sex-determining pathway. Here, I review the various evolutionary forces acting on upstream regulators of sexual development that can cause transitions between sex-determining systems. These include sex-ratio selection and pleiotropic benefits, as well as indirect selection mechanisms involving sex-linked sexually antagonistic loci or recessive deleterious mutations. Most of the current theory concentrates on the population–genetic aspects of sex-determination transitions, using models that do not reflect the developmental mechanisms involved in sex determination. However, the increasing availability of molecular data creates opportunities for the development of mechanistic models that can clarify how selection and developmental architecture interact to direct the evolution of sex-determination genes. PMID:24993578

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2002-01-01

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Comparison of the theoretical and real-world evolutionary potential of a genetic circuit

NASA Astrophysics Data System (ADS)

Razo-Mejia, M.; Boedicker, J. Q.; Jones, D.; DeLuna, A.; Kinney, J. B.; Phillips, R.

2014-04-01

With the development of next-generation sequencing technologies, many large scale experimental efforts aim to map genotypic variability among individuals. This natural variability in populations fuels many fundamental biological processes, ranging from evolutionary adaptation and speciation to the spread of genetic diseases and drug resistance. An interesting and important component of this variability is present within the regulatory regions of genes. As these regions evolve, accumulated mutations lead to modulation of gene expression, which may have consequences for the phenotype. A simple model system where the link between genetic variability, gene regulation and function can be studied in detail is missing. In this article we develop a model to explore how the sequence of the wild-type lac promoter dictates the fold-change in gene expression. The model combines single-base pair resolution maps of transcription factor and RNA polymerase binding energies with a comprehensive thermodynamic model of gene regulation. The model was validated by predicting and then measuring the variability of lac operon regulation in a collection of natural isolates. We then implement the model to analyze the sensitivity of the promoter sequence to the regulatory output, and predict the potential for regulation to evolve due to point mutations in the promoter region.

Priceless GEMMs: genetically engineered mouse models for colorectal cancer drug development.

PubMed

Roper, Jatin; Hung, Kenneth E

2012-08-01

To establish effective drug development for colorectal cancer (CRC), preclinical models that are robust surrogates for human disease are crucial. Mouse models are an attractive platform because of their relatively low cost, short life span, and ease of use. There are two main categories of mouse CRC models: xenografts derived from implantation of CRC cells or tumors in immunodeficient mice; and genetically engineered mouse models (GEMMs) derived from modification of human cancer predisposition genes, resulting in spontaneous tumor formation. Here, we review xenografts and GEMMs and focus on their potential application in translational research. Furthermore, we describe newer GEMMs for sporadic CRC that are particularly suitable for drug testing. Finally, we discuss recent advances in small-animal imaging, such as optical colonoscopy, which allow in vivo assessment of tumors. With the increasing sophistication of GEMMs, our preclinical armamentarium provides new hope for the ongoing war against CRC. Copyright © 2012. Published by Elsevier Ltd.

An approach for modeling cross-immunity of two strains, with application to variants of Bartonella in terms of genetic similarity.

PubMed

Ahn, Kwang Woo; Kosoy, Michael; Chan, Kung-Sik

2014-06-01

We developed a two-strain susceptible-infected-recovered (SIR) model that provides a framework for inferring the cross-immunity between two strains of a bacterial species in the host population with discretely sampled co-infection time-series data. Moreover, the model accounts for seasonality in host reproduction. We illustrate an approach using a dataset describing co-infections by several strains of bacteria circulating within a population of cotton rats (Sigmodon hispidus). Bartonella strains were clustered into three genetically close groups, between which the divergence is correspondent to the accepted level of separate bacterial species. The proposed approach revealed no cross-immunity between genetic clusters while limited cross-immunity might exist between subgroups within the clusters. Copyright © 2014. Published by Elsevier B.V.

Endogenous Reference Genes and Their Quantitative Real-Time PCR Assays for Genetically Modified Bread Wheat (Triticum aestivum L.) Detection.

PubMed

Yang, Litao; Quan, Sheng; Zhang, Dabing

2017-01-01

Endogenous reference genes (ERG) and their derivate analytical methods are standard requirements for analysis of genetically modified organisms (GMOs). Development and validation of suitable ERGs is the primary step for establishing assays that monitoring the genetically modified (GM) contents in food/feed samples. Herein, we give a review of the ERGs currently used for GM wheat analysis, such as ACC1, PKABA1, ALMT1, and Waxy-D1, as well as their performances in GM wheat analysis. Also, we discussed one model for developing and validating one ideal RG for one plant species based on our previous research work.

Structural equation models based on multivariate diversity assessment of diploid and tetraploid hulled wheat species

USDA-ARS?s Scientific Manuscript database

Hulled wheats are largely untapped genetic resources with >10,000 years of genetic memory and diversity that can be used for wheat quality improvement, development of healthy products, and adaptation to climate change. Multivariate diversity was assessed in the diploid Triticum monococcum L. var mon...

A Method for the Determination of Genetic Sex in the Fathead Minnow, Pimephales promelas, to Support Testing of Endocrine-active Chemicals

EPA Science Inventory

Fathead minnows are used as a model fish species for the characterization of the endocrine-disrupting potential of environmental contaminants. This research describes the development of a PCR method that can determine the genetic sex in this species. This method, when incorpora...

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.

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.

Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

PubMed Central

Mas, Sergi; Gassó, Patricia; Morer, Astrid; Calvo, Anna; Bargalló, Nuria; Lafuente, Amalia; Lázaro, Luisa

2016-01-01

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder. PMID:27093171

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

The future: genetics advances in MEN1 therapeutic approaches and management strategies.

PubMed

Agarwal, Sunita K

2017-10-01

The identification of the multiple endocrine neoplasia type 1 ( MEN1 ) gene in 1997 has shown that germline heterozygous mutations in the MEN1 gene located on chromosome 11q13 predisposes to the development of tumors in the MEN1 syndrome. Tumor development occurs upon loss of the remaining normal copy of the MEN1 gene in MEN1-target tissues. Therefore, MEN1 is a classic tumor suppressor gene in the context of MEN1. This tumor suppressor role of the protein encoded by the MEN1 gene, menin, holds true in mouse models with germline heterozygous Men1 loss, wherein MEN1-associated tumors develop in adult mice after spontaneous loss of the remaining non-targeted copy of the Men1 gene. The availability of genetic testing for mutations in the MEN1 gene has become an essential part of the diagnosis and management of MEN1. Genetic testing is also helping to exclude mutation-negative cases in MEN1 families from the burden of lifelong clinical screening. In the past 20 years, efforts of various groups world-wide have been directed at mutation analysis, molecular genetic studies, mouse models, gene expression studies, epigenetic regulation analysis, biochemical studies and anti-tumor effects of candidate therapies in mouse models. This review will focus on the findings and advances from these studies to identify MEN1 germline and somatic mutations, the genetics of MEN1-related states, several protein partners of menin, the three-dimensional structure of menin and menin-dependent target genes. The ongoing impact of all these studies on disease prediction, management and outcomes will continue in the years to come. © 2017 Society for Endocrinology.

Paternal Age Alters Social Development in Offspring.

PubMed

Janecka, Magdalena; Haworth, Claire M A; Ronald, Angelica; Krapohl, Eva; Happé, Francesca; Mill, Jonathan; Schalkwyk, Leonard C; Fernandes, Cathy; Reichenberg, Abraham; Rijsdijk, Frühling

2017-05-01

Advanced paternal age (APA) at conception has been linked with autism and schizophrenia in offspring, neurodevelopmental disorders that affect social functioning. The current study explored the effects of paternal age on social development in the general population. We used multilevel growth modeling to investigate APA effects on socioemotional development from early childhood until adolescence, as measured by the Strengths and Difficulties Questionnaire (SDQ) in the Twins Early Development Study (TEDS) sample. We also investigated genetic and environmental underpinnings of the paternal age effects on development, using the Additive genetics, Common environment, unique Environment (ACE) and gene-environment (GxE) models. In the general population, both very young and advanced paternal ages were associated with altered trajectory of social development (intercept: p = .01; slope: p = .03). No other behavioral domain was affected by either young or advanced age at fatherhood, suggesting specificity of paternal age effects. Increased importance of genetic factors in social development was recorded in the offspring of older but not very young fathers, suggesting distinct underpinnings of the paternal age effects at these two extremes. Our findings highlight that the APA-related deficits that lead to autism and schizophrenia are likely continuously distributed in the population. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Genetic counselling in the era of genomic medicine

PubMed Central

Middleton, Anna

2018-01-01

Abstract Background Genomic technology can now deliver cost effective, targeted diagnosis and treatment for patients. Genetic counselling is a communication process empowering patients and families to make autonomous decisions and effectively use new genetic information. The skills of genetic counselling and expertise of genetic counsellors are integral to the effective implementation of genomic medicine. Sources of data Original papers, reviews, guidelines, policy papers and web-resources. Areas of agreement An international consensus on the definition of genetic counselling. Genetic counselling is necessary for implementation of genomic medicine. Areas of controversy Models of genetic counselling. Growing points Genomic medicine is a growing and strategic priority for many health care systems. Genetic counselling is part of this. Areas timely for developing research An evidence base is necessary, incorporating implementation and outcome research, to enable health care systems, practitioners, patients and families to maximize the utility (medically and psychologically) of the new genomic possibilities. PMID:29617718

Evolution and development in cave animals: from fish to crustaceans.

PubMed

Protas, Meredith; Jeffery, William R

2012-01-01

Cave animals are excellent models to study the general principles of evolution as well as the mechanisms of adaptation to a novel environment: the perpetual darkness of caves. In this article, two of the major model systems used to study the evolution and development (evo-devo) of cave animals are described: the teleost fish Astyanax mexicanus and the isopod crustacean Asellus aquaticus. The ways in which these animals match the major attributes expected of an evo-devo cave animal model system are described. For both species, we enumerate the regressive and constructive troglomorphic traits that have evolved during their adaptation to cave life, the developmental and genetic basis of these traits, the possible evolutionary forces responsible for them, and potential new areas in which these model systems could be used for further exploration of the evolution of cave animals. Furthermore, we compare the two model cave animals to investigate the mechanisms of troglomorphic evolution. Finally, we propose a few other cave animal systems that would be suitable for development as additional models to obtain a more comprehensive understanding of the developmental and genetic mechanisms involved in troglomorphic evolution.

Animal Models of Fibrotic Lung Disease

PubMed Central

Lawson, William E.; Oury, Tim D.; Sisson, Thomas H.; Raghavendran, Krishnan; Hogaboam, Cory M.

2013-01-01

Interstitial lung fibrosis can develop as a consequence of occupational or medical exposure, as a result of genetic defects, and after trauma or acute lung injury leading to fibroproliferative acute respiratory distress syndrome, or it can develop in an idiopathic manner. The pathogenesis of each form of lung fibrosis remains poorly understood. They each result in a progressive loss of lung function with increasing dyspnea, and most forms ultimately result in mortality. To better understand the pathogenesis of lung fibrotic disorders, multiple animal models have been developed. This review summarizes the common and emerging models of lung fibrosis to highlight their usefulness in understanding the cell–cell and soluble mediator interactions that drive fibrotic responses. Recent advances have allowed for the development of models to study targeted injuries of Type II alveolar epithelial cells, fibroblastic autonomous effects, and targeted genetic defects. Repetitive dosing in some models has more closely mimicked the pathology of human fibrotic lung disease. We also have a much better understanding of the fact that the aged lung has increased susceptibility to fibrosis. Each of the models reviewed in this report offers a powerful tool for studying some aspect of fibrotic lung disease. PMID:23526222

Petri net modeling of high-order genetic systems using grammatical evolution.

PubMed

Moore, Jason H; Hahn, Lance W

2003-11-01

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.

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

Introduction to focus issue: quantitative approaches to genetic networks.

PubMed

Albert, Réka; Collins, James J; Glass, Leon

2013-06-01

All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks using field-programmable gate arrays. Mathematical analyses will be essential for understanding naturally occurring genetic networks in diverse organisms and for providing a foundation for the improved development of synthetic genetic networks.

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials

PubMed Central

2018-01-01

Objective The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. Methods A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. Results All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. Conclusion These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins. PMID:28823122

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials.

PubMed

Ben Zaabza, Hafedh; Ben Gara, Abderrahmen; Rekik, Boulbaba

2018-05-01

The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

NASA Astrophysics Data System (ADS)

Albert, Réka; Collins, James J.; Glass, Leon

2013-06-01

All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks using field-programmable gate arrays. Mathematical analyses will be essential for understanding naturally occurring genetic networks in diverse organisms and for providing a foundation for the improved development of synthetic genetic networks.

All the world’s a (clinical) stage: Rethinking bipolar disorder from a longitudinal perspective

PubMed Central

Frank, Ellen; Nimgaonkar, Vishwajit L.; Phillips, Mary L.; Kupfer, David J.

2014-01-01

Psychiatric disorders have traditionally been classified using a static, categorical approach. However, this approach falls short in facilitating understanding of the development, common comorbid diagnoses, prognosis, and treatment of these disorders. We propose a “staging” model of bipolar disorder that integrates genetic and neural information with mood and activity symptoms to describe how the disease progresses over time. From an early, asymptomatic, but “at risk” stage to severe, chronic illness, each stage is described with associated neuroimaging findings as well as strategies for mapping genetic risk factors. Integrating more biologic information relating to cardiovascular and endocrine systems, refining methodology for modeling dimensional approaches to disease, and developing outcome measures will all be crucial in examining the validity of this model. Ultimately, this approach should aid in developing targeted interventions for each group that will reduce the significant morbidity and mortality associated with bipolar disorder. PMID:25048003

Transgenic and gene knockout mice in gastric cancer research

PubMed Central

Jiang, Yannan; Yu, Yingyan

2017-01-01

Mouse models are useful tool for carcinogenic study. They will greatly enrich the understanding of pathogenesis and molecular mechanisms for gastric cancer. However, only few of mice could develop gastric cancer spontaneously. With the development and improvement of gene transfer technology, investigators created a variety of transgenic and knockout/knockin mouse models of gastric cancer, such as INS-GAS mice and gastrin knockout mice. Combined with helicobacter infection and carcinogens treatment, these transgenic/knockout/knockin mice developed precancerous or cancerous lesions, which are proper for gene function study or experimental therapy. Here we review the progression of genetically engineered mouse models on gastric cancer research, and emphasize the effects of chemical carcinogens or infectious factors on carcinogenesis of genetically modified mouse. We also emphasize the histological examination on mouse stomach. We expect to provide researchers with some inspirations on this field. PMID:27713138

Cycles of Exploration, Reflection, and Consolidation in Model-Based Learning of Genetics

NASA Astrophysics Data System (ADS)

Kim, Beaumie; Pathak, Suneeta A.; Jacobson, Michael J.; Zhang, Baohui; Gobert, Janice D.

