Quantitative maps of genetic interactions in yeast - comparative evaluation and integrative analysis.

PubMed

Lindén, Rolf O; Eronen, Ville-Pekka; Aittokallio, Tero

2011-03-24

High-throughput genetic screening approaches have enabled systematic means to study how interactions among gene mutations contribute to quantitative fitness phenotypes, with the aim of providing insights into the functional wiring diagrams of genetic interaction networks on a global scale. However, it is poorly known how well these quantitative interaction measurements agree across the screening approaches, which hinders their integrated use toward improving the coverage and quality of the genetic interaction maps in yeast and other organisms. Using large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies. We have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.

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.

Quantitative genetics of disease traits.

PubMed

Wray, N R; Visscher, P M

2015-04-01

John James authored two key papers on the theory of risk to relatives for binary disease traits and the relationship between parameters on the observed binary scale and an unobserved scale of liability (James Annals of Human Genetics, 1971; 35: 47; Reich, James and Morris Annals of Human Genetics, 1972; 36: 163). These two papers are John James' most cited papers (198 and 328 citations, November 2014). They have been influential in human genetics and have recently gained renewed popularity because of their relevance to the estimation of quantitative genetics parameters for disease traits using SNP data. In this review, we summarize the two early papers and put them into context. We show recent extensions of the theory for ascertained case-control data and review recent applications in human genetics. © 2015 Blackwell Verlag GmbH.

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

Quantitative genetic versions of Hamilton's rule with empirical applications

PubMed Central

McGlothlin, Joel W.; Wolf, Jason B.; Brodie, Edmund D.; Moore, Allen J.

2014-01-01

Hamilton's theory of inclusive fitness revolutionized our understanding of the evolution of social interactions. Surprisingly, an incorporation of Hamilton's perspective into the quantitative genetic theory of phenotypic evolution has been slow, despite the popularity of quantitative genetics in evolutionary studies. Here, we discuss several versions of Hamilton's rule for social evolution from a quantitative genetic perspective, emphasizing its utility in empirical applications. Although evolutionary quantitative genetics offers methods to measure each of the critical parameters of Hamilton's rule, empirical work has lagged behind theory. In particular, we lack studies of selection on altruistic traits in the wild. Fitness costs and benefits of altruism can be estimated using a simple extension of phenotypic selection analysis that incorporates the traits of social interactants. We also discuss the importance of considering the genetic influence of the social environment, or indirect genetic effects (IGEs), in the context of Hamilton's rule. Research in social evolution has generated an extensive body of empirical work focusing—with good reason—almost solely on relatedness. We argue that quantifying the roles of social and non-social components of selection and IGEs, in addition to relatedness, is now timely and should provide unique additional insights into social evolution. PMID:24686930

Genomic Quantitative Genetics to Study Evolution in the Wild.

PubMed

Gienapp, Phillip; Fior, Simone; Guillaume, Frédéric; Lasky, Jesse R; Sork, Victoria L; Csilléry, Katalin

2017-12-01

Quantitative genetic theory provides a means of estimating the evolutionary potential of natural populations. However, this approach was previously only feasible in systems where the genetic relatedness between individuals could be inferred from pedigrees or experimental crosses. The genomic revolution opened up the possibility of obtaining the realized proportion of genome shared among individuals in natural populations of virtually any species, which could promise (more) accurate estimates of quantitative genetic parameters in virtually any species. Such a 'genomic' quantitative genetics approach relies on fewer assumptions, offers a greater methodological flexibility, and is thus expected to greatly enhance our understanding of evolution in natural populations, for example, in the context of adaptation to environmental change, eco-evolutionary dynamics, and biodiversity conservation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Universality and predictability in molecular quantitative genetics.

PubMed

Nourmohammad, Armita; Held, Torsten; Lässig, Michael

2013-12-01

Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait's genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology. Copyright © 2013. Published by Elsevier Ltd.

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

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

Quantitative genetic models of sexual conflict based on interacting phenotypes.

PubMed

Moore, Allen J; Pizzari, Tommaso

2005-05-01

Evolutionary conflict arises between reproductive partners when alternative reproductive opportunities are available. Sexual conflict can generate sexually antagonistic selection, which mediates sexual selection and intersexual coevolution. However, despite intense interest, the evolutionary implications of sexual conflict remain unresolved. We propose a novel theoretical approach to study the evolution of sexually antagonistic phenotypes based on quantitative genetics and the measure of social selection arising from male-female interactions. We consider the phenotype of one sex as both a genetically influenced evolving trait as well as the (evolving) social environment in which the phenotype of the opposite sex evolves. Several important points emerge from our analysis, including the relationship between direct selection on one sex and indirect effects through selection on the opposite sex. We suggest that the proposed approach may be a valuable tool to complement other theoretical approaches currently used to study sexual conflict. Most importantly, our approach highlights areas where additional empirical data can help clarify the role of sexual conflict in the evolutionary process.

Quantitative trait nucleotide analysis using Bayesian model selection.

PubMed

Blangero, John; Goring, Harald H H; Kent, Jack W; Williams, Jeff T; Peterson, Charles P; Almasy, Laura; Dyer, Thomas D

2005-10-01

Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis.

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

PubMed

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

2018-03-28

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

Quantitative characterization of genetic parts and circuits for plant synthetic biology.

PubMed

Schaumberg, Katherine A; Antunes, Mauricio S; Kassaw, Tessema K; Xu, Wenlong; Zalewski, Christopher S; Medford, June I; Prasad, Ashok

2016-01-01

Plant synthetic biology promises immense technological benefits, including the potential development of a sustainable bio-based economy through the predictive design of synthetic gene circuits. Such circuits are built from quantitatively characterized genetic parts; however, this characterization is a significant obstacle in work with plants because of the time required for stable transformation. We describe a method for rapid quantitative characterization of genetic plant parts using transient expression in protoplasts and dual luciferase outputs. We observed experimental variability in transient-expression assays and developed a mathematical model to describe, as well as statistical normalization methods to account for, this variability, which allowed us to extract quantitative parameters. We characterized >120 synthetic parts in Arabidopsis and validated our method by comparing transient expression with expression in stably transformed plants. We also tested >100 synthetic parts in sorghum (Sorghum bicolor) protoplasts, and the results showed that our method works in diverse plant groups. Our approach enables the construction of tunable gene circuits in complex eukaryotic organisms.

Can genetics help psychometrics? Improving dimensionality assessment through genetic factor modeling.

PubMed

Franić, Sanja; Dolan, Conor V; Borsboom, Denny; Hudziak, James J; van Beijsterveldt, Catherina E M; Boomsma, Dorret I

2013-09-01

In the present article, we discuss the role that quantitative genetic methodology may play in assessing and understanding the dimensionality of psychological (psychometric) instruments. Specifically, we study the relationship between the observed covariance structures, on the one hand, and the underlying genetic and environmental influences giving rise to such structures, on the other. We note that this relationship may be such that it hampers obtaining a clear estimate of dimensionality using standard tools for dimensionality assessment alone. One situation in which dimensionality assessment may be impeded is that in which genetic and environmental influences, of which the observed covariance structure is a function, differ from each other in structure and dimensionality. We demonstrate that in such situations settling dimensionality issues may be problematic, and propose using quantitative genetic modeling to uncover the (possibly different) dimensionalities of the underlying genetic and environmental structures. We illustrate using simulations and an empirical example on childhood internalizing problems.

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.

EvolQG - An R package for evolutionary quantitative genetics

PubMed Central

Melo, Diogo; Garcia, Guilherme; Hubbe, Alex; Assis, Ana Paula; Marroig, Gabriel

2016-01-01

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the \\textbf{EvolQG} package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification. PMID:27785352

Harnessing quantitative genetics and genomics for understanding and improving complex traits in crops

USDA-ARS?s Scientific Manuscript database

Classical quantitative genetics aids crop improvement by providing the means to estimate heritability, genetic correlations, and predicted responses to various selection schemes. Genomics has the potential to aid quantitative genetics and applied crop improvement programs via large-scale, high-thro...

Quantitative genetic analysis of injury liability in infants and toddlers

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

Phillips, K.; Matheny, A.P. Jr.

1995-02-27

A threshold model of latent liability was applied to infant and toddler twin data on total count of injuries sustained during the interval from birth to 36 months of age. A quantitative genetic analysis of estimated twin correlations in injury liability indicated strong genetic dominance effects, but no additive genetic variance was detected. Because interpretations involving overdominance have little research support, the results may be due to low order epistasis or other interaction effects. Boys had more injuries than girls, but this effect was found only for groups whose parents were prompted and questioned in detail about their children`s injuries.more » Activity and impulsivity are two behavioral predictors of childhood injury, and the results are discussed in relation to animal research on infant and adult activity levels, and impulsivity in adult humans. Genetic epidemiological approaches to childhood injury should aid in targeting higher risk children for preventive intervention. 30 refs., 4 figs., 3 tabs.« less

Breeding and quantitative genetics advances in sunflower Sclerotinia research

USDA-ARS?s Scientific Manuscript database

Genetic research of the sunflower research unit, USDA-ARS, in Fargo, ND, was discussed in a presentation to a group of producers, industry representatives, and scientists. The need for sunflower quantitative genetics research to find and capture Sclerotinia resistance is increasing with every year t...

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.

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

Using genetic markers to orient the edges in quantitative trait networks: the NEO software.

PubMed

Aten, Jason E; Fuller, Tova F; Lusis, Aldons J; Horvath, Steve

2008-04-15

Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers. We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue. The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait

Quantitative Genetics in the Era of Molecular Genetics: Learning Abilities and Disabilities as an Example

ERIC Educational Resources Information Center

Haworth, Claire M. A.; Plomin, Robert

2010-01-01

Objective: To consider recent findings from quantitative genetic research in the context of molecular genetic research, especially genome-wide association studies. We focus on findings that go beyond merely estimating heritability. We use learning abilities and disabilities as examples. Method: Recent twin research in the area of learning…

Laboratory evolution of the migratory polymorphism in the sand cricket: combining physiology with quantitative genetics.

PubMed

Roff, Derek A; Fairbairn, Daphne J

2007-01-01

Predicting evolutionary change is the central goal of evolutionary biology because it is the primary means by which we can test evolutionary hypotheses. In this article, we analyze the pattern of evolutionary change in a laboratory population of the wing-dimorphic sand cricket Gryllus firmus resulting from relaxation of selection favoring the migratory (long-winged) morph. Based on a well-characterized trade-off between fecundity and flight capability, we predict that evolution in the laboratory environment should result in a reduction in the proportion of long-winged morphs. We also predict increased fecundity and reduced functionality and weight of the major flight muscles in long-winged females but little change in short-winged (flightless) females. Based on quantitative genetic theory, we predict that the regression equation describing the trade-off between ovary weight and weight of the major flight muscles will show a change in its intercept but not in its slope. Comparisons across generations verify all of these predictions. Further, using values of genetic parameters estimated from previous studies, we show that a quantitative genetic simulation model can account for not only the qualitative changes but also the evolutionary trajectory. These results demonstrate the power of combining quantitative genetic and physiological approaches for understanding the evolution of complex traits.

QUANTITATIVE GENETIC ACTIVITY GRAPHICAL PROFILES FOR USE IN CHEMICAL EVALUATION

EPA Science Inventory

A graphic approach termed a Genetic Activity Profile (GAP) has been developed to display a matrix of data on the genetic and related effects of selected chemical agents. he profiles provide a visual overview of the quantitative (doses) and qualitative (test results) data for each...

The Quantitative Basis of the Arabidopsis Innate Immune System to Endemic Pathogens Depends on Pathogen Genetics

PubMed Central

Corwin, Jason A.; Copeland, Daniel; Feusier, Julie; Subedy, Anushriya; Eshbaugh, Robert; Palmer, Christine; Maloof, Julin; Kliebenstein, Daniel J.

2016-01-01

The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used the Arabidopsis-Botrytis pathosystem to explore the quantitative genetic architecture underlying host innate immune system in a population of Arabidopsis thaliana. By infecting a diverse panel of Arabidopsis accessions with four phenotypically and genotypically distinct isolates of the fungal necrotroph B. cinerea, we identified a total of 2,982 genes associated with quantitative resistance using lesion area and 3,354 genes associated with camalexin production as measures of the interaction. Most genes were associated with resistance to a specific Botrytis isolate, which demonstrates the influence of pathogen genetic variation in analyzing host quantitative resistance. While known resistance genes, such as receptor-like kinases (RLKs) and nucleotide-binding site leucine-rich repeat proteins (NLRs), were found to be enriched among associated genes, they only account for a small fraction of the total genes associated with quantitative resistance. Using publically available co-expression data, we condensed the quantitative resistance associated genes into co-expressed gene networks. GO analysis of these networks implicated several biological processes commonly connected to disease resistance, including defense hormone signaling and ROS production, as well as novel processes, such as leaf development. Validation of single gene T-DNA knockouts in a Col-0 background demonstrate a high success rate (60%) when accounting for differences in environmental and Botrytis genetic variation. This study shows that the genetic architecture underlying host innate immune system is extremely complex and is likely able to sense and respond to differential virulence among pathogen genotypes. PMID:26866607

The Quantitative Basis of the Arabidopsis Innate Immune System to Endemic Pathogens Depends on Pathogen Genetics.

PubMed

Corwin, Jason A; Copeland, Daniel; Feusier, Julie; Subedy, Anushriya; Eshbaugh, Robert; Palmer, Christine; Maloof, Julin; Kliebenstein, Daniel J

2016-02-01

The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used the Arabidopsis-Botrytis pathosystem to explore the quantitative genetic architecture underlying host innate immune system in a population of Arabidopsis thaliana. By infecting a diverse panel of Arabidopsis accessions with four phenotypically and genotypically distinct isolates of the fungal necrotroph B. cinerea, we identified a total of 2,982 genes associated with quantitative resistance using lesion area and 3,354 genes associated with camalexin production as measures of the interaction. Most genes were associated with resistance to a specific Botrytis isolate, which demonstrates the influence of pathogen genetic variation in analyzing host quantitative resistance. While known resistance genes, such as receptor-like kinases (RLKs) and nucleotide-binding site leucine-rich repeat proteins (NLRs), were found to be enriched among associated genes, they only account for a small fraction of the total genes associated with quantitative resistance. Using publically available co-expression data, we condensed the quantitative resistance associated genes into co-expressed gene networks. GO analysis of these networks implicated several biological processes commonly connected to disease resistance, including defense hormone signaling and ROS production, as well as novel processes, such as leaf development. Validation of single gene T-DNA knockouts in a Col-0 background demonstrate a high success rate (60%) when accounting for differences in environmental and Botrytis genetic variation. This study shows that the genetic architecture underlying host innate immune system is extremely complex and is likely able to sense and respond to differential virulence among pathogen genotypes.

Genetic toxicology at the crossroads-from qualitative hazard evaluation to quantitative risk assessment.

PubMed

White, Paul A; Johnson, George E

2016-05-01

Applied genetic toxicology is undergoing a transition from qualitative hazard identification to quantitative dose-response analysis and risk assessment. To facilitate this change, the Health and Environmental Sciences Institute (HESI) Genetic Toxicology Technical Committee (GTTC) sponsored a workshop held in Lancaster, UK on July 10-11, 2014. The event included invited speakers from several institutions and the contents was divided into three themes-1: Point-of-departure Metrics for Quantitative Dose-Response Analysis in Genetic Toxicology; 2: Measurement and Estimation of Exposures for Better Extrapolation to Humans and 3: The Use of Quantitative Approaches in Genetic Toxicology for human health risk assessment (HHRA). A host of pertinent issues were discussed relating to the use of in vitro and in vivo dose-response data, the development of methods for in vitro to in vivo extrapolation and approaches to use in vivo dose-response data to determine human exposure limits for regulatory evaluations and decision-making. This Special Issue, which was inspired by the workshop, contains a series of papers that collectively address topics related to the aforementioned themes. The Issue includes contributions that collectively evaluate, describe and discuss in silico, in vitro, in vivo and statistical approaches that are facilitating the shift from qualitative hazard evaluation to quantitative risk assessment. The use and application of the benchmark dose approach was a central theme in many of the workshop presentations and discussions, and the Special Issue includes several contributions that outline novel applications for the analysis and interpretation of genetic toxicity data. Although the contents of the Special Issue constitutes an important step towards the adoption of quantitative methods for regulatory assessment of genetic toxicity, formal acceptance of quantitative methods for HHRA and regulatory decision-making will require consensus regarding the

Genetic Architectures of Quantitative Variation in RNA Editing Pathways

PubMed Central

Gu, Tongjun; Gatti, Daniel M.; Srivastava, Anuj; Snyder, Elizabeth M.; Raghupathy, Narayanan; Simecek, Petr; Svenson, Karen L.; Dotu, Ivan; Chuang, Jeffrey H.; Keller, Mark P.; Attie, Alan D.; Braun, Robert E.; Churchill, Gary A.

2016-01-01

RNA editing refers to post-transcriptional processes that alter the base sequence of RNA. Recently, hundreds of new RNA editing targets have been reported. However, the mechanisms that determine the specificity and degree of editing are not well understood. We examined quantitative variation of site-specific editing in a genetically diverse multiparent population, Diversity Outbred mice, and mapped polymorphic loci that alter editing ratios globally for C-to-U editing and at specific sites for A-to-I editing. An allelic series in the C-to-U editing enzyme Apobec1 influences the editing efficiency of Apob and 58 additional C-to-U editing targets. We identified 49 A-to-I editing sites with polymorphisms in the edited transcript that alter editing efficiency. In contrast to the shared genetic control of C-to-U editing, most of the variable A-to-I editing sites were determined by local nucleotide polymorphisms in proximity to the editing site in the RNA secondary structure. Our results indicate that RNA editing is a quantitative trait subject to genetic variation and that evolutionary constraints have given rise to distinct genetic architectures in the two canonical types of RNA editing. PMID:26614740

Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture

PubMed Central

Monir, Md. Mamun; Zhu, Jun

2017-01-01

Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits. PMID:28079101

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

Genetic Complexity and Quantitative Trait Loci Mapping of Yeast Morphological Traits

PubMed Central

Nogami, Satoru; Ohya, Yoshikazu; Yvert, Gaël

2007-01-01

Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes. PMID:17319748

Statistical genetics and evolution of quantitative traits

NASA Astrophysics Data System (ADS)

Neher, Richard A.; Shraiman, Boris I.

2011-10-01

The distribution and heritability of many traits depends on numerous loci in the genome. In general, the astronomical number of possible genotypes makes the system with large numbers of loci difficult to describe. Multilocus evolution, however, greatly simplifies in the limit of weak selection and frequent recombination. In this limit, populations rapidly reach quasilinkage equilibrium (QLE) in which the dynamics of the full genotype distribution, including correlations between alleles at different loci, can be parametrized by the allele frequencies. This review provides a simplified exposition of the concept and mathematics of QLE which is central to the statistical description of genotypes in sexual populations. Key results of quantitative genetics such as the generalized Fisher’s “fundamental theorem,” along with Wright’s adaptive landscape, are shown to emerge within QLE from the dynamics of the genotype distribution. This is followed by a discussion under what circumstances QLE is applicable, and what the breakdown of QLE implies for the population structure and the dynamics of selection. Understanding the fundamental aspects of multilocus evolution obtained through simplified models may be helpful in providing conceptual and computational tools to address the challenges arising in the studies of complex quantitative phenotypes of practical interest.

A population genetic interpretation of GWAS findings for human quantitative traits

PubMed Central

Bullaughey, Kevin; Hudson, Richard R.; Sella, Guy

2018-01-01

Human genome-wide association studies (GWASs) are revealing the genetic architecture of anthropomorphic and biomedical traits, i.e., the frequencies and effect sizes of variants that contribute to heritable variation in a trait. To interpret these findings, we need to understand how genetic architecture is shaped by basic population genetics processes—notably, by mutation, natural selection, and genetic drift. Because many quantitative traits are subject to stabilizing selection and because genetic variation that affects one trait often affects many others, we model the genetic architecture of a focal trait that arises under stabilizing selection in a multidimensional trait space. We solve the model for the phenotypic distribution and allelic dynamics at steady state and derive robust, closed-form solutions for summary statistics of the genetic architecture. Our results provide a simple interpretation for missing heritability and why it varies among traits. They predict that the distribution of variances contributed by loci identified in GWASs is well approximated by a simple functional form that depends on a single parameter: the expected contribution to genetic variance of a strongly selected site affecting the trait. We test this prediction against the results of GWASs for height and body mass index (BMI) and find that it fits the data well, allowing us to make inferences about the degree of pleiotropy and mutational target size for these traits. Our findings help to explain why the GWAS for height explains more of the heritable variance than the similarly sized GWAS for BMI and to predict the increase in explained heritability with study sample size. Considering the demographic history of European populations, in which these GWASs were performed, we further find that most of the associations they identified likely involve mutations that arose shortly before or during the Out-of-Africa bottleneck at sites with selection coefficients around s = 10−3. PMID

Quantitative genetics of immunity and life history under different photoperiods.

PubMed

Hammerschmidt, K; Deines, P; Wilson, A J; Rolff, J

2012-05-01

Insects with complex life-cycles should optimize age and size at maturity during larval development. When inhabiting seasonal environments, organisms have limited reproductive periods and face fundamental decisions: individuals that reach maturity late in season have to either reproduce at a small size or increase their growth rates. Increasing growth rates is costly in insects because of higher juvenile mortality, decreased adult survival or increased susceptibility to parasitism by bacteria and viruses via compromised immune function. Environmental changes such as seasonality can also alter the quantitative genetic architecture. Here, we explore the quantitative genetics of life history and immunity traits under two experimentally induced seasonal environments in the cricket Gryllus bimaculatus. Seasonality affected the life history but not the immune phenotypes. Individuals under decreasing day length developed slower and grew to a bigger size. We found ample additive genetic variance and heritability for components of immunity (haemocyte densities, proPhenoloxidase activity, resistance against Serratia marcescens), and for the life history traits, age and size at maturity. Despite genetic covariance among traits, the structure of G was inconsistent with genetically based trade-off between life history and immune traits (for example, a strong positive genetic correlation between growth rate and haemocyte density was estimated). However, conditional evolvabilities support the idea that genetic covariance structure limits the capacity of individual traits to evolve independently. We found no evidence for G × E interactions arising from the experimentally induced seasonality.

Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure

PubMed Central

Yee, Jaeyong; Kwon, Min-Seok; Park, Taesung; Park, Mira

2015-01-01

A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits. However, many phenotype measures are intrinsically quantitative and categorizing continuous traits may not always be straightforward and meaningful. Association of gene-gene interactions with an observed distribution of such phenotypes needs to be investigated directly without categorization. Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits. We extend the usefulness of this information gain by proposing a nonparametric evaluation method of conditional entropy of a quantitative phenotype associated with a given genotype. Hence, the information gain can be obtained for any phenotype distribution. Because any functional form, such as Gaussian, is not assumed for the entire distribution of a trait or a given genotype, this method is expected to be robust enough to be applied to any phenotypic association data. Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait. PMID:26339620

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

PubMed

Fan, Ruzong; Wang, Yifan; Boehnke, Michael; Chen, Wei; Li, Yun; Ren, Haobo; Lobach, Iryna; Xiong, Momiao

2015-08-01

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies. Copyright © 2015 by the Genetics Society of America.

Quantitative genetic tools for insecticide resistance risk assessment: estimating the heritability of resistance

Treesearch

Michael J. Firko; Jane Leslie Hayes

1990-01-01

Quantitative genetic studies of resistance can provide estimates of genetic parameters not available with other types of genetic analyses. Three methods are discussed for estimating the amount of additive genetic variation in resistance to individual insecticides and subsequent estimation of heritability (h2) of resistance. Sibling analysis and...

Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

PubMed

Morgante, Fabio; Huang, Wen; Maltecca, Christian; Mackay, Trudy F C

2018-06-01

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

Metabolite profiling and quantitative genetics of natural variation for flavonoids in Arabidopsis

PubMed Central

Routaboul, Jean-Marc; Dubos, Christian; Beck, Gilles; Marquis, Catherine; Bidzinski, Przemyslaw; Loudet, Olivier; Lepiniec, Loïc

2012-01-01

Little is known about the range and the genetic bases of naturally occurring variation for flavonoids. Using Arabidopsis thaliana seed as a model, the flavonoid content of 41 accessions and two recombinant inbred line (RIL) sets derived from divergent accessions (Cvi-0×Col-0 and Bay-0×Shahdara) were analysed. These accessions and RILs showed mainly quantitative rather than qualitative changes. To dissect the genetic architecture underlying these differences, a quantitative trait locus (QTL) analysis was performed on the two segregating populations. Twenty-two flavonoid QTLs were detected that accounted for 11–64% of the observed trait variations, only one QTL being common to both RIL sets. Sixteen of these QTLs were confirmed and coarsely mapped using heterogeneous inbred families (HIFs). Three genes, namely TRANSPARENT TESTA (TT)7, TT15, and MYB12, were proposed to underlie their variations since the corresponding mutants and QTLs displayed similar specific flavonoid changes. Interestingly, most loci did not co-localize with any gene known to be involved in flavonoid metabolism. This latter result shows that novel functions have yet to be characterized and paves the way for their isolation. PMID:22442426

Genetic and environmental determinants of violence risk in psychotic disorders: a multivariate quantitative genetic study of 1.8 million Swedish twins and siblings

PubMed Central

Sariaslan, A; Larsson, H; Fazel, S

2016-01-01

Patients diagnosed with psychotic disorders (for example, schizophrenia and bipolar disorder) have elevated risks of committing violent acts, particularly if they are comorbid with substance misuse. Despite recent insights from quantitative and molecular genetic studies demonstrating considerable pleiotropy in the genetic architecture of these phenotypes, there is currently a lack of large-scale studies that have specifically examined the aetiological links between psychotic disorders and violence. Using a sample of all Swedish individuals born between 1958 and 1989 (n=3 332 101), we identified a total of 923 259 twin-sibling pairs. Patients were identified using the National Patient Register using validated algorithms based on International Classification of Diseases (ICD) 8–10. Univariate quantitative genetic models revealed that all phenotypes (schizophrenia, bipolar disorder, substance misuse, and violent crime) were highly heritable (h2=53–71%). Multivariate models further revealed that schizophrenia was a stronger predictor of violence (r=0.32; 95% confidence interval: 0.30–0.33) than bipolar disorder (r=0.23; 0.21–0.25), and large proportions (51–67%) of these phenotypic correlations were explained by genetic factors shared between each disorder, substance misuse, and violence. Importantly, we found that genetic influences that were unrelated to substance misuse explained approximately a fifth (21% 20–22%) of the correlation with violent criminality in bipolar disorder but none of the same correlation in schizophrenia (Pbipolar disorder<0.001; Pschizophrenia=0.55). These findings highlight the problems of not disentangling common and unique sources of covariance across genetically similar phenotypes as the latter sources may include aetiologically important clues. Clinically, these findings underline the importance of assessing risk of different phenotypes together and integrating interventions for psychiatric disorders, substance misuse, and

Genetic and environmental determinants of violence risk in psychotic disorders: a multivariate quantitative genetic study of 1.8 million Swedish twins and siblings.

PubMed

Sariaslan, A; Larsson, H; Fazel, S

2016-09-01

Patients diagnosed with psychotic disorders (for example, schizophrenia and bipolar disorder) have elevated risks of committing violent acts, particularly if they are comorbid with substance misuse. Despite recent insights from quantitative and molecular genetic studies demonstrating considerable pleiotropy in the genetic architecture of these phenotypes, there is currently a lack of large-scale studies that have specifically examined the aetiological links between psychotic disorders and violence. Using a sample of all Swedish individuals born between 1958 and 1989 (n=3 332 101), we identified a total of 923 259 twin-sibling pairs. Patients were identified using the National Patient Register using validated algorithms based on International Classification of Diseases (ICD) 8-10. Univariate quantitative genetic models revealed that all phenotypes (schizophrenia, bipolar disorder, substance misuse, and violent crime) were highly heritable (h(2)=53-71%). Multivariate models further revealed that schizophrenia was a stronger predictor of violence (r=0.32; 95% confidence interval: 0.30-0.33) than bipolar disorder (r=0.23; 0.21-0.25), and large proportions (51-67%) of these phenotypic correlations were explained by genetic factors shared between each disorder, substance misuse, and violence. Importantly, we found that genetic influences that were unrelated to substance misuse explained approximately a fifth (21%; 20-22%) of the correlation with violent criminality in bipolar disorder but none of the same correlation in schizophrenia (Pbipolar disorder<0.001; Pschizophrenia=0.55). These findings highlight the problems of not disentangling common and unique sources of covariance across genetically similar phenotypes as the latter sources may include aetiologically important clues. Clinically, these findings underline the importance of assessing risk of different phenotypes together and integrating interventions for psychiatric disorders, substance misuse, and violence.

Reinventing the ames test as a quantitative lab that connects classical and molecular genetics.

PubMed

Goodson-Gregg, Nathan; De Stasio, Elizabeth A

2009-01-01

While many institutions use a version of the Ames test in the undergraduate genetics laboratory, students typically are not exposed to techniques or procedures beyond qualitative analysis of phenotypic reversion, thereby seriously limiting the scope of learning. We have extended the Ames test to include both quantitative analysis of reversion frequency and molecular analysis of revertant gene sequences. By giving students a role in designing their quantitative methods and analyses, students practice and apply quantitative skills. To help students connect classical and molecular genetic concepts and techniques, we report here procedures for characterizing the molecular lesions that confer a revertant phenotype. We suggest undertaking reversion of both missense and frameshift mutants to allow a more sophisticated molecular genetic analysis. These modifications and additions broaden the educational content of the traditional Ames test teaching laboratory, while simultaneously enhancing students' skills in experimental design, quantitative analysis, and data interpretation.

Eco-genetic modeling of contemporary life-history evolution.

PubMed

Dunlop, Erin S; Heino, Mikko; Dieckmann, Ulf

2009-10-01

We present eco-genetic modeling as a flexible tool for exploring the course and rates of multi-trait life-history evolution in natural populations. We build on existing modeling approaches by combining features that facilitate studying the ecological and evolutionary dynamics of realistically structured populations. In particular, the joint consideration of age and size structure enables the analysis of phenotypically plastic populations with more than a single growth trajectory, and ecological feedback is readily included in the form of density dependence and frequency dependence. Stochasticity and life-history trade-offs can also be implemented. Critically, eco-genetic models permit the incorporation of salient genetic detail such as a population's genetic variances and covariances and the corresponding heritabilities, as well as the probabilistic inheritance and phenotypic expression of quantitative traits. These inclusions are crucial for predicting rates of evolutionary change on both contemporary and longer timescales. An eco-genetic model can be tightly coupled with empirical data and therefore may have considerable practical relevance, in terms of generating testable predictions and evaluating alternative management measures. To illustrate the utility of these models, we present as an example an eco-genetic model used to study harvest-induced evolution of multiple traits in Atlantic cod. The predictions of our model (most notably that harvesting induces a genetic reduction in age and size at maturation, an increase or decrease in growth capacity depending on the minimum-length limit, and an increase in reproductive investment) are corroborated by patterns observed in wild populations. The predicted genetic changes occur together with plastic changes that could phenotypically mask the former. Importantly, our analysis predicts that evolutionary changes show little signs of reversal following a harvest moratorium. This illustrates how predictions offered by

An assessment of the reliability of quantitative genetics estimates in study systems with high rate of extra-pair reproduction and low recruitment.

PubMed

Bourret, A; Garant, D

2017-03-01

Quantitative genetics approaches, and particularly animal models, are widely used to assess the genetic (co)variance of key fitness related traits and infer adaptive potential of wild populations. Despite the importance of precision and accuracy of genetic variance estimates and their potential sensitivity to various ecological and population specific factors, their reliability is rarely tested explicitly. Here, we used simulations and empirical data collected from an 11-year study on tree swallow (Tachycineta bicolor), a species showing a high rate of extra-pair paternity and a low recruitment rate, to assess the importance of identity errors, structure and size of the pedigree on quantitative genetic estimates in our dataset. Our simulations revealed an important lack of precision in heritability and genetic-correlation estimates for most traits, a low power to detect significant effects and important identifiability problems. We also observed a large bias in heritability estimates when using the social pedigree instead of the genetic one (deflated heritabilities) or when not accounting for an important cause of resemblance among individuals (for example, permanent environment or brood effect) in model parameterizations for some traits (inflated heritabilities). We discuss the causes underlying the low reliability observed here and why they are also likely to occur in other study systems. Altogether, our results re-emphasize the difficulties of generalizing quantitative genetic estimates reliably from one study system to another and the importance of reporting simulation analyses to evaluate these important issues.

Genetic background effects in quantitative genetics: gene-by-system interactions.

PubMed

Sardi, Maria; Gasch, Audrey P

2018-04-11

Proper cell function depends on networks of proteins that interact physically and functionally to carry out physiological processes. Thus, it seems logical that the impact of sequence variation in one protein could be significantly influenced by genetic variants at other loci in a genome. Nonetheless, the importance of such genetic interactions, known as epistasis, in explaining phenotypic variation remains a matter of debate in genetics. Recent work from our lab revealed that genes implicated from an association study of toxin tolerance in Saccharomyces cerevisiae show extensive interactions with the genetic background: most implicated genes, regardless of allele, are important for toxin tolerance in only one of two tested strains. The prevalence of background effects in our study adds to other reports of widespread genetic-background interactions in model organisms. We suggest that these effects represent many-way interactions with myriad features of the cellular system that vary across classes of individuals. Such gene-by-system interactions may influence diverse traits and require new modeling approaches to accurately represent genotype-phenotype relationships across individuals.

Genetic Variants Associated With Quantitative Glucose Homeostasis Traits Translate to Type 2 Diabetes in Mexican Americans: The GUARDIAN (Genetics Underlying Diabetes in Hispanics) Consortium.