2015-12-01

Model-based reasoning has been introduced as an authentic way of learning science, and many researchers have developed technological tools for learning with models. This paper describes how a model-based tool, BioLogica™, was used to facilitate genetics learning in secondary 3-level biology in Singapore. The research team co-designed two different pedagogical approaches with teachers, both of which involved learner-centered "exploration and reflection" with BioLogica and teacher-led "telling" or "consolidation." One group went through the stand-alone BioLogica units for all topics prior to a series of teacher-led instructions, whereas the other group was engaged in teacher-led activities after using BioLogica for each topic. Based on the results of a series of tests on genetics, the groups performed differently from what the teacher had expected. We explore how the design of the two approaches and interactions among students might have contributed to the results.

The ethics of disclosing genetic diagnosis for Alzheimer's disease: do we need a new paradigm?

PubMed

Arribas-Ayllon, Michael

2011-01-01

Genetic testing for rare Mendelian disorders represents the dominant ethical paradigm in clinical and professional practice. Predictive testing for Huntington's disease is the model against which other kinds of genetic testing are evaluated, including testing for Alzheimer's disease. This paper retraces the historical development of ethical reasoning in relation to predictive genetic testing and reviews a range of ethical, sociological and psychological literature from the 1970s to the present. In the past, ethical reasoning has embodied a distinct style whereby normative principles are developed from a dominant disease exemplar. This reductionist approach to formulating ethical frameworks breaks down in the case of disease susceptibility. Recent developments in the genetics of Alzheimer's disease present a significant case for reconsidering the ethics of disclosing risk for common complex diseases. Disclosing the results of susceptibility testing for Alzheimer's disease has different social, psychological and behavioural consequences. Furthermore, what genetic susceptibility means to individuals and their families is diffuse and often mitigated by other factors and concerns. The ethics of disclosing a genetic diagnosis of susceptibility is contingent on whether professionals accept that probabilistic risk information is in fact 'diagnostic' and it will rely substantially on empirical evidence of how people actually perceive, recall and communicate complex risk information.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Candidate gene database and transcript map for peach, a model species for fruit trees.

PubMed

Horn, Renate; Lecouls, Anne-Claire; Callahan, Ann; Dandekar, Abhaya; Garay, Lilibeth; McCord, Per; Howad, Werner; Chan, Helen; Verde, Ignazio; Main, Doreen; Jung, Sook; Georgi, Laura; Forrest, Sam; Mook, Jennifer; Zhebentyayeva, Tatyana; Yu, Yeisoo; Kim, Hye Ran; Jesudurai, Christopher; Sosinski, Bryon; Arús, Pere; Baird, Vance; Parfitt, Dan; Reighard, Gregory; Scorza, Ralph; Tomkins, Jeffrey; Wing, Rod; Abbott, Albert Glenn

2005-05-01

Peach (Prunus persica) is a model species for the Rosaceae, which includes a number of economically important fruit tree species. To develop an extensive Prunus expressed sequence tag (EST) database for identifying and cloning the genes important to fruit and tree development, we generated 9,984 high-quality ESTs from a peach cDNA library of developing fruit mesocarp. After assembly and annotation, a putative peach unigene set consisting of 3,842 ESTs was defined. Gene ontology (GO) classification was assigned based on the annotation of the single "best hit" match against the Swiss-Prot database. No significant homology could be found in the GenBank nr databases for 24.3% of the sequences. Using core markers from the general Prunus genetic map, we anchored bacterial artificial chromosome (BAC) clones on the genetic map, thereby providing a framework for the construction of a physical and transcript map. A transcript map was developed by hybridizing 1,236 ESTs from the putative peach unigene set and an additional 68 peach cDNA clones against the peach BAC library. Hybridizing ESTs to genetically anchored BACs immediately localized 11.2% of the ESTs on the genetic map. ESTs showed a clustering of expressed genes in defined regions of the linkage groups. [The data were built into a regularly updated Genome Database for Rosaceae (GDR), available at (http://www.genome.clemson.edu/gdr/).].

Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

PubMed

Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

2015-01-01

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

Drosophila sex combs as a model of evolutionary innovations.

PubMed

Kopp, Artyom

2011-01-01

The diversity of animal and plant forms is shaped by nested evolutionary innovations. Understanding the genetic and molecular changes responsible for these innovations is therefore one of the key goals of evolutionary biology. From the genetic point of view, the origin of novel traits implies the origin of new regulatory pathways to control their development. To understand how these new pathways are assembled in the course of evolution, we need model systems that combine relatively recent innovations with a powerful set of genetic and molecular tools. One such model is provided by the Drosophila sex comb-a male-specific morphological structure that evolved in a relatively small lineage related to the model species D. melanogaster. Our extensive knowledge of sex comb development in D. melanogaster provides the basis for investigating the genetic changes responsible for sex comb origin and diversification. At the same time, sex combs can change on microevolutionary timescales and differ spectacularly among closely related species, providing opportunities for direct genetic analysis and for integrating developmental and population-genetic approaches. Sex comb evolution is associated with the origin of novel interactions between Hox and sex determination genes. Activity of the sex determination pathway was brought under the control of the Hox code to become segment-specific, while Hox gene expression became sexually dimorphic. At the same time, both Hox and sex determination genes were integrated into the intrasegmental spatial patterning network, and acquired new joint downstream targets. Phylogenetic analysis shows that similar sex comb morphologies evolved independently in different lineages. Convergent evolution at the phenotypic level reflects convergent changes in the expression of Hox and sex determination genes, involving both independent gains and losses of regulatory interactions. However, the downstream cell-differentiation programs have diverged between species, and in some lineages, similar adult morphologies are produced by different morphogenetic mechanisms. These features make the sex comb an excellent model for examining not only the genetic changes responsible for its evolution, but also the cellular processes that translate DNA sequence changes into morphological diversity. The origin and diversification of sex combs provides insights into the roles of modularity, cooption, and regulatory changes in evolutionary innovations, and can serve as a model for understanding the origin of the more drastic novelties that define higher order taxa. © 2011 Wiley Periodicals, Inc.

Drosophila Sex Combs as a Model of Evolutionary Innovations

PubMed Central

Kopp, Artyom

2011-01-01

The diversity of animal and plant forms is shaped by nested evolutionary innovations. Understanding the genetic and molecular changes responsible for these innovations is therefore one of the key goals of evolutionary biology. From the genetic point of view, the origin of novel traits implies the origin of new regulatory pathways to control their development. To understand how these new pathways are assembled in the course of evolution, we need model systems that combine relatively recent innovations with a powerful set of genetic and molecular tools. One such model is provided by the Drosophila sex comb – a male-specific morphological structure that evolved in a relatively small lineage related to the model species D. melanogaster. Our extensive knowledge of sex comb development in D. melanogaster provides the basis for investigating the genetic changes responsible for sex comb origin and diversification. At the same time, sex combs can change on microevolutionary timescales and differ spectacularly among closely related species, providing opportunities for direct genetic analysis and for integrating developmental and population-genetic approaches. Sex comb evolution is associated with the origin of novel interactions between HOX and sex determination genes. Activity of the sex determination pathway was brought under the control of the HOX code to become segment-specific, while HOX gene expression became sexually dimorphic. At the same time, both HOX and sex determination genes were integrated into the intrasegmental spatial patterning network, and acquired new joint downstream targets. Phylogenetic analysis shows that similar sex comb morphologies evolved independently in different lineages. Convergent evolution at the phenotypic level reflects convergent changes in the expression of HOX and sex determination genes, involving both independent gains and losses of regulatory interactions. However, the downstream cell differentiation programs have diverged between species, and in some lineages similar adult morphologies are produced by different morphogenetic mechanisms. These features make the sex comb an excellent model for examining not only the genetic changes responsible for its evolution, but also the cellular processes that translate DNA sequence changes into morphological diversity. The origin and diversification of sex combs provides insights into the roles of modularity, cooption, and regulatory changes in evolutionary innovations, and can serve as a model for understanding the origin of the more drastic novelties that define higher-order taxa. PMID:23016935

Setaria viridis as a Model System to Advance Millet Genetics and Genomics

PubMed Central

Huang, Pu; Shyu, Christine; Coelho, Carla P.; Cao, Yingying; Brutnell, Thomas P.

2016-01-01

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail (Setaria viridis) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops. PMID:27965689

Setaria viridis as a Model System to Advance Millet Genetics and Genomics.

PubMed

Huang, Pu; Shyu, Christine; Coelho, Carla P; Cao, Yingying; Brutnell, Thomas P

2016-01-01

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail ( Setaria viridis ) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica . These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops.

Challenges and advances in mouse modeling for human pancreatic tumorigenesis and metastasis

PubMed Central

Qiu, Wanglong

2013-01-01

Pancreatic cancer is critical for developed countries, where its rate of diagnosis has been increasing steadily annually. In the past decade, the advances of pancreatic cancer research have not contributed to the decline in mortality rates from pancreatic cancer—the overall 5-year survival rate remains about 5% low. This number only underscores an obvious urgency for us to better understand the biological features of pancreatic carcinogenesis, to develop early detection methods, and to improve novel therapeutic treatments. To achieve these goals, animal modeling that faithfully recapitulates the whole process of human pancreatic cancer is central to making the advancements. In this review, we summarize the currently available animal models for pancreatic cancer and the advances in pancreatic cancer animal modeling. We compare and contrast the advantages and disadvantages of three major categories of these models: (1) carcinogen-induced; (2) xenograft and allograft; and (3) genetically engineered mouse models. We focus more on the genetically engineered mouse models, a category which has been rapidly expanded recently for their capacities to mimic human pancreatic cancer and metastasis, and highlight the combinations of these models with various newly developed strategies and cell-lineage labeling systems. PMID:23114842

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.

Generation of genetically-engineered animals using engineered endonucleases.

PubMed

Lee, Jong Geol; Sung, Young Hoon; Baek, In-Jeoung

2018-05-17

The key to successful drug discovery and development is to find the most suitable animal model of human diseases for the preclinical studies. The recent emergence of engineered endonucleases is allowing for efficient and precise genome editing, which can be used to develop potentially useful animal models for human diseases. In particular, zinc finger nucleases, transcription activator-like effector nucleases, and the clustered regularly interspaced short palindromic repeat systems are revolutionizing the generation of diverse genetically-engineered experimental animals including mice, rats, rabbits, dogs, pigs, and even non-human primates that are commonly used for preclinical studies of the drug discovery. Here, we describe recent advances in engineered endonucleases and their application in various laboratory animals. We also discuss the importance of genome editing in animal models for more closely mimicking human diseases.

Ovariectomy results in inbred strain-specific increases in anxiety-like behavior in mice

PubMed Central

Schoenrock, Sarah Adams; Oreper, Daniel; Young, Nancy; Ervin, Robin Betsch; Bogue, Molly A.; Valdar, William; Tarantino, Lisa M.

2017-01-01

Women are at an increased risk for developing affective disorders during times of hormonal flux, including menopause when the ovaries cease production of estrogen. However, while all women undergo menopause, not all develop an affective disorder. Increased vulnerability can result from genetic predisposition, environmental factors and gene by environment interactions. In order to investigate interactions between genetic background and estrogen depletion, we performed bilateral ovariectomy, a surgical procedure that results in estrogen depletion and is thought to model the post-menopausal state, in a genetically defined panel of 37 inbred mouse strains. Seventeen days post-ovariectomy, we assessed behavior in two standard rodent assays of anxiety- and depressive-like behavior, the open field and forced swim tests. We detected a significant interaction between ovariectomy and genetic background on anxiety-like behavior in the open field. No strain specific effects of ovariectomy were observed in the forced swim assay. However, we did observe significant strain effects for all behaviors in both the open field and forced swim tests. This study is the largest to date to look at the effects of ovariectomy on behavior and provides evidence that ovariectomy interacts with genetic background to alter anxiety-like behavior in an animal model of menopause. PMID:27693591

Genomic Model with Correlation Between Additive and Dominance Effects.

PubMed

Xiang, Tao; Christensen, Ole Fredslund; Vitezica, Zulma Gladis; Legarra, Andres

2018-05-09

Dominance genetic effects are rarely included in pedigree-based genetic evaluation. With the availability of single nucleotide polymorphism markers and the development of genomic evaluation, estimates of dominance genetic effects have become feasible using genomic best linear unbiased prediction (GBLUP). Usually, studies involving additive and dominance genetic effects ignore possible relationships between them. It has been often suggested that the magnitude of functional additive and dominance effects at the quantitative trait loci are related, but there is no existing GBLUP-like approach accounting for such correlation. Wellmann and Bennewitz showed two ways of considering directional relationships between additive and dominance effects, which they estimated in a Bayesian framework. However, these relationships cannot be fitted at the level of individuals instead of loci in a mixed model and are not compatible with standard animal or plant breeding software. This comes from a fundamental ambiguity in assigning the reference allele at a given locus. We show that, if there has been selection, assigning the most frequent as the reference allele orients the correlation between functional additive and dominance effects. As a consequence, the most frequent reference allele is expected to have a positive value. We also demonstrate that selection creates negative covariance between genotypic additive and dominance genetic values. For parameter estimation, it is possible to use a combined additive and dominance relationship matrix computed from marker genotypes, and to use standard restricted maximum likelihood (REML) algorithms based on an equivalent model. Through a simulation study, we show that such correlations can easily be estimated by mixed model software and accuracy of prediction for genetic values is slightly improved if such correlations are used in GBLUP. However, a model assuming uncorrelated effects and fitting orthogonal breeding values and dominant deviations performed similarly for prediction. Copyright © 2018, Genetics.

Oceanography and life history predict contrasting genetic population structure in two Antarctic fish species

PubMed Central

Young, Emma F; Belchier, Mark; Hauser, Lorenz; Horsburgh, Gavin J; Meredith, Michael P; Murphy, Eugene J; Pascoal, Sonia; Rock, Jennifer; Tysklind, Niklas; Carvalho, Gary R

2015-01-01

Understanding the key drivers of population connectivity in the marine environment is essential for the effective management of natural resources. Although several different approaches to evaluating connectivity have been used, they are rarely integrated quantitatively. Here, we use a ‘seascape genetics’ approach, by combining oceanographic modelling and microsatellite analyses, to understand the dominant influences on the population genetic structure of two Antarctic fishes with contrasting life histories, Champsocephalus gunnari and Notothenia rossii. The close accord between the model projections and empirical genetic structure demonstrated that passive dispersal during the planktonic early life stages is the dominant influence on patterns and extent of genetic structuring in both species. The shorter planktonic phase of C. gunnari restricts direct transport of larvae between distant populations, leading to stronger regional differentiation. By contrast, geographic distance did not affect differentiation in N. rossii, whose longer larval period promotes long-distance dispersal. Interannual variability in oceanographic flows strongly influenced the projected genetic structure, suggesting that shifts in circulation patterns due to climate change are likely to impact future genetic connectivity and opportunities for local adaptation, resilience and recovery from perturbations. Further development of realistic climate models is required to fully assess such potential impacts. PMID:26029262

Modeling the influence of genetic and environmental variation on the expression of plant life cycles across landscapes.