PubMed

Palmer, Nicholette D; Goodarzi, Mark O; Langefeld, Carl D; Wang, Nan; Guo, Xiuqing; Taylor, Kent D; Fingerlin, Tasha E; Norris, Jill M; Buchanan, Thomas A; Xiang, Anny H; Haritunians, Talin; Ziegler, Julie T; Williams, Adrienne H; Stefanovski, Darko; Cui, Jinrui; Mackay, Adrienne W; Henkin, Leora F; Bergman, Richard N; Gao, Xiaoyi; Gauderman, James; Varma, Rohit; Hanis, Craig L; Cox, Nancy J; Highland, Heather M; Below, Jennifer E; Williams, Amy L; Burtt, Noel P; Aguilar-Salinas, Carlos A; Huerta-Chagoya, Alicia; Gonzalez-Villalpando, Clicerio; Orozco, Lorena; Haiman, Christopher A; Tsai, Michael Y; Johnson, W Craig; Yao, Jie; Rasmussen-Torvik, Laura; Pankow, James; Snively, Beverly; Jackson, Rebecca D; Liu, Simin; Nadler, Jerry L; Kandeel, Fouad; Chen, Yii-Der I; Bowden, Donald W; Rich, Stephen S; Raffel, Leslie J; Rotter, Jerome I; Watanabe, Richard M; Wagenknecht, Lynne E

2015-05-01

Insulin sensitivity, insulin secretion, insulin clearance, and glucose effectiveness exhibit strong genetic components, although few studies have examined their genetic architecture or influence on type 2 diabetes (T2D) risk. We hypothesized that loci affecting variation in these quantitative traits influence T2D. We completed a multicohort genome-wide association study to search for loci influencing T2D-related quantitative traits in 4,176 Mexican Americans. Quantitative traits were measured by the frequently sampled intravenous glucose tolerance test (four cohorts) or euglycemic clamp (three cohorts), and random-effects models were used to test the association between loci and quantitative traits, adjusting for age, sex, and admixture proportions (Discovery). Analysis revealed a significant (P < 5.00 × 10(-8)) association at 11q14.3 (MTNR1B) with acute insulin response. Loci with P < 0.0001 among the quantitative traits were examined for translation to T2D risk in 6,463 T2D case and 9,232 control subjects of Mexican ancestry (Translation). Nonparametric meta-analysis of the Discovery and Translation cohorts identified significant associations at 6p24 (SLC35B3/TFAP2A) with glucose effectiveness/T2D, 11p15 (KCNQ1) with disposition index/T2D, and 6p22 (CDKAL1) and 11q14 (MTNR1B) with acute insulin response/T2D. These results suggest that T2D and insulin secretion and sensitivity have both shared and distinct genetic factors, potentially delineating genomic components of these quantitative traits that drive the risk for T2D. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

Quantitative genetics of age at reproduction in wild swans: Support for antagonistic pleiotropy models of senescence

PubMed Central

Charmantier, Anne; Perrins, Christopher; McCleery, Robin H.; Sheldon, Ben C.

2006-01-01

Why do individuals stop reproducing after a certain age, and how is this age determined? The antagonistic pleiotropy theory for the evolution of senescence predicts that increased early-life performance should be accompanied by earlier (or faster) senescence. Hence, an individual that has started to breed early should also lose its reproductive capacities early. We investigate here the relationship between age at first reproduction (AFR) and age at last reproduction (ALR) in a free-ranging mute swan (Cygnus olor) population monitored for 36 years. Using multivariate analyses on the longitudinal data, we show that both traits are strongly selected in opposite directions. Analysis of the phenotypic covariance between these characters shows that individuals vary in their inherent quality, such that some individuals have earlier AFR and later ALR than expected. Quantitative genetic pedigree analyses show that both traits possess additive genetic variance but also that AFR and ALR are positively genetically correlated. Hence, although both traits display heritable variation and are under opposing directional selection, their evolution is constrained by a strong evolutionary tradeoff. These results are consistent with the theory that increased early-life performance comes with faster senescence because of genetic tradeoffs. PMID:16618935

Quantitative genetic correlation between trait and preference supports a sexually selected sperm process

PubMed Central

Simmons, Leigh W.; Kotiaho, Janne S.

2007-01-01

Sperm show patterns of rapid and divergent evolution that are characteristic of sexual selection. Sperm competition has been proposed as an important selective agent in the evolution of sperm morphology. However, several comparative analyses have revealed evolutionary associations between sperm length and female reproductive tract morphology that suggest patterns of male–female coevolution. In the dung beetle Onthophagus taurus, males with short sperm have a fertilization advantage that depends on the size of the female's sperm storage organ, the spermatheca; large spermathecae select for short sperm. Sperm length is heritable and is genetically correlated with male condition. Here we report significant additive genetic variation and heritability for spermatheca size and genetic covariance between spermatheca size and sperm length predicted by both the “good-sperm” and “sexy-sperm” models of postcopulatory female preference. Our data thus provide quantitative genetic support for the role of a sexually selected sperm process in the evolutionary divergence of sperm morphology, in much the same manner as precopulatory female preferences drive the evolutionary divergence of male secondary sexual traits. PMID:17921254

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast

PubMed Central

Bloom, Joshua S.; Kotenko, Iulia; Sadhu, Meru J.; Treusch, Sebastian; Albert, Frank W.; Kruglyak, Leonid

2015-01-01

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies. PMID:26537231

Refining Intervention Targets in Family-Based Research: Lessons From Quantitative Behavioral Genetics

PubMed Central

Leve, Leslie D.; Harold, Gordon T.; Ge, Xiaojia; Neiderhiser, Jenae M.; Patterson, Gerald

2010-01-01

The results from a large body of family-based research studies indicate that modifying the environment (specifically dimensions of the social environment) through intervention is an effective mechanism for achieving positive outcomes. Parallel to this work is a growing body of evidence from genetically informed studies indicating that social environmental factors are central to enhancing or offsetting genetic influences. Increased precision in the understanding of the role of the social environment in offsetting genetic risk might provide new information about environmental mechanisms that could be applied to prevention science. However, at present, the multifaceted conceptualization of the environment in prevention science is mismatched with the more limited measurement of the environment in many genetically informed studies. A framework for translating quantitative behavioral genetic research to inform the development of preventive interventions is presented in this article. The measurement of environmental indices amenable to modification is discussed within the context of quantitative behavioral genetic studies. In particular, emphasis is placed on the necessary elements that lead to benefits in prevention science, specifically the development of evidence-based interventions. An example from an ongoing prospective adoption study is provided to illustrate the potential of this translational process to inform the selection of preventive intervention targets. PMID:21188273

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.

Rapid climate change and the rate of adaptation: insight from experimental quantitative genetics.

PubMed

Shaw, Ruth G; Etterson, Julie R

2012-09-01

Evolution proceeds unceasingly in all biological populations. It is clear that climate-driven evolution has molded plants in deep time and within extant populations. However, it is less certain whether adaptive evolution can proceed sufficiently rapidly to maintain the fitness and demographic stability of populations subjected to exceptionally rapid contemporary climate change. Here, we consider this question, drawing on current evidence on the rate of plant range shifts and the potential for an adaptive evolutionary response. We emphasize advances in understanding based on theoretical studies that model interacting evolutionary processes, and we provide an overview of quantitative genetic approaches that can parameterize these models to provide more meaningful predictions of the dynamic interplay between genetics, demography and evolution. We outline further research that can clarify both the adaptive potential of plant populations as climate continues to change and the role played by ongoing adaptation in their persistence. © 2012 The Authors. New Phytologist © 2012 New Phytologist Trust.

Quantitative genetic-interaction mapping in mammalian cells

PubMed Central

Roguev, Assen; Talbot, Dale; Negri, Gian Luca; Shales, Michael; Cagney, Gerard; Bandyopadhyay, Sourav; Panning, Barbara; Krogan, Nevan J

2013-01-01

Mapping genetic interactions (GIs) by simultaneously perturbing pairs of genes is a powerful tool for understanding complex biological phenomena. Here we describe an experimental platform for generating quantitative GI maps in mammalian cells using a combinatorial RNA interference strategy. We performed ~11,000 pairwise knockdowns in mouse fibroblasts, focusing on 130 factors involved in chromatin regulation to create a GI map. Comparison of the GI and protein-protein interaction (PPI) data revealed that pairs of genes exhibiting positive GIs and/or similar genetic profiles were predictive of the corresponding proteins being physically associated. The mammalian GI map identified pathways and complexes but also resolved functionally distinct submodules within larger protein complexes. By integrating GI and PPI data, we created a functional map of chromatin complexes in mouse fibroblasts, revealing that the PAF complex is a central player in the mammalian chromatin landscape. PMID:23407553

Entering the second century of maize quantitative genetics

USDA-ARS?s Scientific Manuscript database

Maize is the most widely grown cereal in the world. In addition to its role in global agriculture, it has also long served as a model organism for genetic research. Maize stands at a genetic crossroads, as it has access to all the tools available for plant genetics but exhibits a genetic architectur...

Quantitative genetic analysis of anxiety trait in bipolar disorder.

PubMed

Contreras, J; Hare, E; Chavarría, G; Raventós, H

2018-01-01

Bipolar disorder type I (BPI) affects approximately 1% of the world population. Although genetic influences on bipolar disorder are well established, identification of genes that predispose to the illness has been difficult. Most genetic studies are based on categorical diagnosis. One strategy to overcome this obstacle is the use of quantitative endophenotypes, as has been done for other medical disorders. We studied 619 individuals, 568 participants from 61 extended families and 51 unrelated healthy controls. The sample was 55% female and had a mean age of 43.25 (SD 13.90; range 18-78). Heritability and genetic correlation of the trait scale from the Anxiety State and Trait Inventory (STAI) was computed by using the general linear model (SOLAR package software). we observed that anxiety trait meets the following criteria for an endophenotype of bipolar disorder type I (BPI): 1) association with BPI (individuals with BPI showed the highest trait score (F = 15.20 [5,24], p = 0.009), 2) state-independence confirmed after conducting a test-retest in 321 subjects, 3) co-segregation within families 4) heritability of 0.70 (SE: 0.060), p = 2.33 × 10 -14 and 5) genetic correlation with BPI was 0.20, (SE = 0.17, p = 3.12 × 10 -5 ). Confounding factors such as comorbid disorders and pharmacological treatment could affect the clinical relationship between BPI and anxiety trait. Further research is needed to evaluate if anxiety traits are specially related to BPI in comparison with other traits such as anger, attention or response inhibition deficit, pathological impulsivity or low self-directedness. Anxiety trait is a heritable phenotype that follows a normal distribution when measured not only in subjects with BPI but also in unrelated healthy controls. It could be used as an endophenotype in BPI for the identification of genomic regions with susceptibility genes for this disorder. Published by Elsevier B.V.

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

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm.

PubMed

Namkung, Junghyun; Nam, Jin-Wu; Park, Taesung

2007-01-01

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene x gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene x gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms.

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

PubMed

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

2015-04-01

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

Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models.

PubMed

Reed, Thomas E; Gienapp, Phillip; Visser, Marcel E

2016-10-01

Key life history traits such as breeding time and clutch size are frequently both heritable and under directional selection, yet many studies fail to document microevolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have causal effects on fitness, but few valid tests of this exist. Here, we show, using a quantitative genetic framework and six decades of life-history data on two free-living populations of great tits Parus major, that selection estimates for egg-laying date and clutch size are relatively unbiased. Predicted responses to selection based on the Robertson-Price Identity were similar to those based on the multivariate breeder's equation (MVBE), indicating that unmeasured covarying traits were not missing from the analysis. Changing patterns of phenotypic selection on these traits (for laying date, linked to climate change) therefore reflect changing selection on breeding values, and genetic constraints appear not to limit their independent evolution. Quantitative genetic analysis of correlational data from pedigreed populations can be a valuable complement to experimental approaches to help identify whether apparent associations between traits and fitness are biased by missing traits, and to parse the roles of direct versus indirect selection across a range of environments. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Quantitative genetic bases of anthocyanin variation in grape (Vitis vinifera L. ssp. sativa) berry: a quantitative trait locus to quantitative trait nucleotide integrated study.

PubMed

Fournier-Level, Alexandre; Le Cunff, Loïc; Gomez, Camila; Doligez, Agnès; Ageorges, Agnès; Roux, Catherine; Bertrand, Yves; Souquet, Jean-Marc; Cheynier, Véronique; This, Patrice

2009-11-01

The combination of QTL mapping studies of synthetic lines and association mapping studies of natural diversity represents an opportunity to throw light on the genetically based variation of quantitative traits. With the positional information provided through quantitative trait locus (QTL) mapping, which often leads to wide intervals encompassing numerous genes, it is now feasible to directly target candidate genes that are likely to be responsible for the observed variation in completely sequenced genomes and to test their effects through association genetics. This approach was performed in grape, a newly sequenced genome, to decipher the genetic architecture of anthocyanin content. Grapes may be either white or colored, ranging from the lightest pink to the darkest purple tones according to the amount of anthocyanin accumulated in the berry skin, which is a crucial trait for both wine quality and human nutrition. Although the determinism of the white phenotype has been fully identified, the genetic bases of the quantitative variation of anthocyanin content in berry skin remain unclear. A single QTL responsible for up to 62% of the variation in the anthocyanin content was mapped on a Syrah x Grenache F(1) pseudo-testcross. Among the 68 unigenes identified in the grape genome within the QTL interval, a cluster of four Myb-type genes was selected on the basis of physiological evidence (VvMybA1, VvMybA2, VvMybA3, and VvMybA4). From a core collection of natural resources (141 individuals), 32 polymorphisms revealed significant association, and extended linkage disequilibrium was observed. Using a multivariate regression method, we demonstrated that five polymorphisms in VvMybA genes except VvMybA4 (one retrotransposon, three single nucleotide polymorphisms and one 2-bp insertion/deletion) accounted for 84% of the observed variation. All these polymorphisms led to either structural changes in the MYB proteins or differences in the VvMybAs promoters. We concluded that

Genetic variation affecting host-parasite interactions: major-effect quantitative trait loci affect the transmission of sigma virus in Drosophila melanogaster.

PubMed

Bangham, Jenny; Knott, Sara A; Kim, Kang-Wook; Young, Robert S; Jiggins, Francis M

2008-09-01

In natural populations, genetic variation affects resistance to disease. Whether that genetic variation comprises lots of small-effect polymorphisms or a small number of large-effect polymorphisms has implications for adaptation, selection and how genetic variation is maintained in populations. Furthermore, how much genetic variation there is, and the genes that underlie this variation, affects models of co-evolution between parasites and their hosts. We are studying the genetic variation that affects the resistance of Drosophila melanogaster to its natural pathogen--the vertically transmitted sigma virus. We have carried out three separate quantitative trait locus mapping analyses to map gene variants on the second chromosome that cause variation in the rate at which males transmit the infection to their offspring. All three crosses identified a locus in a similar chromosomal location that causes a large drop in the rate at which the virus is transmitted. We also found evidence for an additional smaller-effect quantitative trait locus elsewhere on the chromosome. Our data, together with previous experiments on the sigma virus and parasitoid wasps, indicate that the resistance of D. melanogaster to co-evolved pathogens is controlled by a limited number of major-effect polymorphisms.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

Wang, Yifan; Liu, Aiyi; Mills, James L.; Boehnke, Michael; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao; Wu, Colin O.; Fan, Ruzong

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

PubMed

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

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

PubMed Central

2011-01-01

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

Quantitative autistic trait measurements index background genetic risk for ASD in Hispanic families.

PubMed

Page, Joshua; Constantino, John Nicholas; Zambrana, Katherine; Martin, Eden; Tunc, Ilker; Zhang, Yi; Abbacchi, Anna; Messinger, Daniel

2016-01-01

Recent studies have indicated that quantitative autistic traits (QATs) of parents reflect inherited liabilities that may index background genetic risk for clinical autism spectrum disorder (ASD) in their offspring. Moreover, preferential mating for QATs has been observed as a potential factor in concentrating autistic liabilities in some families across generations. Heretofore, intergenerational studies of QATs have focused almost exclusively on Caucasian populations-the present study explored these phenomena in a well-characterized Hispanic population. The present study examined QAT scores in siblings and parents of 83 Hispanic probands meeting research diagnostic criteria for ASD, and 64 non-ASD controls, using the Social Responsiveness Scale-2 (SRS-2). Ancestry of the probands was characterized by genotype, using information from 541,929 single nucleotide polymorphic markers. In families of Hispanic children with an ASD diagnosis, the pattern of quantitative trait correlations observed between ASD-affected children and their first-degree relatives (ICCs on the order of 0.20), between unaffected first-degree relatives in ASD-affected families (sibling/mother ICC = 0.36; sibling/father ICC = 0.53), and between spouses (mother/father ICC = 0.48) were in keeping with the influence of transmitted background genetic risk and strong preferential mating for variation in quantitative autistic trait burden. Results from analysis of ancestry-informative genetic markers among probands in this sample were consistent with that from other Hispanic populations. Quantitative autistic traits represent measurable indices of inherited liability to ASD in Hispanic families. The accumulation of autistic traits occurs within generations, between spouses, and across generations, among Hispanic families affected by ASD. The occurrence of preferential mating for QATs-the magnitude of which may vary across cultures-constitutes a mechanism by which background genetic liability

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

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm

PubMed Central

Namkung, Junghyun; Nam, Jin-Wu; Park, Taesung

2007-01-01

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene × gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene × gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms. PMID:18466570

Quantitative Genetic Interactions Reveal Layers of Biological Modularity

PubMed Central

Beltrao, Pedro; Cagney, Gerard; Krogan, Nevan J.

2010-01-01

In the past, biomedical research has embraced a reductionist approach, primarily focused on characterizing the individual components that comprise a system of interest. Recent technical developments have significantly increased the size and scope of data describing biological systems. At the same time, advances in the field of systems biology have evoked a broader view of how the underlying components are interconnected. In this essay, we discuss how quantitative genetic interaction mapping has enhanced our view of biological systems, allowing a deeper functional interrogation at different biological scales. PMID:20510918

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions.

PubMed

Clark, Michelle M; Blangero, John; Dyer, Thomas D; Sobel, Eric M; Sinsheimer, Janet S

2016-01-01

Maternal-offspring gene interactions, aka maternal-fetal genotype (MFG) incompatibilities, are neglected in complex diseases and quantitative trait studies. They are implicated in birth to adult onset diseases but there are limited ways to investigate their influence on quantitative traits. We present the quantitative-MFG (QMFG) test, a linear mixed model where maternal and offspring genotypes are fixed effects and residual correlations between family members are random effects. The QMFG handles families of any size, common or general scenarios of MFG incompatibility, and additional covariates. We develop likelihood ratio tests (LRTs) and rapid score tests and show they provide correct inference. In addition, the LRT's alternative model provides unbiased parameter estimates. We show that testing the association of SNPs by fitting a standard model, which only considers the offspring genotypes, has very low power or can lead to incorrect conclusions. We also show that offspring genetic effects are missed if the MFG modeling assumptions are too restrictive. With genome-wide association study data from the San Antonio Family Heart Study, we demonstrate that the QMFG score test is an effective and rapid screening tool. The QMFG test therefore has important potential to identify pathways of complex diseases for which the genetic etiology remains to be discovered. © 2015 John Wiley & Sons Ltd/University College London.

Heritability and quantitative genetic divergence of serotiny, a fire-persistence plant trait

PubMed Central

Hernández-Serrano, Ana; Verdú, Miguel; Santos-del-Blanco, Luís; Climent, José; González-Martínez, Santiago C.; Pausas, Juli G.

2014-01-01

Background and Aims Although it is well known that fire acts as a selective pressure shaping plant phenotypes, there are no quantitative estimates of the heritability of any trait related to plant persistence under recurrent fires, such as serotiny. In this study, the heritability of serotiny in Pinus halepensis is calculated, and an evaluation is made as to whether fire has left a selection signature on the level of serotiny among populations by comparing the genetic divergence of serotiny with the expected divergence of neutral molecular markers (QST–FST comparison). Methods A common garden of P. halepensis was used, located in inland Spain and composed of 145 open-pollinated families from 29 provenances covering the entire natural range of P. halepensis in the Iberian Peninsula and Balearic Islands. Narrow-sense heritability (h2) and quantitative genetic differentiation among populations for serotiny (QST) were estimated by means of an ‘animal model’ fitted by Bayesian inference. In order to determine whether genetic differentiation for serotiny is the result of differential natural selection, QST estimates for serotiny were compared with FST estimates obtained from allozyme data. Finally, a test was made of whether levels of serotiny in the different provenances were related to different fire regimes, using summer rainfall as a proxy for fire regime in each provenance. Key Results Serotiny showed a significant narrow-sense heritability (h2) of 0·20 (credible interval 0·09–0·40). Quantitative genetic differentiation among provenances for serotiny (QST = 0·44) was significantly higher than expected under a neutral process (FST = 0·12), suggesting adaptive differentiation. A significant negative relationship was found between the serotiny level of trees in the common garden and summer rainfall of their provenance sites. Conclusions Serotiny is a heritable trait in P. halepensis, and selection acts on it, giving rise to contrasting serotiny levels

[The study of tomato fruit weight quantitative trait locus and its application in genetics teaching].

PubMed

Wang, Hai-yan

2015-08-01

The classical research cases, which have greatly promoted the development of genetics in history, can be combined with the content of courses in genetics teaching to train students' ability of scientific thinking and genetic analysis. The localization and clone of gene controlling tomato fruit weight is a pioneer work in quantitative trait locus (QTL) studies and represents a complete process of QTL research in plants. Application of this integrated case in genetics teaching, which showed a wonderful process of scientific discovery and the fascination of genetic research, has inspired students' interest in genetics and achieved a good teaching effect.

Quantitative genetics of secondary hip joint osteoarthritis in a Labrador Retriever-Greyhound pedigree.

PubMed

Hays, Laurel; Zhang, Zhiwu; Mateescu, Raluca G; Lust, George; Burton-Wurster, Nancy I; Todhunter, Rory J

2007-01-01

To evaluate the quantitative inheritance of secondary hip joint osteoarthritis in a canine pedigree. 137 Labrador Retrievers, Greyhounds, and mixed-breed dogs. Necropsy scores ranging from 0 to 4 were obtained for each hip joint. Seven unaffected Greyhounds with normal hip joint conformation were also used for genetic modeling, but were not euthanized. Sixty-six male and 71 female dogs were allocated to 2 groups (< or = 12 months of age and > 12 months of age). Statistical models were developed to establish the inheritance pattern of hip joint osteoarthritis that developed secondary to hip dysplasia. 62 dogs had evidence of osteoarthritis in a hip joint, and 75 had no evidence of osteoarthritis. After sex was adjusted for, the necropsy score was found to be inherited additively but without dominance. Each Labrador Retriever allele increased the necropsy score by 0.7 to 0.9 points, compared with the Greyhound allele, and male sex increased the necropsy score 0.74 over female sex. Approximately 10% of the variation in necropsy score was attributable to the litter of puppies' origin. Because secondary hip joint osteoarthritis is inherited additively, selection pressure could be applied to reduce its incidence. Similar statistical models can be used in linkage and association mapping to detect the genes in the underlying quantitative trait loci that contribute to hip joint osteoarthritis.

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

PubMed

Zhu, J

1995-12-01

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

FRET-based genetically-encoded sensors for quantitative monitoring of metabolites.

PubMed

Mohsin, Mohd; Ahmad, Altaf; Iqbal, Muhammad

2015-10-01

Neighboring cells in the same tissue can exist in different states of dynamic activities. After genomics, proteomics and metabolomics, fluxomics is now equally important for generating accurate quantitative information on the cellular and sub-cellular dynamics of ions and metabolite, which is critical for functional understanding of organisms. Various spectrometry techniques are used for monitoring ions and metabolites, although their temporal and spatial resolutions are limited. Discovery of the fluorescent proteins and their variants has revolutionized cell biology. Therefore, novel tools and methods targeting sub-cellular compartments need to be deployed in specific cells and targeted to sub-cellular compartments in order to quantify the target-molecule dynamics directly. We require tools that can measure cellular activities and protein dynamics with sub-cellular resolution. Biosensors based on fluorescence resonance energy transfer (FRET) are genetically encoded and hence can specifically target sub-cellular organelles by fusion to proteins or targetted sequences. Since last decade, FRET-based genetically encoded sensors for molecules involved in energy production, reactive oxygen species and secondary messengers have helped to unravel key aspects of cellular physiology. This review, describing the design and principles of sensors, presents a database of sensors for different analytes/processes, and illustrate examples of application in quantitative live cell imaging.

Sex-specific genetic effects in physical activity: results from a quantitative genetic analysis.

PubMed

Diego, Vincent P; de Chaves, Raquel Nichele; Blangero, John; de Souza, Michele Caroline; Santos, Daniel; Gomes, Thayse Natacha; dos Santos, Fernanda Karina; Garganta, Rui; Katzmarzyk, Peter T; Maia, José A R

2015-08-01

The objective of this study is to present a model to estimate sex-specific genetic effects on physical activity (PA) levels and sedentary behaviour (SB) using three generation families. The sample consisted of 100 families covering three generations from Portugal. PA and SB were assessed via the International Physical Activity Questionnaire short form (IPAQ-SF). Sex-specific effects were assessed by genotype-by-sex interaction (GSI) models and sex-specific heritabilities. GSI effects and heterogeneity were tested in the residual environmental variance. SPSS 17 and SOLAR v. 4.1 were used in all computations. The genetic component for PA and SB domains varied from low to moderate (11% to 46%), when analyzing both genders combined. We found GSI effects for vigorous PA (p = 0.02) and time spent watching television (WT) (p < 0.001) that showed significantly higher additive genetic variance estimates in males. The heterogeneity in the residual environmental variance was significant for moderate PA (p = 0.02), vigorous PA (p = 0.006) and total PA (p = 0.001). Sex-specific heritability estimates were significantly higher in males only for WT, with a male-to-female difference in heritability of 42.5 (95% confidence interval: 6.4, 70.4). Low to moderate genetic effects on PA and SB traits were found. Results from the GSI model show that there are sex-specific effects in two phenotypes, VPA and WT with a stronger genetic influence in males.

Uncovering the genetic signature of quantitative trait evolution with replicated time series data.

PubMed

Franssen, S U; Kofler, R; Schlötterer, C

2017-01-01

The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. Although much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied, as the associated genomic signature is extremely difficult to detect. We propose 'Evolve and Resequence' as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: (1) directional frequency changes towards the new trait optimum, (2) plateauing of allele frequencies when the new trait optimum has been reached and (3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole-genome population genetics data.

Marker-based quantitative genetics in the wild?: the heritability and genetic correlation of chemical defenses in eucalyptus.

PubMed

Andrew, R L; Peakall, R; Wallis, I R; Wood, J T; Knight, E J; Foley, W J

2005-12-01

Marker-based methods for estimating heritability and genetic correlation in the wild have attracted interest because traditional methods may be impractical or introduce bias via G x E effects, mating system variation, and sampling effects. However, they have not been widely used, especially in plants. A regression-based approach, which uses a continuous measure of genetic relatedness, promises to be particularly appropriate for use in plants with mixed-mating systems and overlapping generations. Using this method, we found significant narrow-sense heritability of foliar defense chemicals in a natural population of Eucalyptus melliodora. We also demonstrated a genetic basis for the phenotypic correlation underlying an ecological example of conditioned flavor aversion involving different biosynthetic pathways. Our results revealed that heritability estimates depend on the spatial scale of the analysis in a way that offers insight into the distribution of genetic and environmental variance. This study is the first to successfully use a marker-based method to measure quantitative genetic parameters in a tree. We suggest that this method will prove to be a useful tool in other studies and offer some recommendations for future applications of the method.

Effects of functionally asexual reproduction on quantitative genetic variation in the evening primroses (Oenothera, Onagraceae).

PubMed

Godfrey, Ryan M; Johnson, Marc T J

2014-11-01

It has long been predicted that a loss of sexual reproduction leads to decreased heritable variation within populations and increased differentiation between populations. Despite an abundance of theory, there are few empirical tests of how sex affects genetic variation in phenotypic traits, especially for plants. Here we test whether repeated losses of two critical components of sex (recombination and segregation) in the evening primroses (Oenothera L., Onagraceae) affect quantitative genetic variation within and between populations. We sampled multiple genetic families from 3-5 populations from each of eight Oenothera species, which represented four independent transitions between sexual reproduction and a functionally asexual genetic system called "permanent translocation heterozygosity." We used quantitative genetics methods to partition genetic variation within and between populations for eight plant traits related to growth, leaf physiology, flowering, and resistance to herbivores. Heritability was, on average, 74% higher in sexual Oenothera populations than in functionally asexual populations, with plant growth rate, specific leaf area, and the percentage of leaf water content showing the strongest differences. By contrast, genetic differentiation among populations was 2.8× higher in functionally asexual vs. sexual Oenothera species. This difference was particularly strong for specific leaf area. Sexual populations tended to exhibit higher genetic correlations among traits, but this difference was weakly supported. These results support the prediction that sexual reproduction maintains higher genetic variation within populations, which may facilitate adaptive evolution. We also found partial support for the prediction that a loss of sex leads to greater population differentiation, which may elevate speciation rates. © 2014 Botanical Society of America, Inc.

Quantitative PCR for Detection and Enumeration of Genetic Markers of Bovine Fecal Pollution

EPA Science Inventory

Accurate assessment of health risks associated with bovine (cattle) fecal pollution requires a reliable host-specific genetic marker and a rapid quantification method. We report the development of quantitative PCR assays for the detection of two recently described cow feces-spec...

Limits to behavioral evolution: the quantitative genetics of a complex trait under directional selection.

PubMed

Careau, Vincent; Wolak, Matthew E; Carter, Patrick A; Garland, Theodore

2013-11-01

Replicated selection experiments provide a powerful way to study how "multiple adaptive solutions" may lead to differences in the quantitative-genetic architecture of selected traits and whether this may translate into differences in the timing at which evolutionary limits are reached. We analyze data from 31 generations (n=17,988) of selection on voluntary wheel running in house mice. The rate of initial response, timing of selection limit, and height of the plateau varied significantly between sexes and among the four selected lines. Analyses of litter size and realized selection differentials seem to rule out counterposing natural selection as a cause of the selection limits. Animal-model analyses showed that although the additive genetic variance was significantly lower in selected than control lines, both before and after the limits, the decrease was not sufficient to explain the limits. Moreover, directional selection promoted a negative covariance between additive and maternal genetic variance over the first 10 generations. These results stress the importance of replication in selection studies of higher-level traits and highlight the fact that long-term predictions of response to selection are not necessarily expected to be linear because of the variable effects of selection on additive genetic variance and maternal effects. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

PubMed

Fu, Guifang; Dai, Xiaotian; Symanzik, Jürgen; Bushman, Shaun

2017-01-01

Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Analysis of Conditional Genetic Effects and Variance Components in Developmental Genetics

PubMed Central

Zhu, J.

1995-01-01

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

Dissecting genetic architecture of grape proanthocyanidin composition through quantitative trait locus mapping

PubMed Central

2012-01-01

Background Proanthocyanidins (PAs), or condensed tannins, are flavonoid polymers, widespread throughout the plant kingdom, which provide protection against herbivores while conferring organoleptic and nutritive values to plant-derived foods, such as wine. However, the genetic basis of qualitative and quantitative PA composition variation is still poorly understood. To elucidate the genetic architecture of the complex grape PA composition, we first carried out quantitative trait locus (QTL) analysis on a 191-individual pseudo-F1 progeny. Three categories of PA variables were assessed: total content, percentages of constitutive subunits and composite ratio variables. For nine functional candidate genes, among which eight co-located with QTLs, we performed association analyses using a diversity panel of 141 grapevine cultivars in order to identify causal SNPs. Results Multiple QTL analysis revealed a total of 103 and 43 QTLs, respectively for seed and skin PA variables. Loci were mainly of additive effect while some loci were primarily of dominant effect. Results also showed a large involvement of pairwise epistatic interactions in shaping PA composition. QTLs for PA variables in skin and seeds differed in number, position, involvement of epistatic interaction and allelic effect, thus revealing different genetic determinisms for grape PA composition in seeds and skin. Association results were consistent with QTL analyses in most cases: four out of nine tested candidate genes (VvLAR1, VvMYBPA2, VvCHI1, VvMYBPA1) showed at least one significant association with PA variables, especially VvLAR1 revealed as of great interest for further functional investigation. Some SNP-phenotype associations were observed only in the diversity panel. Conclusions This study presents the first QTL analysis on grape berry PA composition with a comparison between skin and seeds, together with an association study. Our results suggest a complex genetic control for PA traits and different

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

PubMed

Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

2016-04-01

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

Bigger Is Fitter? Quantitative Genetic Decomposition of Selection Reveals an Adaptive Evolutionary Decline of Body Mass in a Wild Rodent Population.

PubMed

Bonnet, Timothée; Wandeler, Peter; Camenisch, Glauco; Postma, Erik

2017-01-01

In natural populations, quantitative trait dynamics often do not appear to follow evolutionary predictions. Despite abundant examples of natural selection acting on heritable traits, conclusive evidence for contemporary adaptive evolution remains rare for wild vertebrate populations, and phenotypic stasis seems to be the norm. This so-called "stasis paradox" highlights our inability to predict evolutionary change, which is especially concerning within the context of rapid anthropogenic environmental change. While the causes underlying the stasis paradox are hotly debated, comprehensive attempts aiming at a resolution are lacking. Here, we apply a quantitative genetic framework to individual-based long-term data for a wild rodent population and show that despite a positive association between body mass and fitness, there has been a genetic change towards lower body mass. The latter represents an adaptive response to viability selection favouring juveniles growing up to become relatively small adults, i.e., with a low potential adult mass, which presumably complete their development earlier. This selection is particularly strong towards the end of the snow-free season, and it has intensified in recent years, coinciding which a change in snowfall patterns. Importantly, neither the negative evolutionary change, nor the selective pressures that drive it, are apparent on the phenotypic level, where they are masked by phenotypic plasticity and a non causal (i.e., non genetic) positive association between body mass and fitness, respectively. Estimating selection at the genetic level enabled us to uncover adaptive evolution in action and to identify the corresponding phenotypic selective pressure. We thereby demonstrate that natural populations can show a rapid and adaptive evolutionary response to a novel selective pressure, and that explicitly (quantitative) genetic models are able to provide us with an understanding of the causes and consequences of selection that is

Bigger Is Fitter? Quantitative Genetic Decomposition of Selection Reveals an Adaptive Evolutionary Decline of Body Mass in a Wild Rodent Population

PubMed Central

Wandeler, Peter; Camenisch, Glauco

2017-01-01

In natural populations, quantitative trait dynamics often do not appear to follow evolutionary predictions. Despite abundant examples of natural selection acting on heritable traits, conclusive evidence for contemporary adaptive evolution remains rare for wild vertebrate populations, and phenotypic stasis seems to be the norm. This so-called “stasis paradox” highlights our inability to predict evolutionary change, which is especially concerning within the context of rapid anthropogenic environmental change. While the causes underlying the stasis paradox are hotly debated, comprehensive attempts aiming at a resolution are lacking. Here, we apply a quantitative genetic framework to individual-based long-term data for a wild rodent population and show that despite a positive association between body mass and fitness, there has been a genetic change towards lower body mass. The latter represents an adaptive response to viability selection favouring juveniles growing up to become relatively small adults, i.e., with a low potential adult mass, which presumably complete their development earlier. This selection is particularly strong towards the end of the snow-free season, and it has intensified in recent years, coinciding which a change in snowfall patterns. Importantly, neither the negative evolutionary change, nor the selective pressures that drive it, are apparent on the phenotypic level, where they are masked by phenotypic plasticity and a non causal (i.e., non genetic) positive association between body mass and fitness, respectively. Estimating selection at the genetic level enabled us to uncover adaptive evolution in action and to identify the corresponding phenotypic selective pressure. We thereby demonstrate that natural populations can show a rapid and adaptive evolutionary response to a novel selective pressure, and that explicitly (quantitative) genetic models are able to provide us with an understanding of the causes and consequences of selection that is

Quantitative genetics of circulating Hyaluronic Acid (HA) and its correlation with hand osteoarthritis and obesity-related phenotypes in a community-based sample.