PubMed

Burghardt, Liana T; Metcalf, C Jessica E; Wilczek, Amity M; Schmitt, Johanna; Donohue, Kathleen

2015-02-01

Organisms develop through multiple life stages that differ in environmental tolerances. The seasonal timing, or phenology, of life-stage transitions determines the environmental conditions to which each life stage is exposed and the length of time required to complete a generation. Both environmental and genetic factors contribute to phenological variation, yet predicting their combined effect on life cycles across a geographic range remains a challenge. We linked submodels of the plasticity of individual life stages to create an integrated model that predicts life-cycle phenology in complex environments. We parameterized the model for Arabidopsis thaliana and simulated life cycles in four locations. We compared multiple "genotypes" by varying two parameters associated with natural genetic variation in phenology: seed dormancy and floral repression. The model predicted variation in life cycles across locations that qualitatively matches observed natural phenology. Seed dormancy had larger effects on life-cycle length than floral repression, and results suggest that a genetic cline in dormancy maintains a life-cycle length of 1 year across the geographic range of this species. By integrating across life stages, this approach demonstrates how genetic variation in one transition can influence subsequent transitions and the geographic distribution of life cycles more generally.

Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data.

PubMed

Gim, Jungsoo; Kim, Wonji; Kwak, Soo Heon; Choi, Hosik; Park, Changyi; Park, Kyong Soo; Kwon, Sunghoon; Park, Taesung; Won, Sungho

2017-11-01

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance. Copyright © 2017 by the Genetics Society of America.

Developing a Risk-scoring Model for Ankylosing Spondylitis Based on a Combination of HLA-B27, Single-nucleotide Polymorphism, and Copy Number Variant Markers.

PubMed

Jung, Seung-Hyun; Cho, Sung-Min; Yim, Seon-Hee; Kim, So-Hee; Park, Hyeon-Chun; Cho, Mi-La; Shim, Seung-Cheol; Kim, Tae-Hwan; Park, Sung-Hwan; Chung, Yeun-Jun

2016-12-01

To develop a genotype-based ankylosing spondylitis (AS) risk prediction model that is more sensitive and specific than HLA-B27 typing. To develop the AS genetic risk scoring (AS-GRS) model, 648 individuals (285 cases and 363 controls) were examined for 5 copy number variants (CNV), 7 single-nucleotide polymorphisms (SNP), and an HLA-B27 marker by TaqMan assays. The AS-GRS model was developed using logistic regression and validated with a larger independent set (576 cases and 680 controls). Through logistic regression, we built the AS-GRS model consisting of 5 genetic components: HLA-B27, 3 CNV (1q32.2, 13q13.1, and 16p13.3), and 1 SNP (rs10865331). All significant associations of genetic factors in the model were replicated in the independent validation set. The discriminative ability of the AS-GRS model measured by the area under the curve was excellent: 0.976 (95% CI 0.96-0.99) in the model construction set and 0.951 (95% CI 0.94-0.96) in the validation set. The AS-GRS model showed higher specificity and accuracy than the HLA-B27-only model when the sensitivity was set to over 94%. When we categorized the individuals into quartiles based on the AS-GRS scores, OR of the 4 groups (low, intermediate-1, intermediate-2, and high risk) showed an increasing trend with the AS-GRS scores (r 2 = 0.950) and the highest risk group showed a 494× higher risk of AS than the lowest risk group (95% CI 237.3-1029.1). Our AS-GRS could be used to identify individuals at high risk for AS before major symptoms appear, which may improve the prognosis for them through early treatment.

A longitudinal twin study of physical aggression during early childhood: evidence for a developmentally dynamic genome.

PubMed

Lacourse, E; Boivin, M; Brendgen, M; Petitclerc, A; Girard, A; Vitaro, F; Paquin, S; Ouellet-Morin, I; Dionne, G; Tremblay, R E

2014-09-01

Physical aggression (PA) tends to have its onset in infancy and to increase rapidly in frequency. Very little is known about the genetic and environmental etiology of PA development during early childhood. We investigated the temporal pattern of genetic and environmental etiology of PA during this crucial developmental period. Participants were 667 twin pairs, including 254 monozygotic and 413 dizygotic pairs, from the ongoing longitudinal Quebec Newborn Twin Study. Maternal reports of PA were obtained from three waves of data at 20, 32 and 50 months. These reports were analysed using a biometric Cholesky decomposition and linear latent growth curve model. The best-fitting Cholesky model revealed developmentally dynamic effects, mostly genetic attenuation and innovation. The contribution of genetic factors at 20 months substantially decreased over time, while new genetic effects appeared later on. The linear latent growth curve model revealed a significant moderate increase in PA from 20 to 50 months. Two separate sets of uncorrelated genetic factors accounted for the variation in initial level and growth rate. Non-shared and shared environments had no effect on the stability, initial status and growth rate in PA. Genetic factors underlie PA frequency and stability during early childhood; they are also responsible for initial status and growth rate in PA. The contribution of shared environment is modest, and perhaps limited, as it appears only at 50 months. Future research should investigate the complex nature of these dynamic genetic factors through genetic-environment correlation (r GE) and interaction (G×E) analyses.

The epicurean fly: using Drosophila melanogaster to study metabolism.

PubMed

Bharucha, Kamal N

2009-02-01

In this review, the utility of Drosophila melanogaster as a model organism for research in metabolism will be demonstrated. Importantly, many metabolic pathways are conserved in both man and the fly. Recent work has highlighted that these conserved molecular pathways have the potential to give rise to similar phenotypes. For example, it has proven possible to generate obese and diabetic Drosophila; conversely, genetic manipulation can also generate lean and hypoglycemic phenotypes. From conserved circulating hormones to key enzymes, the fly is host to a variety of homologous, metabolically active signaling mechanisms. The world of Drosophila research has not only a rich history of developing techniques for exquisite genetic manipulation, but also continues to develop genetic methodologies at an exciting rate. Many of these techniques add to the cadre of experimental tools available for the use of the fly as a model organism for studying carbohydrate and lipid homeostasis. This review is written for the pediatric-scientist with little background in Drosophila, with the goal of relaying the potential of this model organism for contributing to a better understanding of diseases affecting today's children.

Gene and environment interaction: is the differential susceptibility hypothesis relevant for obesity?

PubMed Central

Dalle Molle, Roberta; Fatemi, Hajar; Dagher, Alain; Levitan, Robert D.; Silveira, Patricia P.; Dubé, Laurette

2017-01-01

The differential susceptibility model states that a given genetic variant is associated with an increased risk of pathology in negative environments but greater than average resilience in enriched ones. While this theory was first implemented in psychiatric-genetic research, it may also help us to unravel the complex ways that genes and environments interact to influence feeding behavior and obesity. We reviewed evidence on gene vs. environment interactions that influence obesity development, aiming to support the applicability of the differential susceptibility model for this condition, and propose that various environmental “layers” relevant for human development should be considered when bearing the differential susceptibility model in mind. Mother-child relationship, socioeconomic status and individual's response are important modifiers of BMI and food intake when interacting with gene variants, “for better and for worse”. While only a few studies to date have investigated obesity outcomes using this approach, we propose that the differential susceptibility hypothesis is in fact highly applicable to the study of genetic and environmental influences on feeding behavior and obesity risk. PMID:28024828

Rendering the Intractable More Tractable: Tools from Caenorhabditis elegans Ripe for Import into Parasitic Nematodes

PubMed Central

Ward, Jordan D.

2015-01-01

Recent and rapid advances in genetic and molecular tools have brought spectacular tractability to Caenorhabditis elegans, a model that was initially prized because of its simple design and ease of imaging. C. elegans has long been a powerful model in biomedical research, and tools such as RNAi and the CRISPR/Cas9 system allow facile knockdown of genes and genome editing, respectively. These developments have created an additional opportunity to tackle one of the most debilitating burdens on global health and food security: parasitic nematodes. I review how development of nonparasitic nematodes as genetic models informs efforts to import tools into parasitic nematodes. Current tools in three commonly studied parasites (Strongyloides spp., Brugia malayi, and Ascaris suum) are described, as are tools from C. elegans that are ripe for adaptation and the benefits and barriers to doing so. These tools will enable dissection of a huge array of questions that have been all but completely impenetrable to date, allowing investigation into host–parasite and parasite–vector interactions, and the genetic basis of parasitism. PMID:26644478

The evolution of the cognitive model of depression and its neurobiological correlates.

PubMed

Beck, Aaron T

2008-08-01

Although the cognitive model of depression has evolved appreciably since its first formulation over 40 years ago, the potential interaction of genetic, neurochemical, and cognitive factors has only recently been demonstrated. Combining findings from behavioral genetics and cognitive neuroscience with the accumulated research on the cognitive model opens new opportunities for integrated research. Drawing on advances in cognitive, personality, and social psychology as well as clinical observations, expansions of the original cognitive model have incorporated in successive stages automatic thoughts, cognitive distortions, dysfunctional beliefs, and information-processing biases. The developmental model identified early traumatic experiences and the formation of dysfunctional beliefs as predisposing events and congruent stressors in later life as precipitating factors. It is now possible to sketch out possible genetic and neurochemical pathways that interact with or are parallel to cognitive variables. A hypersensitive amygdala is associated with both a genetic polymorphism and a pattern of negative cognitive biases and dysfunctional beliefs, all of which constitute risk factors for depression. Further, the combination of a hyperactive amygdala and hypoactive prefrontal regions is associated with diminished cognitive appraisal and the occurrence of depression. Genetic polymorphisms also are involved in the overreaction to the stress and the hypercortisolemia in the development of depression--probably mediated by cognitive distortions. I suggest that comprehensive study of the psychological as well as biological correlates of depression can provide a new understanding of this debilitating disorder.

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.

Political Attitudes Develop Independently of Personality Traits

PubMed Central

Hatemi, Peter K.; Verhulst, Brad

2015-01-01

The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not significant or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, latent genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data. Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question. We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them. Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area. PMID:25734580

Political attitudes develop independently of personality traits.

PubMed

Hatemi, Peter K; Verhulst, Brad

2015-01-01

The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not significant or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, latent genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data. Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question. We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them. Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area.

Genetic Simulation Resources: a website for the registration and discovery of genetic data simulators

PubMed Central

Peng, Bo; Chen, Huann-Sheng; Mechanic, Leah E.; Racine, Ben; Clarke, John; Clarke, Lauren; Gillanders, Elizabeth; Feuer, Eric J.

2013-01-01

Summary: Many simulation methods and programs have been developed to simulate genetic data of the human genome. These data have been widely used, for example, to predict properties of populations retrospectively or prospectively according to mathematically intractable genetic models, and to assist the validation, statistical inference and power analysis of a variety of statistical models. However, owing to the differences in type of genetic data of interest, simulation methods, evolutionary features, input and output formats, terminologies and assumptions for different applications, choosing the right tool for a particular study can be a resource-intensive process that usually involves searching, downloading and testing many different simulation programs. Genetic Simulation Resources (GSR) is a website provided by the National Cancer Institute (NCI) that aims to help researchers compare and choose the appropriate simulation tools for their studies. This website allows authors of simulation software to register their applications and describe them with well-defined attributes, thus allowing site users to search and compare simulators according to specified features. Availability: http://popmodels.cancercontrol.cancer.gov/gsr. Contact: gsr@mail.nih.gov PMID:23435068

Workers make the queens in melipona bees: identification of geraniol as a caste determining compound from labial glands of nurse bees.

PubMed

Jarau, Stefan; van Veen, Johan W; Twele, Robert; Reichle, Christian; Gonzales, Eduardo Herrera; Aguilar, Ingrid; Francke, Wittko; Ayasse, Manfred

2010-06-01

Reproductive division of labor in advanced eusocial honey bees and stingless bees is based on the ability of totipotent female larvae to develop into either workers or queens. In nearly all species, caste is determined by larval nutrition. However, the mechanism that triggers queen development in Melipona bees is still unresolved. Several hypotheses have been proposed, ranging from the proximate (a genetic determination of caste development) to the ultimate (a model in which larvae have complete control over their own caste fate). Here, we showed that the addition of geraniol, the main compound in labial gland secretions of nurse workers, to the larval food significantly increases the number of larvae that develop into queens. Interestingly, the proportion of queens in treated brood exactly matched the value (25%) predicted by the two-locus, two-allele model of genetic queen determination, in which only females that are heterozygous at both loci are capable of developing into queens. We conclude that labial gland secretions, added to the food of some cells by nurse bees, trigger queen development, provided that the larvae are genetically predisposed towards this developmental pathway. In Melipona beecheii, geraniol acts as a primer pheromone representing the first caste determination substance identified to date.

A toolkit for incorporating genetics into mainstream medical services: Learning from service development pilots in England

PubMed Central

2010-01-01

Background As advances in genetics are becoming increasingly relevant to mainstream healthcare, a major challenge is to ensure that these are integrated appropriately into mainstream medical services. In 2003, the Department of Health for England announced the availability of start-up funding for ten 'Mainstreaming Genetics' pilot services to develop models to achieve this. Methods Multiple methods were used to explore the pilots' experiences of incorporating genetics which might inform the development of new services in the future. A workshop with project staff, an email questionnaire, interviews and a thematic analysis of pilot final reports were carried out. Results Seven themes relating to the integration of genetics into mainstream medical services were identified: planning services to incorporate genetics; the involvement of genetics departments; the establishment of roles incorporating genetic activities; identifying and involving stakeholders; the challenges of working across specialty boundaries; working with multiple healthcare organisations; and the importance of cultural awareness of genetic conditions. Pilots found that the planning phase often included the need to raise awareness of genetic conditions and services and that early consideration of organisational issues such as clinic location was essential. The formal involvement of genetics departments was crucial to success; benefits included provision of clinical and educational support for staff in new roles. Recruitment and retention for new roles outside usual career pathways sometimes proved difficult. Differences in specialties' working practices and working with multiple healthcare organisations also brought challenges such as the 'genetic approach' of working with families, incompatible record systems and different approaches to health professionals' autonomous practice. 'Practice points' have been collated into a Toolkit which includes resources from the pilots, including job descriptions and clinical tools. These can be customised for reuse by other services. Conclusions Healthcare services need to translate advances in genetics into benefits for patients. Consideration of the issues presented here when incorporating genetics into mainstream medical services will help ensure that new service developments build on the body of experience gained by the pilots, to provide high quality services for patients with or at risk of genetic conditions. PMID:20470377

A toolkit for incorporating genetics into mainstream medical services: Learning from service development pilots in England.

PubMed

Bennett, Catherine L; Burke, Sarah E; Burton, Hilary; Farndon, Peter A

2010-05-14

As advances in genetics are becoming increasingly relevant to mainstream healthcare, a major challenge is to ensure that these are integrated appropriately into mainstream medical services. In 2003, the Department of Health for England announced the availability of start-up funding for ten 'Mainstreaming Genetics' pilot services to develop models to achieve this. Multiple methods were used to explore the pilots' experiences of incorporating genetics which might inform the development of new services in the future. A workshop with project staff, an email questionnaire, interviews and a thematic analysis of pilot final reports were carried out. Seven themes relating to the integration of genetics into mainstream medical services were identified: planning services to incorporate genetics; the involvement of genetics departments; the establishment of roles incorporating genetic activities; identifying and involving stakeholders; the challenges of working across specialty boundaries; working with multiple healthcare organisations; and the importance of cultural awareness of genetic conditions. Pilots found that the planning phase often included the need to raise awareness of genetic conditions and services and that early consideration of organisational issues such as clinic location was essential. The formal involvement of genetics departments was crucial to success; benefits included provision of clinical and educational support for staff in new roles. Recruitment and retention for new roles outside usual career pathways sometimes proved difficult. Differences in specialties' working practices and working with multiple healthcare organisations also brought challenges such as the 'genetic approach' of working with families, incompatible record systems and different approaches to health professionals' autonomous practice. 'Practice points' have been collated into a Toolkit which includes resources from the pilots, including job descriptions and clinical tools. These can be customised for reuse by other services. Healthcare services need to translate advances in genetics into benefits for patients. Consideration of the issues presented here when incorporating genetics into mainstream medical services will help ensure that new service developments build on the body of experience gained by the pilots, to provide high quality services for patients with or at risk of genetic conditions.

Incorporation of genetic gain into growth projections of Douglas-Fir using ORGANON and the Forest Vegetation Simulator

Treesearch

Peter J. Gould; David D. Marshall

2010-01-01

Growth models for coast Douglas-fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) are generally based on measurements of stands that are genetically unimproved (or woods-run); therefore, they cannot be expected to accurately project the development of stands that originate from improved seedlots. In this report, we...

Genetic Variance in the F2 Generation of Divergently Selected Parents

Treesearch

M.P. Koshy; G. Namkoong; J.H. Roberds

1998-01-01

Either by selective breeding for population divergence or by using natural population differences, F2 and advanced generation hybrids can be developed with high variances. We relate the size of the genetic variance to the population divergence based on a forward and backward mutation model at a locus with two alleles with additive gene action....

EvoluZion: A Computer Simulator for Teaching Genetic and Evolutionary Concepts

ERIC Educational Resources Information Center

Zurita, Adolfo R.

2017-01-01

EvoluZion is a forward-in-time genetic simulator developed in Java and designed to perform real time simulations on the evolutionary history of virtual organisms. These model organisms harbour a set of 13 genes that codify an equal number of phenotypic features. These genes change randomly during replication, and mutant genes can have null,…

Hybrid Deterministic Views about Genes in Biology Textbooks: A Key Problem in Genetics Teaching

ERIC Educational Resources Information Center

dos Santos, Vanessa Carvalho; Joaquim, Leyla Mariane; El-Hani, Charbel Nino

2012-01-01

A major source of difficulties in promoting students' understanding of genetics lies in the presentation of gene concepts and models in an inconsistent and largely ahistorical manner, merely amalgamated in hybrid views, as if they constituted linear developments, instead of being built for different purposes and employed in specific contexts. In…

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

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity.

PubMed

Almendro, Vanessa; Cheng, Yu-Kang; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; Gonzalez-Farre, Xavier; Muñoz, Montse; Russnes, Hege G; Helland, Aslaug; Rye, Inga H; Borresen-Dale, Anne-Lise; Maruyama, Reo; van Oudenaarden, Alexander; Dowsett, Mitchell; Jones, Robin L; Reis-Filho, Jorge; Gascon, Pere; Gönen, Mithat; Michor, Franziska; Polyak, Kornelia

2014-02-13

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and posttreatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

A next-generation dual-recombinase system for time and host specific targeting of pancreatic cancer

PubMed Central

Schachtler, Christina; Zukowska, Magdalena; Eser, Stefan; Feyerabend, Thorsten B.; Paul, Mariel C.; Eser, Philipp; Klein, Sabine; Lowy, Andrew M.; Banerjee, Ruby; Yang, Fangtang; Lee, Chang-Lung; Moding, Everett J.; Kirsch, David G.; Scheideler, Angelika; Alessi, Dario R.; Varela, Ignacio; Bradley, Allan; Kind, Alexander; Schnieke, Angelika E.; Rodewald, Hans-Reimer; Rad, Roland; Schmid, Roland M.; Schneider, Günter; Saur, Dieter

2014-01-01

Genetically engineered mouse models (GEMMs) have dramatically improved our understanding of tumor evolution and therapeutic resistance. However, sequential genetic manipulation of gene expression and targeting of the host is almost impossible using conventional Cre-loxP–based models. We have developed an inducible dual-recombinase system by combining flippase-FRT (Flp-FRT) and Cre-loxP recombination technologies to improve GEMMs of pancreatic cancer. This enables investigation of multistep carcinogenesis, genetic manipulation of tumor subpopulations (such as cancer stem cells), selective targeting of the tumor microenvironment and genetic validation of therapeutic targets in autochthonous tumors on a genome-wide scale. As a proof of concept, we performed tumor cell–autonomous and nonautonomous targeting, recapitulated hallmarks of human multistep carcinogenesis, validated genetic therapy by 3-phosphoinositide-dependent protein kinase inactivation as well as cancer cell depletion and show that mast cells in the tumor microenvironment, which had been thought to be key oncogenic players, are dispensable for tumor formation. PMID:25326799

A next-generation dual-recombinase system for time- and host-specific targeting of pancreatic cancer.

PubMed

Schönhuber, Nina; Seidler, Barbara; Schuck, Kathleen; Veltkamp, Christian; Schachtler, Christina; Zukowska, Magdalena; Eser, Stefan; Feyerabend, Thorsten B; Paul, Mariel C; Eser, Philipp; Klein, Sabine; Lowy, Andrew M; Banerjee, Ruby; Yang, Fangtang; Lee, Chang-Lung; Moding, Everett J; Kirsch, David G; Scheideler, Angelika; Alessi, Dario R; Varela, Ignacio; Bradley, Allan; Kind, Alexander; Schnieke, Angelika E; Rodewald, Hans-Reimer; Rad, Roland; Schmid, Roland M; Schneider, Günter; Saur, Dieter

2014-11-01

Genetically engineered mouse models (GEMMs) have dramatically improved our understanding of tumor evolution and therapeutic resistance. However, sequential genetic manipulation of gene expression and targeting of the host is almost impossible using conventional Cre-loxP-based models. We have developed an inducible dual-recombinase system by combining flippase-FRT (Flp-FRT) and Cre-loxP recombination technologies to improve GEMMs of pancreatic cancer. This enables investigation of multistep carcinogenesis, genetic manipulation of tumor subpopulations (such as cancer stem cells), selective targeting of the tumor microenvironment and genetic validation of therapeutic targets in autochthonous tumors on a genome-wide scale. As a proof of concept, we performed tumor cell-autonomous and nonautonomous targeting, recapitulated hallmarks of human multistep carcinogenesis, validated genetic therapy by 3-phosphoinositide-dependent protein kinase inactivation as well as cancer cell depletion and show that mast cells in the tumor microenvironment, which had been thought to be key oncogenic players, are dispensable for tumor formation.

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of cellular diversity for genetic and phenotypic features

PubMed Central

Almendro, Vanessa; Cheng, Yu-Kang; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; Gonzalez-Farre, Xavier; Muñoz, Montse; Russnes, Hege G.; Helland, Åslaug; Rye, Inga H.; Borresen-Dale, Anne-Lise; Maruyama, Reo; van Oudenaarden, Alexander; Dowsett, Mitchell; Jones, Robin L.; Reis-Filho, Jorge; Gascon, Pere; Gönen, Mithat; Michor, Franziska; Polyak, Kornelia

2014-01-01

SUMMARY Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor subtype-specific and it did not change during treatment in tumors with partial or no response. However, lower pre-treatment genetic diversity was significantly associated with complete pathologic response. In contrast, phenotypic diversity was different between pre- and post-treatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution. PMID:24462293

Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction.

PubMed

Hill, William G

2014-01-01

Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives' performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher's infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with "genomic selection" is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.

Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity

DOE PAGES

Almendro, Vanessa; Cheng, Yu -Kang; Randles, Amanda; ...

2014-02-01

Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and post-treatment samples. We also observed significant changes in the spatialmore » distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution.« less

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.

Parenting and adolescents' psychological adjustment: Longitudinal moderation by adolescents' genetic sensitivity.

PubMed

Stocker, Clare M; Masarik, April S; Widaman, Keith F; Reeb, Ben T; Boardman, Jason D; Smolen, Andrew; Neppl, Tricia K; Conger, Katherine J

2017-10-01

We examined whether adolescents' genetic sensitivity, measured by a polygenic index score, moderated the longitudinal associations between parenting and adolescents' psychological adjustment. The sample included 323 mothers, fathers, and adolescents (177 female, 146 male; Time 1 [T1] average age = 12.61 years, SD = 0.54 years; Time 2 [T2] average age = 13.59 years, SD = 0.59 years). Parents' warmth and hostility were rated by trained, independent observers using videotapes of family discussions. Adolescents reported their symptoms of anxiety, depressed mood, and hostility at T1 and T2. The results from autoregressive linear regression models showed that adolescents' genetic sensitivity moderated associations between observations of both mothers' and fathers' T1 parenting and adolescents' T2 composite maladjustment, depression, anxiety, and hostility. Compared to adolescents with low genetic sensitivity, adolescents with high genetic sensitivity had worse adjustment outcomes when parenting was low on warmth and high on hostility. When parenting was characterized by high warmth and low hostility, adolescents with high genetic sensitivity had better adjustment outcomes than their counterparts with low genetic sensitivity. The results support the differential susceptibility model and highlight the complex ways that genes and environment interact to influence development.

Biological Aging - Criteria for Modeling and a New Mechanistic Model

NASA Astrophysics Data System (ADS)

Pletcher, Scott D.; Neuhauser, Claudia

To stimulate interaction and collaboration across scientific fields, we introduce a minimum set of biological criteria that theoretical models of aging should satisfy. We review results of several recent experiments that examined changes in age-specific mortality rates caused by genetic and environmental manipulation. The empirical data from these experiments is then used to test mathematical models of aging from several different disciplines, including molecular biology, reliability theory, physics, and evolutionary biology/population genetics. We find that none of the current models are consistent with all of the published experimental findings. To provide an example of how our criteria might be applied in practice, we develop a new conceptual model of aging that is consistent with our observations.

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.

The role of the transcription factor Ets1 in lupus and other autoimmune diseases

PubMed Central

Garrett-Sinha, Lee Ann; Kearly, Alyssa; Satterthwaite, Anne B.

2017-01-01

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by excess B and T cell activation, the development of autoantibodies against self-antigens including nuclear antigens, and immune complex deposition in target organs which triggers an inflammatory response and tissue damage. The genetic and environmental factors that contribute to development of SLE have been extensively studied in both humans and mouse models of the disease. One of the important genetic contributions to SLE development is an alteration in the expression of the transcription factor Ets1, which regulates the functional differentiation of lymphocytes. Here we review the genetic, biochemical and immunological studies that have linked low levels of Ets1 to aberrant lymphocyte differentiation and to the pathogenesis of SLE. PMID:28845756

Genetic evaluation of mastitis liability and recovery through longitudinal analysis of transition probabilities

PubMed Central

2012-01-01

Background Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis. Methods Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations. Results Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process. Conclusions The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health. PMID:22475575

Animal models of the non-motor features of Parkinson’s disease

PubMed Central

McDowell, Kimberly; Chesselet, Marie-Françoise

2012-01-01

The non-motor symptoms (NMS) of Parkinson’s disease (PD) occur in roughly 90% of patients, have a profound negative impact on their quality of life, and often go undiagnosed. NMS typically involve many functional systems, and include sleep disturbances, neuropsychiatric and cognitive deficits, and autonomic and sensory dysfunction. The development and use of animal models have provided valuable insight into the classical motor symptoms of PD over the past few decades. Toxin-induced models provide a suitable approach to study aspects of the disease that derive from the loss of nigrostriatal dopaminergic neurons, a cardinal feature of PD. This also includes some NMS, primarily cognitive dysfunction. However, several NMS poorly respond to dopaminergic treatments, suggesting that they may be due to other pathologies. Recently developed genetic models of PD are providing new ways to model these NMS and identify their mechanisms. This review summarizes the current available literature on the ability of both toxin-induced and genetically-based animal models to reproduce the NMS of PD. PMID:22236386

Modeling AEC—New Approaches to Study Rare Genetic Disorders

PubMed Central

Koch, Peter J.; Dinella, Jason; Fete, Mary; Siegfried, Elaine C.; Koster, Maranke I.

2015-01-01

Ankyloblepharon-ectodermal defects-cleft lip/palate (AEC) syndrome is a rare monogenetic disorder that is characterized by severe abnormalities in ectoderm-derived tissues, such as skin and its appendages. A major cause of morbidity among affected infants is severe and chronic skin erosions. Currently, supportive care is the only available treatment option for AEC patients. Mutations in TP63, a gene that encodes key regulators of epidermal development, are the genetic cause of AEC. However, it is currently not clear how mutations in TP63 lead to the various defects seen in the patients’ skin. In this review, we will discuss current knowledge of the AEC disease mechanism obtained by studying patient tissue and genetically engineered mouse models designed to mimic aspects of the disorder. We will then focus on new approaches to model AEC, including the use of patient cells and stem cell technology to replicate the disease in a human tissue culture model. The latter approach will advance our understanding of the disease and will allow for the development of new in vitro systems to identify drugs for the treatment of skin erosions in AEC patients. Further, the use of stem cell technology, in particular induced pluripotent stem cells (iPSC), will enable researchers to develop new therapeutic approaches to treat the disease using the patient’s own cells (autologous keratinocyte transplantation) after correction of the disease-causing mutations. PMID:24665072

Engineered probiotic Escherichia coli can eliminate and prevent Pseudomonas aeruginosa gut infection in animal models

PubMed Central

Hwang, In Young; Koh, Elvin; Wong, Adison; March, John C.; Bentley, William E.; Lee, Yung Seng; Chang, Matthew Wook

2017-01-01

Bacteria can be genetically engineered to kill specific pathogens or inhibit their virulence. We previously developed a synthetic genetic system that allows a laboratory strain of Escherichia coli to sense and kill Pseudomonas aeruginosa in vitro. Here, we generate a modified version of the system, including a gene encoding an anti-biofilm enzyme, and use the probiotic strain Escherichia coli Nissle 1917 as host. The engineered probiotic shows in vivo prophylactic and therapeutic activity against P. aeruginosa during gut infection in two animal models (Caenorhabditis elegans and mice). These findings support the further development of engineered microorganisms with potential prophylactic and therapeutic activities against gut infections. PMID:28398304

Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants.