PubMed

Prakash, Jai; Gabdulina, Gulzhan; Trofimov, Svetlana; Livshits, Gregory

2017-09-01

One of the potential molecular biomarkers of osteoarthritis (OA) is hyaluronic acid (HA). HA levels may be related to the severity and progression of OA. However, little is known about the contribution of major risk factors for osteoarthritis, e.g. obesity-related phenotypes and genetics to HA variation. To clarify the quantitative effect of these factors on HA. An ethnically homogeneous sample of 911 apparently healthy European-derived individuals, assessed for radiographic hand osteoarthritis (RHOA), HA, leptin, adiponectin, and several anthropometrical measures of obesity-related phenotypes was studied. Model-based quantitative genetic analysis was used to reveal genetic and shared environmental factors affecting the variation of the study's phenotypes. The HA levels significantly correlated with the age, RHOA, adiponectin, obesity-related phenotypes, and the waist-to-hip ratio. The putative genetic effects contributed significantly to the variation of HA (66.2 ± 9.3%) and they were also significant factors in the variations of all the other studied phenotypes, with the heritability estimate ranging between 0.122 ± 4.4% (WHR) and 45.7 ± 2.2% (joint space narrowing). This is the first study to report heritability estimates of HA variation and its correlation with obesity-related phenotypes, ADP and RHOA. However, the nature of genetic effects on HA and its correlation with other study phenotypes require further clarification.

Population size is weakly related to quantitative genetic variation and trait differentiation in a stream fish.

PubMed

Wood, Jacquelyn L A; Tezel, Defne; Joyal, Destin; Fraser, Dylan J

2015-09-01

How population size influences quantitative genetic variation and differentiation among natural, fragmented populations remains unresolved. Small, isolated populations might occupy poor quality habitats and lose genetic variation more rapidly due to genetic drift than large populations. Genetic drift might furthermore overcome selection as population size decreases. Collectively, this might result in directional changes in additive genetic variation (VA ) and trait differentiation (QST ) from small to large population size. Alternatively, small populations might exhibit larger variation in VA and QST if habitat fragmentation increases variability in habitat types. We explored these alternatives by investigating VA and QST using nine fragmented populations of brook trout varying 50-fold in census size N (179-8416) and 10-fold in effective number of breeders, Nb (18-135). Across 15 traits, no evidence was found for consistent differences in VA and QST with population size and almost no evidence for increased variability of VA or QST estimates at small population size. This suggests that (i) small populations of some species may retain adaptive potential according to commonly adopted quantitative genetic measures and (ii) populations of varying sizes experience a variety of environmental conditions in nature, however extremely large studies are likely required before any firm conclusions can be made. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

NASA Astrophysics Data System (ADS)

Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

Routine Discovery of Complex Genetic Models using Genetic Algorithms

PubMed Central

Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.

2010-01-01

Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes. PMID:20948983

Filling the knowledge gap: Integrating quantitative genetics and genomics in graduate education and outreach

USDA-ARS?s Scientific Manuscript database

The genomics revolution provides vital tools to address global food security. Yet to be incorporated into livestock breeding, molecular techniques need to be integrated into a quantitative genetics framework. Within the U.S., with shrinking faculty numbers with the requisite skills, the capacity to ...

Quantitative Genetic Architecture at Latitudinal Range Boundaries: Reduced Variation but Higher Trait Independence.

PubMed

Paccard, Antoine; Van Buskirk, Josh; Willi, Yvonne

2016-05-01

Species distribution limits are hypothesized to be caused by small population size and limited genetic variation in ecologically relevant traits, but earlier studies have not evaluated genetic variation in multivariate phenotypes. We asked whether populations at the latitudinal edges of the distribution have altered quantitative genetic architecture of ecologically relevant traits compared with midlatitude populations. We calculated measures of evolutionary potential in nine Arabidopsis lyrata populations spanning the latitudinal range of the species in eastern and midwestern North America. Environments at the latitudinal extremes have reduced water availability, and therefore plants were assessed under wet and dry treatments. We estimated genetic variance-covariance (G-) matrices for 10 traits related to size, development, and water balance. Populations at southern and northern distribution edges had reduced levels of genetic variation across traits, but their G-matrices were more spherical; G-matrix orientation was unrelated to latitude. As a consequence, the predicted short-term response to selection was at least as strong in edge populations as in central populations. These results are consistent with genetic drift eroding variation and reducing the effectiveness of correlational selection at distribution margins. We conclude that genetic variation of isolated traits poorly predicts the capacity to evolve in response to multivariate selection and that the response to selection may frequently be greater than expected at species distribution margins because of genetic drift.

Improving breeding efficiency in potato using molecular and quantitative genetics.

PubMed

Slater, Anthony T; Cogan, Noel O I; Hayes, Benjamin J; Schultz, Lee; Dale, M Finlay B; Bryan, Glenn J; Forster, John W

2014-11-01

Potatoes are highly heterozygous and the conventional breeding of superior germplasm is challenging, but use of a combination of MAS and EBVs can accelerate genetic gain. Cultivated potatoes are highly heterozygous due to their outbreeding nature, and suffer acute inbreeding depression. Modern potato cultivars also exhibit tetrasomic inheritance. Due to this genetic heterogeneity, the large number of target traits and the specific requirements of commercial cultivars, potato breeding is challenging. A conventional breeding strategy applies phenotypic recurrent selection over a number of generations, a process which can take over 10 years. Recently, major advances in genetics and molecular biology have provided breeders with molecular tools to accelerate gains for some traits. Marker-assisted selection (MAS) can be effectively used for the identification of major genes and quantitative trait loci that exhibit large effects. There are also a number of complex traits of interest, such as yield, that are influenced by a large number of genes of individual small effect where MAS will be difficult to deploy. Progeny testing and the use of pedigree in the analysis can provide effective identification of the superior genetic factors that underpin these complex traits. Recently, it has been shown that estimated breeding values (EBVs) can be developed for complex potato traits. Using a combination of MAS and EBVs for simple and complex traits can lead to a significant reduction in the length of the breeding cycle for the identification of superior germplasm.

Quantitative reactive modeling and verification.

PubMed

Henzinger, Thomas A

Formal verification aims to improve the quality of software by detecting errors before they do harm. At the basis of formal verification is the logical notion of correctness , which purports to capture whether or not a program behaves as desired. We suggest that the boolean partition of software into correct and incorrect programs falls short of the practical need to assess the behavior of software in a more nuanced fashion against multiple criteria. We therefore propose to introduce quantitative fitness measures for programs, specifically for measuring the function, performance, and robustness of reactive programs such as concurrent processes. This article describes the goals of the ERC Advanced Investigator Project QUAREM. The project aims to build and evaluate a theory of quantitative fitness measures for reactive models. Such a theory must strive to obtain quantitative generalizations of the paradigms that have been success stories in qualitative reactive modeling, such as compositionality, property-preserving abstraction and abstraction refinement, model checking, and synthesis. The theory will be evaluated not only in the context of software and hardware engineering, but also in the context of systems biology. In particular, we will use the quantitative reactive models and fitness measures developed in this project for testing hypotheses about the mechanisms behind data from biological experiments.

Highly multiplexed and quantitative cell-surface protein profiling using genetically barcoded antibodies.

PubMed

Pollock, Samuel B; Hu, Amy; Mou, Yun; Martinko, Alexander J; Julien, Olivier; Hornsby, Michael; Ploder, Lynda; Adams, Jarrett J; Geng, Huimin; Müschen, Markus; Sidhu, Sachdev S; Moffat, Jason; Wells, James A

2018-03-13

Human cells express thousands of different surface proteins that can be used for cell classification, or to distinguish healthy and disease conditions. A method capable of profiling a substantial fraction of the surface proteome simultaneously and inexpensively would enable more accurate and complete classification of cell states. We present a highly multiplexed and quantitative surface proteomic method using genetically barcoded antibodies called phage-antibody next-generation sequencing (PhaNGS). Using 144 preselected antibodies displayed on filamentous phage (Fab-phage) against 44 receptor targets, we assess changes in B cell surface proteins after the development of drug resistance in a patient with acute lymphoblastic leukemia (ALL) and in adaptation to oncogene expression in a Myc-inducible Burkitt lymphoma model. We further show PhaNGS can be applied at the single-cell level. Our results reveal that a common set of proteins including FLT3, NCR3LG1, and ROR1 dominate the response to similar oncogenic perturbations in B cells. Linking high-affinity, selective, genetically encoded binders to NGS enables direct and highly multiplexed protein detection, comparable to RNA-sequencing for mRNA. PhaNGS has the potential to profile a substantial fraction of the surface proteome simultaneously and inexpensively to enable more accurate and complete classification of cell states. Copyright © 2018 the Author(s). Published by PNAS.

Quantitative and Comparative Profiling of Protease Substrates through a Genetically Encoded Multifunctional Photocrosslinker.

PubMed

He, Dan; Xie, Xiao; Yang, Fan; Zhang, Heng; Su, Haomiao; Ge, Yun; Song, Haiping; Chen, Peng R

2017-11-13

A genetically encoded, multifunctional photocrosslinker was developed for quantitative and comparative proteomics. By bearing a bioorthogonal handle and a releasable linker in addition to its photoaffinity warhead, this probe enables the enrichment of transient and low-abundance prey proteins after intracellular photocrosslinking and prey-bait separation, which can be subject to stable isotope dimethyl labeling and mass spectrometry analysis. This quantitative strategy (termed isoCAPP) allowed a comparative proteomic approach to be adopted to identify the proteolytic substrates of an E. coli protease-chaperone dual machinery DegP. Two newly identified substrates were subsequently confirmed by proteolysis experiments. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

PubMed

Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S

2015-11-13

The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science

Validation of PCR methods for quantitation of genetically modified plants in food.

PubMed

Hübner, P; Waiblinger, H U; Pietsch, K; Brodmann, P

2001-01-01

For enforcement of the recently introduced labeling threshold for genetically modified organisms (GMOs) in food ingredients, quantitative detection methods such as quantitative competitive (QC-PCR) and real-time PCR are applied by official food control laboratories. The experiences of 3 European food control laboratories in validating such methods were compared to describe realistic performance characteristics of quantitative PCR detection methods. The limit of quantitation (LOQ) of GMO-specific, real-time PCR was experimentally determined to reach 30-50 target molecules, which is close to theoretical prediction. Starting PCR with 200 ng genomic plant DNA, the LOQ depends primarily on the genome size of the target plant and ranges from 0.02% for rice to 0.7% for wheat. The precision of quantitative PCR detection methods, expressed as relative standard deviation (RSD), varied from 10 to 30%. Using Bt176 corn containing test samples and applying Bt176 specific QC-PCR, mean values deviated from true values by -7to 18%, with an average of 2+/-10%. Ruggedness of real-time PCR detection methods was assessed in an interlaboratory study analyzing commercial, homogeneous food samples. Roundup Ready soybean DNA contents were determined in the range of 0.3 to 36%, relative to soybean DNA, with RSDs of about 25%. Taking the precision of quantitative PCR detection methods into account, suitable sample plans and sample sizes for GMO analysis are suggested. Because quantitative GMO detection methods measure GMO contents of samples in relation to reference material (calibrants), high priority must be given to international agreements and standardization on certified reference materials.

Genetic and Environmental Influences on Behavior: Capturing All the Interplay

ERIC Educational Resources Information Center

Johnson, Wendy

2007-01-01

Basic quantitative genetic models of human behavioral variation have made clear that individual differences in behavior cannot be understood without acknowledging the importance of genetic influences. Yet these basic models estimate average, population-level genetic and environmental influences, obscuring differences that might exist within the…

Missing heritability in the tails of quantitative traits? A simulation study on the impact of slightly altered true genetic models.

PubMed

Pütter, Carolin; Pechlivanis, Sonali; Nöthen, Markus M; Jöckel, Karl-Heinz; Wichmann, Heinz-Erich; Scherag, André

2011-01-01

Genome-wide association studies have identified robust associations between single nucleotide polymorphisms and complex traits. As the proportion of phenotypic variance explained is still limited for most of the traits, larger and larger meta-analyses are being conducted to detect additional associations. Here we investigate the impact of the study design and the underlying assumption about the true genetic effect in a bimodal mixture situation on the power to detect associations. We performed simulations of quantitative phenotypes analysed by standard linear regression and dichotomized case-control data sets from the extremes of the quantitative trait analysed by standard logistic regression. Using linear regression, markers with an effect in the extremes of the traits were almost undetectable, whereas analysing extremes by case-control design had superior power even for much smaller sample sizes. Two real data examples are provided to support our theoretical findings and to explore our mixture and parameter assumption. Our findings support the idea to re-analyse the available meta-analysis data sets to detect new loci in the extremes. Moreover, our investigation offers an explanation for discrepant findings when analysing quantitative traits in the general population and in the extremes. Copyright © 2011 S. Karger AG, Basel.

The mathematics of cancer: integrating quantitative models.

PubMed

Altrock, Philipp M; Liu, Lin L; Michor, Franziska

2015-12-01

Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.

Real-time quantitative polymerase chain reaction methods for four genetically modified maize varieties and maize DNA content in food.

PubMed

Brodmann, Peter D; Ilg, Evelyn C; Berthoud, Hélène; Herrmann, Andre

2002-01-01

Quantitative detection methods are needed for enforcement of the recently introduced labeling threshold for genetically modified organisms (GMOs) in food ingredients. This labeling threshold, which is set to 1% in the European Union and Switzerland, must be applied to all approved GMOs. Four different varieties of maize are approved in the European Union: the insect-resistant Bt176 maize (Maximizer), Btl 1 maize, Mon810 (YieldGard) maize, and the herbicide-tolerant T25 (Liberty Link) maize. Because the labeling must be considered individually for each ingredient, a quantitation system for the endogenous maize content is needed in addition to the GMO-specific detection systems. Quantitative real-time polymerase chain reaction detection methods were developed for the 4 approved genetically modified maize varieties and for an endogenous maize (invertase) gene system.

Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs.

PubMed

Longin, Carl Friedrich Horst; Gowda, Manje; Mühleisen, Jonathan; Ebmeyer, Erhard; Kazman, Ebrahim; Schachschneider, Ralf; Schacht, Johannes; Kirchhoff, Martin; Zhao, Yusheng; Reif, Jochen Christoph

2013-11-01

Commercial heterosis for grain yield is present in hybrid wheat but long-term competiveness of hybrid versus line breeding depends on the development of heterotic groups to improve hybrid prediction. Detailed knowledge of the amount of heterosis and quantitative genetic parameters are of paramount importance to assess the potential of hybrid breeding. Our objectives were to (1) examine the extent of midparent, better-parent and commercial heterosis in a vast population of 1,604 wheat (Triticum aestivum L.) hybrids and their parental elite inbred lines and (2) discuss the consequences of relevant quantitative parameters for the design of hybrid wheat breeding programs. Fifteen male lines were crossed in a factorial mating design with 120 female lines, resulting in 1,604 of the 1,800 potential single-cross hybrid combinations. The hybrids, their parents, and ten commercial wheat varieties were evaluated in multi-location field experiments for grain yield, plant height, heading time and susceptibility to frost, lodging, septoria tritici blotch, yellow rust, leaf rust, and powdery mildew at up to five locations. We observed that hybrids were superior to the mean of their parents for grain yield (10.7 %) and susceptibility to frost (-7.2 %), leaf rust (-8.4 %) and septoria tritici blotch (-9.3 %). Moreover, 69 hybrids significantly (P < 0.05) outyielded the best commercial inbred line variety underlining the potential of hybrid wheat breeding. The estimated quantitative genetic parameters suggest that the establishment of reciprocal recurrent selection programs is pivotal for a successful long-term hybrid wheat breeding.

A generalised individual-based algorithm for modelling the evolution of quantitative herbicide resistance in arable weed populations.

PubMed

Liu, Chun; Bridges, Melissa E; Kaundun, Shiv S; Glasgow, Les; Owen, Micheal Dk; Neve, Paul

2017-02-01

Simulation models are useful tools for predicting and comparing the risk of herbicide resistance in weed populations under different management strategies. Most existing models assume a monogenic mechanism governing herbicide resistance evolution. However, growing evidence suggests that herbicide resistance is often inherited in a polygenic or quantitative fashion. Therefore, we constructed a generalised modelling framework to simulate the evolution of quantitative herbicide resistance in summer annual weeds. Real-field management parameters based on Amaranthus tuberculatus (Moq.) Sauer (syn. rudis) control with glyphosate and mesotrione in Midwestern US maize-soybean agroecosystems demonstrated that the model can represent evolved herbicide resistance in realistic timescales. Sensitivity analyses showed that genetic and management parameters were impactful on the rate of quantitative herbicide resistance evolution, whilst biological parameters such as emergence and seed bank mortality were less important. The simulation model provides a robust and widely applicable framework for predicting the evolution of quantitative herbicide resistance in summer annual weed populations. The sensitivity analyses identified weed characteristics that would favour herbicide resistance evolution, including high annual fecundity, large resistance phenotypic variance and pre-existing herbicide resistance. Implications for herbicide resistance management and potential use of the model are discussed. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Complex Adaptive System Models and the Genetic Analysis of Plasma HDL-Cholesterol Concentration

PubMed Central

Rea, Thomas J.; Brown, Christine M.; Sing, Charles F.

2006-01-01

Despite remarkable advances in diagnosis and therapy, ischemic heart disease (IHD) remains a leading cause of morbidity and mortality in industrialized countries. Recent efforts to estimate the influence of genetic variation on IHD risk have focused on predicting individual plasma high-density lipoprotein cholesterol (HDL-C) concentration. Plasma HDL-C concentration (mg/dl), a quantitative risk factor for IHD, has a complex multifactorial etiology that involves the actions of many genes. Single gene variations may be necessary but are not individually sufficient to predict a statistically significant increase in risk of disease. The complexity of phenotype-genotype-environment relationships involved in determining plasma HDL-C concentration has challenged commonly held assumptions about genetic causation and has led to the question of which combination of variations, in which subset of genes, in which environmental strata of a particular population significantly improves our ability to predict high or low risk phenotypes. We document the limitations of inferences from genetic research based on commonly accepted biological models, consider how evidence for real-world dynamical interactions between HDL-C determinants challenges the simplifying assumptions implicit in traditional linear statistical genetic models, and conclude by considering research options for evaluating the utility of genetic information in predicting traits with complex etiologies. PMID:17146134

Island Rule, quantitative genetics and brain–body size evolution in Homo floresiensis

PubMed Central

2017-01-01

Colonization of islands often activate a complex chain of adaptive events that, over a relatively short evolutionary time, may drive strong shifts in body size, a pattern known as the Island Rule. It is arguably difficult to perform a direct analysis of the natural selection forces behind such a change in body size. Here, we used quantitative evolutionary genetic models, coupled with simulations and pattern-oriented modelling, to analyse the evolution of brain and body size in Homo floresiensis, a diminutive hominin species that appeared around 700 kya and survived up to relatively recent times (60–90 kya) on Flores Island, Indonesia. The hypothesis of neutral evolution was rejected in 97% of the simulations, and estimated selection gradients are within the range found in living natural populations. We showed that insularity may have triggered slightly different evolutionary trajectories for body and brain size, which means explaining the exceedingly small cranial volume of H. floresiensis requires additional selective forces acting on brain size alone. Our analyses also support previous conclusions that H. floresiensis may be most likely derived from an early Indonesian H. erectus, which is coherent with currently accepted biogeographical scenario for Homo expansion out of Africa. PMID:28637851

Island Rule, quantitative genetics and brain-body size evolution in Homo floresiensis.

PubMed

Diniz-Filho, José Alexandre Felizola; Raia, Pasquale

2017-06-28

Colonization of islands often activate a complex chain of adaptive events that, over a relatively short evolutionary time, may drive strong shifts in body size, a pattern known as the Island Rule. It is arguably difficult to perform a direct analysis of the natural selection forces behind such a change in body size. Here, we used quantitative evolutionary genetic models, coupled with simulations and pattern-oriented modelling, to analyse the evolution of brain and body size in Homo floresiensis , a diminutive hominin species that appeared around 700 kya and survived up to relatively recent times (60-90 kya) on Flores Island, Indonesia. The hypothesis of neutral evolution was rejected in 97% of the simulations, and estimated selection gradients are within the range found in living natural populations. We showed that insularity may have triggered slightly different evolutionary trajectories for body and brain size, which means explaining the exceedingly small cranial volume of H. floresiensis requires additional selective forces acting on brain size alone. Our analyses also support previous conclusions that H. floresiensis may be most likely derived from an early Indonesian H. erectus , which is coherent with currently accepted biogeographical scenario for Homo expansion out of Africa. © 2017 The Author(s).

Development and application of SINE multilocus and quantitative genetic markers to study oilseed rape (Brassica napus L.) crops.

PubMed

Allnutt, T R; Roper, K; Henry, C

2008-01-23

A genetic marker system based on the S1 Short Interspersed Elements (SINEs) in the important commercial crop, oilseed rape ( Brassica napus L.) has been developed. SINEs provided a successful multilocus, dominant marker system that was capable of clearly delineating winter- and spring-type crop varieties. Sixteen of 20 varieties tested showed unique profiles from the 17 polymorphic SINE markers generated. The 3' or 5' flank region of nine SINE markers were cloned, and DNA was sequenced. In addition, one putative pre-transposition SINE allele was cloned and sequenced. Two SINE flanking sequences were used to design real-time PCR assays. These quantitative SINE assays were applied to study the genetic structure of eight fields of oilseed rape crops. Studied fields were more genetically diverse than expected for the chosen loci (mean H T = 0.23). The spatial distribution of SINE marker frequencies was highly structured in some fields, suggesting locations of volunteer impurities within the crop. In one case, the assay identified a mislabeling of the crop variety. SINE markers were a useful tool for crop genetics, phylogenetics, variety identification, and purity analysis. The use and further application of quantitative, real-time PCR markers are discussed.

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.

Quantitative analysis of terahertz spectra for illicit drugs using adaptive-range micro-genetic algorithm

NASA Astrophysics Data System (ADS)

Chen, Yi; Ma, Yong; Lu, Zheng; Peng, Bei; Chen, Qin

2011-08-01

In the field of anti-illicit drug applications, many suspicious mixture samples might consist of various drug components—for example, a mixture of methamphetamine, heroin, and amoxicillin—which makes spectral identification very difficult. A terahertz spectroscopic quantitative analysis method using an adaptive range micro-genetic algorithm with a variable internal population (ARVIPɛμGA) has been proposed. Five mixture cases are discussed using ARVIPɛμGA driven quantitative terahertz spectroscopic analysis in this paper. The devised simulation results show agreement with the previous experimental results, which suggested that the proposed technique has potential applications for terahertz spectral identifications of drug mixture components. The results show agreement with the results obtained using other experimental and numerical techniques.

A quantitative genetic analysis of hibernation emergence date in a wild population of Columbian ground squirrels.

PubMed

Lane, J E; Kruuk, L E B; Charmantier, A; Murie, J O; Coltman, D W; Buoro, M; Raveh, S; Dobson, F S

2011-09-01

The life history schedules of wild organisms have long attracted scientific interest, and, in light of ongoing climate change, an understanding of their genetic and environmental underpinnings is increasingly becoming of applied concern. We used a multi-generation pedigree and detailed phenotypic records, spanning 18 years, to estimate the quantitative genetic influences on the timing of hibernation emergence in a wild population of Columbian ground squirrels (Urocitellus columbianus). Emergence date was significantly heritable [h(2) = 0.22 ± 0.05 (in females) and 0.34 ± 0.14 (in males)], and there was a positive genetic correlation (r(G) = 0.76 ± 0.22) between male and female emergence dates. In adult females, the heritabilities of body mass at emergence and oestrous date were h(2) = 0.23 ± 0.09 and h(2) = 0.18 ± 0.12, respectively. The date of hibernation emergence has been hypothesized to have evolved so as to synchronize subsequent reproduction with upcoming peaks in vegetation abundance. In support of this hypothesis, although levels of phenotypic variance in emergence date were higher than oestrous date, there was a highly significant genetic correlation between the two (r(G) = 0.98 ± 0.01). Hibernation is a prominent feature in the annual cycle of many small mammals, but our understanding of its influences lags behind that for phenological traits in many other taxa. Our results provide the first insight into its quantitative genetic influences and thus help contribute to a more general understanding of its evolutionary significance. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Decay Of Bacterial Pathogens, Fecal Indicators, And Real-Time Quantitative PCR Genetic Markers In Manure-Amended Soils

EPA Science Inventory

This study examined persistence and decay of bacterial pathogens, fecal indicator bacteria (FIB), and emerging real-time quantitative PCR (qPCR) genetic markers for rapid detection of fecal pollution in manure-amended agricultural soils. Known concentrations of transformed green...

Decay Of Bacterial Pathogen, Fecal Indicators, And Real-Time Quantitative PCR Genetic Markers In Manure Amended Soils

EPA Science Inventory

This study examined persistence and decay of bacterial pathogens, fecal indicator bacteria, and emerging real-time quantitative PCR (qPCR) genetic markers for rapid detection of fecal pollution in manre-amended agricultural soils. Known concentrations of transformed green fluore...

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

PubMed Central

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

2001-01-01

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

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-ling; Xiong, Momiao; Fan, Ruzong

2017-01-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data. PMID:28000696

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.

PubMed

Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-Ling; Xiong, Momiao; Fan, Ruzong

2017-02-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

PubMed

Naik, P K; Singh, T; Singh, H

2009-07-01

Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

NASA Astrophysics Data System (ADS)

Chung, K. K.; Do, D. Q.

2010-09-01

In order to model relationships between chemical structures and biological effects in quantitative structure-activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data.

Beyond Punnett Squares: Student Word Association and Explanations of Phenotypic Variation through an Integrative Quantitative Genetics Unit Investigating Anthocyanin Inheritance and Expression in Brassica rapa Fast Plants

PubMed Central

Smith, Amber R.; Williams, Paul H.; McGee, Seth A.; Dósa, Katalin; Pfammatter, Jesse

2014-01-01

Genetics instruction in introductory biology is often confined to Mendelian genetics and avoids the complexities of variation in quantitative traits. Given the driving question “What determines variation in phenotype (Pv)? (Pv=Genotypic variation Gv + environmental variation Ev),” we developed a 4-wk unit for an inquiry-based laboratory course focused on the inheritance and expression of a quantitative trait in varying environments. We utilized Brassica rapa Fast Plants as a model organism to study variation in the phenotype anthocyanin pigment intensity. As an initial curriculum assessment, we used free word association to examine students’ cognitive structures before and after the unit and explanations in students’ final research posters with particular focus on variation (Pv = Gv + Ev). Comparison of pre- and postunit word frequency revealed a shift in words and a pattern of co-occurring concepts indicative of change in cognitive structure, with particular focus on “variation” as a proposed threshold concept and primary goal for students’ explanations. Given review of 53 posters, we found ∼50% of students capable of intermediate to high-level explanations combining both Gv and Ev influence on expression of anthocyanin intensity (Pv). While far from “plug and play,” this conceptually rich, inquiry-based unit holds promise for effective integration of quantitative and Mendelian genetics. PMID:25185225

Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans

PubMed Central

Saykin, Andrew J.; Shen, Li; Foroud, Tatiana M.; Potkin, Steven G.; Swaminathan, Shanker; Kim, Sungeun; Risacher, Shannon L.; Nho, Kwangsik; Huentelman, Matthew J.; Craig, David W.; Thompson, Paul M.; Stein, Jason L.; Moore, Jason H.; Farrer, Lindsay A.; Green, Robert C.; Bertram, Lars; Jack, Clifford R.; Weiner, Michael W.

2010-01-01

The role of the Alzheimer’s Disease Neuroimaging Initiative Genetics Core is to facilitate the investigation of genetic influences on disease onset and trajectory as reflected in structural, functional, and molecular imaging changes; fluid biomarkers; and cognitive status. Major goals include (1) blood sample processing, genotyping, and dissemination, (2) genome-wide association studies (GWAS) of longitudinal phenotypic data, and (3) providing a central resource, point of contact and planning group for genetics within Alzheimer’s Disease Neuroimaging Initiative. Genome-wide array data have been publicly released and updated, and several neuroimaging GWAS have recently been reported examining baseline magnetic resonance imaging measures as quantitative phenotypes. Other preliminary investigations include copy number variation in mild cognitive impairment and Alzheimer’s disease and GWAS of baseline cerebrospinal fluid biomarkers and longitudinal changes on magnetic resonance imaging. Blood collection for RNA studies is a new direction. Genetic studies of longitudinal phenotypes hold promise for elucidating disease mechanisms and risk, development of therapeutic strategies, and refining selection criteria for clinical trials. PMID:20451875

Combining Quantitative Genetic Footprinting and Trait Enrichment Analysis to Identify Fitness Determinants of a Bacterial Pathogen

PubMed Central

Wiles, Travis J.; Norton, J. Paul; Russell, Colin W.; Dalley, Brian K.; Fischer, Kael F.; Mulvey, Matthew A.

2013-01-01

Strains of Extraintestinal Pathogenic Escherichia c oli (ExPEC) exhibit an array of virulence strategies and are a major cause of urinary tract infections, sepsis and meningitis. Efforts to understand ExPEC pathogenesis are challenged by the high degree of genetic and phenotypic variation that exists among isolates. Determining which virulence traits are widespread and which are strain-specific will greatly benefit the design of more effective therapies. Towards this goal, we utilized a quantitative genetic footprinting technique known as transposon insertion sequencing (Tn-seq) in conjunction with comparative pathogenomics to functionally dissect the genetic repertoire of a reference ExPEC isolate. Using Tn-seq and high-throughput zebrafish infection models, we tracked changes in the abundance of ExPEC variants within saturated transposon mutant libraries following selection within distinct host niches. Nine hundred and seventy bacterial genes (18% of the genome) were found to promote pathogen fitness in either a niche-dependent or independent manner. To identify genes with the highest therapeutic and diagnostic potential, a novel Trait Enrichment Analysis (TEA) algorithm was developed to ascertain the phylogenetic distribution of candidate genes. TEA revealed that a significant portion of the 970 genes identified by Tn-seq have homologues more often contained within the genomes of ExPEC and other known pathogens, which, as suggested by the first axiom of molecular Koch's postulates, is considered to be a key feature of true virulence determinants. Three of these Tn-seq-derived pathogen-associated genes—a transcriptional repressor, a putative metalloendopeptidase toxin and a hypothetical DNA binding protein—were deleted and shown to independently affect ExPEC fitness in zebrafish and mouse models of infection. Together, the approaches and observations reported herein provide a resource for future pathogenomics-based research and highlight the diversity of

Generalized PSF modeling for optimized quantitation in PET imaging.

PubMed

Ashrafinia, Saeed; Mohy-Ud-Din, Hassan; Karakatsanis, Nicolas A; Jha, Abhinav K; Casey, Michael E; Kadrmas, Dan J; Rahmim, Arman

2017-06-21

Point-spread function (PSF) modeling offers the ability to account for resolution degrading phenomena within the PET image generation framework. PSF modeling improves resolution and enhances contrast, but at the same time significantly alters image noise properties and induces edge overshoot effect. Thus, studying the effect of PSF modeling on quantitation task performance can be very important. Frameworks explored in the past involved a dichotomy of PSF versus no-PSF modeling. By contrast, the present work focuses on quantitative performance evaluation of standard uptake value (SUV) PET images, while incorporating a wide spectrum of PSF models, including those that under- and over-estimate the true PSF, for the potential of enhanced quantitation of SUVs. The developed framework first analytically models the true PSF, considering a range of resolution degradation phenomena (including photon non-collinearity, inter-crystal penetration and scattering) as present in data acquisitions with modern commercial PET systems. In the context of oncologic liver FDG PET imaging, we generated 200 noisy datasets per image-set (with clinically realistic noise levels) using an XCAT anthropomorphic phantom with liver tumours of varying sizes. These were subsequently reconstructed using the OS-EM algorithm with varying PSF modelled kernels. We focused on quantitation of both SUV mean and SUV max , including assessment of contrast recovery coefficients, as well as noise-bias characteristics (including both image roughness and coefficient of-variability), for different tumours/iterations/PSF kernels. It was observed that overestimated PSF yielded more accurate contrast recovery for a range of tumours, and typically improved quantitative performance. For a clinically reasonable number of iterations, edge enhancement due to PSF modeling (especially due to over-estimated PSF) was in fact seen to lower SUV mean bias in small tumours. Overall, the results indicate that exactly matched PSF

Nonparametric modeling of longitudinal covariance structure in functional mapping of quantitative trait loci.

PubMed

Yap, John Stephen; Fan, Jianqing; Wu, Rongling

2009-12-01

Estimation of the covariance structure of longitudinal processes is a fundamental prerequisite for the practical deployment of functional mapping designed to study the genetic regulation and network of quantitative variation in dynamic complex traits. We present a nonparametric approach for estimating the covariance structure of a quantitative trait measured repeatedly at a series of time points. Specifically, we adopt Huang et al.'s (2006, Biometrika 93, 85-98) approach of invoking the modified Cholesky decomposition and converting the problem into modeling a sequence of regressions of responses. A regularized covariance estimator is obtained using a normal penalized likelihood with an L(2) penalty. This approach, embedded within a mixture likelihood framework, leads to enhanced accuracy, precision, and flexibility of functional mapping while preserving its biological relevance. Simulation studies are performed to reveal the statistical properties and advantages of the proposed method. A real example from a mouse genome project is analyzed to illustrate the utilization of the methodology. The new method will provide a useful tool for genome-wide scanning for the existence and distribution of quantitative trait loci underlying a dynamic trait important to agriculture, biology, and health sciences.

Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease.

PubMed

Schmidt, Vanessa; Baum, Katharina; Lao, Angelyn; Rateitschak, Katja; Schmitz, Yvonne; Teichmann, Anke; Wiesner, Burkhard; Petersen, Claus Munck; Nykjaer, Anders; Wolf, Jana; Wolkenhauer, Olaf; Willnow, Thomas E

2012-01-04

The extent of proteolytic processing of the amyloid precursor protein (APP) into neurotoxic amyloid-β (Aβ) peptides is central to the pathology of Alzheimer's disease (AD). Accordingly, modifiers that increase Aβ production rates are risk factors in the sporadic form of AD. In a novel systems biology approach, we combined quantitative biochemical studies with mathematical modelling to establish a kinetic model of amyloidogenic processing, and to evaluate the influence by SORLA/SORL1, an inhibitor of APP processing and important genetic risk factor. Contrary to previous hypotheses, our studies demonstrate that secretases represent allosteric enzymes that require cooperativity by APP oligomerization for efficient processing. Cooperativity enables swift adaptive changes in secretase activity with even small alterations in APP concentration. We also show that SORLA prevents APP oligomerization both in cultured cells and in the brain in vivo, eliminating the preferred form of the substrate and causing secretases to switch to a less efficient non-allosteric mode of action. These data represent the first mathematical description of the contribution of genetic risk factors to AD substantiating the relevance of subtle changes in SORLA levels for amyloidogenic processing as proposed for patients carrying SORL1 risk alleles.

Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease

PubMed Central

Schmidt, Vanessa; Baum, Katharina; Lao, Angelyn; Rateitschak, Katja; Schmitz, Yvonne; Teichmann, Anke; Wiesner, Burkhard; Petersen, Claus Munck; Nykjaer, Anders; Wolf, Jana; Wolkenhauer, Olaf; Willnow, Thomas E

2012-01-01

The extent of proteolytic processing of the amyloid precursor protein (APP) into neurotoxic amyloid-β (Aβ) peptides is central to the pathology of Alzheimer's disease (AD). Accordingly, modifiers that increase Aβ production rates are risk factors in the sporadic form of AD. In a novel systems biology approach, we combined quantitative biochemical studies with mathematical modelling to establish a kinetic model of amyloidogenic processing, and to evaluate the influence by SORLA/SORL1, an inhibitor of APP processing and important genetic risk factor. Contrary to previous hypotheses, our studies demonstrate that secretases represent allosteric enzymes that require cooperativity by APP oligomerization for efficient processing. Cooperativity enables swift adaptive changes in secretase activity with even small alterations in APP concentration. We also show that SORLA prevents APP oligomerization both in cultured cells and in the brain in vivo, eliminating the preferred form of the substrate and causing secretases to switch to a less efficient non-allosteric mode of action. These data represent the first mathematical description of the contribution of genetic risk factors to AD substantiating the relevance of subtle changes in SORLA levels for amyloidogenic processing as proposed for patients carrying SORL1 risk alleles. PMID:21989385

Quantitative trait loci from the host genetic background modulate the durability of a resistance gene: a rational basis for sustainable resistance breeding in plants.

PubMed

Quenouille, J; Paulhiac, E; Moury, B; Palloix, A

2014-06-01

The combination of major resistance genes with quantitative resistance factors is hypothesized as a promising breeding strategy to preserve the durability of resistant cultivar, as recently observed in different pathosystems. Using the pepper (Capsicum annuum)/Potato virus Y (PVY, genus Potyvirus) pathosystem, we aimed at identifying plant genetic factors directly affecting the frequency of virus adaptation to the major resistance gene pvr2(3) and at comparing them with genetic factors affecting quantitative resistance. The resistance breakdown frequency was a highly heritable trait (h(2)=0.87). Four loci including additive quantitative trait loci (QTLs) and epistatic interactions explained together 70% of the variance of pvr2(3) breakdown frequency. Three of the four QTLs controlling pvr2(3) breakdown frequency were also involved in quantitative resistance, strongly suggesting that QTLs controlling quantitative resistance have a pleiotropic effect on the durability of the major resistance gene. With the first mapping of QTLs directly affecting resistance durability, this study provides a rationale for sustainable resistance breeding. Surprisingly, a genetic trade-off was observed between the durability of PVY resistance controlled by pvr2(3) and the spectrum of the resistance against different potyviruses. This trade-off seemed to have been resolved by the combination of minor-effect durability QTLs under long-term farmer selection.

IWGT report on quantitative approaches to genotoxicity risk ...

EPA Pesticide Factsheets

This is the second of two reports from the International Workshops on Genotoxicity Testing (IWGT) Working Group on Quantitative Approaches to Genetic Toxicology Risk Assessment (the QWG). The first report summarized the discussions and recommendations of the QWG related to the need for quantitative dose–response analysis of genetic toxicology data, the existence and appropriate evaluation of threshold responses, and methods to analyze exposure-response relationships and derive points of departure (PoDs) from which acceptable exposure levels could be determined. This report summarizes the QWG discussions and recommendations regarding appropriate approaches to evaluate exposure-related risks of genotoxic damage, including extrapolation below identified PoDs and across test systems and species. Recommendations include the selection of appropriate genetic endpoints and target tissues, uncertainty factors and extrapolation methods to be considered, the importance and use of information on mode of action, toxicokinetics, metabolism, and exposure biomarkers when using quantitative exposure-response data to determine acceptable exposure levels in human populations or to assess the risk associated with known or anticipated exposures. The empirical relationship between genetic damage (mutation and chromosomal aberration) and cancer in animal models was also examined. It was concluded that there is a general correlation between cancer induction and mutagenic and/or clast

A Developmental-Genetic Model of Alcoholism: Implications for Genetic Research.

ERIC Educational Resources Information Center

Devor, Eric J.

1994-01-01

Research for biological-genetic markers of alcoholism is discussed in context of a multifactorial, heterogeneous, developmental model. Suggested that strategies used in linkage and association studies will require modification. Also suggested several extant associations of genetic markers represent true secondary interactive phenomena that alter…

Joint effects of pleiotropic selection and stabilizing selection on the maintenance of quantitative genetic variation at mutation-selection balance.

PubMed Central

Zhang, Xu-Sheng; Hill, William G

2002-01-01

In quantitative genetics, there are two basic "conflicting" observations: abundant polygenic variation and strong stabilizing selection that should rapidly deplete that variation. This conflict, although having attracted much theoretical attention, still stands open. Two classes of model have been proposed: real stabilizing selection directly on the metric trait under study and apparent stabilizing selection caused solely by the deleterious pleiotropic side effects of mutations on fitness. Here these models are combined and the total stabilizing selection observed is assumed to derive simultaneously through these two different mechanisms. Mutations have effects on a metric trait and on fitness, and both effects vary continuously. The genetic variance (V(G)) and the observed strength of total stabilizing selection (V(s,t)) are analyzed with a rare-alleles model. Both kinds of selection reduce V(G) but their roles in depleting it are not independent: The magnitude of pleiotropic selection depends on real stabilizing selection and such dependence is subject to the shape of the distributions of mutational effects. The genetic variation maintained thus depends on the kurtosis as well as the variance of mutational effects: All else being equal, V(G) increases with increasing leptokurtosis of mutational effects on fitness, while for a given distribution of mutational effects on fitness, V(G) decreases with increasing leptokurtosis of mutational effects on the trait. The V(G) and V(s,t) are determined primarily by real stabilizing selection while pleiotropic effects, which can be large, have only a limited impact. This finding provides some promise that a high heritability can be explained under strong total stabilizing selection for what are regarded as typical values of mutation and selection parameters. PMID:12242254

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

PubMed Central

Lorenz, Kim; Cohen, Barak A.

2012-01-01

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL. PMID:22942125

Quantitative Chemical-Genetic Interaction Map Connects Gene Alterations to Drug Responses | Office of Cancer Genomics

Cancer.gov

In a recent Cancer Discovery report, CTD2 researchers at the University of California in San Francisco developed a new quantitative chemical-genetic interaction mapping approach to evaluate drug sensitivity or resistance in isogenic cell lines. Performing a high-throughput screen with isogenic cell lines allowed the researchers to explore the impact of a panel of emerging and established drugs on cells overexpressing a single cancer-associated gene in isolation.

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

PubMed

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

2016-02-01

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

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

Quantitative Modeling of Earth Surface Processes

NASA Astrophysics Data System (ADS)

Pelletier, Jon D.

This textbook describes some of the most effective and straightforward quantitative techniques for modeling Earth surface processes. By emphasizing a core set of equations and solution techniques, the book presents state-of-the-art models currently employed in Earth surface process research, as well as a set of simple but practical research tools. Detailed case studies demonstrate application of the methods to a wide variety of processes including hillslope, fluvial, aeolian, glacial, tectonic, and climatic systems. Exercises at the end of each chapter begin with simple calculations and then progress to more sophisticated problems that require computer programming. All the necessary computer codes are available online at www.cambridge.org/9780521855976. Assuming some knowledge of calculus and basic programming experience, this quantitative textbook is designed for advanced geomorphology courses and as a reference book for professional researchers in Earth and planetary science looking for a quantitative approach to Earth surface processes.

The influence of genetic drift and selection on quantitative traits in a plant pathogenic fungus.

PubMed

Stefansson, Tryggvi S; McDonald, Bruce A; Willi, Yvonne

2014-01-01

Genetic drift and selection are ubiquitous evolutionary forces acting to shape genetic variation in populations. While their relative importance has been well studied in plants and animals, less is known about their relative importance in fungal pathogens. Because agro-ecosystems are more homogeneous environments than natural ecosystems, stabilizing selection may play a stronger role than genetic drift or diversifying selection in shaping genetic variation among populations of fungal pathogens in agro-ecosystems. We tested this hypothesis by conducting a QST/FST analysis using agricultural populations of the barley pathogen Rhynchosporium commune. Population divergence for eight quantitative traits (QST) was compared with divergence at eight neutral microsatellite loci (FST) for 126 pathogen strains originating from nine globally distributed field populations to infer the effects of genetic drift and types of selection acting on each trait. Our analyses indicated that five of the eight traits had QST values significantly lower than FST, consistent with stabilizing selection, whereas one trait, growth under heat stress (22°C), showed evidence of diversifying selection and local adaptation (QST>FST). Estimates of heritability were high for all traits (means ranging between 0.55-0.84), and average heritability across traits was negatively correlated with microsatellite gene diversity. Some trait pairs were genetically correlated and there was significant evidence for a trade-off between spore size and spore number, and between melanization and growth under benign temperature. Our findings indicate that many ecologically and agriculturally important traits are under stabilizing selection in R. commune and that high within-population genetic variation is maintained for these traits.

How Surrogate and Chemical Genetics in Model Organisms Can Suggest Therapies for Human Genetic Diseases.

PubMed

Strynatka, Katherine A; Gurrola-Gal, Michelle C; Berman, Jason N; McMaster, Christopher R

2018-03-01

Genetic diseases are both inherited and acquired. Many genetic diseases fall under the paradigm of orphan diseases, a disease found in < 1 in 2000 persons. With rapid and cost-effective genome sequencing becoming the norm, many causal mutations for genetic diseases are being rapidly determined. In this regard, model organisms are playing an important role in validating if specific mutations identified in patients drive the observed phenotype. An emerging challenge for model organism researchers is the application of genetic and chemical genetic platforms to discover drug targets and drugs/drug-like molecules for potential treatment options for patients with genetic disease. This review provides an overview of how model organisms have contributed to our understanding of genetic disease, with a focus on the roles of yeast and zebrafish in gene discovery and the identification of compounds that could potentially treat human genetic diseases. Copyright © 2018 by the Genetics Society of America.

How Surrogate and Chemical Genetics in Model Organisms Can Suggest Therapies for Human Genetic Diseases

PubMed Central

Strynatka, Katherine A.; Gurrola-Gal, Michelle C.; Berman, Jason N.; McMaster, Christopher R.

2018-01-01

Genetic diseases are both inherited and acquired. Many genetic diseases fall under the paradigm of orphan diseases, a disease found in < 1 in 2000 persons. With rapid and cost-effective genome sequencing becoming the norm, many causal mutations for genetic diseases are being rapidly determined. In this regard, model organisms are playing an important role in validating if specific mutations identified in patients drive the observed phenotype. An emerging challenge for model organism researchers is the application of genetic and chemical genetic platforms to discover drug targets and drugs/drug-like molecules for potential treatment options for patients with genetic disease. This review provides an overview of how model organisms have contributed to our understanding of genetic disease, with a focus on the roles of yeast and zebrafish in gene discovery and the identification of compounds that could potentially treat human genetic diseases. PMID:29487144

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

PubMed

Cobben, Marleen M P; van Noordwijk, Arie J

2017-10-01

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

A two-locus model of spatially varying stabilizing or directional selection on a quantitative trait

PubMed Central

Geroldinger, Ludwig; Bürger, Reinhard

2014-01-01

The consequences of spatially varying, stabilizing or directional selection on a quantitative trait in a subdivided population are studied. A deterministic two-locus two-deme model is employed to explore the effects of migration, the degree of divergent selection, and the genetic architecture, i.e., the recombination rate and ratio of locus effects, on the maintenance of genetic variation. The possible equilibrium configurations are determined as functions of the migration rate. They depend crucially on the strength of divergent selection and the genetic architecture. The maximum migration rates are investigated below which a stable fully polymorphic equilibrium or a stable single-locus polymorphism can exist. Under stabilizing selection, but with different optima in the demes, strong recombination may facilitate the maintenance of polymorphism. However usually, and in particular with directional selection in opposite direction, the critical migration rates are maximized by a concentrated genetic architecture, i.e., by a major locus and a tightly linked minor one. Thus, complementing previous work on the evolution of genetic architectures in subdivided populations subject to diversifying selection, it is shown that concentrated architectures may aid the maintenance of polymorphism. Conditions are obtained when this is the case. Finally, the dependence of the phenotypic variance, linkage disequilibrium, and various measures of local adaptation and differentiation on the parameters is elaborated. PMID:24726489

A two-locus model of spatially varying stabilizing or directional selection on a quantitative trait.

PubMed

Geroldinger, Ludwig; Bürger, Reinhard

2014-06-01

The consequences of spatially varying, stabilizing or directional selection on a quantitative trait in a subdivided population are studied. A deterministic two-locus two-deme model is employed to explore the effects of migration, the degree of divergent selection, and the genetic architecture, i.e., the recombination rate and ratio of locus effects, on the maintenance of genetic variation. The possible equilibrium configurations are determined as functions of the migration rate. They depend crucially on the strength of divergent selection and the genetic architecture. The maximum migration rates are investigated below which a stable fully polymorphic equilibrium or a stable single-locus polymorphism can exist. Under stabilizing selection, but with different optima in the demes, strong recombination may facilitate the maintenance of polymorphism. However usually, and in particular with directional selection in opposite direction, the critical migration rates are maximized by a concentrated genetic architecture, i.e., by a major locus and a tightly linked minor one. Thus, complementing previous work on the evolution of genetic architectures in subdivided populations subject to diversifying selection, it is shown that concentrated architectures may aid the maintenance of polymorphism. Conditions are obtained when this is the case. Finally, the dependence of the phenotypic variance, linkage disequilibrium, and various measures of local adaptation and differentiation on the parameters is elaborated. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Quantitative genetic analysis of the bTB diagnostic single intradermal comparative cervical test (SICCT).

PubMed

Tsairidou, Smaragda; Brotherstone, Susan; Coffey, Mike; Bishop, Stephen C; Woolliams, John A

2016-11-24

Bovine tuberculosis (bTB) is a disease of significant economic importance and is a persistent animal health problem with implications for public health worldwide. Control of bTB in the UK has relied on diagnosis through the single intradermal comparative cervical test (SICCT). However, limitations in the sensitivity of this test hinder successful eradication and the control of bTB remains a major challenge. Genetic selection for cattle that are more resistant to bTB infection can assist in bTB control. The aim of this study was to conduct a quantitative genetic analysis of SICCT measurements collected during bTB herd testing. Genetic selection for bTB resistance will be partially informed by SICCT-based diagnosis; therefore it is important to know whether, in addition to increasing bTB resistance, this might also alter genetically the epidemiological characteristics of SICCT. Our main findings are that: (1) the SICCT test is robust at the genetic level, since its hierarchy and comparative nature provide substantial protection against random genetic changes that arise from genetic drift and from correlated responses among its components due to either natural or artificial selection; (2) the comparative nature of SICCT provides effective control for initial skin thickness and age-dependent differences; and (3) continuous variation in SICCT is only lowly heritable and has a weak correlation with SICCT positivity among healthy animals which was not significantly different from zero (P > 0.05). These emerging results demonstrate that genetic selection for bTB resistance is unlikely to change the probability of correctly identifying non-infected animals, i.e. the test's specificity, while reducing the overall number of cases. This study cannot exclude all theoretical risks from selection on resistance to bTB infection but the role of SICCT in disease control is unlikely to be rapidly undermined, with any adverse correlated responses expected to be weak and slow, which

Quantitative Predictive Models for Systemic Toxicity (SOT)

EPA Science Inventory

Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...

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.

Explanatory Models of Genetics and Genetic Risk among a Selected Group of Students.

PubMed

Goltz, Heather Honoré; Bergman, Margo; Goodson, Patricia

2016-01-01

This exploratory qualitative study focuses on how college students conceptualize genetics and genetic risk, concepts essential for genetic literacy (GL) and genetic numeracy (GN), components of overall health literacy (HL). HL is dependent on both the background knowledge and culture of a patient, and lower HL is linked to increased morbidity and mortality for a number of chronic health conditions (e.g., diabetes and cancer). A purposive sample of 86 students from three Southwestern universities participated in eight focus groups. The sample ranged in age from 18 to 54 years, and comprised primarily of female (67.4%), single (74.4%), and non-White (57%) participants, none of whom were genetics/biology majors. A holistic-content approach revealed broad categories concerning participants' explanatory models (EMs) of genetics and genetic risk. Participants' EMs were grounded in highly contextualized narratives that only partially overlapped with biomedical models. While higher education levels should be associated with predominately knowledge-based EM of genetic risk, this study shows that even in well-educated populations cultural factors can dominate. Study findings reveal gaps in how this sample of young adults obtains, processes, and understands genetic/genomic concepts. Future studies should assess how individuals with low GL and GN obtain and process genetics and genetic risk information and incorporate this information into health decision making. Future work should also address the interaction of communication between health educators, providers, and genetic counselors, to increase patient understanding of genetic risk.

Development and evaluation of event-specific quantitative PCR method for genetically modified soybean A2704-12.

PubMed

Takabatake, Reona; Akiyama, Hiroshi; Sakata, Kozue; Onishi, Mari; Koiwa, Tomohiro; Futo, Satoshi; Minegishi, Yasutaka; Teshima, Reiko; Mano, Junichi; Furui, Satoshi; Kitta, Kazumi

2011-01-01

A novel real-time PCR-based analytical method was developed for the event-specific quantification of a genetically modified (GM) soybean event; A2704-12. During the plant transformation, DNA fragments derived from pUC19 plasmid were integrated in A2704-12, and the region was found to be A2704-12 specific. The pUC19-derived DNA sequences were used as primers for the specific detection of A2704-12. We first tried to construct a standard plasmid for A2704-12 quantification using pUC19. However, non-specific signals appeared with both qualitative and quantitative PCR analyses using the specific primers with pUC19 as a template, and we then constructed a plasmid using pBR322. The conversion factor (C(f)), which is required to calculate the amount of the genetically modified organism (GMO), was experimentally determined with two real-time PCR instruments, the Applied Biosystems 7900HT and the Applied Biosystems 7500. The determined C(f) values were both 0.98. The quantitative method was evaluated by means of blind tests in multi-laboratory trials using the two real-time PCR instruments. The limit of quantitation for the method was estimated to be 0.1%. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSD(R)), and the determined bias and RSD(R) values for the method were each less than 20%. These results suggest that the developed method would be suitable for practical analyses for the detection and quantification of A2704-12.

Latent spatial models and sampling design for landscape genetics

USGS Publications Warehouse

Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

Revisiting the case for genetically engineered mouse models in human myelodysplastic syndrome research.

PubMed

Zhou, Ting; Kinney, Marsha C; Scott, Linda M; Zinkel, Sandra S; Rebel, Vivienne I

2015-08-27

Much-needed attention has been given of late to diseases specifically associated with an expanding elderly population. Myelodysplastic syndrome (MDS), a hematopoietic stem cell-based blood disease, is one of these. The lack of clear understanding of the molecular mechanisms underlying the pathogenesis of this disease has hampered the development of efficacious therapies, especially in the presence of comorbidities. Mouse models could potentially provide new insights into this disease, although primary human MDS cells grow poorly in xenografted mice. This makes genetically engineered murine models a more attractive proposition, although this approach is not without complications. In particular, it is unclear if or how myelodysplasia (abnormal blood cell morphology), a key MDS feature in humans, presents in murine cells. Here, we evaluate the histopathologic features of wild-type mice and 23 mouse models with verified myelodysplasia. We find that certain features indicative of myelodysplasia in humans, such as Howell-Jolly bodies and low neutrophilic granularity, are commonplace in healthy mice, whereas other features are similarly abnormal in humans and mice. Quantitative hematopoietic parameters, such as blood cell counts, are required to distinguish between MDS and related diseases. We provide data that mouse models of MDS can be genetically engineered and faithfully recapitulate human disease. © 2015 by The American Society of Hematology.

Estimation of genetic parameters and detection of quantitative trait loci for metabolites in Danish Holstein milk.

PubMed

Buitenhuis, A J; Sundekilde, U K; Poulsen, N A; Bertram, H C; Larsen, L B; Sørensen, P

2013-05-01

Small components and metabolites in milk are significant for the utilization of milk, not only in dairy food production but also as disease predictors in dairy cattle. This study focused on estimation of genetic parameters and detection of quantitative trait loci for metabolites in bovine milk. For this purpose, milk samples were collected in mid lactation from 371 Danish Holstein cows in first to third parity. A total of 31 metabolites were detected and identified in bovine milk by using (1)H nuclear magnetic resonance (NMR) spectroscopy. Cows were genotyped using a bovine high-density single nucleotide polymorphism (SNP) chip. Based on the SNP data, a genomic relationship matrix was calculated and used as a random factor in a model together with 2 fixed factors (herd and lactation stage) to estimate the heritability and breeding value for individual metabolites in the milk. Heritability was in the range of 0 for lactic acid to >0.8 for orotic acid and β-hydroxybutyrate. A single SNP association analysis revealed 7 genome-wide significant quantitative trait loci [malonate: Bos taurus autosome (BTA)2 and BTA7; galactose-1-phosphate: BTA2; cis-aconitate: BTA11; urea: BTA12; carnitine: BTA25; and glycerophosphocholine: BTA25]. These results demonstrate that selection for metabolites in bovine milk may be possible. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster.

PubMed

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

2015-05-06

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

Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster

PubMed Central

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

2015-01-01

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

A Quantitative Chemotherapy Genetic Interaction Map Reveals Factors Associated with PARP Inhibitor Resistance.

PubMed

Hu, Hsien-Ming; Zhao, Xin; Kaushik, Swati; Robillard, Lilliane; Barthelet, Antoine; Lin, Kevin K; Shah, Khyati N; Simmons, Andy D; Raponi, Mitch; Harding, Thomas C; Bandyopadhyay, Sourav

2018-04-17

Chemotherapy is used to treat most cancer patients, yet our understanding of factors that dictate response and resistance to such drugs remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells charting the impact of the knockdown of 625 genes related to cancer and DNA repair on sensitivity to 29 drugs, covering all classes of chemotherapy. This quantitative map is predictive of interactions maintained in other cell lines, identifies DNA-repair factors, predicts cancer cell line responses to therapy, and prioritizes synergistic drug combinations. We identify that ARID1A loss confers resistance to PARP inhibitors in cells and ovarian cancer patients and that loss of GPBP1 causes resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map helps navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Global genetic architecture of an erythroid quantitative trait locus, HMIP-2.

PubMed

Menzel, Stephan; Rooks, Helen; Zelenika, Diana; Mtatiro, Siana N; Gnanakulasekaran, Akshala; Drasar, Emma; Cox, Sharon; Liu, Li; Masood, Mariam; Silver, Nicholas; Garner, Chad; Vasavda, Nisha; Howard, Jo; Makani, Julie; Adekile, Adekunle; Pace, Betty; Spector, Tim; Farrall, Martin; Lathrop, Mark; Thein, Swee Lay

2014-11-01

HMIP-2 is a human quantitative trait locus affecting peripheral numbers, size and hemoglobin composition of red blood cells, with a marked effect on the persistence of the fetal form of hemoglobin, HbF, in adults. The locus consists of multiple common variants in an enhancer region for MYB (chr 6q23.3), which encodes the hematopoietic transcription factor cMYB. Studying a European population cohort and four African-descended groups of patients with sickle cell anemia, we found that all share a set of two spatially separate HbF-promoting alleles at HMIP-2, termed "A" and "B." These typically occurred together ("A-B") on European chromosomes, but existed on separate homologous chromosomes in Africans. Using haplotype signatures for "A" and "B," we interrogated public population datasets. Haplotypes carrying only "A" or "B" were typical for populations in Sub-Saharan Africa. The "A-B" combination was frequent in European, Asian, and Amerindian populations. Both alleles were infrequent in tropical regions, possibly undergoing negative selection by geographical factors, as has been reported for malaria with other hematological traits. We propose that the ascertainment of worldwide distribution patterns for common, HbF-promoting alleles can aid their further genetic characterization, including the investigation of gene-environment interaction during human migration and adaptation. © 2014 The Authors. Annals of Human Genetics published by University College London (UCL) and John Wiley & Sons Ltd.

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens.

PubMed

Abdollahi-Arpanahi, Rostam; Morota, Gota; Valente, Bruno D; Kranis, Andreas; Rosa, Guilherme J M; Gianola, Daniel

2016-02-03

Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations. A dataset including 1351 birds that were genotyped with the 600K Affymetrix platform was used. We partitioned SNPs according to genome annotation data into six classes to characterize the relative contribution of genic and non-genic regions to genetic variation as well as their predictive power using all available quality-filtered SNPs. Target traits were body weight, ultrasound measurement of breast muscle and hen house egg production in broiler chickens. Six genomic regions were considered: intergenic regions, introns, missense, synonymous, 5' and 3' untranslated regions, and regions that are located 5 kb upstream and downstream of coding genes. Genomic relationship matrices were constructed for each genomic region and fitted in the models, separately or simultaneously. Kernel-based ridge regression was used to estimate variance components and assess predictive ability. Contribution of each class of genomic regions to dominance variance was also considered. Variance component estimates indicated that all genomic regions contributed to marked additive genetic variation and that the class of synonymous regions tended to have the greatest contribution. The marked dominance genetic variation explained by each class of genomic regions was similar and negligible (~0.05). In terms of prediction mean-square error, the whole-genome approach showed the best predictive ability. All genic and non-genic regions contributed to

Testing the Structure of Hydrological Models using Genetic Programming

NASA Astrophysics Data System (ADS)

Selle, B.; Muttil, N.

2009-04-01

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

Modeling the Diagnostic Criteria for Alcohol Dependence with Genetic Animal Models

PubMed Central

Kendler, Kenneth S.; Hitzemann, Robert J.

2012-01-01

A diagnosis of alcohol dependence (AD) using the DSM-IV-R is categorical, based on an individual’s manifestation of three or more symptoms from a list of seven. AD risk can be traced to both genetic and environmental sources. Most genetic studies of AD risk implicitly assume that an AD diagnosis represents a single underlying genetic factor. We recently found that the criteria for an AD diagnosis represent three somewhat distinct genetic paths to individual risk. Specifically, heavy use and tolerance versus withdrawal and continued use despite problems reflected separate genetic factors. However, some data suggest that genetic risk for AD is adequately described with a single underlying genetic risk factor. Rodent animal models for alcohol-related phenotypes typically target discrete aspects of the complex human AD diagnosis. Here, we review the literature derived from genetic animal models in an attempt to determine whether they support a single-factor or multiple-factor genetic structure. We conclude that there is modest support in the animal literature that alcohol tolerance and withdrawal reflect distinct genetic risk factors, in agreement with our human data. We suggest areas where more research could clarify this attempt to align the rodent and human data. PMID:21910077

PCR-free quantitative detection of genetically modified organism from raw materials. An electrochemiluminescence-based bio bar code method.

PubMed

Zhu, Debin; Tang, Yabing; Xing, Da; Chen, Wei R

2008-05-15

A bio bar code assay based on oligonucleotide-modified gold nanoparticles (Au-NPs) provides a PCR-free method for quantitative detection of nucleic acid targets. However, the current bio bar code assay requires lengthy experimental procedures including the preparation and release of bar code DNA probes from the target-nanoparticle complex and immobilization and hybridization of the probes for quantification. Herein, we report a novel PCR-free electrochemiluminescence (ECL)-based bio bar code assay for the quantitative detection of genetically modified organism (GMO) from raw materials. It consists of tris-(2,2'-bipyridyl) ruthenium (TBR)-labeled bar code DNA, nucleic acid hybridization using Au-NPs and biotin-labeled probes, and selective capture of the hybridization complex by streptavidin-coated paramagnetic beads. The detection of target DNA is realized by direct measurement of ECL emission of TBR. It can quantitatively detect target nucleic acids with high speed and sensitivity. This method can be used to quantitatively detect GMO fragments from real GMO products.

Quantitative Assessment of Eye Phenotypes for Functional Genetic Studies Using Drosophila melanogaster

PubMed Central

Iyer, Janani; Wang, Qingyu; Le, Thanh; Pizzo, Lucilla; Grönke, Sebastian; Ambegaokar, Surendra S.; Imai, Yuzuru; Srivastava, Ashutosh; Troisí, Beatriz Llamusí; Mardon, Graeme; Artero, Ruben; Jackson, George R.; Isaacs, Adrian M.; Partridge, Linda; Lu, Bingwei; Kumar, Justin P.; Girirajan, Santhosh

2016-01-01

About two-thirds of the vital genes in the Drosophila genome are involved in eye development, making the fly eye an excellent genetic system to study cellular function and development, neurodevelopment/degeneration, and complex diseases such as cancer and diabetes. We developed a novel computational method, implemented as Flynotyper software (http://flynotyper.sourceforge.net), to quantitatively assess the morphological defects in the Drosophila eye resulting from genetic alterations affecting basic cellular and developmental processes. Flynotyper utilizes a series of image processing operations to automatically detect the fly eye and the individual ommatidium, and calculates a phenotypic score as a measure of the disorderliness of ommatidial arrangement in the fly eye. As a proof of principle, we tested our method by analyzing the defects due to eye-specific knockdown of Drosophila orthologs of 12 neurodevelopmental genes to accurately document differential sensitivities of these genes to dosage alteration. We also evaluated eye images from six independent studies assessing the effect of overexpression of repeats, candidates from peptide library screens, and modifiers of neurotoxicity and developmental processes on eye morphology, and show strong concordance with the original assessment. We further demonstrate the utility of this method by analyzing 16 modifiers of sine oculis obtained from two genome-wide deficiency screens of Drosophila and accurately quantifying the effect of its enhancers and suppressors during eye development. Our method will complement existing assays for eye phenotypes, and increase the accuracy of studies that use fly eyes for functional evaluation of genes and genetic interactions. PMID:26994292

A quantitative framework for the forward design of synthetic miRNA circuits.

PubMed

Bloom, Ryan J; Winkler, Sally M; Smolke, Christina D

2014-11-01

Synthetic genetic circuits incorporating regulatory components based on RNA interference (RNAi) have been used in a variety of systems. A comprehensive understanding of the parameters that determine the relationship between microRNA (miRNA) and target expression levels is lacking. We describe a quantitative framework supporting the forward engineering of gene circuits that incorporate RNAi-based regulatory components in mammalian cells. We developed a model that captures the quantitative relationship between miRNA and target gene expression levels as a function of parameters, including mRNA half-life and miRNA target-site number. We extended the model to synthetic circuits that incorporate protein-responsive miRNA switches and designed an optimized miRNA-based protein concentration detector circuit that noninvasively measures small changes in the nuclear concentration of β-catenin owing to induction of the Wnt signaling pathway. Our results highlight the importance of methods for guiding the quantitative design of genetic circuits to achieve robust, reliable and predictable behaviors in mammalian cells.

Genetics Home Reference: prostate cancer

MedlinePlus

... prostate cancer Genetic Testing Registry: Prostate cancer aggressiveness quantitative trait locus on chromosome 19 Genetic Testing Registry: ... OMIM (25 links) PROSTATE CANCER PROSTATE CANCER AGGRESSIVENESS QUANTITATIVE TRAIT LOCUS ON CHROMOSOME 19 PROSTATE CANCER ANTIGEN ...

Quantitative analysis of fatty-acid-based biofuels produced by wild-type and genetically engineered cyanobacteria by gas chromatography-mass spectrometry.

PubMed

Guan, Wenna; Zhao, Hui; Lu, Xuefeng; Wang, Cong; Yang, Menglong; Bai, Fali

2011-11-11

Simple and rapid quantitative determination of fatty-acid-based biofuels is greatly important for the study of genetic engineering progress for biofuels production by microalgae. Ideal biofuels produced from biological systems should be chemically similar to petroleum, like fatty-acid-based molecules including free fatty acids, fatty acid methyl esters, fatty acid ethyl esters, fatty alcohols and fatty alkanes. This study founded a gas chromatography-mass spectrometry (GC-MS) method for simultaneous quantification of seven free fatty acids, nine fatty acid methyl esters, five fatty acid ethyl esters, five fatty alcohols and three fatty alkanes produced by wild-type Synechocystis PCC 6803 and its genetically engineered strain. Data obtained from GC-MS analyses were quantified using internal standard peak area comparisons. The linearity, limit of detection (LOD) and precision (RSD) of the method were evaluated. The results demonstrated that fatty-acid-based biofuels can be directly determined by GC-MS without derivation. Therefore, rapid and reliable quantitative analysis of fatty-acid-based biofuels produced by wild-type and genetically engineered cyanobacteria can be achieved using the GC-MS method founded in this work. Copyright © 2011 Elsevier B.V. All rights reserved.

Genetic and Environmental Influences on Negative Life Events from Late Childhood to Adolescence

ERIC Educational Resources Information Center

Johnson, Daniel P.; Rhee, Soo Hyun; Whisman, Mark A.; Corley, Robin P.; Hewitt, John K.

2013-01-01

This multiwave longitudinal study tested two quantitative genetic developmental models to examine genetic and environmental influences on exposure to negative dependent and independent life events. Participants (N = 457 twin pairs) completed measures of life events annually from ages 9 to 16. The same genetic factors influenced exposure to…

Genetic Diversity of Globally Dispersed Lacustrine Group I Haptophytes: Implications for Quantitative Temperature Reconstructions

NASA Astrophysics Data System (ADS)

Richter, N.; Longo, W. M.; Amaral-Zettler, L. A.; Huang, Y.

2017-12-01

There are significant uncertainties surrounding the forcings that drive terrestrial temperature changes on local and regional scales. Quantitative temperature reconstructions from terrestrial sites, such as lakes, help to unravel the fundamental processes that drive changes in temperature on different temporal and spatial scales. Recent studies at Brown University show that distinct alkenones, long chain ketones produced by haptophytes, are found in many freshwater, alkaline lakes in the Northern Hemisphere, highlighting these systems as targets for quantitative continental temperature reconstructions. These freshwater alkenones are produced by the Group I haptophyte phylotype and are characterized by a distinct signature: the presence of isomeric tri-unsaturated ketones and absence of alkenoates. There are currently no cultured representatives of the "Group I" haptophytes, hence they are only known based on their rRNA gene signatures. Here we present robust evidence that Northern Hemispheric freshwater, alkaline lakes with the characteristic "Group I" alkenone signature all host the same clade of Isochrysidales haptophytes. We employed next generation DNA amplicon sequencing to target haptophyte specific hypervariable regions of the large and small-subunit ribosomal RNA gene from 13 different lakes from three continents (i.e., North America, Europe, and Asia). Combined with previously published sequences, our genetic data show that the Group I haptophyte is genetically diverse on a regional and global scale, and even within the same lake. We present two case studies from a suite of five lakes in Alaska and three in Iceland to assess the impact of various environmental factors affecting Group I diversity and alkenone production. Despite the genetic diversity in this group, the overall ketone signature is conserved. Based on global surface sediment samples and in situ Alaskan lake calibrations, alkenones produced by different operational taxonomic units of the Group

A Spatial Statistical Model for Landscape Genetics

PubMed Central

Guillot, Gilles; Estoup, Arnaud; Mortier, Frédéric; Cosson, Jean François

2005-01-01

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites. PMID:15520263

Modeling Host Genetic Regulation of Influenza Pathogenesis in the Collaborative Cross

PubMed Central

Ferris, Martin T.; Aylor, David L.; Bottomly, Daniel; Whitmore, Alan C.; Aicher, Lauri D.; Bell, Timothy A.; Bradel-Tretheway, Birgit; Bryan, Janine T.; Buus, Ryan J.; Gralinski, Lisa E.; Haagmans, Bart L.; McMillan, Leonard; Miller, Darla R.; Rosenzweig, Elizabeth; Valdar, William; Wang, Jeremy; Churchill, Gary A.; Threadgill, David W.; McWeeney, Shannon K.; Katze, Michael G.; Pardo-Manuel de Villena, Fernando; Baric, Ralph S.; Heise, Mark T.