PubMed

Romanos, Jihane; Rosén, Anna; Kumar, Vinod; Trynka, Gosia; Franke, Lude; Szperl, Agata; Gutierrez-Achury, Javier; van Diemen, Cleo C; Kanninga, Roan; Jankipersadsing, Soesma A; Steck, Andrea; Eisenbarth, Georges; van Heel, David A; Cukrowska, Bozena; Bruno, Valentina; Mazzilli, Maria Cristina; Núñez, Concepcion; Bilbao, Jose Ramon; Mearin, M Luisa; Barisani, Donatella; Rewers, Marian; Norris, Jill M; Ivarsson, Anneli; Boezen, H Marieke; Liu, Edwin; Wijmenga, Cisca

2014-03-01

The majority of coeliac disease (CD) patients are not being properly diagnosed and therefore remain untreated, leading to a greater risk of developing CD-associated complications. The major genetic risk heterodimer, HLA-DQ2 and DQ8, is already used clinically to help exclude disease. However, approximately 40% of the population carry these alleles and the majority never develop CD. We explored whether CD risk prediction can be improved by adding non-HLA-susceptible variants to common HLA testing. We developed an average weighted genetic risk score with 10, 26 and 57 single nucleotide polymorphisms (SNP) in 2675 cases and 2815 controls and assessed the improvement in risk prediction provided by the non-HLA SNP. Moreover, we assessed the transferability of the genetic risk model with 26 non-HLA variants to a nested case-control population (n=1709) and a prospective cohort (n=1245) and then tested how well this model predicted CD outcome for 985 independent individuals. Adding 57 non-HLA variants to HLA testing showed a statistically significant improvement compared to scores from models based on HLA only, HLA plus 10 SNP and HLA plus 26 SNP. With 57 non-HLA variants, the area under the receiver operator characteristic curve reached 0.854 compared to 0.823 for HLA only, and 11.1% of individuals were reclassified to a more accurate risk group. We show that the risk model with HLA plus 26 SNP is useful in independent populations. Predicting risk with 57 additional non-HLA variants improved the identification of potential CD patients. This demonstrates a possible role for combined HLA and non-HLA genetic testing in diagnostic work for CD.

Mouse Model for the Preclinical Study of Metastatic Disease | NCI Technology Transfer Center | TTC

Cancer.gov

The Laboratory of Cancer Biology and Genetics, National Cancer Institute seeks partners for collaborative research to co-develop a mouse model that shows preclinical therapeutic response of residual metastatic disease.

Modeling Human Cancers in Drosophila.

PubMed

Sonoshita, M; Cagan, R L

2017-01-01

Cancer is a complex disease that affects multiple organs. Whole-body animal models provide important insights into oncology that can lead to clinical impact. Here, we review novel concepts that Drosophila studies have established for cancer biology, drug discovery, and patient therapy. Genetic studies using Drosophila have explored the roles of oncogenes and tumor-suppressor genes that when dysregulated promote cancer formation, making Drosophila a useful model to study multiple aspects of transformation. Not limited to mechanism analyses, Drosophila has recently been showing its value in facilitating drug development. Flies offer rapid, efficient platforms by which novel classes of drugs can be identified as candidate anticancer leads. Further, we discuss the use of Drosophila as a platform to develop therapies for individual patients by modeling the tumor's genetic complexity. Drosophila provides both a classical and a novel tool to identify new therapeutics, complementing other more traditional cancer tools. © 2017 Elsevier Inc. All rights reserved.

Bordetella pertussis transmission

PubMed Central

Trainor, Elizabeth A.; Nicholson, Tracy L.; Merkel, Tod J.

2015-01-01

Bordetella pertussis and B. bronchiseptica are Gram-negative bacterial respiratory pathogens. Bordetella pertussis is the causative agent of whooping cough and is considered a human-adapted variant of B. bronchiseptica. Bordetella pertussis and B. bronchiseptica share mechanisms of pathogenesis and are genetically closely related. However, despite the close genetic relatedness, these Bordetella species differ in several classic fundamental aspects of bacterial pathogens such as host range, pathologies and persistence. The development of the baboon model for the study of B. pertussis transmission, along with the development of the swine and mouse model for the study of B. bronchiseptica, has enabled the investigation of different aspects of transmission including the route, attack rate, role of bacterial and host factors, and the impact of vaccination on transmission. This review will focus on B. pertussis transmission and how animal models of B. pertussis transmission and transmission models using the closely related B. bronchiseptica have increased our understanding of B. pertussis transmission. PMID:26374235

Developmental analysis and influence of genetic background on the Lhx3 W227ter mouse model of combined pituitary hormone deficiency disease.

PubMed

Prince, Kelly L; Colvin, Stephanie C; Park, Soyoung; Lai, Xianyin; Witzmann, Frank A; Rhodes, Simon J

2013-02-01

Combined pituitary hormone deficiency (CPHD) diseases result in severe outcomes for patients including short stature, developmental delays, and reproductive deficiencies. Little is known about their etiology, especially the developmental profiles and the influences of genetic background on disease progression. Animal models for CPHD provide valuable tools to investigate disease mechanisms and inform diagnostic and treatment protocols. Here we examined hormone production during pituitary development and the influence of genetic background on phenotypic severity in the Lhx3(W227ter/W227ter) mouse model. Lhx3(W227ter/W227ter) embryos have deficiencies of ACTH, α-glycoprotein subunit, GH, PRL, TSHβ, and LHβ during prenatal development. Furthermore, mutant mice have significant reduction in the critical pituitary transcriptional activator-1 (PIT1). Through breeding, the Lhx3(W227ter/W227ter) genotype was placed onto the 129/Sv and C57BL/6 backgrounds. Intriguingly, the genetic background significantly affected viability: whereas Lhx3(W227ter/W227ter) animals were found in the expected frequencies in C57BL/6, homozygous animals were not viable in the 129/Sv genetic environment. The hormone marker and PIT1 reductions observed in Lhx3(W227ter/W227ter) mice on a mixed background were also seen in the separate strains but in some cases were more severe in 129/Sv. To further characterize the molecular changes in diseased mice, we conducted a quantitative proteomic analysis of pituitary proteins. This showed significantly lower levels of PRL, pro-opiomelanocortin (ACTH), and α-glycoprotein subunit proteins in Lhx3(W227ter/W227ter) mice. Together, these data show that hormone deficiency disease is apparent in early prenatal stages in this CPHD model system. Furthermore, as is noted in human disease, genetic background significantly impacts the phenotypic outcome of these monogenic endocrine diseases.

Developmental Analysis and Influence of Genetic Background on the Lhx3 W227ter Mouse Model of Combined Pituitary Hormone Deficiency Disease

PubMed Central

Prince, Kelly L.; Colvin, Stephanie C.; Park, Soyoung; Lai, Xianyin; Witzmann, Frank A.

2013-01-01

Combined pituitary hormone deficiency (CPHD) diseases result in severe outcomes for patients including short stature, developmental delays, and reproductive deficiencies. Little is known about their etiology, especially the developmental profiles and the influences of genetic background on disease progression. Animal models for CPHD provide valuable tools to investigate disease mechanisms and inform diagnostic and treatment protocols. Here we examined hormone production during pituitary development and the influence of genetic background on phenotypic severity in the Lhx3W227ter/W227ter mouse model. Lhx3W227ter/W227ter embryos have deficiencies of ACTH, α-glycoprotein subunit, GH, PRL, TSHβ, and LHβ during prenatal development. Furthermore, mutant mice have significant reduction in the critical pituitary transcriptional activator-1 (PIT1). Through breeding, the Lhx3W227ter/W227ter genotype was placed onto the 129/Sv and C57BL/6 backgrounds. Intriguingly, the genetic background significantly affected viability: whereas Lhx3W227ter/W227ter animals were found in the expected frequencies in C57BL/6, homozygous animals were not viable in the 129/Sv genetic environment. The hormone marker and PIT1 reductions observed in Lhx3W227ter/W227ter mice on a mixed background were also seen in the separate strains but in some cases were more severe in 129/Sv. To further characterize the molecular changes in diseased mice, we conducted a quantitative proteomic analysis of pituitary proteins. This showed significantly lower levels of PRL, pro-opiomelanocortin (ACTH), and α-glycoprotein subunit proteins in Lhx3W227ter/W227ter mice. Together, these data show that hormone deficiency disease is apparent in early prenatal stages in this CPHD model system. Furthermore, as is noted in human disease, genetic background significantly impacts the phenotypic outcome of these monogenic endocrine diseases. PMID:23288907

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.

Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

PubMed Central

Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun

2015-01-01

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. PMID:25802512

Progress toward generating a ferret model of cystic fibrosis by somatic cell nuclear transfer

PubMed Central

Li, Ziyi; Engelhardt, John F

2003-01-01

Mammalian cloning by nuclear transfer from somatic cells has created new opportunities to generate animal models of genetic diseases in species other than mice. Although genetic mouse models play a critical role in basic and applied research for numerous diseases, often mouse models do not adequately reproduce the human disease phenotype. Cystic fibrosis (CF) is one such disease. Targeted ablation of the cystic fibrosis transmembrane conductance regulator (CFTR) gene in mice does not adequately replicate spontaneous bacterial infections observed in the human CF lung. Hence, several laboratories are pursuing alternative animal models of CF in larger species such as the pig, sheep, rabbits, and ferrets. Our laboratory has focused on developing the ferret as a CF animal model. Over the past few years, we have investigated several experimental parameters required for gene targeting and nuclear transfer (NT) cloning in the ferret using somatic cells. In this review, we will discuss our progress and the hurdles to NT cloning and gene-targeting that accompany efforts to generate animal models of genetic diseases in species such as the ferret. PMID:14613541

Applications of genetic programming in cancer research.

PubMed

Worzel, William P; Yu, Jianjun; Almal, Arpit A; Chinnaiyan, Arul M

2009-02-01

The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.

Genetic biomarkers for brain hemisphere differentiation in Parkinson's Disease

NASA Astrophysics Data System (ADS)

Hourani, Mou'ath; Mendes, Alexandre; Berretta, Regina; Moscato, Pablo

2007-11-01

This work presents a study on the genetic profile of the left and right hemispheres of the brain of a mouse model of Parkinson's disease (PD). The goal is to characterize, in a genetic basis, PD as a disease that affects these two brain regions in different ways. Using the same whole-genome microarray expression data introduced by Brown et al. (2002) [1], we could find significant differences in the expression of some key genes, well-known to be involved in the mechanisms of dopamine production control and PD. The problem of selecting such genes was modeled as the MIN (α,β)—FEATURE SET problem [2]; a similar approach to that employed previously to find biomarkers for different types of cancer using gene expression microarray data [3]. The Feature Selection method produced a series of genetic signatures for PD, with distinct expression profiles in the Parkinson's model and control mice experiments. In addition, a close examination of the genes composing those signatures shows that many of them belong to genetic pathways or have ontology annotations considered to be involved in the onset and development of PD. Such elements could provide new clues on which mechanisms are implicated in hemisphere differentiation in PD.

Mapping Heart Development in Flies: Src42A Acts Non-Autonomously to Promote Heart Tube Formation in Drosophila

PubMed Central

Vanderploeg, Jessica; Jacobs, J. Roger

2017-01-01

Congenital heart defects, clinically identified in both small and large animals, are multifactorial and complex. Although heritable factors are known to have a role in cardiovascular disease, the full genetic aetiology remains unclear. Model organism research has proven valuable in providing a deeper understanding of the essential factors in heart development. For example, mouse knock-out studies reveal a role for the Integrin adhesion receptor in cardiac tissue. Recent research in Drosophila melanogaster (the fruit fly), a powerful experimental model, has demonstrated that the link between the extracellular matrix and the cell, mediated by Integrins, is required for multiple aspects of cardiogenesis. Here we test the hypothesis that Integrins signal to the heart cells through Src42A kinase. Using the powerful genetics and cell biology analysis possible in Drosophila, we demonstrate that Src42A acts in early events of heart tube development. Careful examination of mutant heart tissue and genetic interaction data suggests that Src42A’s role is independent of Integrin and the Integrin-related Focal Adhesion Kinase. Rather, Src42A acts non-autonomously by promoting programmed cell death of the amnioserosa, a transient tissue that neighbors the developing heart. PMID:29056682

A recombinant vesicular stomatitis virus-based Lassa fever vaccine protects guinea pigs and macaques against challenge with geographically and genetically distinct Lassa viruses.

PubMed

Safronetz, David; Mire, Chad; Rosenke, Kyle; Feldmann, Friederike; Haddock, Elaine; Geisbert, Thomas; Feldmann, Heinz

2015-04-01

Lassa virus (LASV) is endemic in several West African countries and is the etiological agent of Lassa fever. Despite the high annual incidence and significant morbidity and mortality rates, currently there are no approved vaccines to prevent infection or disease in humans. Genetically, LASV demonstrates a high degree of diversity that correlates with geographic distribution. The genetic heterogeneity observed between geographically distinct viruses raises concerns over the potential efficacy of a "universal" LASV vaccine. To date, several experimental LASV vaccines have been developed; however, few have been evaluated against challenge with various genetically unique Lassa virus isolates in relevant animal models. Here we demonstrate that a single, prophylactic immunization with a recombinant vesicular stomatitis virus (VSV) expressing the glycoproteins of LASV strain Josiah from Sierra Leone protects strain 13 guinea pigs from infection / disease following challenge with LASV isolates originating from Liberia, Mali and Nigeria. Similarly, the VSV-based LASV vaccine yields complete protection against a lethal challenge with the Liberian LASV isolate in the gold-standard macaque model of Lassa fever. Our results demonstrate the VSV-based LASV vaccine is capable of preventing morbidity and mortality associated with non-homologous LASV challenge in two animal models of Lassa fever. Additionally, this work highlights the need for the further development of disease models for geographical distinct LASV strains, particularly those from Nigeria, in order to comprehensively evaluate potential vaccines and therapies against this prominent agent of viral hemorrhagic fever.

Networking in autism: leveraging genetic, biomarker and model system findings in the search for new treatments.

PubMed

Veenstra-VanderWeele, Jeremy; Blakely, Randy D

2012-01-01

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder affecting approximately 1% of children. ASD is defined by core symptoms in two domains: negative symptoms of impairment in social and communication function, and positive symptoms of restricted and repetitive behaviors. Available treatments are inadequate for treating both core symptoms and associated conditions. Twin studies indicate that ASD susceptibility has a large heritable component. Genetic studies have identified promising leads, with converging insights emerging from single-gene disorders that bear ASD features, with particular interest in mammalian target of rapamycin (mTOR)-linked synaptic plasticity mechanisms. Mouse models of these disorders are revealing not only opportunities to model behavioral perturbations across species, but also evidence of postnatal rescue of brain and behavioral phenotypes. An intense search for ASD biomarkers has consistently pointed to elevated platelet serotonin (5-HT) levels and a surge in brain growth in the first 2 years of life. Following a review of the diversity of ASD phenotypes and its genetic origins and biomarkers, we discuss opportunities for translation of these findings into novel ASD treatments, focusing on mTor- and 5-HT-signaling pathways, and their possible intersection. Paralleling the progress made in understanding the root causes of rare genetic syndromes that affect cognitive development, we anticipate progress in models systems using bona fide ASD-associated molecular changes that have the potential to accelerate the development of ASD diagnostics and therapeutics.