2013-01-01

Genetic variation contributes to host responses and outcomes following infection by influenza A virus or other viral infections. Yet narrow windows of disease symptoms and confounding environmental factors have made it difficult to identify polymorphic genes that contribute to differential disease outcomes in human populations. Therefore, to control for these confounding environmental variables in a system that models the levels of genetic diversity found in outbred populations such as humans, we used incipient lines of the highly genetically diverse Collaborative Cross (CC) recombinant inbred (RI) panel (the pre-CC population) to study how genetic variation impacts influenza associated disease across a genetically diverse population. A wide range of variation in influenza disease related phenotypes including virus replication, virus-induced inflammation, and weight loss was observed. Many of the disease associated phenotypes were correlated, with viral replication and virus-induced inflammation being predictors of virus-induced weight loss. Despite these correlations, pre-CC mice with unique and novel disease phenotype combinations were observed. We also identified sets of transcripts (modules) that were correlated with aspects of disease. In order to identify how host genetic polymorphisms contribute to the observed variation in disease, we conducted quantitative trait loci (QTL) mapping. We identified several QTL contributing to specific aspects of the host response including virus-induced weight loss, titer, pulmonary edema, neutrophil recruitment to the airways, and transcriptional expression. Existing whole-genome sequence data was applied to identify high priority candidate genes within QTL regions. A key host response QTL was located at the site of the known anti-influenza Mx1 gene. We sequenced the coding regions of Mx1 in the eight CC founder strains, and identified a novel Mx1 allele that showed reduced ability to inhibit viral replication, while

A Quantitative Chemotherapy Genetic Interaction Map Reveals Factors Associated with PARP Inhibitor Resistance. | Office of Cancer Genomics

Cancer.gov

Chemotherapy is used to treat most cancer patients, yet our understanding of factors that dictate response and resistance to such drugs remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells charting the impact of the knockdown of 625 genes related to cancer and DNA repair on sensitivity to 29 drugs, covering all classes of chemotherapy.

Making Quantitative Genetics Relevant: Effectiveness of a Laboratory Investigation that Links Scientific Research, Commercial Applications, and Legal Issues

ERIC Educational Resources Information Center

Rutledge, Michael L.; Mathis, Philip M.; Seipelt, Rebecca L.

2005-01-01

As students apply their knowledge of scientific concepts and of science as a method of inquiry, learning becomes relevant. This laboratory exercise is designed to foster students' understanding of the genetics of quantitative traits and of the nature of science as a method of inquiry by engaging them in a real-world business scenario. During the…

Beyond Punnett squares: Student word association and explanations of phenotypic variation through an integrative quantitative genetics unit investigating anthocyanin inheritance and expression in Brassica rapa Fast plants.

PubMed

Batzli, Janet M; Smith, Amber R; Williams, Paul H; McGee, Seth A; Dósa, Katalin; Pfammatter, Jesse

2014-01-01

Genetics instruction in introductory biology is often confined to Mendelian genetics and avoids the complexities of variation in quantitative traits. Given the driving question "What determines variation in phenotype (Pv)? (Pv=Genotypic variation Gv + environmental variation Ev)," we developed a 4-wk unit for an inquiry-based laboratory course focused on the inheritance and expression of a quantitative trait in varying environments. We utilized Brassica rapa Fast Plants as a model organism to study variation in the phenotype anthocyanin pigment intensity. As an initial curriculum assessment, we used free word association to examine students' cognitive structures before and after the unit and explanations in students' final research posters with particular focus on variation (Pv = Gv + Ev). Comparison of pre- and postunit word frequency revealed a shift in words and a pattern of co-occurring concepts indicative of change in cognitive structure, with particular focus on "variation" as a proposed threshold concept and primary goal for students' explanations. Given review of 53 posters, we found ∼50% of students capable of intermediate to high-level explanations combining both Gv and Ev influence on expression of anthocyanin intensity (Pv). While far from "plug and play," this conceptually rich, inquiry-based unit holds promise for effective integration of quantitative and Mendelian genetics. © 2014 J. M. Batzli et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Modeling with Young Students--Quantitative and Qualitative.

ERIC Educational Resources Information Center

Bliss, Joan; Ogborn, Jon; Boohan, Richard; Brosnan, Tim; Mellar, Harvey; Sakonidis, Babis

1999-01-01

A project created tasks and tools to investigate quality and nature of 11- to 14-year-old pupils' reasoning with quantitative and qualitative computer-based modeling tools. Tasks and tools were used in two innovative modes of learning: expressive, where pupils created their own models, and exploratory, where pupils investigated an expert's model.…

A Tri-part Model for Genetics Literacy: Exploring Undergraduate Student Reasoning About Authentic Genetics Dilemmas

NASA Astrophysics Data System (ADS)

Shea, Nicole A.; Duncan, Ravit Golan; Stephenson, Celeste

2015-08-01

Genetics literacy is becoming increasingly important as advancements in our application of genetic technologies such as stem cell research, cloning, and genetic screening become more prevalent. Very few studies examine how genetics literacy is applied when reasoning about authentic genetic dilemmas. However, there is evidence that situational features of a reasoning task may influence how students apply content knowledge as they generate and support arguments. Understanding how students apply content knowledge to reason about authentic and complex issues is important for considering instructional practices that best support student thinking and reasoning. In this conceptual report, we present a tri-part model for genetics literacy that embodies the relationships between content knowledge use, argumentation quality, and the role of situational features in reasoning to support genetics literacy. Using illustrative examples from an interview study with early career undergraduate students majoring in the biological sciences and late career undergraduate students majoring in genetics, we provide insights into undergraduate student reasoning about complex genetics issues and discuss implications for teaching and learning. We further discuss the need for research about how the tri-part model of genetics literacy can be used to explore students' thinking and reasoning abilities in genetics.

Detection of nonauthorized genetically modified organisms using differential quantitative polymerase chain reaction: application to 35S in maize.

PubMed

Cankar, Katarina; Chauvensy-Ancel, Valérie; Fortabat, Marie-Noelle; Gruden, Kristina; Kobilinsky, André; Zel, Jana; Bertheau, Yves

2008-05-15

Detection of nonauthorized genetically modified organisms (GMOs) has always presented an analytical challenge because the complete sequence data needed to detect them are generally unavailable although sequence similarity to known GMOs can be expected. A new approach, differential quantitative polymerase chain reaction (PCR), for detection of nonauthorized GMOs is presented here. This method is based on the presence of several common elements (e.g., promoter, genes of interest) in different GMOs. A statistical model was developed to study the difference between the number of molecules of such a common sequence and the number of molecules identifying the approved GMO (as determined by border-fragment-based PCR) and the donor organism of the common sequence. When this difference differs statistically from zero, the presence of a nonauthorized GMO can be inferred. The interest and scope of such an approach were tested on a case study of different proportions of genetically modified maize events, with the P35S promoter as the Cauliflower Mosaic Virus common sequence. The presence of a nonauthorized GMO was successfully detected in the mixtures analyzed and in the presence of (donor organism of P35S promoter). This method could be easily transposed to other common GMO sequences and other species and is applicable to other detection areas such as microbiology.

Comparing estimates of genetic variance across different relationship models.

PubMed

Legarra, Andres

2016-02-01

Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, "missing heritabilities". Copyright © 2015 Elsevier Inc. All rights reserved.

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

Testing the structure of a hydrological model using Genetic Programming

NASA Astrophysics Data System (ADS)

Selle, Benny; Muttil, Nitin

2011-01-01

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

Genetic dissection of fruiting body-related traits using quantitative trait loci mapping in Lentinula edodes.

PubMed

Gong, Wen-Bing; Li, Lei; Zhou, Yan; Bian, Yin-Bing; Kwan, Hoi-Shan; Cheung, Man-Kit; Xiao, Yang

2016-06-01

To provide a better understanding of the genetic architecture of fruiting body formation of Lentinula edodes, quantitative trait loci (QTLs) mapping was employed to uncover the loci underlying seven fruiting body-related traits (FBRTs). An improved L. edodes genetic linkage map, comprising 572 markers on 12 linkage groups with a total map length of 983.7 cM, was constructed by integrating 82 genomic sequence-based insertion-deletion (InDel) markers into a previously published map. We then detected a total of 62 QTLs for seven target traits across two segregating testcross populations, with individual QTLs contributing 5.5 %-30.2 % of the phenotypic variation. Fifty-three out of the 62 QTLs were clustered in six QTL hotspots, suggesting the existence of main genomic regions regulating the morphological characteristics of fruiting bodies in L. edodes. A stable QTL hotspot on MLG2, containing QTLs for all investigated traits, was identified in both testcross populations. QTLs for related traits were frequently co-located on the linkage groups, demonstrating the genetic basis for phenotypic correlation of traits. Meta-QTL (mQTL) analysis was performed and identified 16 mQTLs with refined positions and narrow confidence intervals (CIs). Nine genes, including those encoding MAP kinase, blue-light photoreceptor, riboflavin-aldehyde-forming enzyme and cyclopropane-fatty-acyl-phospholipid synthase, and cytochrome P450s, were likely to be candidate genes controlling the shape of fruiting bodies. The study has improved our understanding of the genetic architecture of fruiting body formation in L. edodes. To our knowledge, this is the first genome-wide QTL detection of FBRTs in L. edodes. The improved genetic map, InDel markers and QTL hotspot regions revealed here will assist considerably in the conduct of future genetic and breeding studies of L. edodes.

Latent spatial models and sampling design for landscape genetics

Treesearch

Ephraim M. Hanks; Melvin B. Hooten; Steven T. Knick; Sara J. Oyler-McCance; Jennifer A. Fike; Todd B. Cross; Michael K. Schwartz

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial...

Rapid changes in genetic architecture of behavioural syndromes following colonization of a novel environment.

PubMed

Karlsson Green, K; Eroukhmanoff, F; Harris, S; Pettersson, L B; Svensson, E I

2016-01-01

Behavioural syndromes, that is correlated behaviours, may be a result from adaptive correlational selection, but in a new environmental setting, the trait correlation might act as an evolutionary constraint. However, knowledge about the quantitative genetic basis of behavioural syndromes, and the stability and evolvability of genetic correlations under different ecological conditions, is limited. We investigated the quantitative genetic basis of correlated behaviours in the freshwater isopod Asellus aquaticus. In some Swedish lakes, A. aquaticus has recently colonized a novel habitat and diverged into two ecotypes, presumably due to habitat-specific selection from predation. Using a common garden approach and animal model analyses, we estimated quantitative genetic parameters for behavioural traits and compared the genetic architecture between the ecotypes. We report that the genetic covariance structure of the behavioural traits has been altered in the novel ecotype, demonstrating divergence in behavioural correlations. Thus, our study confirms that genetic correlations behind behaviours can change rapidly in response to novel selective environments. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.

Determination of Mycotoxin Production of Fusarium Species in Genetically Modified Maize Varieties by Quantitative Flow Immunocytometry.

PubMed

Bánáti, Hajnalka; Darvas, Béla; Fehér-Tóth, Szilvia; Czéh, Árpád; Székács, András

2017-02-22

Levels of mycotoxins produced by Fusarium species in genetically modified (GM) and near-isogenic maize, were determined using multi-analyte, microbead-based flow immunocytometry with fluorescence detection, for the parallel quantitative determination of fumonisin B1, deoxynivalenol, zearalenone, T-2, ochratoxin A, and aflatoxin B1. Maize varieties included the genetic events MON 810 and DAS-59122-7 , and their isogenic counterparts. Cobs were artificially infested by F. verticillioides and F. proliferatum conidia, and contained F. graminearum and F. sporotrichoides natural infestation. The production of fumonisin B1 and deoxynivalenol was substantially affected in GM maize lines: F. verticillioides , with the addition of F. graminearum and F. sporotrichoides , produced significantly lower levels of fumonisin B1 (~300 mg·kg -1 ) in DAS-59122-7 than in its isogenic line (~580 mg·kg -1 ), while F. proliferatum , in addition to F. graminearum and F. sporotrichoides , produced significantly higher levels of deoxynivalenol (~18 mg·kg -1 ) in MON 810 than in its isogenic line (~5 mg·kg -1 ). Fusarium verticillioides , with F. graminearum and F. sporotrichoides , produced lower amounts of deoxynivalenol and zearalenone than F. proliferatum , with F. graminearum and F. sporotrichoides . T-2 toxin production remained unchanged when considering the maize variety. The results demonstrate the utility of the Fungi-Plex™ quantitative flow immunocytometry method, applied for the high throughput parallel determination of the target mycotoxins.

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

PubMed

Dechow, C D; Rogers, G W

2018-05-01

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

Broad Bandwidth or High Fidelity? Evidence from the Structure of Genetic and Environmental Effects on the Facets of the Five Factor Model

PubMed Central

Briley, Daniel A.; Tucker-Drob, Elliot M.

2017-01-01

The Five Factor Model (FFM) of personality is well-established at the phenotypic level, but much less is known about the coherence of the genetic and environmental influences within each personality domain. Univariate behavioral genetic analyses have consistently found the influence of additive genes and nonshared environment on multiple personality facets, but the extent to which genetic and environmental influences on specific facets reflect more general influences on higher order factors is less clear. We applied a multivariate quantitative-genetic approach to scores on the CPI-Big Five facets for 490 monozygotic and 317 dizygotic twins who took part in the National Merit Twin Study. Our results revealed a complex genetic structure for facets composing all five factors, with both domain-general and facet-specific genetic and environmental influences. Models that required common genetic and environmental influences on each facet to occur by way of effects on a higher order trait did not fit as well as models allowing for common genetic and environmental effects to act directly on the facets for three of the Big Five domains. These results add to the growing body of literature indicating that important variation in personality occurs at the facet level which may be overshadowed by aggregating to the trait level. Research at the facet level, rather than the factor level, is likely to have pragmatic advantages in future research on the genetics of personality. PMID:22695681

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.

Quantitative trait loci and metabolic pathways

PubMed Central

McMullen, M. D.; Byrne, P. F.; Snook, M. E.; Wiseman, B. R.; Lee, E. A.; Widstrom, N. W.; Coe, E. H.

1998-01-01

The interpretation of quantitative trait locus (QTL) studies is limited by the lack of information on metabolic pathways leading to most economic traits. Inferences about the roles of the underlying genes with a pathway or the nature of their interaction with other loci are generally not possible. An exception is resistance to the corn earworm Helicoverpa zea (Boddie) in maize (Zea mays L.) because of maysin, a C-glycosyl flavone synthesized in silks via a branch of the well characterized flavonoid pathway. Our results using flavone synthesis as a model QTL system indicate: (i) the importance of regulatory loci as QTLs, (ii) the importance of interconnecting biochemical pathways on product levels, (iii) evidence for “channeling” of intermediates, allowing independent synthesis of related compounds, (iv) the utility of QTL analysis in clarifying the role of specific genes in a biochemical pathway, and (v) identification of a previously unknown locus on chromosome 9S affecting flavone level. A greater understanding of the genetic basis of maysin synthesis and associated corn earworm resistance should lead to improved breeding strategies. More broadly, the insights gained in relating a defined genetic and biochemical pathway affecting a quantitative trait should enhance interpretation of the biological basis of variation for other quantitative traits. PMID:9482823

PCR-free quantitative detection of genetically modified organism from raw materials – A novel electrochemiluminescence-based bio-barcode method

PubMed Central

Zhu, Debin; Tang, Yabing; Xing, Da; Chen, Wei R.

2018-01-01

Bio-barcode assay based on oligonucleotide-modified gold nanoparticles (Au-NPs) provides a PCR-free method for quantitative detection of nucleic acid targets. However, the current bio-barcode assay requires lengthy experimental procedures including the preparation and release of barcode DNA probes from the target-nanoparticle complex, and immobilization and hybridization of the probes for quantification. Herein, we report a novel PCR-free electrochemiluminescence (ECL)-based bio-barcode assay for the quantitative detection of genetically modified organism (GMO) from raw materials. It consists of tris-(2’2’-bipyridyl) ruthenium (TBR)-labele barcode DNA, nucleic acid hybridization using Au-NPs and biotin-labeled probes, and selective capture of the hybridization complex by streptavidin-coated paramagnetic beads. The detection of target DNA is realized by direct measurement of ECL emission of TBR. It can quantitatively detect target nucleic acids with high speed and sensitivity. This method can be used to quantitatively detect GMO fragments from real GMO products. PMID:18386909

Quantitative model validation of manipulative robot systems

NASA Astrophysics Data System (ADS)

Kartowisastro, Iman Herwidiana

This thesis is concerned with applying the distortion quantitative validation technique to a robot manipulative system with revolute joints. Using the distortion technique to validate a model quantitatively, the model parameter uncertainties are taken into account in assessing the faithfulness of the model and this approach is relatively more objective than the commonly visual comparison method. The industrial robot is represented by the TQ MA2000 robot arm. Details of the mathematical derivation of the distortion technique are given which explains the required distortion of the constant parameters within the model and the assessment of model adequacy. Due to the complexity of a robot model, only the first three degrees of freedom are considered where all links are assumed rigid. The modelling involves the Newton-Euler approach to obtain the dynamics model, and the Denavit-Hartenberg convention is used throughout the work. The conventional feedback control system is used in developing the model. The system behavior to parameter changes is investigated as some parameters are redundant. This work is important so that the most important parameters to be distorted can be selected and this leads to a new term called the fundamental parameters. The transfer function approach has been chosen to validate an industrial robot quantitatively against the measured data due to its practicality. Initially, the assessment of the model fidelity criterion indicated that the model was not capable of explaining the transient record in term of the model parameter uncertainties. Further investigations led to significant improvements of the model and better understanding of the model properties. After several improvements in the model, the fidelity criterion obtained was almost satisfied. Although the fidelity criterion is slightly less than unity, it has been shown that the distortion technique can be applied in a robot manipulative system. Using the validated model, the importance of

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.

Quantitative Resistance: More Than Just Perception of a Pathogen.

PubMed

Corwin, Jason A; Kliebenstein, Daniel J

2017-04-01

Molecular plant pathology has focused on studying large-effect qualitative resistance loci that predominantly function in detecting pathogens and/or transmitting signals resulting from pathogen detection. By contrast, less is known about quantitative resistance loci, particularly the molecular mechanisms controlling variation in quantitative resistance. Recent studies have provided insight into these mechanisms, showing that genetic variation at hundreds of causal genes may underpin quantitative resistance. Loci controlling quantitative resistance contain some of the same causal genes that mediate qualitative resistance, but the predominant mechanisms of quantitative resistance extend beyond pathogen recognition. Indeed, most causal genes for quantitative resistance encode specific defense-related outputs such as strengthening of the cell wall or defense compound biosynthesis. Extending previous work on qualitative resistance to focus on the mechanisms of quantitative resistance, such as the link between perception of microbe-associated molecular patterns and growth, has shown that the mechanisms underlying these defense outputs are also highly polygenic. Studies that include genetic variation in the pathogen have begun to highlight a potential need to rethink how the field considers broad-spectrum resistance and how it is affected by genetic variation within pathogen species and between pathogen species. These studies are broadening our understanding of quantitative resistance and highlighting the potentially vast scale of the genetic basis of quantitative resistance. © 2017 American Society of Plant Biologists. All rights reserved.

Quantitative Resistance: More Than Just Perception of a Pathogen

PubMed Central

2017-01-01

Molecular plant pathology has focused on studying large-effect qualitative resistance loci that predominantly function in detecting pathogens and/or transmitting signals resulting from pathogen detection. By contrast, less is known about quantitative resistance loci, particularly the molecular mechanisms controlling variation in quantitative resistance. Recent studies have provided insight into these mechanisms, showing that genetic variation at hundreds of causal genes may underpin quantitative resistance. Loci controlling quantitative resistance contain some of the same causal genes that mediate qualitative resistance, but the predominant mechanisms of quantitative resistance extend beyond pathogen recognition. Indeed, most causal genes for quantitative resistance encode specific defense-related outputs such as strengthening of the cell wall or defense compound biosynthesis. Extending previous work on qualitative resistance to focus on the mechanisms of quantitative resistance, such as the link between perception of microbe-associated molecular patterns and growth, has shown that the mechanisms underlying these defense outputs are also highly polygenic. Studies that include genetic variation in the pathogen have begun to highlight a potential need to rethink how the field considers broad-spectrum resistance and how it is affected by genetic variation within pathogen species and between pathogen species. These studies are broadening our understanding of quantitative resistance and highlighting the potentially vast scale of the genetic basis of quantitative resistance. PMID:28302676

Genetics of SLE: evidence from mouse models.

PubMed

Morel, Laurence

2010-06-01

Great progress has been made in the field of lupus genetics in the past few years, notably with the publication of genome-wide association studies in humans and the identification of susceptibility genes (including Fcgr2b, Ly108, Kallikrein genes and Coronin-1A) in mouse models of spontaneous lupus. This influx of new information has revealed an ever-increasing interdependence between the mouse and human systems for unraveling the genetic basis of lupus susceptibility. Studies in the 1980s and 1990s established that mice prone to spontaneous lupus constitute excellent models of the genetic architecture of human systemic lupus erythematosus (SLE). This notion has been greatly strengthened by the convergence of the functional pathways that are defective in both human and murine lupus. Within these pathways, variants in a number of genes have now been shown to be directly associated with lupus in both species. Consequently, mouse models will continue to serve a pre-eminent role in lupus genetics research, with an increased emphasis on mechanistic and molecular studies of human susceptibility alleles.

Genetic and Quantitative Trait Locus Analysis for Bio-Oil Compounds after Fast Pyrolysis in Maize Cobs.

PubMed

Jeffrey, Brandon; Kuzhiyil, Najeeb; de Leon, Natalia; Lübberstedt, Thomas

2016-01-01

Fast pyrolysis has been identified as one of the biorenewable conversion platforms that could be a part of an alternative energy future, but it has not yet received the same attention as cellulosic ethanol in the analysis of genetic inheritance within potential feedstocks such as maize. Ten bio-oil compounds were measured via pyrolysis/gas chromatography-mass spectrometry (Py/GC-MS) in maize cobs. 184 recombinant inbred lines (RILs) of the intermated B73 x Mo17 (IBM) Syn4 population were analyzed in two environments, using 1339 markers, for quantitative trait locus (QTL) mapping. QTL mapping was performed using composite interval mapping with significance thresholds established by 1000 permutations at α = 0.05. 50 QTL were found in total across those ten traits with R2 values ranging from 1.7 to 5.8%, indicating a complex quantitative inheritance of these traits.

Development and Evaluation of Event-Specific Quantitative PCR Method for Genetically Modified Soybean MON87701.

PubMed

Tsukahara, Keita; Takabatake, Reona; Masubuchi, Tomoko; Futo, Satoshi; Minegishi, Yasutaka; Noguchi, Akio; Kondo, Kazunari; Nishimaki-Mogami, Tomoko; Kurashima, Takeyo; Mano, Junichi; Kitta, Kazumi

2016-01-01

A real-time PCR-based analytical method was developed for the event-specific quantification of a genetically modified (GM) soybean event, MON87701. First, a standard plasmid for MON87701 quantification was constructed. The conversion factor (C f ) required to calculate the amount of genetically modified organism (GMO) was experimentally determined for a real-time PCR instrument. The determined C f for the real-time PCR instrument was 1.24. For the evaluation of the developed method, a blind test was carried out in an inter-laboratory trial. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSDr), respectively. The determined biases and the RSDr values were less than 30 and 13%, respectively, at all evaluated concentrations. The limit of quantitation of the method was 0.5%, and the developed method would thus be applicable for practical analyses for the detection and quantification of MON87701.

A unifying theory for genetic epidemiological analysis of binary disease data

PubMed Central

2014-01-01

Background Genetic selection for host resistance offers a desirable complement to chemical treatment to control infectious disease in livestock. Quantitative genetics disease data frequently originate from field studies and are often binary. However, current methods to analyse binary disease data fail to take infection dynamics into account. Moreover, genetic analyses tend to focus on host susceptibility, ignoring potential variation in infectiousness, i.e. the ability of a host to transmit the infection. This stands in contrast to epidemiological studies, which reveal that variation in infectiousness plays an important role in the progression and severity of epidemics. In this study, we aim at filling this gap by deriving an expression for the probability of becoming infected that incorporates infection dynamics and is an explicit function of both host susceptibility and infectiousness. We then validate this expression according to epidemiological theory and by simulating epidemiological scenarios, and explore implications of integrating this expression into genetic analyses. Results Our simulations show that the derived expression is valid for a range of stochastic genetic-epidemiological scenarios. In the particular case of variation in susceptibility only, the expression can be incorporated into conventional quantitative genetic analyses using a complementary log-log link function (rather than probit or logit). Similarly, if there is moderate variation in both susceptibility and infectiousness, it is possible to use a logarithmic link function, combined with an indirect genetic effects model. However, in the presence of highly infectious individuals, i.e. super-spreaders, the use of any model that is linear in susceptibility and infectiousness causes biased estimates. Thus, in order to identify super-spreaders, novel analytical methods using our derived expression are required. Conclusions We have derived a genetic-epidemiological function for quantitative

A unifying theory for genetic epidemiological analysis of binary disease data.

PubMed

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

2014-02-19

Genetic selection for host resistance offers a desirable complement to chemical treatment to control infectious disease in livestock. Quantitative genetics disease data frequently originate from field studies and are often binary. However, current methods to analyse binary disease data fail to take infection dynamics into account. Moreover, genetic analyses tend to focus on host susceptibility, ignoring potential variation in infectiousness, i.e. the ability of a host to transmit the infection. This stands in contrast to epidemiological studies, which reveal that variation in infectiousness plays an important role in the progression and severity of epidemics. In this study, we aim at filling this gap by deriving an expression for the probability of becoming infected that incorporates infection dynamics and is an explicit function of both host susceptibility and infectiousness. We then validate this expression according to epidemiological theory and by simulating epidemiological scenarios, and explore implications of integrating this expression into genetic analyses. Our simulations show that the derived expression is valid for a range of stochastic genetic-epidemiological scenarios. In the particular case of variation in susceptibility only, the expression can be incorporated into conventional quantitative genetic analyses using a complementary log-log link function (rather than probit or logit). Similarly, if there is moderate variation in both susceptibility and infectiousness, it is possible to use a logarithmic link function, combined with an indirect genetic effects model. However, in the presence of highly infectious individuals, i.e. super-spreaders, the use of any model that is linear in susceptibility and infectiousness causes biased estimates. Thus, in order to identify super-spreaders, novel analytical methods using our derived expression are required. We have derived a genetic-epidemiological function for quantitative genetic analyses of binary

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

PubMed

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

2017-06-01

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

Genetically Modified Foods and Consumer Perspective.

PubMed

Boccia, Flavio; Sarnacchiaro, Pasquale

2015-01-01

Genetically modified food is able to oppose the world's hunger and preserve the environment, even if the patents in this matter are symptomatic of several doubts. And also, transgenic consumption causes problems and skepticism among consumers in several European countries, but above all in Italy, where there is a strong opposition over recent years. So, the present study conducted a research to study the consumption of genetically modified food products by Italian young generation. This research presented the following purposes: firstly, to analyze genetically modified products' consumption among a particular category of consumers; secondly, to implement a quantitative model to understand behaviour about this particular kind of consumption and identify the factors that determine their purchase. The proposed model shows that transgenic consumption is especially linked to knowledge and impact on environment and mankind's health.

A quantitative chemotherapy genetic interaction map reveals new factors associated with PARP inhibitor resistance | Office of Cancer Genomics

Cancer.gov

Nearly every cancer patient is treated with chemotherapy yet our understanding of factors that dictate response and resistance to such agents remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells that charts the impact of knockdown of 625 cancer and DNA repair related genes on sensitivity to 29 drugs, covering all classes of cancer chemotherapeutics.

What Are We Doing When We Translate from Quantitative Models?

PubMed Central

Critchfield, Thomas S; Reed, Derek D

2009-01-01

Although quantitative analysis (in which behavior principles are defined in terms of equations) has become common in basic behavior analysis, translational efforts often examine everyday events through the lens of narrative versions of laboratory-derived principles. This approach to translation, although useful, is incomplete because equations may convey concepts that are difficult to capture in words. To support this point, we provide a nontechnical introduction to selected aspects of quantitative analysis; consider some issues that translational investigators (and, potentially, practitioners) confront when attempting to translate from quantitative models; and discuss examples of relevant translational studies. We conclude that, where behavior-science translation is concerned, the quantitative features of quantitative models cannot be ignored without sacrificing conceptual precision, scientific and practical insights, and the capacity of the basic and applied wings of behavior analysis to communicate effectively. PMID:22478533

[The genetic control of mouse coat color and its applications in genetics teaching].

PubMed

Xing, Wanjin; Morigen, Morigen

2014-10-01

Mice are the most commonly used mammalian model. The coat colors of mice are typical Mendelian traits, which have various colors such as white, black, yellow and agouti. The inheritance of mouse coat color is usually stated as an example only in teaching the knowledge of recessive lethal alleles. After searched the related literatures and summarized the molecular mechanisms of mouse coat color inheritance, we further expanded the application of this example into the introduction of the basic concepts of alleles and Mendelian laws, demonstration of the gene structure and function, regulation of gene expression, gene interaction, epigenetic modification, quantitative genetics, as well as evolutionary genetics. By running this example through the whole genetics-teaching lectures, we help the student to form a systemic and developmental view of genetic analysis. At the same time, this teaching approach not only highlights the advancement and integrity of genetics, but also results in a good teaching effect on inspiring the students' interest and attracting students' attention.

6 Principles for Quantitative Reasoning and Modeling

ERIC Educational Resources Information Center

Weber, Eric; Ellis, Amy; Kulow, Torrey; Ozgur, Zekiye

2014-01-01

Encouraging students to reason with quantitative relationships can help them develop, understand, and explore mathematical models of real-world phenomena. Through two examples--modeling the motion of a speeding car and the growth of a Jactus plant--this article describes how teachers can use six practical tips to help students develop quantitative…

CMCpy: Genetic Code-Message Coevolution Models in Python

PubMed Central

Becich, Peter J.; Stark, Brian P.; Bhat, Harish S.; Ardell, David H.

2013-01-01

Code-message coevolution (CMC) models represent coevolution of a genetic code and a population of protein-coding genes (“messages”). Formally, CMC models are sets of quasispecies coupled together for fitness through a shared genetic code. Although CMC models display plausible explanations for the origin of multiple genetic code traits by natural selection, useful modern implementations of CMC models are not currently available. To meet this need we present CMCpy, an object-oriented Python API and command-line executable front-end that can reproduce all published results of CMC models. CMCpy implements multiple solvers for leading eigenpairs of quasispecies models. We also present novel analytical results that extend and generalize applications of perturbation theory to quasispecies models and pioneer the application of a homotopy method for quasispecies with non-unique maximally fit genotypes. Our results therefore facilitate the computational and analytical study of a variety of evolutionary systems. CMCpy is free open-source software available from http://pypi.python.org/pypi/CMCpy/. PMID:23532367

High-Density Genetic Linkage Map Construction and Quantitative Trait Locus Mapping for Hawthorn (Crataegus pinnatifida Bunge).

PubMed

Zhao, Yuhui; Su, Kai; Wang, Gang; Zhang, Liping; Zhang, Jijun; Li, Junpeng; Guo, Yinshan

2017-07-14

Genetic linkage maps are an important tool in genetic and genomic research. In this study, two hawthorn cultivars, Qiujinxing and Damianqiu, and 107 progenies from a cross between them were used for constructing a high-density genetic linkage map using the 2b-restriction site-associated DNA (2b-RAD) sequencing method, as well as for mapping quantitative trait loci (QTL) for flavonoid content. In total, 206,411,693 single-end reads were obtained, with an average sequencing depth of 57× in the parents and 23× in the progeny. After quality trimming, 117,896 high-quality 2b-RAD tags were retained, of which 42,279 were polymorphic; of these, 12,951 markers were used for constructing the genetic linkage map. The map contained 17 linkage groups and 3,894 markers, with a total map length of 1,551.97 cM and an average marker interval of 0.40 cM. QTL mapping identified 21 QTLs associated with flavonoid content in 10 linkage groups, which explained 16.30-59.00% of the variance. This is the first high-density linkage map for hawthorn, which will serve as a basis for fine-scale QTL mapping and marker-assisted selection of important traits in hawthorn germplasm and will facilitate chromosome assignment for hawthorn whole-genome assemblies in the future.

Quantitative modeling of soil genesis processes

NASA Technical Reports Server (NTRS)

Levine, E. R.; Knox, R. G.; Kerber, A. G.

1992-01-01

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

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.

Little effect of HSP90 inhibition on the quantitative wing traits variation in Drosophila melanogaster.

PubMed

Takahashi, Kazuo H

2017-02-01

Drosophila wings have been a model system to study the effect of HSP90 on quantitative trait variation. The effect of HSP90 inhibition on environmental buffering of wing morphology varies among studies while the genetic buffering effect of it was examined in only one study and was not detected. Variable results so far might show that the genetic background influences the environmental and genetic buffering effect of HSP90. In the previous studies, the number of the genetic backgrounds used is limited. To examine the effect of HSP90 inhibition with a larger number of genetic backgrounds than the previous studies, 20 wild-type strains of Drosophila melanogaster were used in this study. Here I investigated the effect of HSP90 inhibition on the environmental buffering of wing shape and size by assessing within-individual and among-individual variations, and as a result, I found little or very weak effects on environmental and genetic buffering. The current results suggest that the role of HSP90 as a global regulator of environmental and genetic buffering is limited at least in quantitative traits.

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

PubMed

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

2018-06-01

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

[The emphases and basic procedures of genetic counseling in psychotherapeutic model].

PubMed

Zhang, Yuan-Zhi; Zhong, Nanbert

2006-11-01

The emphases and basic procedures of genetic counseling are all different with those in old models. In the psychotherapeutic model, genetic counseling will not only focus on counselees' genetic disorders and birth defects, but also their psychological problems. "Client-centered therapy" termed by Carl Rogers plays an important role in genetic counseling process. The basic procedures of psychotherapeutic model of genetic counseling include 7 steps: initial contact, introduction, agendas, inquiry of family history, presenting information, closing the session and follow-up.

A synthetic genetic edge detection program.

PubMed

Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

2009-06-26

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

A Synthetic Genetic Edge Detection Program

PubMed Central

Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

2009-01-01

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

Quantitative analysis of bristle number in Drosophila mutants identifies genes involved in neural development

NASA Technical Reports Server (NTRS)

Norga, Koenraad K.; Gurganus, Marjorie C.; Dilda, Christy L.; Yamamoto, Akihiko; Lyman, Richard F.; Patel, Prajal H.; Rubin, Gerald M.; Hoskins, Roger A.; Mackay, Trudy F.; Bellen, Hugo J.

2003-01-01

BACKGROUND: The identification of the function of all genes that contribute to specific biological processes and complex traits is one of the major challenges in the postgenomic era. One approach is to employ forward genetic screens in genetically tractable model organisms. In Drosophila melanogaster, P element-mediated insertional mutagenesis is a versatile tool for the dissection of molecular pathways, and there is an ongoing effort to tag every gene with a P element insertion. However, the vast majority of P element insertion lines are viable and fertile as homozygotes and do not exhibit obvious phenotypic defects, perhaps because of the tendency for P elements to insert 5' of transcription units. Quantitative genetic analysis of subtle effects of P element mutations that have been induced in an isogenic background may be a highly efficient method for functional genome annotation. RESULTS: Here, we have tested the efficacy of this strategy by assessing the extent to which screening for quantitative effects of P elements on sensory bristle number can identify genes affecting neural development. We find that such quantitative screens uncover an unusually large number of genes that are known to function in neural development, as well as genes with yet uncharacterized effects on neural development, and novel loci. CONCLUSIONS: Our findings establish the use of quantitative trait analysis for functional genome annotation through forward genetics. Similar analyses of quantitative effects of P element insertions will facilitate our understanding of the genes affecting many other complex traits in Drosophila.

Novel Autism Subtype-Dependent Genetic Variants Are Revealed by Quantitative Trait and Subphenotype Association Analyses of Published GWAS Data

PubMed Central

Hu, Valerie W.; Addington, Anjene; Hyman, Alexander

2011-01-01

The heterogeneity of symptoms associated with autism spectrum disorders (ASDs) has presented a significant challenge to genetic analyses. Even when associations with genetic variants have been identified, it has been difficult to associate them with a specific trait or characteristic of autism. Here, we report that quantitative trait analyses of ASD symptoms combined with case-control association analyses using distinct ASD subphenotypes identified on the basis of symptomatic profiles result in the identification of highly significant associations with 18 novel single nucleotide polymorphisms (SNPs). The symptom categories included deficits in language usage, non-verbal communication, social development, and play skills, as well as insistence on sameness or ritualistic behaviors. Ten of the trait-associated SNPs, or quantitative trait loci (QTL), were associated with more than one subtype, providing partial replication of the identified QTL. Notably, none of the novel SNPs is located within an exonic region, suggesting that these hereditary components of ASDs are more likely related to gene regulatory processes (or gene expression) than to structural or functional changes in gene products. Seven of the QTL reside within intergenic chromosomal regions associated with rare copy number variants that have been previously reported in autistic samples. Pathway analyses of the genes associated with the QTL identified in this study implicate neurological functions and disorders associated with autism pathophysiology. This study underscores the advantage of incorporating both quantitative traits as well as subphenotypes into large-scale genome-wide analyses of complex disorders. PMID:21556359

Testing process predictions of models of risky choice: a quantitative model comparison approach

PubMed Central

Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

2013-01-01

This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information.

PubMed

Ristov, Strahil; Brajkovic, Vladimir; Cubric-Curik, Vlatka; Michieli, Ivan; Curik, Ino

2016-09-10

Identification of genes or even nucleotides that are responsible for quantitative and adaptive trait variation is a difficult task due to the complex interdependence between a large number of genetic and environmental factors. The polymorphism of the mitogenome is one of the factors that can contribute to quantitative trait variation. However, the effects of the mitogenome have not been comprehensively studied, since large numbers of mitogenome sequences and recorded phenotypes are required to reach the adequate power of analysis. Current research in our group focuses on acquiring the necessary mitochondria sequence information and analysing its influence on the phenotype of a quantitative trait. To facilitate these tasks we have produced software for processing pedigrees that is optimised for maternal lineage analysis. We present MaGelLAn 1.0 (maternal genealogy lineage analyser), a suite of four Python scripts (modules) that is designed to facilitate the analysis of the impact of mitogenome polymorphism on quantitative trait variation by combining molecular and pedigree information. MaGelLAn 1.0 is primarily used to: (1) optimise the sampling strategy for molecular analyses; (2) identify and correct pedigree inconsistencies; and (3) identify maternal lineages and assign the corresponding mitogenome sequences to all individuals in the pedigree, this information being used as input to any of the standard software for quantitative genetic (association) analysis. In addition, MaGelLAn 1.0 allows computing the mitogenome (maternal) effective population sizes and probability of mitogenome (maternal) identity that are useful for conservation management of small populations. MaGelLAn is the first tool for pedigree analysis that focuses on quantitative genetic analyses of mitogenome data. It is conceived with the purpose to significantly reduce the effort in handling and preparing large pedigrees for processing the information linked to maternal lines. The software source

Genetically Engineered Mouse Models for Studying Inflammatory Bowel Disease

PubMed Central

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

2015-01-01

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. PMID:26387641

Quantitative genetic analysis of brain copper and zinc in BXD recombinant inbred mice.

PubMed

Jones, Leslie C; McCarthy, Kristin A; Beard, John L; Keen, Carl L; Jones, Byron C

2006-01-01

Copper and zinc are trace nutrients essential for normal brain function, yet an excess of these elements can be toxic. It is important therefore that these metals be closely regulated. We recently conducted a quantitative trait loci (QTL) analysis to identify chromosomal regions in the mouse containing possible regulatory genes. The animals came from 15 strains of the BXD/Ty recombinant inbred (RI) strain panel and the brain regions analyzed were frontal cortex, caudate-putamen, nucleus accumbens and ventral midbrain. Several QTL were identified for copper and/or zinc, most notably on chromosomes 1, 8, 16 and 17. Genetic correlational analysis also revealed associations between these metals and dopamine, cocaine responses, saccharine preference, immune response and seizure susceptibility. Notably, the QTL on chromosome 17 is also associated with seizure susceptibility and contains the histocompatibility H2 complex. This work shows that regulation of zinc and copper is under polygenic influence and is intimately related to CNS function. Future work will reveal genes underlying the QTL and how they interact with other genes and the environment. More importantly, revelation of the genetic underpinnings of copper and zinc brain homeostasis will aid our understanding of neurological diseases that are related to copper and zinc imbalance.

Building a Database for a Quantitative Model

NASA Technical Reports Server (NTRS)

Kahn, C. Joseph; Kleinhammer, Roger

2014-01-01

A database can greatly benefit a quantitative analysis. The defining characteristic of a quantitative risk, or reliability, model is the use of failure estimate data. Models can easily contain a thousand Basic Events, relying on hundreds of individual data sources. Obviously, entering so much data by hand will eventually lead to errors. Not so obviously entering data this way does not aid linking the Basic Events to the data sources. The best way to organize large amounts of data on a computer is with a database. But a model does not require a large, enterprise-level database with dedicated developers and administrators. A database built in Excel can be quite sufficient. A simple spreadsheet database can link every Basic Event to the individual data source selected for them. This database can also contain the manipulations appropriate for how the data is used in the model. These manipulations include stressing factors based on use and maintenance cycles, dormancy, unique failure modes, the modeling of multiple items as a single "Super component" Basic Event, and Bayesian Updating based on flight and testing experience. A simple, unique metadata field in both the model and database provides a link from any Basic Event in the model to its data source and all relevant calculations. The credibility for the entire model often rests on the credibility and traceability of the data.

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.

Testing natural selection vs. genetic drift in phenotypic evolution using quantitative trait locus data.

PubMed Central

Orr, H A

1998-01-01

Evolutionary biologists have long sought a way to determine whether a phenotypic difference between two taxa was caused by natural selection or random genetic drift. Here I argue that data from quantitative trait locus (QTL) analyses can be used to test the null hypothesis of neutral phenotypic evolution. I propose a sign test that compares the observed number of plus and minus alleles in the "high line" with that expected under neutrality, conditioning on the known phenotypic difference between the taxa. Rejection of the null hypothesis implies a role for directional natural selection. This test is applicable to any character in any organism in which QTL analysis can be performed. PMID:9691061

Beyond Punnett Squares: Student Word Association and Explanations of Phenotypic Variation through an Integrative Quantitative Genetics Unit Investigating Anthocyanin Inheritance and Expression in "Brassica rapa" Fast Plants

ERIC Educational Resources Information Center

Batzli, Janet M.; Smith, Amber R.; Williams, Paul H.; McGee, Seth A.; Dosa, Katalin; Pfammatter, Jesse

2014-01-01

Genetics instruction in introductory biology is often confined to Mendelian genetics and avoids the complexities of variation in quantitative traits. Given the driving question "What determines variation in phenotype (Pv)? (Pv=Genotypic variation Gv + environmental variation Ev)," we developed a 4-wk unit for an inquiry-based laboratory…

Integrated Environmental Modeling: Quantitative Microbial Risk Assessment

EPA Science Inventory

The presentation discusses the need for microbial assessments and presents a road map associated with quantitative microbial risk assessments, through an integrated environmental modeling approach. A brief introduction and the strengths of the current knowledge are illustrated. W...

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses

PubMed Central

Zhang, S; Meng, L; Wang, J; Zhang, L

2017-01-01

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population. PMID:28722705

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses.

PubMed

Zhang, S; Meng, L; Wang, J; Zhang, L

2017-10-01

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population.

Development and validation of an event-specific quantitative PCR method for genetically modified maize MIR162.

PubMed

Takabatake, Reona; Masubuchi, Tomoko; Futo, Satoshi; Minegishi, Yasutaka; Noguchi, Akio; Kondo, Kazunari; Teshima, Reiko; Kurashima, Takeyo; Mano, Junichi; Kitta, Kazumi

2014-01-01

A novel real-time PCR-based analytical method was developed for the event-specific quantification of a genetically modified (GM) maize event, MIR162. We first prepared a standard plasmid for MIR162 quantification. The conversion factor (Cf) required to calculate the genetically modified organism (GMO) amount was empirically determined for two real-time PCR instruments, the Applied Biosystems 7900HT (ABI7900) and the Applied Biosystems 7500 (ABI7500) for which the determined Cf values were 0.697 and 0.635, respectively. To validate the developed method, a blind test was carried out in an interlaboratory study. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSDr). The determined biases were less than 25% and the RSDr values were less than 20% at all evaluated concentrations. These results suggested that the limit of quantitation of the method was 0.5%, and that the developed method would thus be suitable for practical analyses for the detection and quantification of MIR162.

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

PubMed

Ziyatdinov, Andrey; Vázquez-Santiago, Miquel; Brunel, Helena; Martinez-Perez, Angel; Aschard, Hugues; Soria, Jose Manuel

2018-02-27

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids.

PubMed

Bonnafous, Fanny; Fievet, Ghislain; Blanchet, Nicolas; Boniface, Marie-Claude; Carrère, Sébastien; Gouzy, Jérôme; Legrand, Ludovic; Marage, Gwenola; Bret-Mestries, Emmanuelle; Munos, Stéphane; Pouilly, Nicolas; Vincourt, Patrick; Langlade, Nicolas; Mangin, Brigitte

2018-02-01

This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

Shaping asteroid models using genetic evolution (SAGE)

NASA Astrophysics Data System (ADS)

Bartczak, P.; Dudziński, G.

2018-02-01

In this work, we present SAGE (shaping asteroid models using genetic evolution), an asteroid modelling algorithm based solely on photometric lightcurve data. It produces non-convex shapes, orientations of the rotation axes and rotational periods of asteroids. The main concept behind a genetic evolution algorithm is to produce random populations of shapes and spin-axis orientations by mutating a seed shape and iterating the process until it converges to a stable global minimum. We tested SAGE on five artificial shapes. We also modelled asteroids 433 Eros and 9 Metis, since ground truth observations for them exist, allowing us to validate the models. We compared the derived shape of Eros with the NEAR Shoemaker model and that of Metis with adaptive optics and stellar occultation observations since other models from various inversion methods were available for Metis.

Fumonisin B1 Toxicity in Grower-Finisher Pigs: A Comparative Analysis of Genetically Engineered Bt Corn and non-Bt Corn by Using Quantitative Dietary Exposure Assessment Modeling

PubMed Central

Delgado, James E.; Wolt, Jeffrey D.

2011-01-01

In this study, we investigate the long-term exposure (20 weeks) to fumonisin B1 (FB1) in grower-finisher pigs by conducting a quantitative exposure assessment (QEA). Our analytical approach involved both deterministic and semi-stochastic modeling for dietary comparative analyses of FB1 exposures originating from genetically engineered Bacillus thuringiensis (Bt)-corn, conventional non-Bt corn and distiller’s dried grains with solubles (DDGS) derived from Bt and/or non-Bt corn. Results from both deterministic and semi-stochastic demonstrated a distinct difference of FB1 toxicity in feed between Bt corn and non-Bt corn. Semi-stochastic results predicted the lowest FB1 exposure for Bt grain with a mean of 1.5 mg FB1/kg diet and the highest FB1 exposure for a diet consisting of non-Bt grain and non-Bt DDGS with a mean of 7.87 mg FB1/kg diet; the chronic toxicological incipient level of concern is 1.0 mg of FB1/kg of diet. Deterministic results closely mirrored but tended to slightly under predict the mean result for the semi-stochastic analysis. This novel comparative QEA model reveals that diet scenarios where the source of grain is derived from Bt corn presents less potential to induce FB1 toxicity than diets containing non-Bt corn. PMID:21909298

An evidential reasoning extension to quantitative model-based failure diagnosis

NASA Technical Reports Server (NTRS)

Gertler, Janos J.; Anderson, Kenneth C.

1992-01-01

The detection and diagnosis of failures in physical systems characterized by continuous-time operation are studied. A quantitative diagnostic methodology has been developed that utilizes the mathematical model of the physical system. On the basis of the latter, diagnostic models are derived each of which comprises a set of orthogonal parity equations. To improve the robustness of the algorithm, several models may be used in parallel, providing potentially incomplete and/or conflicting inferences. Dempster's rule of combination is used to integrate evidence from the different models. The basic probability measures are assigned utilizing quantitative information extracted from the mathematical model and from online computation performed therewith.

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.

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.

Advances in Genetical Genomics of Plants

PubMed Central

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

2009-01-01

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

Genetic demographic networks: Mathematical model and applications.

PubMed

Kimmel, Marek; Wojdyła, Tomasz

2016-10-01

Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise

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

PubMed

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

2013-01-01

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

Quantitative genetics and utilization of mutants

USDA-ARS?s Scientific Manuscript database

The relatively low level of genetic variability currently available in cotton makes mutagenesis attractive to overcome this problem. Mutations can occur either spontaneously or be induced. The majority of the genes we use today are spontaneous mutants that developed over a long period of time. Induc...

Event-specific qualitative and quantitative detection of five genetically modified rice events using a single standard reference molecule.

PubMed

Kim, Jae-Hwan; Park, Saet-Byul; Roh, Hyo-Jeong; Shin, Min-Ki; Moon, Gui-Im; Hong, Jin-Hwan; Kim, Hae-Yeong

2017-07-01

One novel standard reference plasmid, namely pUC-RICE5, was constructed as a positive control and calibrator for event-specific qualitative and quantitative detection of genetically modified (GM) rice (Bt63, Kemingdao1, Kefeng6, Kefeng8, and LLRice62). pUC-RICE5 contained fragments of a rice-specific endogenous reference gene (sucrose phosphate synthase) as well as the five GM rice events. An existing qualitative PCR assay approach was modified using pUC-RICE5 to create a quantitative method with limits of detection correlating to approximately 1-10 copies of rice haploid genomes. In this quantitative PCR assay, the square regression coefficients ranged from 0.993 to 1.000. The standard deviation and relative standard deviation values for repeatability ranged from 0.02 to 0.22 and 0.10% to 0.67%, respectively. The Ministry of Food and Drug Safety (Korea) validated the method and the results suggest it could be used routinely to identify five GM rice events. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

PubMed Central

Welch, Stephen M.; White, Jeffrey W.; Thorp, Kelly R.; Bello, Nora M.

2018-01-01

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10−6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops. PMID:29672629

Development and interlaboratory validation of quantitative polymerase chain reaction method for screening analysis of genetically modified soybeans.

PubMed

Takabatake, Reona; Onishi, Mari; Koiwa, Tomohiro; Futo, Satoshi; Minegishi, Yasutaka; Akiyama, Hiroshi; Teshima, Reiko; Kurashima, Takeyo; Mano, Junichi; Furui, Satoshi; Kitta, Kazumi

2013-01-01

A novel real-time polymerase chain reaction (PCR)-based quantitative screening method was developed for three genetically modified soybeans: RRS, A2704-12, and MON89788. The 35S promoter (P35S) of cauliflower mosaic virus is introduced into RRS and A2704-12 but not MON89788. We then designed a screening method comprised of the combination of the quantification of P35S and the event-specific quantification of MON89788. The conversion factor (Cf) required to convert the amount of a genetically modified organism (GMO) from a copy number ratio to a weight ratio was determined experimentally. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSDR), respectively. The determined RSDR values for the method were less than 25% for both targets. We consider that the developed method would be suitable for the simple detection and approximate quantification of GMO.

Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone.

PubMed

Geerts, Hugo; Roberts, Patrick; Spiros, Athan; Potkin, Steven

2015-04-01

The concept of targeted therapies remains a holy grail for the pharmaceutical drug industry for identifying responder populations or new drug targets. Here we provide quantitative systems pharmacology as an alternative to the more traditional approach of retrospective responder pharmacogenomics analysis and applied this to the case of iloperidone in schizophrenia. This approach implements the actual neurophysiological effect of genotypes in a computer-based biophysically realistic model of human neuronal circuits, is parameterized with human imaging and pathology, and is calibrated by clinical data. We keep the drug pharmacology constant, but allowed the biological model coupling values to fluctuate in a restricted range around their calibrated values, thereby simulating random genetic mutations and representing variability in patient response. Using hypothesis-free Design of Experiments methods the dopamine D4 R-AMPA (receptor-alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptor coupling in cortical neurons was found to drive the beneficial effect of iloperidone, likely corresponding to the rs2513265 upstream of the GRIA4 gene identified in a traditional pharmacogenomics analysis. The serotonin 5-HT3 receptor-mediated effect on interneuron gamma-aminobutyric acid conductance was identified as the process that moderately drove the differentiation of iloperidone versus ziprasidone. This paper suggests that reverse-engineered quantitative systems pharmacology is a powerful alternative tool to characterize the underlying neurobiology of a responder population and possibly identifying new targets. © The Author(s) 2015.

Refining the quantitative pathway of the Pathways to Mathematics model.

PubMed

Sowinski, Carla; LeFevre, Jo-Anne; Skwarchuk, Sheri-Lynn; Kamawar, Deepthi; Bisanz, Jeffrey; Smith-Chant, Brenda

2015-03-01

In the current study, we adopted the Pathways to Mathematics model of LeFevre et al. (2010). In this model, there are three cognitive domains--labeled as the quantitative, linguistic, and working memory pathways--that make unique contributions to children's mathematical development. We attempted to refine the quantitative pathway by combining children's (N=141 in Grades 2 and 3) subitizing, counting, and symbolic magnitude comparison skills using principal components analysis. The quantitative pathway was examined in relation to dependent numerical measures (backward counting, arithmetic fluency, calculation, and number system knowledge) and a dependent reading measure, while simultaneously accounting for linguistic and working memory skills. Analyses controlled for processing speed, parental education, and gender. We hypothesized that the quantitative, linguistic, and working memory pathways would account for unique variance in the numerical outcomes; this was the case for backward counting and arithmetic fluency. However, only the quantitative and linguistic pathways (not working memory) accounted for unique variance in calculation and number system knowledge. Not surprisingly, only the linguistic pathway accounted for unique variance in the reading measure. These findings suggest that the relative contributions of quantitative, linguistic, and working memory skills vary depending on the specific cognitive task. Copyright © 2014 Elsevier Inc. All rights reserved.

Modeling conflict : research methods, quantitative modeling, and lessons learned.

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

Rexroth, Paul E.; Malczynski, Leonard A.; Hendrickson, Gerald A.

2004-09-01

This study investigates the factors that lead countries into conflict. Specifically, political, social and economic factors may offer insight as to how prone a country (or set of countries) may be for inter-country or intra-country conflict. Largely methodological in scope, this study examines the literature for quantitative models that address or attempt to model conflict both in the past, and for future insight. The analysis concentrates specifically on the system dynamics paradigm, not the political science mainstream approaches of econometrics and game theory. The application of this paradigm builds upon the most sophisticated attempt at modeling conflict as a resultmore » of system level interactions. This study presents the modeling efforts built on limited data and working literature paradigms, and recommendations for future attempts at modeling conflict.« less

Multi-scale genetic dynamic modelling I : an algorithm to compute generators.

PubMed

Kirkilionis, Markus; Janus, Ulrich; Sbano, Luca

2011-09-01

We present a new approach or framework to model dynamic regulatory genetic activity. The framework is using a multi-scale analysis based upon generic assumptions on the relative time scales attached to the different transitions of molecular states defining the genetic system. At micro-level such systems are regulated by the interaction of two kinds of molecular players: macro-molecules like DNA or polymerases, and smaller molecules acting as transcription factors. The proposed genetic model then represents the larger less abundant molecules with a finite discrete state space, for example describing different conformations of these molecules. This is in contrast to the representations of the transcription factors which are-like in classical reaction kinetics-represented by their particle number only. We illustrate the method by considering the genetic activity associated to certain configurations of interacting genes that are fundamental to modelling (synthetic) genetic clocks. A largely unknown question is how different molecular details incorporated via this more realistic modelling approach lead to different macroscopic regulatory genetic models which dynamical behaviour might-in general-be different for different model choices. The theory will be applied to a real synthetic clock in a second accompanying article (Kirkilioniset al., Theory Biosci, 2011).

Reliably Discriminating Stock Structure with Genetic Markers:Mixture Models with Robust and Fast Computation.

PubMed

Foster, Scott D; Feutry, Pierre; Grewe, Peter M; Berry, Oliver; Hui, Francis K C; Davies, Campbell R

2018-06-26

Delineating naturally occurring and self-sustaining sub-populations (stocks) of a species is an important task, especially for species harvested from the wild. Despite its central importance to natural resource management, analytical methods used to delineate stocks are often, and increasingly, borrowed from superficially similar analytical tasks in human genetics even though models specifically for stock identification have been previously developed. Unfortunately, the analytical tasks in resource management and human genetics are not identical { questions about humans are typically aimed at inferring ancestry (often referred to as 'admixture') rather than breeding stocks. In this article, we argue, and show through simulation experiments and an analysis of yellowfin tuna data, that ancestral analysis methods are not always appropriate for stock delineation. In this work, we advocate a variant of a previouslyintroduced and simpler model that identifies stocks directly. We also highlight that the computational aspects of the analysis, irrespective of the model, are difficult. We introduce some alternative computational methods and quantitatively compare these methods to each other and to established methods. We also present a method for quantifying uncertainty in model parameters and in assignment probabilities. In doing so, we demonstrate that point estimates can be misleading. One of the computational strategies presented here, based on an expectation-maximisation algorithm with judiciously chosen starting values, is robust and has a modest computational cost. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Mathematical modelling and quantitative methods.

PubMed

Edler, L; Poirier, K; Dourson, M; Kleiner, J; Mileson, B; Nordmann, H; Renwick, A; Slob, W; Walton, K; Würtzen, G

2002-01-01

The present review reports on the mathematical methods and statistical techniques presently available for hazard characterisation. The state of the art of mathematical modelling and quantitative methods used currently for regulatory decision-making in Europe and additional potential methods for risk assessment of chemicals in food and diet are described. Existing practices of JECFA, FDA, EPA, etc., are examined for their similarities and differences. A framework is established for the development of new and improved quantitative methodologies. Areas for refinement, improvement and increase of efficiency of each method are identified in a gap analysis. Based on this critical evaluation, needs for future research are defined. It is concluded from our work that mathematical modelling of the dose-response relationship would improve the risk assessment process. An adequate characterisation of the dose-response relationship by mathematical modelling clearly requires the use of a sufficient number of dose groups to achieve a range of different response levels. This need not necessarily lead to an increase in the total number of animals in the study if an appropriate design is used. Chemical-specific data relating to the mode or mechanism of action and/or the toxicokinetics of the chemical should be used for dose-response characterisation whenever possible. It is concluded that a single method of hazard characterisation would not be suitable for all kinds of risk assessments, and that a range of different approaches is necessary so that the method used is the most appropriate for the data available and for the risk characterisation issue. Future refinements to dose-response characterisation should incorporate more clearly the extent of uncertainty and variability in the resulting output.

Novel Applications of Multi-task Learning and Multiple Output Regression to Multiple Genetic Trait Prediction

USDA-ARS?s Scientific Manuscript database

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait predicti...

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.

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.

A Second-Generation Device for Automated Training and Quantitative Behavior Analyses of Molecularly-Tractable Model Organisms

PubMed Central

Blackiston, Douglas; Shomrat, Tal; Nicolas, Cindy L.; Granata, Christopher; Levin, Michael

2010-01-01

A deep understanding of cognitive processes requires functional, quantitative analyses of the steps leading from genetics and the development of nervous system structure to behavior. Molecularly-tractable model systems such as Xenopus laevis and planaria offer an unprecedented opportunity to dissect the mechanisms determining the complex structure of the brain and CNS. A standardized platform that facilitated quantitative analysis of behavior would make a significant impact on evolutionary ethology, neuropharmacology, and cognitive science. While some animal tracking systems exist, the available systems do not allow automated training (feedback to individual subjects in real time, which is necessary for operant conditioning assays). The lack of standardization in the field, and the numerous technical challenges that face the development of a versatile system with the necessary capabilities, comprise a significant barrier keeping molecular developmental biology labs from integrating behavior analysis endpoints into their pharmacological and genetic perturbations. Here we report the development of a second-generation system that is a highly flexible, powerful machine vision and environmental control platform. In order to enable multidisciplinary studies aimed at understanding the roles of genes in brain function and behavior, and aid other laboratories that do not have the facilities to undergo complex engineering development, we describe the device and the problems that it overcomes. We also present sample data using frog tadpoles and flatworms to illustrate its use. Having solved significant engineering challenges in its construction, the resulting design is a relatively inexpensive instrument of wide relevance for several fields, and will accelerate interdisciplinary discovery in pharmacology, neurobiology, regenerative medicine, and cognitive science. PMID:21179424

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.

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

PubMed

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

2017-02-27

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

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

PubMed Central

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

2017-01-01

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

Basal exon skipping and genetic pleiotropy: A predictive model of disease pathogenesis.

PubMed

Drivas, Theodore G; Wojno, Adam P; Tucker, Budd A; Stone, Edwin M; Bennett, Jean

2015-06-10

Genetic pleiotropy, the phenomenon by which mutations in the same gene result in markedly different disease phenotypes, has proven difficult to explain with traditional models of disease pathogenesis. We have developed a model of pleiotropic disease that explains, through the process of basal exon skipping, how different mutations in the same gene can differentially affect protein production, with the total amount of protein produced correlating with disease severity. Mutations in the centrosomal protein of 290 kDa (CEP290) gene are associated with a spectrum of phenotypically distinct human diseases (the ciliopathies). Molecular biologic examination of CEP290 transcript and protein expression in cells from patients carrying CEP290 mutations, measured by quantitative polymerase chain reaction and Western blotting, correlated with disease severity and corroborated our model. We show that basal exon skipping may be the mechanism underlying the disease pleiotropy caused by CEP290 mutations. Applying our model to a different disease gene, CC2D2A (coiled-coil and C2 domains-containing protein 2A), we found that the same correlations held true. Our model explains the phenotypic diversity of two different inherited ciliopathies and may establish a new model for the pathogenesis of other pleiotropic human diseases. Copyright © 2015, American Association for the Advancement of Science.

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.

Quantitative model analysis with diverse biological data: applications in developmental pattern formation.

PubMed

Pargett, Michael; Umulis, David M

2013-07-15

Mathematical modeling of transcription factor and signaling networks is widely used to understand if and how a mechanism works, and to infer regulatory interactions that produce a model consistent with the observed data. Both of these approaches to modeling are informed by experimental data, however, much of the data available or even acquirable are not quantitative. Data that is not strictly quantitative cannot be used by classical, quantitative, model-based analyses that measure a difference between the measured observation and the model prediction for that observation. To bridge the model-to-data gap, a variety of techniques have been developed to measure model "fitness" and provide numerical values that can subsequently be used in model optimization or model inference studies. Here, we discuss a selection of traditional and novel techniques to transform data of varied quality and enable quantitative comparison with mathematical models. This review is intended to both inform the use of these model analysis methods, focused on parameter estimation, and to help guide the choice of method to use for a given study based on the type of data available. Applying techniques such as normalization or optimal scaling may significantly improve the utility of current biological data in model-based study and allow greater integration between disparate types of data. Copyright © 2013 Elsevier Inc. All rights reserved.

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

Exploring the possibility of modeling a genetic counseling guideline using agile methodology.

PubMed

Choi, Jeeyae

2013-01-01

Increased demand of genetic counseling services heightened the necessity of a computerized genetic counseling decision support system. In order to develop an effective and efficient computerized system, modeling of genetic counseling guideline is an essential step. Throughout this pilot study, Agile methodology with United Modeling Language (UML) was utilized to model a guideline. 13 tasks and 14 associated elements were extracted. Successfully constructed conceptual class and activity diagrams revealed that Agile methodology with UML was a suitable tool to modeling a genetic counseling guideline.

Developmental Patterning as a Quantitative Trait: Genetic Modulation of the Hoxb6 Mutant Skeletal Phenotype

PubMed Central

Kappen, Claudia

2016-01-01

The process of patterning along the anterior-posterior axis in vertebrates is highly conserved. The function of Hox genes in the axis patterning process is particularly well documented for bone development in the vertebral column and the limbs. We here show that Hoxb6, in skeletal elements at the cervico-thoracic junction, controls multiple independent aspects of skeletal pattern, implicating discrete developmental pathways as substrates for this transcription factor. In addition, we demonstrate that Hoxb6 function is subject to modulation by genetic factors. These results establish Hox-controlled skeletal pattern as a quantitative trait modulated by gene-gene interactions, and provide evidence that distinct modifiers influence the function of conserved developmental genes in fundamental patterning processes. PMID:26800342

Toward an Integration of Cognitive and Genetic Models of Risk for Depression

PubMed Central

Gibb, Brandon E.; Beevers, Christopher G.; McGeary, John E.

2012-01-01

There is growing interest in integrating cognitive and genetic models of depression risk. We review two ways in which these models can be meaningfully integrated. First, information-processing biases may represent intermediate phenotypes for specific genetic influences. These genetic influences may represent main effects on specific cognitive processes or may moderate the impact of environmental influences on information-processing biases. Second, cognitive and genetic influences may combine to increase reactivity to environmental stressors, increasing risk for depression in a gene × cognition × environment model of risk. There is now growing support for both of these ways of integrating cognitive and genetic models of depression risk. Specifically, there is support for genetic influences on information-processing biases, particularly the link between 5-HTTLPR and attentional biases, from both genetic association and gene × environment (G × E) studies. There is also initial support for gene × cognition × environment models of risk in which specific genetic influences contribute to increased reactivity to environmental influences. We review this research and discuss important areas of future research, particularly the need for larger samples that allow for a broader examination of genetic and epigenetic influences as well as the combined influence of variability across a number of genes. PMID:22920216

Interpretation of protein quantitation using the Bradford assay: comparison with two calculation models.

PubMed

Ku, Hyung-Keun; Lim, Hyuk-Min; Oh, Kyong-Hwa; Yang, Hyo-Jin; Jeong, Ji-Seon; Kim, Sook-Kyung

2013-03-01

The Bradford assay is a simple method for protein quantitation, but variation in the results between proteins is a matter of concern. In this study, we compared and normalized quantitative values from two models for protein quantitation, where the residues in the protein that bind to anionic Coomassie Brilliant Blue G-250 comprise either Arg and Lys (Method 1, M1) or Arg, Lys, and His (Method 2, M2). Use of the M2 model yielded much more consistent quantitation values compared with use of the M1 model, which exhibited marked overestimations against protein standards. Copyright © 2012 Elsevier Inc. All rights reserved.

Genetical genomics of Populus leaf shape variation

DOE PAGES

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

2015-06-30

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

Development of a screening method for genetically modified soybean by plasmid-based quantitative competitive polymerase chain reaction.

PubMed

Shimizu, Eri; Kato, Hisashi; Nakagawa, Yuki; Kodama, Takashi; Futo, Satoshi; Minegishi, Yasutaka; Watanabe, Takahiro; Akiyama, Hiroshi; Teshima, Reiko; Furui, Satoshi; Hino, Akihiro; Kitta, Kazumi

2008-07-23

A novel type of quantitative competitive polymerase chain reaction (QC-PCR) system for the detection and quantification of the Roundup Ready soybean (RRS) was developed. This system was designed based on the advantage of a fully validated real-time PCR method used for the quantification of RRS in Japan. A plasmid was constructed as a competitor plasmid for the detection and quantification of genetically modified soy, RRS. The plasmid contained the construct-specific sequence of RRS and the taxon-specific sequence of lectin1 (Le1), and both had 21 bp oligonucleotide insertion in the sequences. The plasmid DNA was used as a reference molecule instead of ground seeds, which enabled us to precisely and stably adjust the copy number of targets. The present study demonstrated that the novel plasmid-based QC-PCR method could be a simple and feasible alternative to the real-time PCR method used for the quantification of genetically modified organism contents.

Development of an event-specific hydrolysis probe quantitative real-time polymerase chain reaction assay for Embrapa 5.1 genetically modified common bean (Phaseolus vulgaris).

PubMed

Treml, Diana; Venturelli, Gustavo L; Brod, Fábio C A; Faria, Josias C; Arisi, Ana C M

2014-12-10

A genetically modified (GM) common bean event, namely Embrapa 5.1, resistant to the bean golden mosaic virus (BGMV), was approved for commercialization in Brazil. Brazilian regulation for genetically modified organism (GMO) labeling requires that any food containing more than 1% GMO be labeled. The event-specific polymerase chain reaction (PCR) method has been the primary trend for GMO identification and quantitation because of its high specificity based on the flanking sequence. This work reports the development of an event-specific assay, named FGM, for Embrapa 5.1 detection and quantitation by use of SYBR Green or hydrolysis probe. The FGM assay specificity was tested for Embrapa 2.3 event (a noncommercial GM common bean also resistant to BGMV), 46 non-GM common bean varieties, and other crop species including maize, GM maize, soybean, and GM soybean. The FGM assay showed high specificity to detect the Embrapa 5.1 event. Standard curves for the FGM assay presented a mean efficiency of 95% and a limit of detection (LOD) of 100 genome copies in the presence of background DNA. The primers and probe developed are suitable for the detection and quantitation of Embrapa 5.1.