Networking in Autism: Leveraging Genetic, Biomarker and Model System Findings in the Search for New Treatments

PubMed Central

Veenstra-VanderWeele, Jeremy; Blakely, Randy D

2012-01-01

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder affecting approximately 1% of children. ASD is defined by core symptoms in two domains: negative symptoms of impairment in social and communication function, and positive symptoms of restricted and repetitive behaviors. Available treatments are inadequate for treating both core symptoms and associated conditions. Twin studies indicate that ASD susceptibility has a large heritable component. Genetic studies have identified promising leads, with converging insights emerging from single-gene disorders that bear ASD features, with particular interest in mammalian target of rapamycin (mTOR)-linked synaptic plasticity mechanisms. Mouse models of these disorders are revealing not only opportunities to model behavioral perturbations across species, but also evidence of postnatal rescue of brain and behavioral phenotypes. An intense search for ASD biomarkers has consistently pointed to elevated platelet serotonin (5-HT) levels and a surge in brain growth in the first 2 years of life. Following a review of the diversity of ASD phenotypes and its genetic origins and biomarkers, we discuss opportunities for translation of these findings into novel ASD treatments, focusing on mTor- and 5-HT-signaling pathways, and their possible intersection. Paralleling the progress made in understanding the root causes of rare genetic syndromes that affect cognitive development, we anticipate progress in models systems using bona fide ASD-associated molecular changes that have the potential to accelerate the development of ASD diagnostics and therapeutics. PMID:21937981

Physiologically based pharmacokinetic model for 6-mercpatopurine: exploring the role of genetic polymorphism in TPMT enzyme activity

PubMed Central

Ogungbenro, Kayode; Aarons, Leon

2015-01-01

Aims To extend the physiologically based pharmacokinetic (PBPK) model developed for 6-mercaptopurine to account for intracellular metabolism and to explore the role of genetic polymorphism in the TPMT enzyme on the pharmacokinetics of 6-mercaptopurine. Methods The developed PBPK model was extended for 6-mercaptopurine to account for intracellular metabolism and genetic polymorphism in TPMT activity. System and drug specific parameters were obtained from the literature or estimated using plasma or intracellular red blood cell concentrations of 6-mercaptopurine and its metabolites. Age-dependent changes in parameters were implemented for scaling, and variability was also introduced for simulation. The model was validated using published data. Results The model was extended successfully. Parameter estimation and model predictions were satisfactory. Prediction of intracellular red blood cell concentrations of 6-thioguanine nucleotide for different TPMT phenotypes (in a clinical study that compared conventional and individualized dosing) showed results that were consistent with observed values and reported incidence of haematopoietic toxicity. Following conventional dosing, the predicted mean concentrations for homozygous and heterozygous variants, respectively, were about 10 times and two times the levels for wild-type. However, following individualized dosing, the mean concentration was around the same level for the three phenotypes despite different doses. Conclusions The developed PBPK model has been extended for 6-mercaptopurine and can be used to predict plasma 6-mercaptopurine and tissue concentration of 6-mercaptopurine, 6-thioguanine nucleotide and 6-methylmercaptopurine ribonucleotide in adults and children. Predictions of reported data from clinical studies showed satisfactory results. The model may help to improve 6-mercaptopurine dosing, achieve better clinical outcome and reduce toxicity. PMID:25614061

The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes.

PubMed

Shields, B M; McDonald, T J; Ellard, S; Campbell, M J; Hyde, C; Hattersley, A T

2012-05-01

Diagnosing MODY is difficult. To date, selection for molecular genetic testing for MODY has used discrete cut-offs of limited clinical characteristics with varying sensitivity and specificity. We aimed to use multiple, weighted, clinical criteria to determine an individual's probability of having MODY, as a crucial tool for rational genetic testing. We developed prediction models using logistic regression on data from 1,191 patients with MODY (n = 594), type 1 diabetes (n = 278) and type 2 diabetes (n = 319). Model performance was assessed by receiver operating characteristic (ROC) curves, cross-validation and validation in a further 350 patients. The models defined an overall probability of MODY using a weighted combination of the most discriminative characteristics. For MODY, compared with type 1 diabetes, these were: lower HbA(1c), parent with diabetes, female sex and older age at diagnosis. MODY was discriminated from type 2 diabetes by: lower BMI, younger age at diagnosis, female sex, lower HbA(1c), parent with diabetes, and not being treated with oral hypoglycaemic agents or insulin. Both models showed excellent discrimination (c-statistic = 0.95 and 0.98, respectively), low rates of cross-validated misclassification (9.2% and 5.3%), and good performance on the external test dataset (c-statistic = 0.95 and 0.94). Using the optimal cut-offs, the probability models improved the sensitivity (91% vs 72%) and specificity (94% vs 91%) for identifying MODY compared with standard criteria of diagnosis <25 years and an affected parent. The models are now available online at www.diabetesgenes.org . We have developed clinical prediction models that calculate an individual's probability of having MODY. This allows an improved and more rational approach to determine who should have molecular genetic testing.

Genetic and Environmental Influences on Pubertal Development: Longitudinal Data from Finnish Twins at Ages 11 and 14

ERIC Educational Resources Information Center

Mustanski, Brian S.; Viken, Richard J.; Kaprio, Jaakko; Pulkkinen, Lea; Rose, Richard J.

2004-01-01

To study sources of individual differences in pubertal development, the authors fit a sex-limitation common factor model to data reported, at ages 11 and 14 years, by 1,891 twin pairs on items that comprise the Pubertal Development Scale (PDS; A. C. Petersen, L. Crockett, M. Richards, & A. Boxer, 1988). The model divides variation into a general…

The Genetic Basis for Variation in Sensitivity to Lead Toxicity in Drosophila melanogaster.

PubMed

Zhou, Shanshan; Morozova, Tatiana V; Hussain, Yasmeen N; Luoma, Sarah E; McCoy, Lenovia; Yamamoto, Akihiko; Mackay, Trudy F C; Anholt, Robert R H

2016-07-01

Lead toxicity presents a worldwide health problem, especially due to its adverse effects on cognitive development in children. However, identifying genes that give rise to individual variation in susceptibility to lead toxicity is challenging in human populations. Our goal was to use Drosophila melanogaster to identify evolutionarily conserved candidate genes associated with individual variation in susceptibility to lead exposure. To identify candidate genes associated with variation in susceptibility to lead toxicity, we measured effects of lead exposure on development time, viability and adult activity in the Drosophila melanogaster Genetic Reference Panel (DGRP) and performed genome-wide association analyses to identify candidate genes. We used mutants to assess functional causality of candidate genes and constructed a genetic network associated with variation in sensitivity to lead exposure, on which we could superimpose human orthologs. We found substantial heritabilities for all three traits and identified candidate genes associated with variation in susceptibility to lead exposure for each phenotype. The genetic architectures that determine variation in sensitivity to lead exposure are highly polygenic. Gene ontology and network analyses showed enrichment of genes associated with early development and function of the nervous system. Drosophila melanogaster presents an advantageous model to study the genetic underpinnings of variation in susceptibility to lead toxicity. Evolutionary conservation of cellular pathways that respond to toxic exposure allows predictions regarding orthologous genes and pathways across phyla. Thus, studies in the D. melanogaster model system can identify candidate susceptibility genes to guide subsequent studies in human populations. Zhou S, Morozova TV, Hussain YN, Luoma SE, McCoy L, Yamamoto A, Mackay TF, Anholt RR. 2016. The genetic basis for variation in sensitivity to lead toxicity in Drosophila melanogaster. Environ Health Perspect 124:1062-1070; http://dx.doi.org/10.1289/ehp.1510513.

Use of intraspecific variation in thermal responses for estimating an elevational cline in the timing of cold hardening in a sub-boreal conifer.

PubMed

Ishizuka, W; Ono, K; Hara, T; Goto, S

2015-01-01

To avoid winter frost damage, evergreen coniferous species develop cold hardiness with suitable phenology for the local climate regime. Along the elevational gradient, a genetic cline in autumn phenology is often recognised among coniferous populations, but further quantification of evolutionary adaptation related to the local environment and its responsible signals generating the phenological variation are poorly understood. We evaluated the timing of cold hardening among populations of Abies sachalinensis, based on time series freezing tests using trees derived from four seed source populations × three planting sites. Furthermore, we constructed a model to estimate the development of hardening from field temperatures and the intraspecific variations occurring during this process. An elevational cline was detected such that high-elevation populations developed cold hardiness earlier than low-elevation populations, representing significant genetic control. Because development occurred earlier at high-elevation planting sites, the genetic trend across elevation overlapped with the environmental trend. Based on the trade-off between later hardening to lengthen the active growth period and earlier hardening to avoid frost damage, this genetic cline would be adaptive to the local climate. Our modelling approach estimated intraspecific variation in two model components: the threshold temperature, which was the criterion for determining whether the trees accumulated the thermal value, and the chilling requirement for trees to achieve adequate cold hardiness. A higher threshold temperature and a lower chilling requirement could be responsible for the earlier phenology of the high-elevation population. These thermal responses may be one of the important factors driving the elevation-dependent adaptation of A. sachalinensis. © 2014 German Botanical Society and The Royal Botanical Society of the Netherlands.

Functional linear models for association analysis of quantitative traits.

PubMed

Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

2013-11-01

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY PERIODICALS, INC.

Current Translational Research and Murine Models For Duchenne Muscular Dystrophy

PubMed Central

Rodrigues, Merryl; Echigoya, Yusuke; Fukada, So-ichiro; Yokota, Toshifumi

2016-01-01

Duchenne muscular dystrophy (DMD) is an X-linked genetic disorder characterized by progressive muscle degeneration. Mutations in the DMD gene result in the absence of dystrophin, a protein required for muscle strength and stability. Currently, there is no cure for DMD. Since murine models are relatively easy to genetically manipulate, cost effective, and easily reproducible due to their short generation time, they have helped to elucidate the pathobiology of dystrophin deficiency and to assess therapies for treating DMD. Recently, several murine models have been developed by our group and others to be more representative of the human DMD mutation types and phenotypes. For instance, mdx mice on a DBA/2 genetic background, developed by Fukada et al., have lower regenerative capacity and exhibit very severe phenotype. Cmah-deficient mdx mice display an accelerated disease onset and severe cardiac phenotype due to differences in glycosylation between humans and mice. Other novel murine models include mdx52, which harbors a deletion mutation in exon 52, a hot spot region in humans, and dystrophin/utrophin double-deficient (dko), which displays a severe dystrophic phenotype due the absence of utrophin, a dystrophin homolog. This paper reviews the pathological manifestations and recent therapeutic developments in murine models of DMD such as standard mdx (C57BL/10), mdx on C57BL/6 background (C57BL/6-mdx), mdx52, dystrophin/utrophin double-deficient (dko), mdxβgeo, Dmd-null, humanized DMD (hDMD), mdx on DBA/2 background (DBA/2-mdx), Cmah-mdx, and mdx/mTRKO murine models. PMID:27854202

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.

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.

Evolutionary Determinants of Genetic Variation in Susceptibility to Infectious Diseases in Humans

PubMed Central

Baker, Christi; Antonovics, Janis

2012-01-01

Although genetic variation among humans in their susceptibility to infectious diseases has long been appreciated, little focus has been devoted to identifying patterns in levels of variation in susceptibility to different diseases. Levels of genetic variation in susceptibility associated with 40 human infectious diseases were assessed by a survey of studies on both pedigree-based quantitative variation, as well as studies on different classes of marker alleles. These estimates were correlated with pathogen traits, epidemiological characteristics, and effectiveness of the human immune response. The strongest predictors of levels of genetic variation in susceptibility were disease characteristics negatively associated with immune effectiveness. High levels of genetic variation were associated with diseases with long infectious periods and for which vaccine development attempts have been unsuccessful. These findings are consistent with predictions based on theoretical models incorporating fitness costs associated with the different types of resistance mechanisms. An appreciation of these observed patterns will be a valuable tool in directing future research given that genetic variation in disease susceptibility has large implications for vaccine development and epidemiology. PMID:22242158

Genetic factors may play a prominent role in the development of coronary heart disease dependent on important environmental factors

PubMed Central

Song, C; Chang, Z; Magnusson, P K E; Ingelsson, E; Pedersen, N L

2014-01-01

Astract Song C, Chang Z, Magnusson PKE, Ingelsson E, Pedersen NL (Karolinska Institutet, Stockholm; Uppsala University, Uppsala; Sweden). Genetic factors may play a prominent role in the developmentofcoronary heart diseasedependenton important environmental factors. J InternMed2014; 275: 631–639. Objective The aim of the study was to examine whether various lifestyle factors modify genetic influences on coronary heart disease (CHD). Design The effect of lifestyle factors [including smoking, sedentary lifestyle, alcohol intake and body mass index (BMI)] on risk of CHD was evaluated via Cox regression models in a twin study of gene–environment interaction. Using structure equation modelling, we estimated genetic variance of CHD dependent on lifestyle factors. Subjects In total, 51 065 same-sex twins from 25 715 twin pairs born before 1958 and registered in the Swedish Twin Registry were eligible for this study. During the 40-year follow-up, 7264 incident CHD events were recorded. Results Smoking, sedentary lifestyle and above average BMI were significantly associated with increased CHD incidence. The heritability of CHD decreased with increasing age, as well as with increasing levels of BMI, in both men and women. Conclusions The difference in the genetic component of CHD as a function of BMI suggests that genetic factors may play a more prominent role for disease development in the absence of important environmental factors. Increased knowledge of gene–environment interactions will be important for a full understanding of the aetiology of CHD. PMID:24330166

Genetic Variation among Plasmodium vivax Isolates Adapted to Non-Human Primates and the Implication for Vaccine Development

PubMed Central

Ntumngia, Francis B.; McHenry, Amy M.; Barnwel, John W.; Cole-Tobian, Jennifer; King, Christopher L.; Adams, John H.

2009-01-01

Plasmodium vivax Duffy binding protein (DBP) is vital for parasite development, thereby making this molecule a good vaccine candidate. Preclinical development of a P. vivax vaccine often involves use of primate models prior to testing efficacy in humans, but primate isolates are poorly characterized. We analyzed the complete gene coding for the DBP in several P. vivax isolates that are used for experimental primate infections and compared these sequences with the Salvador I DBP isolate, which is being used for vaccine development. Our results affirm that primate-adapted isolates are genetically similar to P. vivax circulating in humans, but variability is greatest in the putative target of protective antibodies. In addition, some P. vivax isolates contain multiple genetically different clones. Testing a DBP vaccine may therefore be complicated by heterogeneity and diversity of the P. vivax isolates available for in vivo challenge. PMID:19190217

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

DTIC Science & Technology

by the principal investigators are being mined for ASD relevant biomarkers. Structural and (constrained) functional meta-analyses of previously...ASD and typically developing (TD) individuals. These regions-of-interest will be extended through additional functional meta-analyses, network models will be created, and these models will be applied to primary ASD data .

Ethnic diversity in the genetics of venous thromboembolism.

PubMed

Tang, Liang; Hu, Yu

2015-11-01

Genetic susceptibility is considered as a crucial factor for the development of venous thromboembolism (VTE). Epidemiologic and genetic studies have revealed clear disparities in the incidence of VTE and the distribution of genetic factors for VTE in populations stratified by ethnicity worldwide. While gain-of-function polymorphisms in the procoagulant genes are common inherited factors in European-origin populations, the most prevalent molecular basis for venous thrombosis in Asians is confirmed to be dysfunctional variants in the anticoagulant genes. With the breakthrough of genomic technologies, a set of novel common alleles and rare mutations associated with VTE have also been identified, in different ethnic groups. Several putative pathways contributing to the pathogenesis of thrombophilia in populations of African-ancestry are largely unknown, as current knowledge of hereditary and acquired risk factors do not fully explain the highest risk of VTE in Black groups. In-depth studies across diverse ethnic populations are needed to unravel the whole genetics of VTE, which will help developing individual risk prediction models and strategies to minimise VTE in all populations.