High-Content Screening for Quantitative Cell Biology.

PubMed

Mattiazzi Usaj, Mojca; Styles, Erin B; Verster, Adrian J; Friesen, Helena; Boone, Charles; Andrews, Brenda J

2016-08-01

High-content screening (HCS), which combines automated fluorescence microscopy with quantitative image analysis, allows the acquisition of unbiased multiparametric data at the single cell level. This approach has been used to address diverse biological questions and identify a plethora of quantitative phenotypes of varying complexity in numerous different model systems. Here, we describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions. We review the steps involved in the design of useful biological assays and automated image analysis, and describe major challenges associated with each. Additionally, we highlight emerging technologies and future challenges, and discuss how the field of HCS might be enhanced in the future. Copyright © 2016 Elsevier Ltd. All rights reserved.

ENU mutagenesis to generate genetically modified rat models.

PubMed

van Boxtel, Ruben; Gould, Michael N; Cuppen, Edwin; Smits, Bart M G

2010-01-01

The rat is one of the most preferred model organisms in biomedical research and has been extremely useful for linking physiology and pathology to the genome. However, approaches to genetically modify specific genes in the rat germ line remain relatively scarce. To date, the most efficient approach for generating genetically modified rats has been the target-selected N-ethyl-N-nitrosourea (ENU) mutagenesis-based technology. Here, we describe the detailed protocols for ENU mutagenesis and mutant retrieval in the rat model organism.

Interlaboratory validation of quantitative duplex real-time PCR method for screening analysis of genetically modified maize.

PubMed

Takabatake, Reona; Koiwa, Tomohiro; Kasahara, Masaki; Takashima, Kaori; Futo, Satoshi; Minegishi, Yasutaka; Akiyama, Hiroshi; Teshima, Reiko; Oguchi, Taichi; Mano, Junichi; Furui, Satoshi; Kitta, Kazumi

2011-01-01

To reduce the cost and time required to routinely perform the genetically modified organism (GMO) test, we developed a duplex quantitative real-time PCR method for a screening analysis simultaneously targeting an event-specific segment for GA21 and Cauliflower Mosaic Virus 35S promoter (P35S) segment [Oguchi et al., J. Food Hyg. Soc. Japan, 50, 117-125 (2009)]. To confirm the validity of the method, an interlaboratory collaborative study was conducted. In the collaborative study, conversion factors (Cfs), which are required to calculate the GMO amount (%), were first determined for two real-time PCR instruments, the ABI PRISM 7900HT and the ABI PRISM 7500. A blind test was then conducted. The limit of quantitation for both GA21 and P35S was estimated to be 0.5% or less. The trueness and precision were evaluated as the bias and reproducibility of the relative standard deviation (RSD(R)). The determined bias and RSD(R) were each less than 25%. We believe the developed method would be useful for the practical screening analysis of GM maize.

Effect of genetic algorithm as a variable selection method on different chemometric models applied for the analysis of binary mixture of amoxicillin and flucloxacillin: A comparative study

NASA Astrophysics Data System (ADS)

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

2016-03-01

Different chemometric models were applied for the quantitative analysis of amoxicillin (AMX), and flucloxacillin (FLX) in their binary mixtures, namely, partial least squares (PLS), spectral residual augmented classical least squares (SRACLS), concentration residual augmented classical least squares (CRACLS) and artificial neural networks (ANNs). All methods were applied with and without variable selection procedure (genetic algorithm GA). The methods were used for the quantitative analysis of the drugs in laboratory prepared mixtures and real market sample via handling the UV spectral data. Robust and simpler models were obtained by applying GA. The proposed methods were found to be rapid, simple and required no preliminary separation steps.

Reduction of a metapopulation genetic model to an effective one-island model

NASA Astrophysics Data System (ADS)

Parra-Rojas, César; McKane, Alan J.

2018-04-01

We explore a model of metapopulation genetics which is based on a more ecologically motivated approach than is frequently used in population genetics. The size of the population is regulated by competition between individuals, rather than by artificially imposing a fixed population size. The increased complexity of the model is managed by employing techniques often used in the physical sciences, namely exploiting time-scale separation to eliminate fast variables and then constructing an effective model from the slow modes. We analyse this effective model and show that the predictions for the probability of fixation of the alleles and the mean time to fixation agree well with those found from numerical simulations of the original model. Contribution to the Focus Issue Evolutionary Modeling and Experimental Evolution edited by José Cuesta, Joachim Krug and Susanna Manrubia.

Genetically informed ecological niche models improve climate change predictions.

PubMed

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

2017-01-01

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

The genetic landscape of a physical interaction

PubMed Central

Diss, Guillaume

2018-01-01

A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay – deepPCA – we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. Genetic interactions are abundant both in cis (within one protein) and trans (between the two molecules) and consist of two classes – interactions driven by thermodynamics that can be predicted using a three-parameter global model, and structural interactions between proximally located residues. These results reveal how physical interactions generate quantitatively predictable genetic interactions. PMID:29638215

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

PubMed Central

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

2016-01-01

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

Construction of a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq) and its application to Quantitative Trait Loci (QTL) analysis for boll weight in upland cotton (Gossypium hirsutum.).

PubMed

Zhang, Zhen; Shang, Haihong; Shi, Yuzhen; Huang, Long; Li, Junwen; Ge, Qun; Gong, Juwu; Liu, Aiying; Chen, Tingting; Wang, Dan; Wang, Yanling; Palanga, Koffi Kibalou; Muhammad, Jamshed; Li, Weijie; Lu, Quanwei; Deng, Xiaoying; Tan, Yunna; Song, Weiwu; Cai, Juan; Li, Pengtao; Rashid, Harun or; Gong, Wankui; Yuan, Youlu

2016-04-11

Upland Cotton (Gossypium hirsutum) is one of the most important worldwide crops it provides natural high-quality fiber for the industrial production and everyday use. Next-generation sequencing is a powerful method to identify single nucleotide polymorphism markers on a large scale for the construction of a high-density genetic map for quantitative trait loci mapping. In this research, a recombinant inbred lines population developed from two upland cotton cultivars 0-153 and sGK9708 was used to construct a high-density genetic map through the specific locus amplified fragment sequencing method. The high-density genetic map harbored 5521 single nucleotide polymorphism markers which covered a total distance of 3259.37 cM with an average marker interval of 0.78 cM without gaps larger than 10 cM. In total 18 quantitative trait loci of boll weight were identified as stable quantitative trait loci and were detected in at least three out of 11 environments and explained 4.15-16.70 % of the observed phenotypic variation. In total, 344 candidate genes were identified within the confidence intervals of these stable quantitative trait loci based on the cotton genome sequence. These genes were categorized based on their function through gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis and eukaryotic orthologous groups analysis. This research reported the first high-density genetic map for Upland Cotton (Gossypium hirsutum) with a recombinant inbred line population using single nucleotide polymorphism markers developed by specific locus amplified fragment sequencing. We also identified quantitative trait loci of boll weight across 11 environments and identified candidate genes within the quantitative trait loci confidence intervals. The results of this research would provide useful information for the next-step work including fine mapping, gene functional analysis, pyramiding breeding of functional genes as well as marker-assisted selection.

Using multiple PCR and CE with chemiluminescence detection for simultaneous qualitative and quantitative analysis of genetically modified organism.

PubMed

Guo, Longhua; Qiu, Bin; Chi, Yuwu; Chen, Guonan

2008-09-01

In this paper, an ultrasensitive CE-CL detection system coupled with a novel double-on-column coaxial flow detection interface was developed for the detection of PCR products. A reliable procedure based on this system had been demonstrated for qualitative and quantitative analysis of genetically modified organism-the detection of Roundup Ready Soy (RRS) samples was presented as an example. The promoter, terminator, function and two reference genes of RRS were amplified with multiplex PCR simultaneously. After that, the multiplex PCR products were labeled with acridinium ester at the 5'-terminal through an amino modification and then analyzed by the proposed CE-CL system. Reproducibility of analysis times and peak heights for the CE-CL analysis were determined to be better than 0.91 and 3.07% (RSD, n=15), respectively, for three consecutive days. It was shown that this method could accurately and qualitatively detect RRS standards and the simulative samples. The evaluation in terms of quantitative analysis of RRS provided by this new method was confirmed by comparing our assay results with those of the standard real-time quantitative PCR (RT-QPCR) using SYBR Green I dyes. The results showed a good coherence between the two methods. This approach demonstrated the possibility for accurate qualitative and quantitative detection of GM plants in a single run.

An integrated approach to characterize genetic interaction networks in yeast metabolism

PubMed Central

Szappanos, Balázs; Kovács, Károly; Szamecz, Béla; Honti, Frantisek; Costanzo, Michael; Baryshnikova, Anastasia; Gelius-Dietrich, Gabriel; Lercher, Martin J.; Jelasity, Márk; Myers, Chad L.; Andrews, Brenda J.; Boone, Charles; Oliver, Stephen G.; Pál, Csaba; Papp, Balázs

2011-01-01

Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments. PMID:21623372

Validation and application of quantitative PCR assays using host-specific Bacteroidales genetic markers for swine fecal pollution tracking.

PubMed

Fan, Lihua; Shuai, Jiangbing; Zeng, Ruoxue; Mo, Hongfei; Wang, Suhua; Zhang, Xiaofeng; He, Yongqiang

2017-12-01

Genome fragment enrichment (GFE) method was applied to identify host-specific bacterial genetic markers that differ among different fecal metagenomes. To enrich for swine-specific DNA fragments, swine fecal DNA composite (n = 34) was challenged against a DNA composite consisting of cow, human, goat, sheep, chicken, duck and goose fecal DNA extracts (n = 83). Bioinformatic analyses of 384 non-redundant swine enriched metagenomic sequences indicated a preponderance of Bacteroidales-like regions predicted to encode metabolism-associated, cellular processes and information storage and processing. After challenged against fecal DNA extracted from different animal sources, four sequences from the clone libraries targeting two Bacteroidales- (genes 1-38 and 3-53), a Clostridia- (gene 2-109) as well as a Bacilli-like sequence (gene 2-95), respectively, showed high specificity to swine feces based on PCR analysis. Host-specificity and host-sensitivity analysis confirmed that oligonucleotide primers and probes capable of annealing to select Bacteroidales-like sequences (1-38 and 3-53) exhibited high specificity (>90%) in quantitative PCR assays with 71 fecal DNAs from non-target animal sources. The two assays also demonstrated broad distributions of corresponding genetic markers (>94% positive) among 72 swine feces. After evaluation with environmental water samples from different areas, swine-targeted assays based on two Bacteroidales-like GFE sequences appear to be suitable quantitative tracing tools for swine fecal pollution. Copyright © 2017 Elsevier Ltd. All rights reserved.

Asynchronous adaptive time step in quantitative cellular automata modeling

PubMed Central

Zhu, Hao; Pang, Peter YH; Sun, Yan; Dhar, Pawan

2004-01-01

Background The behaviors of cells in metazoans are context dependent, thus large-scale multi-cellular modeling is often necessary, for which cellular automata are natural candidates. Two related issues are involved in cellular automata based multi-cellular modeling: how to introduce differential equation based quantitative computing to precisely describe cellular activity, and upon it, how to solve the heavy time consumption issue in simulation. Results Based on a modified, language based cellular automata system we extended that allows ordinary differential equations in models, we introduce a method implementing asynchronous adaptive time step in simulation that can considerably improve efficiency yet without a significant sacrifice of accuracy. An average speedup rate of 4–5 is achieved in the given example. Conclusions Strategies for reducing time consumption in simulation are indispensable for large-scale, quantitative multi-cellular models, because even a small 100 × 100 × 100 tissue slab contains one million cells. Distributed and adaptive time step is a practical solution in cellular automata environment. PMID:15222901

The Mapping Model: A Cognitive Theory of Quantitative Estimation

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2008-01-01

How do people make quantitative estimations, such as estimating a car's selling price? Traditionally, linear-regression-type models have been used to answer this question. These models assume that people weight and integrate all information available to estimate a criterion. The authors propose an alternative cognitive theory for quantitative…

Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth.

PubMed

Baker, Robert L; Leong, Wen Fung; An, Nan; Brock, Marcus T; Rubin, Matthew J; Welch, Stephen; Weinig, Cynthia

2018-02-01

We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements. Characterizing the genetic basis of traits that vary during ontogeny and affect plant performance is a major goal in evolutionary biology and agronomy. Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa recombinant inbred lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation (A max ) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of A max , because these two indices were genetically correlated with A max across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including A max improves model fit over the initial model. The mtci and pri2 indices also outperform direct A max measurements. Of particular

Population-genetic models of sex-limited genomic imprinting.

PubMed

Kelly, S Thomas; Spencer, Hamish G

2017-06-01

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

A quantitative method for defining high-arched palate using the Tcof1(+/-) mutant mouse as a model.

PubMed

Conley, Zachary R; Hague, Molly; Kurosaka, Hiroshi; Dixon, Jill; Dixon, Michael J; Trainor, Paul A

2016-07-15

The palate functions as the roof of the mouth in mammals, separating the oral and nasal cavities. Its complex embryonic development and assembly poses unique susceptibilities to intrinsic and extrinsic disruptions. Such disruptions may cause failure of the developing palatal shelves to fuse along the midline resulting in a cleft. In other cases the palate may fuse at an arch, resulting in a vaulted oral cavity, termed high-arched palate. There are many models available for studying the pathogenesis of cleft palate but a relative paucity for high-arched palate. One condition exhibiting either cleft palate or high-arched palate is Treacher Collins syndrome, a congenital disorder characterized by numerous craniofacial anomalies. We quantitatively analyzed palatal perturbations in the Tcof1(+/-) mouse model of Treacher Collins syndrome, which phenocopies the condition in humans. We discovered that 46% of Tcof1(+/-) mutant embryos and new born pups exhibit either soft clefts or full clefts. In addition, 17% of Tcof1(+/-) mutants were found to exhibit high-arched palate, defined as two sigma above the corresponding wild-type population mean for height and angular based arch measurements. Furthermore, palatal shelf length and shelf width were decreased in all Tcof1(+/-) mutant embryos and pups compared to controls. Interestingly, these phenotypes were subsequently ameliorated through genetic inhibition of p53. The results of our study therefore provide a simple, reproducible and quantitative method for investigating models of high-arched palate. Copyright © 2015 Elsevier Inc. All rights reserved.

Applications of Population Genetics to Animal Breeding, from Wright, Fisher and Lush to Genomic Prediction

PubMed Central

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. PMID:24395822

Quantitative genetic analysis of the body composition and blood pressure association in two ethnically diverse populations.

PubMed

Ghosh, Sudipta; Dosaev, Tasbulat; Prakash, Jai; Livshits, Gregory

2017-04-01

The major aim of this study was to conduct comparative quantitative-genetic analysis of the body composition (BCP) and somatotype (STP) variation, as well as their correlations with blood pressure (BP) in two ethnically, culturally and geographically different populations: Santhal, indigenous ethnic group from India and Chuvash, indigenous population from Russia. Correspondently two pedigree-based samples were collected from 1,262 Santhal and1,558 Chuvash individuals, respectively. At the first stage of the study, descriptive statistics and a series of univariate regression analyses were calculated. Finally, multiple and multivariate regression (MMR) analyses, with BP measurements as dependent variables and age, sex, BCP and STP as independent variables were carried out in each sample separately. The significant and independent covariates of BP were identified and used for re-examination in pedigree-based variance decomposition analysis. Despite clear and significant differences between the populations in BCP/STP, both Santhal and Chuvash were found to be predominantly mesomorphic irrespective of their sex. According to MMR analyses variation of BP significantly depended on age and mesomorphic component in both samples, and in addition on sex, ectomorphy and fat mass index in Santhal and on fat free mass index in Chuvash samples, respectively. Additive genetic component contributes to a substantial proportion of blood pressure and body composition variance. Variance component analysis in addition to above mentioned results suggests that additive genetic factors influence BP and BCP/STP associations significantly. © 2017 Wiley Periodicals, Inc.

Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

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

Gupta, Chinmaya; López, José Manuel; Azencott, Robert

Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemicalmore » Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.« less

The system-resonance approach in modeling genetic structures.

PubMed

Petoukhov, Sergey V

2016-01-01

The founder of the theory of resonance in structural chemistry Linus Pauling established the importance of resonance patterns in organization of living systems. Any living organism is a great chorus of coordinated oscillatory processes. From the formal point of view, biological organism is an oscillatory system with a great number of degrees of freedom. Such systems are studied in the theory of oscillations using matrix mathematics of their resonance characteristics. This study is devoted to a new approach for modeling genetically inherited structures and processes in living organisms using mathematical tools of the theory of resonances. This approach reveals hidden relationships in a number of genetic phenomena and gives rise to a new class of bio-mathematical models, which contribute to a convergence of biology with physics and informatics. In addition some relationships of molecular-genetic ensembles with mathematics of noise-immunity coding of information in modern communications technology are shown. Perspectives of applications of the phenomena of vibrational mechanics for modeling in biology are discussed. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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.

Genetic counselor perceptions of genetic counseling session goals: a validation study of the reciprocal-engagement model.

PubMed

Hartmann, Julianne E; Veach, Patricia McCarthy; MacFarlane, Ian M; LeRoy, Bonnie S

2015-04-01

Although some researchers have attempted to define genetic counseling practice goals, no study has obtained consensus about the goals from a large sample of genetic counselors. The Reciprocal-Engagement Model (REM; McCarthy Veach, Bartels & LeRoy, 2007) articulates 17 goals of genetic counseling practice. The present study investigated whether these goals could be generalized as a model of practice, as determined by a larger group of clinical genetic counselors. Accordingly, 194 genetic counselors were surveyed regarding their opinions about the importance of each goal and their perceptions of how frequently they achieve each goal. Mean importance ratings suggest they viewed every goal as important. Factor analysis of the 17 goals yielded four factors: Understanding and Appreciation, Support and Guidance, Facilitative Decision-Making, and Patient-Centered Education. Patient-Centered Education and Facilitative Decision-Making goals received the highest mean importance ratings. Mean frequency ratings were consistently lower than importance ratings, suggesting genetic counseling goals may be difficult to achieve and/or not applicable in all situations. A number of respondents provided comments about the REM goals that offer insight into factors related to implementing the goals in clinical practice. This study presents preliminary evidence concerning the validity of the goals component of the REM.

Study books on ADHD genetics: balanced or biased?

PubMed

Te Meerman, Sanne; Batstra, Laura; Hoekstra, Rink; Grietens, Hans

2017-06-01

Academic study books are essential assets for disseminating knowledge about ADHD to future healthcare professionals. This study examined if they are balanced with regard to genetics. We selected and analyzed study books (N=43) used in (pre) master's programmes at 10 universities in the Netherlands. Because the mere behaviourally informed quantitative genetics give a much higher effect size of the genetic involvement in ADHD, it is important that study books contrast these findings with molecular genetics' outcomes. The latter studies use real genetic data, and their low effect sizes expose the potential weaknesses of quantitative genetics, like underestimating the involvement of the environment. Only a quarter of books mention both effect sizes and contrast these findings, while another quarter does not discuss any effect size. Most importantly, however, roughly half of the books in our sample mention only the effect sizes from quantitative genetic studies without addressing the low explained variance of molecular genetic studies. This may confuse readers by suggesting that the weakly associated genes support the quite spectacular, but potentially flawed estimates of twin, family and adoption studies, while they actually contradict them.

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

PubMed

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

2012-01-01

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

Quantitative Genetics Identifies Cryptic Genetic Variation Involved in the Paternal Regulation of Seed Development

PubMed Central

Pires, Nuno D.; Bemer, Marian; Müller, Lena M.; Baroux, Célia; Spillane, Charles; Grossniklaus, Ueli

2016-01-01

Embryonic development requires a correct balancing of maternal and paternal genetic information. This balance is mediated by genomic imprinting, an epigenetic mechanism that leads to parent-of-origin-dependent gene expression. The parental conflict (or kinship) theory proposes that imprinting can evolve due to a conflict between maternal and paternal alleles over resource allocation during seed development. One assumption of this theory is that paternal alleles can regulate seed growth; however, paternal effects on seed size are often very low or non-existent. We demonstrate that there is a pool of cryptic genetic variation in the paternal control of Arabidopsis thaliana seed development. Such cryptic variation can be exposed in seeds that maternally inherit a medea mutation, suggesting that MEA acts as a maternal buffer of paternal effects. Genetic mapping using recombinant inbred lines, and a novel method for the mapping of parent-of-origin effects using whole-genome sequencing of segregant bulks, indicate that there are at least six loci with small, paternal effects on seed development. Together, our analyses reveal the existence of a pool of hidden genetic variation on the paternal control of seed development that is likely shaped by parental conflict. PMID:26811909

Quantitative Genetics Identifies Cryptic Genetic Variation Involved in the Paternal Regulation of Seed Development.

PubMed

Pires, Nuno D; Bemer, Marian; Müller, Lena M; Baroux, Célia; Spillane, Charles; Grossniklaus, Ueli

2016-01-01

Embryonic development requires a correct balancing of maternal and paternal genetic information. This balance is mediated by genomic imprinting, an epigenetic mechanism that leads to parent-of-origin-dependent gene expression. The parental conflict (or kinship) theory proposes that imprinting can evolve due to a conflict between maternal and paternal alleles over resource allocation during seed development. One assumption of this theory is that paternal alleles can regulate seed growth; however, paternal effects on seed size are often very low or non-existent. We demonstrate that there is a pool of cryptic genetic variation in the paternal control of Arabidopsis thaliana seed development. Such cryptic variation can be exposed in seeds that maternally inherit a medea mutation, suggesting that MEA acts as a maternal buffer of paternal effects. Genetic mapping using recombinant inbred lines, and a novel method for the mapping of parent-of-origin effects using whole-genome sequencing of segregant bulks, indicate that there are at least six loci with small, paternal effects on seed development. Together, our analyses reveal the existence of a pool of hidden genetic variation on the paternal control of seed development that is likely shaped by parental conflict.

The efficiency of close inbreeding to reduce genetic adaptation to captivity

PubMed Central

Theodorou, K; Couvet, D

2015-01-01

Although ex situ conservation is indispensable for thousands of species, captive breeding is associated with negative genetic changes: loss of genetic variance and genetic adaptation to captivity that is deleterious in the wild. We used quantitative genetic individual-based simulations to model the effect of genetic management on the evolution of a quantitative trait and the associated fitness of wild-born individuals that are brought to captivity. We also examined the feasibility of the breeding strategies under a scenario of a large number of loci subject to deleterious mutations. We compared two breeding strategies: repeated half-sib mating and a method of minimizing mean coancestry (referred to as gc/mc). Our major finding was that half-sib mating is more effective in reducing genetic adaptation to captivity than the gc/mc method. Moreover, half-sib mating retains larger allelic and adaptive genetic variance. Relative to initial standing variation, the additive variance of the quantitative trait increased under half-sib mating during the sojourn in captivity. Although fragmentation into smaller populations improves the efficiency of the gc/mc method, half-sib mating still performs better in the scenarios tested. Half-sib mating shows two caveats that could mitigate its beneficial effects: low heterozygosity and high risk of extinction when populations are of low fecundity and size and one of the following conditions are met: (i) the strength of selection in captivity is comparable with that in the wild, (ii) deleterious mutations are numerous and only slightly deleterious. Experimental validation of half-sib mating is therefore needed for the advancement of captive breeding programs. PMID:25052417

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster

PubMed Central

Edwards, Stefan M.; Sørensen, Izel F.; Sarup, Pernille; Mackay, Trudy F. C.; Sørensen, Peter

2016-01-01

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits. PMID:27235308

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

PubMed

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

2014-09-01

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

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

PubMed Central

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

2014-01-01

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

Quantitative modelling in cognitive ergonomics: predicting signals passed at danger.

PubMed

Moray, Neville; Groeger, John; Stanton, Neville

2017-02-01

This paper shows how to combine field observations, experimental data and mathematical modelling to produce quantitative explanations and predictions of complex events in human-machine interaction. As an example, we consider a major railway accident. In 1999, a commuter train passed a red signal near Ladbroke Grove, UK, into the path of an express. We use the Public Inquiry Report, 'black box' data, and accident and engineering reports to construct a case history of the accident. We show how to combine field data with mathematical modelling to estimate the probability that the driver observed and identified the state of the signals, and checked their status. Our methodology can explain the SPAD ('Signal Passed At Danger'), generate recommendations about signal design and placement and provide quantitative guidance for the design of safer railway systems' speed limits and the location of signals. Practitioner Summary: Detailed ergonomic analysis of railway signals and rail infrastructure reveals problems of signal identification at this location. A record of driver eye movements measures attention, from which a quantitative model for out signal placement and permitted speeds can be derived. The paper is an example of how to combine field data, basic research and mathematical modelling to solve ergonomic design problems.

Nonparametric evaluation of quantitative traits in population-based association studies when the genetic model is unknown.

PubMed

Konietschke, Frank; Libiger, Ondrej; Hothorn, Ludwig A

2012-01-01

Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible

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

PubMed

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

2009-12-01

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

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.

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.

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.

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

PubMed Central

Evans, Jonathan P; Gasparini, Clelia

2013-01-01

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

Performance Theories for Sentence Coding: Some Quantitative Models

ERIC Educational Resources Information Center

Aaronson, Doris; And Others

1977-01-01

This study deals with the patterns of word-by-word reading times over a sentence when the subject must code the linguistic information sufficiently for immediate verbatim recall. A class of quantitative models is considered that would account for reading times at phrase breaks. (Author/RM)

Mapping of epistatic quantitative trait loci in four-way crosses.

PubMed

He, Xiao-Hong; Qin, Hongde; Hu, Zhongli; Zhang, Tianzhen; Zhang, Yuan-Ming

2011-01-01

Four-way crosses (4WC) involving four different inbred lines often appear in plant and animal commercial breeding programs. Direct mapping of quantitative trait loci (QTL) in these commercial populations is both economical and practical. However, the existing statistical methods for mapping QTL in a 4WC population are built on the single-QTL genetic model. This simple genetic model fails to take into account QTL interactions, which play an important role in the genetic architecture of complex traits. In this paper, therefore, we attempted to develop a statistical method to detect epistatic QTL in 4WC population. Conditional probabilities of QTL genotypes, computed by the multi-point single locus method, were used to sample the genotypes of all putative QTL in the entire genome. The sampled genotypes were used to construct the design matrix for QTL effects. All QTL effects, including main and epistatic effects, were simultaneously estimated by the penalized maximum likelihood method. The proposed method was confirmed by a series of Monte Carlo simulation studies and real data analysis of cotton. The new method will provide novel tools for the genetic dissection of complex traits, construction of QTL networks, and analysis of heterosis.

NON-HOMOGENEOUS POISSON PROCESS MODEL FOR GENETIC CROSSOVER INTERFERENCE.

PubMed

Leu, Szu-Yun; Sen, Pranab K

2014-01-01

The genetic crossover interference is usually modeled with a stationary renewal process to construct the genetic map. We propose two non-homogeneous, also dependent, Poisson process models applied to the known physical map. The crossover process is assumed to start from an origin and to occur sequentially along the chromosome. The increment rate depends on the position of the markers and the number of crossover events occurring between the origin and the markers. We show how to obtain parameter estimates for the process and use simulation studies and real Drosophila data to examine the performance of the proposed models.

Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology.

PubMed

Metzger, Gregory J; Kalavagunta, Chaitanya; Spilseth, Benjamin; Bolan, Patrick J; Li, Xiufeng; Hutter, Diane; Nam, Jung W; Johnson, Andrew D; Henriksen, Jonathan C; Moench, Laura; Konety, Badrinath; Warlick, Christopher A; Schmechel, Stephen C; Koopmeiners, Joseph S

2016-06-01

Purpose To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for detection of prostate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Methods Institutional review board approval and informed consent were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area under the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (K(trans)), reflux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and noncancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Four-parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual-level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34

Supply of genetic information--amount, format, and frequency.

PubMed

Misztal, I; Lawlor, T J

1999-05-01

The volume and complexity of genetic information is increasing because of new traits and better models. New traits may include reproduction, health, and carcass. More comprehensive models include the test day model in dairy cattle or a growth model in beef cattle. More complex models, which may include nonadditive effects such as inbreeding and dominance, also provide additional information. The amount of information per animal may increase drastically if DNA marker typing becomes routine and quantitative trait loci information is utilized. In many industries, evaluations are run more frequently. They result in faster genetic progress and improved management and marketing opportunities but also in extra costs and information overload. Adopting new technology and making some organizational changes can help realize all the added benefits of the improvements to the genetic evaluation systems at an acceptable cost. Continuous genetic evaluation, in which new records are accepted and breeding values are updated continuously, will relieve time pressures. An online mating system with access to both genetic and marketing information can result in mating recommendations customized for each user. Such a system could utilize inbreeding and dominance information that cannot efficiently be accommodated in the current sire summaries or off-line mating programs. The new systems will require a new organizational approach in which the task of scientists and technicians will not be simply running the evaluations but also providing the research, design, supervision, and maintenance required in the entire system of evaluation, decision making, and distribution.

Easy calculations of lod scores and genetic risks on small computers.

PubMed Central

Lathrop, G M; Lalouel, J M

1984-01-01

A computer program that calculates lod scores and genetic risks for a wide variety of both qualitative and quantitative genetic traits is discussed. An illustration is given of the joint use of a genetic marker, affection status, and quantitative information in counseling situations regarding Duchenne muscular dystrophy. PMID:6585139

Genes, Environment, and Race: Quantitative Genetic Approaches

ERIC Educational Resources Information Center

Whitfield, Keith E.; McClearn, Gerald

2005-01-01

Understanding the origins of racial health disparities is currently a central focus of health-oriented funding agencies and the health policy community. In particular, the role of genetics in the origin of racial health disparities is receiving growing attention and has been susceptible to considerable misinterpretation. In this article, the…

Study books on ADHD genetics: balanced or biased?

PubMed Central

te Meerman, Sanne; Batstra, Laura; Hoekstra, Rink; Grietens, Hans

2017-01-01

ABSTRACT Academic study books are essential assets for disseminating knowledge about ADHD to future healthcare professionals. This study examined if they are balanced with regard to genetics. We selected and analyzed study books (N=43) used in (pre) master’s programmes at 10 universities in the Netherlands. Because the mere behaviourally informed quantitative genetics give a much higher effect size of the genetic involvement in ADHD, it is important that study books contrast these findings with molecular genetics’ outcomes. The latter studies use real genetic data, and their low effect sizes expose the potential weaknesses of quantitative genetics, like underestimating the involvement of the environment. Only a quarter of books mention both effect sizes and contrast these findings, while another quarter does not discuss any effect size. Most importantly, however, roughly half of the books in our sample mention only the effect sizes from quantitative genetic studies without addressing the low explained variance of molecular genetic studies. This may confuse readers by suggesting that the weakly associated genes support the quite spectacular, but potentially flawed estimates of twin, family and adoption studies, while they actually contradict them. PMID:28532325

Quantitative assessment model for gastric cancer screening

PubMed Central

Chen, Kun; Yu, Wei-Ping; Song, Liang; Zhu, Yi-Min

2005-01-01

AIM: To set up a mathematic model for gastric cancer screening and to evaluate its function in mass screening for gastric cancer. METHODS: A case control study was carried on in 66 patients and 198 normal people, then the risk and protective factors of gastric cancer were determined, including heavy manual work, foods such as small yellow-fin tuna, dried small shrimps, squills, crabs, mothers suffering from gastric diseases, spouse alive, use of refrigerators and hot food, etc. According to some principles and methods of probability and fuzzy mathematics, a quantitative assessment model was established as follows: first, we selected some factors significant in statistics, and calculated weight coefficient for each one by two different methods; second, population space was divided into gastric cancer fuzzy subset and non gastric cancer fuzzy subset, then a mathematic model for each subset was established, we got a mathematic expression of attribute degree (AD). RESULTS: Based on the data of 63 patients and 693 normal people, AD of each subject was calculated. Considering the sensitivity and specificity, the thresholds of AD values calculated were configured with 0.20 and 0.17, respectively. According to these thresholds, the sensitivity and specificity of the quantitative model were about 69% and 63%. Moreover, statistical test showed that the identification outcomes of these two different calculation methods were identical (P>0.05). CONCLUSION: The validity of this method is satisfactory. It is convenient, feasible, economic and can be used to determine individual and population risks of gastric cancer. PMID:15655813

A Geographically Explicit Genetic Model of Worldwide Human-Settlement History

PubMed Central

Liu, Hua; Prugnolle, Franck; Manica, Andrea; Balloux, François

2006-01-01

Currently available genetic and archaeological evidence is generally interpreted as supportive of a recent single origin of modern humans in East Africa. However, this is where the near consensus on human settlement history ends, and considerable uncertainty clouds any more detailed aspect of human colonization history. Here, we present a dynamic genetic model of human settlement history coupled with explicit geographical distances from East Africa, the likely origin of modern humans. We search for the best-supported parameter space by fitting our analytical prediction to genetic data that are based on 52 human populations analyzed at 783 autosomal microsatellite markers. This framework allows us to jointly estimate the key parameters of the expansion of modern humans. Our best estimates suggest an initial expansion of modern humans ∼56,000 years ago from a small founding population of ∼1,000 effective individuals. Our model further points to high growth rates in newly colonized habitats. The general fit of the model with the data is excellent. This suggests that coupling analytical genetic models with explicit demography and geography provides a powerful tool for making inferences on human-settlement history. PMID:16826514

Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits.

PubMed

Yadav, Anupama; Dhole, Kaustubh; Sinha, Himanshu

2016-12-01

Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets.

Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits

PubMed Central

Yadav, Anupama; Dhole, Kaustubh

2016-01-01

Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets. PMID:28172852

BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

PubMed Central

2010-01-01

Background Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. Description BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database. Conclusions BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation

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

PubMed

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

2017-11-01

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

Classification of cassava genotypes based on qualitative and quantitative data.

PubMed

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

2015-02-02

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

DRIFTSEL: an R package for detecting signals of natural selection in quantitative traits.

PubMed

Karhunen, M; Merilä, J; Leinonen, T; Cano, J M; Ovaskainen, O

2013-07-01

Approaches and tools to differentiate between natural selection and genetic drift as causes of population differentiation are of frequent demand in evolutionary biology. Based on the approach of Ovaskainen et al. (2011), we have developed an R package (DRIFTSEL) that can be used to differentiate between stabilizing selection, diversifying selection and random genetic drift as causes of population differentiation in quantitative traits when neutral marker and quantitative genetic data are available. Apart from illustrating the use of this method and the interpretation of results using simulated data, we apply the package on data from three-spined sticklebacks (Gasterosteus aculeatus) to highlight its virtues. DRIFTSEL can also be used to perform usual quantitative genetic analyses in common-garden study designs. © 2013 John Wiley & Sons Ltd.