Building a genome engineering toolbox in nonmodel prokaryotic microbes.

PubMed

Freed, Emily; Fenster, Jacob; Smolinski, Sharon L; Walker, Julie; Henard, Calvin A; Gill, Ryan; Eckert, Carrie A

2018-05-11

The realization of a sustainable bioeconomy requires our ability to understand and engineer complex design principles for the development of platform organisms capable of efficient conversion of cheap and sustainable feedstocks (e.g., sunlight, CO 2 , and nonfood biomass) into biofuels and bioproducts at sufficient titers and costs. For model microbes, such as Escherichia coli, advances in DNA reading and writing technologies are driving the adoption of new paradigms for engineering biological systems. Unfortunately, microbes with properties of interest for the utilization of cheap and renewable feedstocks, such as photosynthesis, autotrophic growth, and cellulose degradation, have very few, if any, genetic tools for metabolic engineering. Therefore, it is important to develop "design rules" for building a genetic toolbox for novel microbes. Here, we present an overview of our current understanding of these rules for the genetic manipulation of prokaryotic microbes and the available genetic tools to expand our ability to genetically engineer nonmodel systems. © 2018 Wiley Periodicals, Inc.

The Genetic and Environmental Etiologies of Individual Differences in Early Reading Growth in Australia, the United States, and Scandinavia

PubMed Central

Christopher, Micaela E.; Hulslander, Jacqueline; Byrne, Brian; Samuelsson, Stefan; Keenan, Janice M.; Pennington, Bruce; DeFries, John C.; Wadsworth, Sally J.; Willcutt, Erik; Olson, Richard K.

2013-01-01

This first cross-country twin study of individual differences in reading growth from post-kindergarten to post-2nd grade analyzed data from 487 twin pairs from the United States, 267 pairs from Australia, and 280 pairs from Scandinavia. Data from two reading measures were fit to biometric latent growth models. Individual differences for the reading measures at post-kindergarten in the U.S. and Australia were due primarily to genetic influences, and to both genetic and shared environmental influences in Scandinavia. In contrast, individual differences in growth generally had large genetic influences in all countries. These results suggest that genetic influences are largely responsible for individual differences in early reading development. In addition, the timing of the start of formal literacy instruction may affect the etiology of individual differences in early reading development, but have only limited influence on the etiology of individual differences in growth. PMID:23665180

Developmental neurogenetics of sexual dimorphism in Aedes aegypti

PubMed Central

Duman-Scheel, Molly; Syed, Zainulabeuddin

2015-01-01

Sexual dimorphism, a poorly understood but crucial aspect of vector mosquito biology, encompasses sex-specific physical, physiological, and behavioral traits related to mosquito reproduction. The study of mosquito sexual dimorphism has largely focused on analysis of the differences between adult female and male mosquitoes, particularly with respect to sex-specific behaviors related to disease transmission. However, sexually dimorphic behaviors are the products of differential gene expression that initiates during development and therefore must also be studied during development. Recent technical advancements are facilitating functional genetic studies in the dengue vector Aedes aegypti, an emerging model for mosquito development. These methodologies, many of which could be extended to other non-model insect species, are facilitating analysis of the development of sexual dimorphism in neural tissues, particularly the olfactory system. These studies are providing insight into the neurodevelopmental genetic basis for sexual dimorphism in vector mosquitoes. PMID:26949699

How spatio-temporal habitat connectivity affects amphibian genetic structure.

PubMed

Watts, Alexander G; Schlichting, Peter E; Billerman, Shawn M; Jesmer, Brett R; Micheletti, Steven; Fortin, Marie-Josée; Funk, W Chris; Hapeman, Paul; Muths, Erin; Murphy, Melanie A

2015-01-01

Heterogeneous landscapes and fluctuating environmental conditions can affect species dispersal, population genetics, and genetic structure, yet understanding how biotic and abiotic factors affect population dynamics in a fluctuating environment is critical for species management. We evaluated how spatio-temporal habitat connectivity influences dispersal and genetic structure in a population of boreal chorus frogs (Pseudacris maculata) using a landscape genetics approach. We developed gravity models to assess the contribution of various factors to the observed genetic distance as a measure of functional connectivity. We selected (a) wetland (within-site) and (b) landscape matrix (between-site) characteristics; and (c) wetland connectivity metrics using a unique methodology. Specifically, we developed three networks that quantify wetland connectivity based on: (i) P. maculata dispersal ability, (ii) temporal variation in wetland quality, and (iii) contribution of wetland stepping-stones to frog dispersal. We examined 18 wetlands in Colorado, and quantified 12 microsatellite loci from 322 individual frogs. We found that genetic connectivity was related to topographic complexity, within- and between-wetland differences in moisture, and wetland functional connectivity as contributed by stepping-stone wetlands. Our results highlight the role that dynamic environmental factors have on dispersal-limited species and illustrate how complex asynchronous interactions contribute to the structure of spatially-explicit metapopulations.

Obstructive Sleep Apnea Syndrome: From Phenotype to Genetic Basis

PubMed Central

Casale, M; Pappacena, M; Rinaldi, V; Bressi, F; Baptista, P; Salvinelli, F

2009-01-01

Obstructive sleep apnea syndrome (OSAS) is a complex chronic clinical syndrome, characterized by snoring, periodic apnea, hypoxemia during sleep, and daytime hypersomnolence. It affects 4-5% of the general population. Racial studies and chromosomal mapping, familial studies and twin studies have provided evidence for the possible link between the OSAS and genetic factors and also most of the risk factors involved in the pathogenesis of OSAS are largely genetically determined. A percentage of 35-40% of its variance can be attributed to genetic factors. It is likely that genetic factors associated with craniofacial structure, body fat distribution and neural control of the upper airway muscles interact to produce the OSAS phenotype. Although the role of specific genes that influence the development of OSAS has not yet been identified, current researches, especially in animal model, suggest that several genetic systems may be important. In this chapter, we will first define the OSAS phenotype, the pathogenesis and the risk factors involved in the OSAS that may be inherited, then, we will review the current progress in the genetics of OSAS and suggest a few future perspectives in the development of therapeutic agents for this complex disease entity. PMID:19794884

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

PubMed

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

2018-03-01

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

CRISPR/Cas9 for Human Genome Engineering and Disease Research.

PubMed

Xiong, Xin; Chen, Meng; Lim, Wendell A; Zhao, Dehua; Qi, Lei S

2016-08-31

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated 9 (Cas9) system, a versatile RNA-guided DNA targeting platform, has been revolutionizing our ability to modify, manipulate, and visualize the human genome, which greatly advances both biological research and therapeutics development. Here, we review the current development of CRISPR/Cas9 technologies for gene editing, transcription regulation, genome imaging, and epigenetic modification. We discuss the broad application of this system to the study of functional genomics, especially genome-wide genetic screening, and to therapeutics development, including establishing disease models, correcting defective genetic mutations, and treating diseases.

Genetic and Genomic Toolbox of Zea mays

PubMed Central

Nannas, Natalie J.; Dawe, R. Kelly

2015-01-01

Maize has a long history of genetic and genomic tool development and is considered one of the most accessible higher plant systems. With a fully sequenced genome, a suite of cytogenetic tools, methods for both forward and reverse genetics, and characterized phenotype markers, maize is amenable to studying questions beyond plant biology. Major discoveries in the areas of transposons, imprinting, and chromosome biology came from work in maize. Moving forward in the post-genomic era, this classic model system will continue to be at the forefront of basic biological study. In this review, we outline the basics of working with maize and describe its rich genetic toolbox. PMID:25740912

Teaching and Learning Activity Sequencing System using Distributed Genetic Algorithms

NASA Astrophysics Data System (ADS)

Matsui, Tatsunori; Ishikawa, Tomotake; Okamoto, Toshio

The purpose of this study is development of a supporting system for teacher's design of lesson plan. Especially design of lesson plan which relates to the new subject "Information Study" is supported. In this study, we developed a system which generates teaching and learning activity sequences by interlinking lesson's activities corresponding to the various conditions according to the user's input. Because user's input is multiple information, there will be caused contradiction which the system should solve. This multiobjective optimization problem is resolved by Distributed Genetic Algorithms, in which some fitness functions are defined with reference models on lesson, thinking and teaching style. From results of various experiments, effectivity and validity of the proposed methods and reference models were verified; on the other hand, some future works on reference models and evaluation functions were also pointed out.

ECUT: Energy Conversion and Utilization Technologies program - Biocatalysis research activity

NASA Technical Reports Server (NTRS)

Wilcox, R.

1984-01-01

The activities of the Biocatalysis Research Activity are organized into the Biocatalysis and Molecular Modeling work elements and a supporting planning and analysis function. In the Biocatalysis work element, progress is made in developing a method for stabilizing genetically engineered traits in microorganisms, refining a technique for monitoring cells that are genetically engineered, and identifying strains of fungi for highly efficient preprocessing of biomass for optimizing the efficiency of bioreactors. In the Molecular Modeling work element, a preliminary model of the behavior of enzymes is developed. A preliminary investigation of the potential for synthesizing enzymes for use in electrochemical processes is completed. Contact with industry and universities is made to define key biocatalysis technical issues and to broaden the range of potential participants in the activity. Analyses are conducted to identify and evaluate potential concepts for future research funding.

A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks

PubMed Central

2009-01-01

Background The study of biological networks has led to the development of increasingly large and detailed models. Computer tools are essential for the simulation of the dynamical behavior of the networks from the model. However, as the size of the models grows, it becomes infeasible to manually verify the predictions against experimental data or identify interesting features in a large number of simulation traces. Formal verification based on temporal logic and model checking provides promising methods to automate and scale the analysis of the models. However, a framework that tightly integrates modeling and simulation tools with model checkers is currently missing, on both the conceptual and the implementational level. Results We have developed a generic and modular web service, based on a service-oriented architecture, for integrating the modeling and formal verification of genetic regulatory networks. The architecture has been implemented in the context of the qualitative modeling and simulation tool GNA and the model checkers NUSMV and CADP. GNA has been extended with a verification module for the specification and checking of biological properties. The verification module also allows the display and visual inspection of the verification results. Conclusions The practical use of the proposed web service is illustrated by means of a scenario involving the analysis of a qualitative model of the carbon starvation response in E. coli. The service-oriented architecture allows modelers to define the model and proceed with the specification and formal verification of the biological properties by means of a unified graphical user interface. This guarantees a transparent access to formal verification technology for modelers of genetic regulatory networks. PMID:20042075

Select Neuropeptides and their G-Protein Coupled Receptors in Caenorhabditis Elegans and Drosophila Melanogaster

PubMed Central

Bendena, William G.; Campbell, Jason; Zara, Lian; Tobe, Stephen S.; Chin-Sang, Ian D.

2012-01-01

The G-protein coupled receptor (GPCR) family is comprised of seven transmembrane domain proteins and play important roles in nerve transmission, locomotion, proliferation and development, sensory perception, metabolism, and neuromodulation. GPCR research has been targeted by drug developers as a consequence of the wide variety of critical physiological functions regulated by this protein family. Neuropeptide GPCRs are the least characterized of the GPCR family as genetic systems to characterize their functions have lagged behind GPCR gene discovery. Drosophila melanogaster and Caenorhabditis elegans are genetic model organisms that have proved useful in characterizing neuropeptide GPCRs. The strength of a genetic approach leads to an appreciation of the behavioral plasticity that can result from subtle alterations in GPCRs or regulatory proteins in the pathways that GPCRs control. Many of these invertebrate neuropeptides, GPCRs, and signaling pathway components serve as models for mammalian counterparts as they have conserved sequences and function. This review provides an overview of the methods to match neuropeptides to their cognate receptor and a state of the art account of neuropeptide GPCRs that have been characterized in D. melanogaster and C. elegans and the behaviors that have been uncovered through genetic manipulation. PMID:22908006

Resistance to genetic insect control: Modelling the effects of space.

PubMed

Watkinson-Powell, Benjamin; Alphey, Nina

2017-01-21

Genetic insect control, such as self-limiting RIDL 2 (Release of Insects Carrying a Dominant Lethal) technology, is a development of the sterile insect technique which is proposed to suppress wild populations of a number of major agricultural and public health insect pests. This is achieved by mass rearing and releasing male insects that are homozygous for a repressible dominant lethal genetic construct, which causes death in progeny when inherited. The released genetically engineered ('GE') insects compete for mates with wild individuals, resulting in population suppression. A previous study modelled the evolution of a hypothetical resistance to the lethal construct using a frequency-dependent population genetic and population dynamic approach. This found that proliferation of resistance is possible but can be diluted by the introgression of susceptible alleles from the released homozygous-susceptible GE males. We develop this approach within a spatial context by modelling the spread of a lethal construct and resistance trait, and the effect on population control, in a two deme metapopulation, with GE release in one deme. Results show that spatial effects can drive an increased or decreased evolution of resistance in both the target and non-target demes, depending on the effectiveness and associated costs of the resistant trait, and on the rate of dispersal. A recurrent theme is the potential for the non-target deme to act as a source of resistant or susceptible alleles for the target deme through dispersal. This can in turn have a major impact on the effectiveness of insect population control. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Teleosts as Model Organisms To Understand Host-Microbe Interactions.

PubMed

Lescak, Emily A; Milligan-Myhre, Kathryn C

2017-08-01

Host-microbe interactions are influenced by complex host genetics and environment. Studies across animal taxa have aided our understanding of how intestinal microbiota influence vertebrate development, disease, and physiology. However, traditional mammalian studies can be limited by the use of isogenic strains, husbandry constraints that result in small sample sizes and limited statistical power, reliance on indirect characterization of gut microbial communities from fecal samples, and concerns of whether observations in artificial conditions are actually reflective of what occurs in the wild. Fish models are able to overcome many of these limitations. The extensive variation in the physiology, ecology, and natural history of fish enriches studies of the evolution and ecology of host-microbe interactions. They share physiological and immunological features common among vertebrates, including humans, and harbor complex gut microbiota, which allows identification of the mechanisms driving microbial community assembly. Their accelerated life cycles and large clutch sizes and the ease of sampling both internal and external microbial communities make them particularly well suited for robust statistical studies of microbial diversity. Gnotobiotic techniques, genetic manipulation of the microbiota and host, and transparent juveniles enable novel insights into mechanisms underlying development of the digestive tract and disease states. Many diseases involve a complex combination of genes which are difficult to manipulate in homogeneous model organisms. By taking advantage of the natural genetic variation found in wild fish populations, as well as of the availability of powerful genetic tools, future studies should be able to identify conserved genes and pathways that contribute to human genetic diseases characterized by dysbiosis. Copyright © 2017 Lescak and Milligan-Myhre.

Teleosts as Model Organisms To Understand Host-Microbe Interactions

PubMed Central

2017-01-01

ABSTRACT Host-microbe interactions are influenced by complex host genetics and environment. Studies across animal taxa have aided our understanding of how intestinal microbiota influence vertebrate development, disease, and physiology. However, traditional mammalian studies can be limited by the use of isogenic strains, husbandry constraints that result in small sample sizes and limited statistical power, reliance on indirect characterization of gut microbial communities from fecal samples, and concerns of whether observations in artificial conditions are actually reflective of what occurs in the wild. Fish models are able to overcome many of these limitations. The extensive variation in the physiology, ecology, and natural history of fish enriches studies of the evolution and ecology of host-microbe interactions. They share physiological and immunological features common among vertebrates, including humans, and harbor complex gut microbiota, which allows identification of the mechanisms driving microbial community assembly. Their accelerated life cycles and large clutch sizes and the ease of sampling both internal and external microbial communities make them particularly well suited for robust statistical studies of microbial diversity. Gnotobiotic techniques, genetic manipulation of the microbiota and host, and transparent juveniles enable novel insights into mechanisms underlying development of the digestive tract and disease states. Many diseases involve a complex combination of genes which are difficult to manipulate in homogeneous model organisms. By taking advantage of the natural genetic variation found in wild fish populations, as well as of the availability of powerful genetic tools, future studies should be able to identify conserved genes and pathways that contribute to human genetic diseases characterized by dysbiosis. PMID:28439034

A review of vulnerability and risks for schizophrenia: Beyond the two hit hypothesis

PubMed Central

Davis, Justin; Eyre, Harris; Jacka, Felice N; Dodd, Seetal; Dean, Olivia; McEwen, Sarah; Debnath, Monojit; McGrath, John; Maes, Michael; Amminger, Paul; McGorry, Patrick D; Pantelis, Christos; Berk, Michael

2016-01-01

Schizophrenia risk has often been conceptualized using a model which requires two hits in order to generate the clinical phenotype—the first as an early priming in a genetically predisposed individual and the second a likely environmental insult. The aim of this paper was to review the literature and reformulate this binary risk-vulnerability model. We sourced the data for this narrative review from the electronic database PUBMED. Our search terms were not limited by language or date of publication. The development of schizophrenia may be driven by genetic vulnerability interacting with multiple vulnerability factors including lowered prenatal vitamin D exposure, viral infections, smoking intelligence quotient, social cognition cannabis use, social defeat, nutrition and childhood trauma. It is likely that these genetic risks, environmental risks and vulnerability factors are cumulative and interactive with each other and with critical periods of neurodevelopmental vulnerability. The development of schizophrenia is likely to be more complex and nuanced than the binary two hit model originally proposed nearly thirty years ago. Risk appears influenced by a more complex process involving genetic risk interfacing with multiple potentially interacting hits and vulnerability factors occurring at key periods of neurodevelopmental activity, which culminate in the expression of disease state. These risks are common across a number of neuropsychiatric and medical disorders, which might inform common preventive and intervention strategies across non-communicable disorders. PMID:27073049

A simplified fractional order impedance model and parameter identification method for lithium-ion batteries

PubMed Central

Yang, Qingxia; Xu, Jun; Cao, Binggang; Li, Xiuqing

2017-01-01

Identification of internal parameters of lithium-ion batteries is a useful tool to evaluate battery performance, and requires an effective model and algorithm. Based on the least square genetic algorithm, a simplified fractional order impedance model for lithium-ion batteries and the corresponding parameter identification method were developed. The simplified model was derived from the analysis of the electrochemical impedance spectroscopy data and the transient response of lithium-ion batteries with different states of charge. In order to identify the parameters of the model, an equivalent tracking system was established, and the method of least square genetic algorithm was applied using the time-domain test data. Experiments and computer simulations were carried out to verify the effectiveness and accuracy of the proposed model and parameter identification method. Compared with a second-order resistance-capacitance (2-RC) model and recursive least squares method, small tracing voltage fluctuations were observed. The maximum battery voltage tracing error for the proposed model and parameter identification method is within 0.5%; this demonstrates the good performance of the model and the efficiency of the least square genetic algorithm to estimate the internal parameters of lithium-ion batteries. PMID:28212405

A General Population Genetic Framework for Antagonistic Selection That Accounts for Demography and Recurrent Mutation

PubMed Central

Connallon, Tim; Clark, Andrew G.

2012-01-01

Antagonistic selection—where alleles at a locus have opposing effects on male and female fitness (“sexual antagonism”) or between components of fitness (“antagonistic pleiotropy”)—might play an important role in maintaining population genetic variation and in driving phylogenetic and genomic patterns of sexual dimorphism and life-history evolution. While prior theory has thoroughly characterized the conditions necessary for antagonistic balancing selection to operate, we currently know little about the evolutionary interactions between antagonistic selection, recurrent mutation, and genetic drift, which should collectively shape empirical patterns of genetic variation. To fill this void, we developed and analyzed a series of population genetic models that simultaneously incorporate these processes. Our models identify two general properties of antagonistically selected loci. First, antagonistic selection inflates heterozygosity and fitness variance across a broad parameter range—a result that applies to alleles maintained by balancing selection and by recurrent mutation. Second, effective population size and genetic drift profoundly affect the statistical frequency distributions of antagonistically selected alleles. The “efficacy” of antagonistic selection (i.e., its tendency to dominate over genetic drift) is extremely weak relative to classical models, such as directional selection and overdominance. Alleles meeting traditional criteria for strong selection (Nes >> 1, where Ne is the effective population size, and s is a selection coefficient for a given sex or fitness component) may nevertheless evolve as if neutral. The effects of mutation and demography may generate population differences in overall levels of antagonistic fitness variation, as well as molecular population genetic signatures of balancing selection. PMID:22298707

A general population genetic framework for antagonistic selection that accounts for demography and recurrent mutation.

PubMed

Connallon, Tim; Clark, Andrew G

2012-04-01

Antagonistic selection--where alleles at a locus have opposing effects on male and female fitness ("sexual antagonism") or between components of fitness ("antagonistic pleiotropy")--might play an important role in maintaining population genetic variation and in driving phylogenetic and genomic patterns of sexual dimorphism and life-history evolution. While prior theory has thoroughly characterized the conditions necessary for antagonistic balancing selection to operate, we currently know little about the evolutionary interactions between antagonistic selection, recurrent mutation, and genetic drift, which should collectively shape empirical patterns of genetic variation. To fill this void, we developed and analyzed a series of population genetic models that simultaneously incorporate these processes. Our models identify two general properties of antagonistically selected loci. First, antagonistic selection inflates heterozygosity and fitness variance across a broad parameter range--a result that applies to alleles maintained by balancing selection and by recurrent mutation. Second, effective population size and genetic drift profoundly affect the statistical frequency distributions of antagonistically selected alleles. The "efficacy" of antagonistic selection (i.e., its tendency to dominate over genetic drift) is extremely weak relative to classical models, such as directional selection and overdominance. Alleles meeting traditional criteria for strong selection (N(e)s > 1, where N(e) is the effective population size, and s is a selection coefficient for a given sex or fitness component) may nevertheless evolve as if neutral. The effects of mutation and demography may generate population differences in overall levels of antagonistic fitness variation, as well as molecular population genetic signatures of balancing selection.

Setaria viridis as a Model System to Advance Millet Genetics and Genomics

DOE PAGES

Huang, Pu; Shyu, Christine; Coelho, Carla P.; ...

2016-11-28

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Yet despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools andmore » resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail (Setaria viridis) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops.« less

Setaria viridis as a Model System to Advance Millet Genetics and Genomics

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

Huang, Pu; Shyu, Christine; Coelho, Carla P.

Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Yet despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools andmore » resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail (Setaria viridis) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops.« less

Label-free in vivo imaging of Drosophila melanogaster by multiphoton microscopy

NASA Astrophysics Data System (ADS)

Lin, Chiao-Ying; Hovhannisyan, Vladimir; Wu, June-Tai; Lin, Sung-Jan; Lin, Chii-Wann; Chen, Jyh-Horng; Dong, Chen-Yuan

2008-02-01

The fruit fly Drosophila melanogaster is one of the most valuable organisms in genetic and developmental biology studies. Drosophila is a small organism with a short life cycle, and is inexpensive and easy to maintain. The entire genome of Drosophila has recently been sequenced (cite the reference). These advantages make fruit fly an attractive model organism for biomedical researches. Unlike humans, Drosophila can be subjected to genetic manipulation with relative ease. Originally, Drosophila was mostly used in classical genetics studies. In the model era of molecular biology, the fruit fly has become a model organ for developmental biology researches. In the past, numerous molecularly modified mutants with well defined genetic defects affecting different aspects of the developmental processes have been identified and studied. However, traditionally, the developmental defects of the mutant flies are mostly examined in isolated fixed tissues which preclude the observation of the dynamic interaction of the different cell types and the extracellular matrix. Therefore, the ability to image different organelles of the fruit fly without extrinsic labeling is invaluable for Drosophila biology. In this work, we successfully acquire in vivo images of both developing muscles and axons of motor neurons in the three larval stages by using the minimially invasive imaging modality of multiphoton (SHG) microscopy. We found that while SHG imaging is useful in revealing the muscular architecture of the developing larva, it is the autofluorescence signal that allows label-free imaging of various organelles to be achieved. Our results demonstrate that multiphoton imaging is a powerful technique for investigation the development of Drosophila.

Finger Millet: A "Certain" Crop for an "Uncertain" Future and a Solution to Food Insecurity and Hidden Hunger under Stressful Environments.

PubMed

Gupta, Sanjay Mohan; Arora, Sandeep; Mirza, Neelofar; Pande, Anjali; Lata, Charu; Puranik, Swati; Kumar, J; Kumar, Anil

2017-01-01

Crop growth and productivity has largely been vulnerable to various abiotic and biotic stresses that are only set to be compounded due to global climate change. Therefore developing improved varieties and designing newer approaches for crop improvement against stress tolerance have become a priority now-a-days. However, most of the crop improvement strategies are directed toward staple cereals such as rice, wheat, maize etc., whereas attention on minor cereals such as finger millet [ Eleusine coracana (L.) Gaertn.] lags far behind. It is an important staple in several semi-arid and tropical regions of the world with excellent nutraceutical properties as well as ensuring food security in these areas even during harsh environment. This review highlights the importance of finger millet as a model nutraceutical crop. Progress and prospects in genetic manipulation for the development of abiotic and biotic stress tolerant varieties is also discussed. Although limited studies have been conducted for genetic improvement of finger millets, its nutritional significance in providing minerals, calories and protein makes it an ideal model for nutrition-agriculture research. Therefore, improved genetic manipulation of finger millets for resistance to both abiotic and biotic stresses, as well as for enhancing nutrient content will be very effective in millet improvement. Key message: Apart from the excellent nutraceutical value of finger millet, its ability to tolerate various abiotic stresses and resist pathogens make it an excellent model for exploring vast genetic and genomic potential of this crop, which provide us a wide choice for developing strategies for making climate resilient staple crops.

Studying the Brain in a Dish: 3D Cell Culture Models of Human Brain Development and Disease.

PubMed

Brown, Juliana; Quadrato, Giorgia; Arlotta, Paola

2018-01-01

The study of the cellular and molecular processes of the developing human brain has been hindered by access to suitable models of living human brain tissue. Recently developed 3D cell culture models offer the promise of studying fundamental brain processes in the context of human genetic background and species-specific developmental mechanisms. Here, we review the current state of 3D human brain organoid models and consider their potential to enable investigation of complex aspects of human brain development and the underpinning of human neurological disease. © 2018 Elsevier Inc. All rights reserved.

Continuity of Genetic and Environmental Influences on Cognition across the Life Span: A Meta-Analysis of Longitudinal Twin and Adoption Studies

PubMed Central

Tucker-Drob, Elliot M.; Briley, Daniel A.

2014-01-01

The longitudinal rank-order stability of cognitive ability increases dramatically over the lifespan. Multiple theoretical perspectives have proposed that genetic and/or environmental mechanisms underlie the longitudinal stability of cognition, and developmental trends therein. However, the patterns of stability of genetic and environmental influences on cognition over the lifespan largely remain poorly understood. We searched for longitudinal studies of cognition that reported raw genetically-informative longitudinal correlations or parameter estimates from longitudinal behavior genetic models. We identified 150 combinations of time points and measures from 15 independent longitudinal samples. In total, longitudinal data came from 4,538 monozygotic twin pairs raised together, 7,777 dizygotic twin pairs raised together, 34 monozygotic twin pairs raised apart, 78 dizygotic twin pairs raised apart, 141 adoptive sibling pairs, and 143 non-adoptive sibling pairs, ranging in age from infancy through late adulthood. At all ages, cross-time genetic correlations and shared environmental correlations were substantially larger than cross-time nonshared environmental correlations. Cross-time correlations for genetic and shared environmental components were low during early childhood, increased sharply over child development, and remained relatively high from adolescence through late adulthood. Cross-time correlations for nonshared environmental components were low across childhood and increased gradually to moderate magnitudes in adulthood. Increasing phenotypic stability over child development was almost entirely mediated by genetic factors. Time-based decay of genetic and shared environmental stability was more pronounced earlier in child development. Results are interpreted in reference to theories of gene-environment interaction and correlation. PMID:24611582

On construction of stochastic genetic networks based on gene expression sequences.

PubMed

Ching, Wai-Ki; Ng, Michael M; Fung, Eric S; Akutsu, Tatsuya

2005-08-01

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n2) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

A minimally sufficient model for rib proximal-distal patterning based on genetic analysis and agent-based simulations

PubMed Central

Mah, In Kyoung

2017-01-01

For decades, the mechanism of skeletal patterning along a proximal-distal axis has been an area of intense inquiry. Here, we examine the development of the ribs, simple structures that in most terrestrial vertebrates consist of two skeletal elements—a proximal bone and a distal cartilage portion. While the ribs have been shown to arise from the somites, little is known about how the two segments are specified. During our examination of genetically modified mice, we discovered a series of progressively worsening phenotypes that could not be easily explained. Here, we combine genetic analysis of rib development with agent-based simulations to conclude that proximal-distal patterning and outgrowth could occur based on simple rules. In our model, specification occurs during somite stages due to varying Hedgehog protein levels, while later expansion refines the pattern. This framework is broadly applicable for understanding the mechanisms of skeletal patterning along a proximal-distal axis. PMID:29068314

Expression of rabbit IL-4 by recombinant myxoma viruses enhances virulence and overcomes genetic resistance to myxomatosis.

PubMed

Kerr, P J; Perkins, H D; Inglis, B; Stagg, R; McLaughlin, E; Collins, S V; Van Leeuwen, B H

2004-06-20

Rabbit IL-4 was expressed in the virulent standard laboratory strain (SLS) and the attenuated Uriarra (Ur) strain of myxoma virus with the aim of creating a Th2 cytokine environment and inhibiting the development of an antiviral cell-mediated response to myxomatosis in infected rabbits. This allowed testing of a model for genetic resistance to myxomatosis in wild rabbits that have undergone 50 years of natural selection for resistance to myxomatosis. Expression of IL-4 significantly enhanced virulence of both virulent and attenuated virus strains in susceptible (laboratory) and resistant (wild) rabbits. SLS-IL-4 completely overcame genetic resistance in wild rabbits. The pathogenesis of SLS-IL-4 was compared in susceptible and resistant rabbits. The results support a model for resistance to myxomatosis of an enhanced innate immune response controlling virus replication and allowing an effective antiviral cell-mediated immune response to develop in resistant rabbits. Expression of IL-4 did not overcome immunity to myxomatosis induced by immunization.