Genetical Genomics Identifies the Genetic Architecture for Growth and Weevil Resistance in Spruce

PubMed Central

Porth, Ilga; White, Richard; Jaquish, Barry; Alfaro, René; Ritland, Carol; Ritland, Kermit

2012-01-01

In plants, relationships between resistance to herbivorous insect pests and growth are typically controlled by complex interactions between genetically correlated traits. These relationships often result in tradeoffs in phenotypic expression. In this study we used genetical genomics to elucidate genetic relationships between tree growth and resistance to white pine terminal weevil (Pissodes strobi Peck.) in a pedigree population of interior spruce (Picea glauca, P. engelmannii and their hybrids) that was growing at Vernon, B.C. and segregating for weevil resistance. Genetical genomics uses genetic perturbations caused by allelic segregation in pedigrees to co-locate quantitative trait loci (QTLs) for gene expression and quantitative traits. Bark tissue of apical leaders from 188 trees was assayed for gene expression using a 21.8K spruce EST-spotted microarray; the same individuals were genotyped for 384 SNP markers for the genetic map. Many of the expression QTLs (eQTL) co-localized with resistance trait QTLs. For a composite resistance phenotype of six attack and oviposition traits, 149 positional candidate genes were identified. Resistance and growth QTLs also overlapped with eQTL hotspots along the genome suggesting that: 1) genetic pleiotropy of resistance and growth traits in interior spruce was substantial, and 2) master regulatory genes were important for weevil resistance in spruce. These results will enable future work on functional genetic studies of insect resistance in spruce, and provide valuable information about candidate genes for genetic improvement of spruce. PMID:22973444

A quantitative method for defining high-arched palate using the Tcof1+/− mutant mouse as a model

PubMed Central

Conley, Zachary R.; Hague, Molly; Kurosaka, Hiroshi; Dixon, Jill; Dixon, Michael J.; Trainor, Paul A.

2016-01-01

The palate functions as the roof of the mouth in mammals, separating the oral and nasal cavities. Its complex embryonic development and assembly poses unique susceptibilities to intrinsic and extrinsic disruptions. Such disruptions may cause failure of the developing palatal shelves to fuse along the midline resulting in a cleft. In other cases the palate may fuse at an arch, resulting in a vaulted oral cavity, termed high-arched palate. There are many models available for studying the pathogenesis of cleft palate but a relative paucity for high-arched palate. One condition exhibiting either cleft palate or high-arched palate is Treacher Collins syndrome, a congenital disorder characterized by numerous craniofacial anomalies. We quantitatively analyzed palatal perturbations in the Tcof1+/− mouse model of Treacher Collins syndrome, which phenocopies the condition in humans. We discovered that 46% of Tcof1+/− mutant embryos and new born pups exhibit either soft clefts or full clefts. In addition, 17% of Tcof1+/− mutants were found to exhibit high-arched palate, defined as two sigma above the corresponding wild-type population mean for height and angular based arch measurements. Furthermore, palatal shelf length and shelf width were decreased in all Tcof1+/− mutant embryos and pups compared to controls. Interestingly, these phenotypes were subsequently ameliorated through genetic inhibition of p53. The results of our study therefore provide a simple, reproducible and quantitative method for investigating models of high-arched palate. PMID:26772999

Portfolio optimization by using linear programing models based on genetic algorithm

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

Enhanced hexose fermentation by Saccharomyces cerevisiae through integration of stoichiometric modeling and genetic screening.

PubMed

Quarterman, Josh; Kim, Soo Rin; Kim, Pan-Jun; Jin, Yong-Su

2015-01-20

In order to determine beneficial gene deletions for ethanol production by the yeast Saccharomyces cerevisiae, we performed an in silico gene deletion experiment based on a genome-scale metabolic model. Genes coding for two oxidative phosphorylation reactions (cytochrome c oxidase and ubiquinol cytochrome c reductase) were identified by the model-based simulation as potential deletion targets for enhancing ethanol production and maintaining acceptable overall growth rate in oxygen-limited conditions. Since the two target enzymes are composed of multiple subunits, we conducted a genetic screening study to evaluate the in silico results and compare the effect of deleting various portions of the respiratory enzyme complexes. Over two-thirds of the knockout mutants identified by the in silico study did exhibit experimental behavior in qualitative agreement with model predictions, but the exceptions illustrate the limitation of using a purely stoichiometric model-based approach. Furthermore, there was a substantial quantitative variation in phenotype among the various respiration-deficient mutants that were screened in this study, and three genes encoding respiratory enzyme subunits were identified as the best knockout targets for improving hexose fermentation in microaerobic conditions. Specifically, deletion of either COX9 or QCR9 resulted in higher ethanol production rates than the parental strain by 37% and 27%, respectively, with slight growth disadvantages. Also, deletion of QCR6 led to improved ethanol production rate by 24% with no growth disadvantage. The beneficial effects of these gene deletions were consistently demonstrated in different strain backgrounds and with four common hexoses. The combination of stoichiometric modeling and genetic screening using a systematic knockout collection was useful for narrowing a large set of gene targets and identifying targets of interest. Copyright © 2014 Elsevier B.V. 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

ERIC Educational Resources Information Center

Wendell, Douglas L.; 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…

Power Analysis of Artificial Selection Experiments Using Efficient Whole Genome Simulation of Quantitative Traits

PubMed Central

Kessner, Darren; Novembre, John

2015-01-01

Evolve and resequence studies combine artificial selection experiments with massively parallel sequencing technology to study the genetic basis for complex traits. In these experiments, individuals are selected for extreme values of a trait, causing alleles at quantitative trait loci (QTL) to increase or decrease in frequency in the experimental population. We present a new analysis of the power of artificial selection experiments to detect and localize quantitative trait loci. This analysis uses a simulation framework that explicitly models whole genomes of individuals, quantitative traits, and selection based on individual trait values. We find that explicitly modeling QTL provides qualitatively different insights than considering independent loci with constant selection coefficients. Specifically, we observe how interference between QTL under selection affects the trajectories and lengthens the fixation times of selected alleles. We also show that a substantial portion of the genetic variance of the trait (50–100%) can be explained by detected QTL in as little as 20 generations of selection, depending on the trait architecture and experimental design. Furthermore, we show that power depends crucially on the opportunity for recombination during the experiment. Finally, we show that an increase in power is obtained by leveraging founder haplotype information to obtain allele frequency estimates. PMID:25672748

Dissecting genetic and environmental mutation signatures with model organisms.

PubMed

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

2015-08-01

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

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

PubMed Central

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

2009-01-01

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

Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir

Treesearch

Nicholas C. Wheeler; Kathleen D. Jermstad; Konstantin V. Krutovsky; Sally N. Aitken; Glenn T. Howe; Jodie Krakowski; David B. Neale

2005-01-01

Quantitative trait locus (QTL) analyses are used by geneticists to characterize the genetic architecture of quantitative traits, provide a foundation for marker-aided-selection (MAS), and provide a framework for positional selection of candidate genes. The most useful QTL for breeding applications are those that have been verified in time, space, and/or genetic...

Quantitative assessment of skin, hair, and iris variation in a diverse sample of individuals and associated genetic variation.

PubMed

Norton, Heather L; Edwards, Melissa; Krithika, S; Johnson, Monique; Werren, Elizabeth A; Parra, Esteban J

2016-08-01

The main goals of this study are to 1) quantitatively measure skin, hair, and iris pigmentation in a diverse sample of individuals, 2) describe variation within and between these samples, and 3) demonstrate how quantitative measures can facilitate genotype-phenotype association tests. We quantitatively characterize skin, hair, and iris pigmentation using the Melanin (M) Index (skin) and CIELab values (hair) in 1,450 individuals who self-identify as African American, East Asian, European, Hispanic, or South Asian. We also quantify iris pigmentation in a subset of these individuals using CIELab values from high-resolution iris photographs. We compare mean skin M index and hair and iris CIELab values among populations using ANOVA and MANOVA respectively and test for genotype-phenotype associations in the European sample. All five populations are significantly different for skin (P <2 × 10(-16) ) and hair color (P <2 × 10(-16) ). Our quantitative analysis of iris and hair pigmentation reinforces the continuous, rather than discrete, nature of these traits. We confirm the association of three loci (rs16891982, rs12203592, and rs12913832) with skin pigmentation and four loci (rs12913832, rs12203592, rs12896399, and rs16891982) with hair pigmentation. Interestingly, the derived rs12203592 T allele located within the IRF4 gene is associated with lighter skin but darker hair color. The quantitative methods used here provide a fine-scale assessment of pigmentation phenotype and facilitate genotype-phenotype associations, even with relatively small sample sizes. This represents an important expansion of current investigations into pigmentation phenotype and associated genetic variation by including non-European and admixed populations. Am J Phys Anthropol 160:570-581, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Quantitative metal magnetic memory reliability modeling for welded joints

NASA Astrophysics Data System (ADS)

Xing, Haiyan; Dang, Yongbin; Wang, Ben; Leng, Jiancheng

2016-03-01

Metal magnetic memory(MMM) testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quantitative evaluation. In order to promote the development of quantitative MMM reliability assessment, a new MMM model is presented for welded joints. Steel Q235 welded specimens are tested along the longitudinal and horizontal lines by TSC-2M-8 instrument in the tensile fatigue experiments. The X-ray testing is carried out synchronously to verify the MMM results. It is found that MMM testing can detect the hidden crack earlier than X-ray testing. Moreover, the MMM gradient vector sum K vs is sensitive to the damage degree, especially at early and hidden damage stages. Considering the dispersion of MMM data, the K vs statistical law is investigated, which shows that K vs obeys Gaussian distribution. So K vs is the suitable MMM parameter to establish reliability model of welded joints. At last, the original quantitative MMM reliability model is first presented based on the improved stress strength interference theory. It is shown that the reliability degree R gradually decreases with the decreasing of the residual life ratio T, and the maximal error between prediction reliability degree R 1 and verification reliability degree R 2 is 9.15%. This presented method provides a novel tool of reliability testing and evaluating in practical engineering for welded joints.

A new method of linkage analysis using LOD scores for quantitative traits supports linkage of monoamine oxidase activity to D17S250 in the Collaborative Study on the Genetics of Alcoholism pedigrees.

PubMed

Curtis, David; Knight, Jo; Sham, Pak C

2005-09-01

Although LOD score methods have been applied to diseases with complex modes of inheritance, linkage analysis of quantitative traits has tended to rely on non-parametric methods based on regression or variance components analysis. Here, we describe a new method for LOD score analysis of quantitative traits which does not require specification of a mode of inheritance. The technique is derived from the MFLINK method for dichotomous traits. A range of plausible transmission models is constructed, constrained to yield the correct population mean and variance for the trait but differing with respect to the contribution to the variance due to the locus under consideration. Maximized LOD scores under homogeneity and admixture are calculated, as is a model-free LOD score which compares the maximized likelihoods under admixture assuming linkage and no linkage. These LOD scores have known asymptotic distributions and hence can be used to provide a statistical test for linkage. The method has been implemented in a program called QMFLINK. It was applied to data sets simulated using a variety of transmission models and to a measure of monoamine oxidase activity in 105 pedigrees from the Collaborative Study on the Genetics of Alcoholism. With the simulated data, the results showed that the new method could detect linkage well if the true allele frequency for the trait was close to that specified. However, it performed poorly on models in which the true allele frequency was much rarer. For the Collaborative Study on the Genetics of Alcoholism data set only a modest overlap was observed between the results obtained from the new method and those obtained when the same data were analysed previously using regression and variance components analysis. Of interest is that D17S250 produced a maximized LOD score under homogeneity and admixture of 2.6 but did not indicate linkage using the previous methods. However, this region did produce evidence for linkage in a separate data set

A Quantitative Cost Effectiveness Model for Web-Supported Academic Instruction

ERIC Educational Resources Information Center

Cohen, Anat; Nachmias, Rafi

2006-01-01

This paper describes a quantitative cost effectiveness model for Web-supported academic instruction. The model was designed for Web-supported instruction (rather than distance learning only) characterizing most of the traditional higher education institutions. It is based on empirical data (Web logs) of students' and instructors' usage…

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three

A quantitative systems physiology model of renal function and blood pressure regulation: Model description.

PubMed

Hallow, K M; Gebremichael, Y

2017-06-01

Renal function plays a central role in cardiovascular, kidney, and multiple other diseases, and many existing and novel therapies act through renal mechanisms. Even with decades of accumulated knowledge of renal physiology, pathophysiology, and pharmacology, the dynamics of renal function remain difficult to understand and predict, often resulting in unexpected or counterintuitive therapy responses. Quantitative systems pharmacology modeling of renal function integrates this accumulated knowledge into a quantitative framework, allowing evaluation of competing hypotheses, identification of knowledge gaps, and generation of new experimentally testable hypotheses. Here we present a model of renal physiology and control mechanisms involved in maintaining sodium and water homeostasis. This model represents the core renal physiological processes involved in many research questions in drug development. The model runs in R and the code is made available. In a companion article, we present a case study using the model to explore mechanisms and pharmacology of salt-sensitive hypertension. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Genetic Model Fitting in IQ, Assortative Mating & Components of IQ Variance.

ERIC Educational Resources Information Center

Capron, Christiane; Vetta, Adrian R.; Vetta, Atam

1998-01-01

The biometrical school of scientists who fit models to IQ data traces their intellectual ancestry to R. Fisher (1918), but their genetic models have no predictive value. Fisher himself was critical of the concept of heritability, because assortative mating, such as for IQ, introduces complexities into the study of a genetic trait. (SLD)

Factor analysis in the Genetics of Asthma International Network family study identifies five major quantitative asthma phenotypes.

PubMed

Pillai, S G; Tang, Y; van den Oord, E; Klotsman, M; Barnes, K; Carlsen, K; Gerritsen, J; Lenney, W; Silverman, M; Sly, P; Sundy, J; Tsanakas, J; von Berg, A; Whyte, M; Ortega, H G; Anderson, W H; Helms, P J

2008-03-01

quantitative traits may be better phenotypes in epidemiological and genetic analyses than those categories derived from the presence or absence of combinations of +ve SPTs and/or elevated IgE.

Computer Center: BASIC String Models of Genetic Information Transfer.

ERIC Educational Resources Information Center

Spain, James D., Ed.

1984-01-01

Discusses some of the major genetic information processes which may be modeled by computer program string manipulation, focusing on replication and transcription. Also discusses instructional applications of using string models. (JN)

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

PubMed

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

2014-12-01

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

Genetic Resources in the "Calabaza Pipiana" Squash (Cucurbita argyrosperma) in Mexico: Genetic Diversity, Genetic Differentiation and Distribution Models.

PubMed

Sánchez-de la Vega, Guillermo; Castellanos-Morales, Gabriela; Gámez, Niza; Hernández-Rosales, Helena S; Vázquez-Lobo, Alejandra; Aguirre-Planter, Erika; Jaramillo-Correa, Juan P; Montes-Hernández, Salvador; Lira-Saade, Rafael; Eguiarte, Luis E

2018-01-01

Analyses of genetic variation allow understanding the origin, diversification and genetic resources of cultivated plants. Domesticated taxa and their wild relatives are ideal systems for studying genetic processes of plant domestication and their joint is important to evaluate the distribution of their genetic resources. Such is the case of the domesticated subspecies C. argyrosperma ssp. argyrosperma , known in Mexico as calabaza pipiana , and its wild relative C. argyrosperma ssp. sororia . The main aim of this study was to use molecular data (microsatellites) to assess the levels of genetic variation and genetic differentiation within and among populations of domesticated argyrosperma across its distribution in Mexico in comparison to its wild relative, sororia , and to identify environmental suitability in previously proposed centers of domestication. We analyzed nine unlinked nuclear microsatellite loci to assess levels of diversity and distribution of genetic variation within and among populations in 440 individuals from 19 populations of cultivated landraces of argyrosperma and from six wild populations of sororia , in order to conduct a first systematic analysis of their genetic resources. We also used species distribution models (SDMs) for sororia to identify changes in this wild subspecies' distribution from the Holocene (∼6,000 years ago) to the present, and to assess the presence of suitable environmental conditions in previously proposed domestication sites. Genetic variation was similar among subspecies ( H E = 0.428 in sororia , and H E = 0.410 in argyrosperma ). Nine argyrosperma populations showed significant levels of inbreeding. Both subspecies are well differentiated, and genetic differentiation ( F ST ) among populations within each subspecies ranged from 0.152 to 0.652. Within argyrosperma we found three genetic groups (Northern Mexico, Yucatan Peninsula, including Michoacan and Veracruz, and Pacific coast plus Durango). We detected low

Simulating pattern-process relationships to validate landscape genetic models

Treesearch

A. J. Shirk; S. A. Cushman; E. L. Landguth

2012-01-01

Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all...

Achieving World-Class Schools: Mastering School Improvement Using a Genetic Model.

ERIC Educational Resources Information Center

Kimmelman, Paul L.; Kroeze, David J.

In providing its program for education reform, this book uses, as an analogy, the genetic model taken from the Human Genome project. In the first part, "Theoretical Underpinnings," the book explains why a genetic model can be used to improve school systems; describes the critical components of a world-class school system; and details the…

The historical role of species from the Solanaceae plant family in genetic research.

PubMed

Gebhardt, Christiane

2016-12-01

This article evaluates the main contributions of tomato, tobacco, petunia, potato, pepper and eggplant to classical and molecular plant genetics and genomics since the beginning of the twentieth century. Species from the Solanaceae family form integral parts of human civilizations as food sources and drugs since thousands of years, and, more recently, as ornamentals. Some Solanaceous species were subjects of classical and molecular genetic research over the last 100 years. The tomato was one of the principal models in twentieth century classical genetics and a pacemaker of genome analysis in plants including molecular linkage maps, positional cloning of disease resistance genes and quantitative trait loci (QTL). Besides that, tomato is the model for the genetics of fruit development and composition. Tobacco was the major model used to establish the principals and methods of plant somatic cell genetics including in vitro propagation of cells and tissues, totipotency of somatic cells, doubled haploid production and genetic transformation. Petunia was a model for elucidating the biochemical and genetic basis of flower color and development. The cultivated potato is the economically most important Solanaceous plant and ranks third after wheat and rice as one of the world's great food crops. Potato is the model for studying the genetic basis of tuber development. Molecular genetics and genomics of potato, in particular association genetics, made valuable contributions to the genetic dissection of complex agronomic traits and the development of diagnostic markers for breeding applications. Pepper and eggplant are horticultural crops of worldwide relevance. Genetic and genomic research in pepper and eggplant mostly followed the tomato model. Comparative genome analysis of tomato, potato, pepper and eggplant contributed to the understanding of plant genome evolution.

Designing automation for human use: empirical studies and quantitative models.

PubMed

Parasuraman, R

2000-07-01

An emerging knowledge base of human performance research can provide guidelines for designing automation that can be used effectively by human operators of complex systems. Which functions should be automated and to what extent in a given system? A model for types and levels of automation that provides a framework and an objective basis for making such choices is described. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design when using the model. Four human performance areas are considered--mental workload, situation awareness, complacency and skill degradation. Secondary evaluative criteria include such factors as automation reliability, the risks of decision/action consequences and the ease of systems integration. In addition to this qualitative approach, quantitative models can inform design. Several computational and formal models of human interaction with automation that have been proposed by various researchers are reviewed. An important future research need is the integration of qualitative and quantitative approaches. Application of these models provides an objective basis for designing automation for effective human use.

Identification of Treatment Targets in a Genetic Mouse Model of Voluntary Methamphetamine Drinking.

PubMed

Phillips, T J; Mootz, J R K; Reed, C

2016-01-01

Methamphetamine has powerful stimulant and euphoric effects that are experienced as rewarding and encourage use. Methamphetamine addiction is associated with debilitating illnesses, destroyed relationships, child neglect, violence, and crime; but after many years of research, broadly effective medications have not been identified. Individual differences that may impact not only risk for developing a methamphetamine use disorder but also affect treatment response have not been fully considered. Human studies have identified candidate genes that may be relevant, but lack of control over drug history, the common use or coabuse of multiple addictive drugs, and restrictions on the types of data that can be collected in humans are barriers to progress. To overcome some of these issues, a genetic animal model comprised of lines of mice selectively bred for high and low voluntary methamphetamine intake was developed to identify risk and protective alleles for methamphetamine consumption, and identify therapeutic targets. The mu opioid receptor gene was supported as a target for genes within a top-ranked transcription factor network associated with level of methamphetamine intake. In addition, mice that consume high levels of methamphetamine were found to possess a nonfunctional form of the trace amine-associated receptor 1 (TAAR1). The Taar1 gene is within a mouse chromosome 10 quantitative trait locus for methamphetamine consumption, and TAAR1 function determines sensitivity to aversive effects of methamphetamine that may curb intake. The genes, gene interaction partners, and protein products identified in this genetic mouse model represent treatment target candidates for methamphetamine addiction. © 2016 Elsevier Inc. All rights reserved.

Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate.

PubMed

Jasinska, Anna J; Zelaya, Ivette; Service, Susan K; Peterson, Christine B; Cantor, Rita M; Choi, Oi-Wa; DeYoung, Joseph; Eskin, Eleazar; Fairbanks, Lynn A; Fears, Scott; Furterer, Allison E; Huang, Yu S; Ramensky, Vasily; Schmitt, Christopher A; Svardal, Hannes; Jorgensen, Matthew J; Kaplan, Jay R; Villar, Diego; Aken, Bronwen L; Flicek, Paul; Nag, Rishi; Wong, Emily S; Blangero, John; Dyer, Thomas D; Bogomolov, Marina; Benjamini, Yoav; Weinstock, George M; Dewar, Ken; Sabatti, Chiara; Wilson, Richard K; Jentsch, J David; Warren, Wesley; Coppola, Giovanni; Woods, Roger P; Freimer, Nelson B

2017-12-01

By analyzing multitissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalog of expression quantitative trait loci (eQTLs) in a nonhuman primate model. This catalog contains more genome-wide significant eQTLs per sample than comparable human resources and identifies sex- and age-related expression patterns. Findings include a master regulatory locus that likely has a role in immune function and a locus regulating hippocampal long noncoding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

A two-factor error model for quantitative steganalysis

NASA Astrophysics Data System (ADS)

Böhme, Rainer; Ker, Andrew D.

2006-02-01

Quantitative steganalysis refers to the exercise not only of detecting the presence of hidden stego messages in carrier objects, but also of estimating the secret message length. This problem is well studied, with many detectors proposed but only a sparse analysis of errors in the estimators. A deep understanding of the error model, however, is a fundamental requirement for the assessment and comparison of different detection methods. This paper presents a rationale for a two-factor model for sources of error in quantitative steganalysis, and shows evidence from a dedicated large-scale nested experimental set-up with a total of more than 200 million attacks. Apart from general findings about the distribution functions found in both classes of errors, their respective weight is determined, and implications for statistical hypothesis tests in benchmarking scenarios or regression analyses are demonstrated. The results are based on a rigorous comparison of five different detection methods under many different external conditions, such as size of the carrier, previous JPEG compression, and colour channel selection. We include analyses demonstrating the effects of local variance and cover saturation on the different sources of error, as well as presenting the case for a relative bias model for between-image error.

A model for family-based case-control studies of genetic imprinting and epistasis.

PubMed

Li, Xin; Sui, Yihan; Liu, Tian; Wang, Jianxin; Li, Yongci; Lin, Zhenwu; Hegarty, John; Koltun, Walter A; Wang, Zuoheng; Wu, Rongling

2014-11-01

Genetic imprinting, or called the parent-of-origin effect, has been recognized to play an important role in the formation and pathogenesis of human diseases. Although the epigenetic mechanisms that establish genetic imprinting have been a focus of many genetic studies, our knowledge about the number of imprinting genes and their chromosomal locations and interactions with other genes is still scarce, limiting precise inference of the genetic architecture of complex diseases. In this article, we present a statistical model for testing and estimating the effects of genetic imprinting on complex diseases using a commonly used case-control design with family structure. For each subject sampled from a case and control population, we not only genotype its own single nucleotide polymorphisms (SNPs) but also collect its parents' genotypes. By tracing the transmission pattern of SNP alleles from parental to offspring generation, the model allows the characterization of genetic imprinting effects based on Pearson tests of a 2 × 2 contingency table. The model is expanded to test the interactions between imprinting effects and additive, dominant and epistatic effects in a complex web of genetic interactions. Statistical properties of the model are investigated, and its practical usefulness is validated by a real data analysis. The model will provide a useful tool for genome-wide association studies aimed to elucidate the picture of genetic control over complex human diseases. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A transformative model for undergraduate quantitative biology education.

PubMed

Usher, David C; Driscoll, Tobin A; Dhurjati, Prasad; Pelesko, John A; Rossi, Louis F; Schleiniger, Gilberto; Pusecker, Kathleen; White, Harold B

2010-01-01

The BIO2010 report recommended that students in the life sciences receive a more rigorous education in mathematics and physical sciences. The University of Delaware approached this problem by (1) developing a bio-calculus section of a standard calculus course, (2) embedding quantitative activities into existing biology courses, and (3) creating a new interdisciplinary major, quantitative biology, designed for students interested in solving complex biological problems using advanced mathematical approaches. To develop the bio-calculus sections, the Department of Mathematical Sciences revised its three-semester calculus sequence to include differential equations in the first semester and, rather than using examples traditionally drawn from application domains that are most relevant to engineers, drew models and examples heavily from the life sciences. The curriculum of the B.S. degree in Quantitative Biology was designed to provide students with a solid foundation in biology, chemistry, and mathematics, with an emphasis on preparation for research careers in life sciences. Students in the program take core courses from biology, chemistry, and physics, though mathematics, as the cornerstone of all quantitative sciences, is given particular prominence. Seminars and a capstone course stress how the interplay of mathematics and biology can be used to explain complex biological systems. To initiate these academic changes required the identification of barriers and the implementation of solutions.

A Transformative Model for Undergraduate Quantitative Biology Education

PubMed Central

Driscoll, Tobin A.; Dhurjati, Prasad; Pelesko, John A.; Rossi, Louis F.; Schleiniger, Gilberto; Pusecker, Kathleen; White, Harold B.

2010-01-01

The BIO2010 report recommended that students in the life sciences receive a more rigorous education in mathematics and physical sciences. The University of Delaware approached this problem by (1) developing a bio-calculus section of a standard calculus course, (2) embedding quantitative activities into existing biology courses, and (3) creating a new interdisciplinary major, quantitative biology, designed for students interested in solving complex biological problems using advanced mathematical approaches. To develop the bio-calculus sections, the Department of Mathematical Sciences revised its three-semester calculus sequence to include differential equations in the first semester and, rather than using examples traditionally drawn from application domains that are most relevant to engineers, drew models and examples heavily from the life sciences. The curriculum of the B.S. degree in Quantitative Biology was designed to provide students with a solid foundation in biology, chemistry, and mathematics, with an emphasis on preparation for research careers in life sciences. Students in the program take core courses from biology, chemistry, and physics, though mathematics, as the cornerstone of all quantitative sciences, is given particular prominence. Seminars and a capstone course stress how the interplay of mathematics and biology can be used to explain complex biological systems. To initiate these academic changes required the identification of barriers and the implementation of solutions. PMID:20810949

A Quantitative Model of Expert Transcription Typing

DTIC Science & Technology

1993-03-08

side of pure psychology, several researchers have argued that transcription typing is a particularly good activity for the study of human skilled...phenomenon with a quantitative METT prediction. The first, quick and dirty analysis gives a good prediction of the copy span, in fact, it is even...typing, it should be demonstrated that the mechanism of the model does not get in the way of good predictions. If situations occur where the entire

Impact of implementation choices on quantitative predictions of cell-based computational models

NASA Astrophysics Data System (ADS)

Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

2017-09-01

'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

"Genetically Engineered" Nanoelectronics

NASA Technical Reports Server (NTRS)

Klimeck, Gerhard; Salazar-Lazaro, Carlos H.; Stoica, Adrian; Cwik, Thomas

2000-01-01

The quantum mechanical functionality of nanoelectronic devices such as resonant tunneling diodes (RTDs), quantum well infrared-photodetectors (QWIPs), quantum well lasers, and heterostructure field effect transistors (HFETs) is enabled by material variations on an atomic scale. The design and optimization of such devices requires a fundamental understanding of electron transport in such dimensions. The Nanoelectronic Modeling Tool (NEMO) is a general-purpose quantum device design and analysis tool based on a fundamental non-equilibrium electron transport theory. NEW was combined with a parallelized genetic algorithm package (PGAPACK) to evolve structural and material parameters to match a desired set of experimental data. A numerical experiment that evolves structural variations such as layer widths and doping concentrations is performed to analyze an experimental current voltage characteristic. The genetic algorithm is found to drive the NEMO simulation parameters close to the experimentally prescribed layer thicknesses and doping profiles. With such a quantitative agreement between theory and experiment design synthesis can be performed.

The Consortium on the Genetics of Endophenotypes in Schizophrenia: Model Recruitment, Assessment, and Endophenotyping Methods for a Multisite Collaboration

PubMed Central

Calkins, Monica E.; Dobie, Dorcas J.; Cadenhead, Kristin S.; Olincy, Ann; Freedman, Robert; Green, Michael F.; Greenwood, Tiffany A.; Gur, Raquel E.; Gur, Ruben C.; Light, Gregory A.; Mintz, Jim; Nuechterlein, Keith H.; Radant, Allen D.; Schork, Nicholas J.; Seidman, Larry J.; Siever, Larry J.; Silverman, Jeremy M.; Stone, William S.; Swerdlow, Neal R.; Tsuang, Debby W.; Tsuang, Ming T.; Turetsky, Bruce I.; Braff, David L.

2007-01-01

Background: The Consortium on the Genetics of Schizophrenia (COGS) is an ongoing, National Institute of Mental Health–funded, 7-site collaboration investigating the occurrence and genetic architecture of quantitative endophenotypes related to schizophrenia. The purpose of this article is to provide a description of the COGS structure and methods, including participant recruitment and assessment. Methods: The hypothesis-driven recruitment strategy ascertains families that include a proband with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnosis of schizophrenia, and at least one unaffected full sibling available for genotyping and endophenotyping, along with parents available for genotyping and (optional depending on age) endophenotyping. The family structure is selected to provide contrast in quantitative endophenotypic traits and thus to maximize the power of the planned genetic analyses. Probands are recruited from many sources including clinician referrals, local National Alliance for the Mentally Ill chapters, and advertising via the media. All participants undergo a standardized protocol that includes clinical characterization, a blood draw for genotyping, and endophenotype assessments (P50 suppression, prepulse inhibition, antisaccade performance, continuous performance tasks, letter-number span, verbal memory, and a computerized neurocognitive battery). Investigators participate in weekly teleconferences to coordinate and evaluate recruitment, clinical assessment, endophenotyping, and continuous quality control of data gathering and analyses. Data integrity is maintained through use of a highly quality-assured, centralized web-based database. Results: As of February 2006, 355 families have been enrolled and 688 participants have been endophenotyped, including schizophrenia probands (n = 154, M:F = 110:44), first-degree biological relatives (n = 343, M:F = 151:192), and community comparison subjects (n = 191, M:F = 81

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

PubMed

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

2015-05-01

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

Preferential access to genetic information from endogenous hominin ancient DNA and accurate quantitative SNP-typing via SPEX

PubMed Central

Brotherton, Paul; Sanchez, Juan J.; Cooper, Alan; Endicott, Phillip

2010-01-01

The analysis of targeted genetic loci from ancient, forensic and clinical samples is usually built upon polymerase chain reaction (PCR)-generated sequence data. However, many studies have shown that PCR amplification from poor-quality DNA templates can create sequence artefacts at significant levels. With hominin (human and other hominid) samples, the pervasive presence of highly PCR-amplifiable human DNA contaminants in the vast majority of samples can lead to the creation of recombinant hybrids and other non-authentic artefacts. The resulting PCR-generated sequences can then be difficult, if not impossible, to authenticate. In contrast, single primer extension (SPEX)-based approaches can genotype single nucleotide polymorphisms from ancient fragments of DNA as accurately as modern DNA. A single SPEX-type assay can amplify just one of the duplex DNA strands at target loci and generate a multi-fold depth-of-coverage, with non-authentic recombinant hybrids reduced to undetectable levels. Crucially, SPEX-type approaches can preferentially access genetic information from damaged and degraded endogenous ancient DNA templates over modern human DNA contaminants. The development of SPEX-type assays offers the potential for highly accurate, quantitative genotyping from ancient hominin samples. PMID:19864251

Genetics of Adiposity in Large Animal Models for Human Obesity-Studies on Pigs and Dogs.

PubMed

Stachowiak, M; Szczerbal, I; Switonski, M

2016-01-01

The role of domestic mammals in the development of human biomedical sciences has been widely documented. Among these model species the pig and dog are of special importance. Both are useful for studies on the etiology of human obesity. Genome sequences of both species are known and advanced genetic tools [eg, microarray SNP for genome wide association studies (GWAS), next generation sequencing (NGS), etc.] are commonly used in such studies. In the domestic pig the accumulation of adipose tissue is an important trait, which influences meat quality and fattening efficiency. Numerous quantitative trait loci (QTLs) for pig fatness traits were identified, while gene polymorphisms associated with these traits were also described. The situation is different in dog population. Generally, excessive accumulation of adipose tissue is considered, similar to humans, as a complex disease. However, research on the genetic background of canine obesity is still in its infancy. Between-breed differences in terms of adipose tissue accumulation are well known in both animal species. In this review we show recent advances of studies on adipose tissue accumulation in pigs and dogs, and their potential importance for studies on human obesity. Copyright © 2016 Elsevier Inc. All rights reserved.

Effect of Inherited Genetic Information on Stochastic Predator-Prey Model

NASA Astrophysics Data System (ADS)

Duda, Artur; Dyś, Paweł; Nowicka, Alekandra; Dudek, Mirosław R.

We discuss the Lotka-Volterra dynamics of two populations, preys and predators, in the case when the predators posses a genetic information. The genetic information is inherited according to the rules of the Penna model of genetic evolution. Each individual of the predator population is uniquely determined by sex, genotype and phenotype. In our case, the genes are represented by 8-bit integers and the phenotypes are defined with the help of the 8-state Potts model Hamiltonian. We showed that during time evolution, the population of the predators can experience a series of dynamical phase transitions which are connected with the different types of the dominant phenotypes present in the population.

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops.

PubMed

Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi

2016-01-01

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an "island model" inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic

Development and in-house validation of the event-specific qualitative and quantitative PCR detection methods for genetically modified cotton MON15985.

PubMed

Jiang, Lingxi; Yang, Litao; Rao, Jun; Guo, Jinchao; Wang, Shu; Liu, Jia; Lee, Seonghun; Zhang, Dabing

2010-02-01

To implement genetically modified organism (GMO) labeling regulations, an event-specific analysis method based on the junction sequence between exogenous integration and host genomic DNA has become the preferential approach for GMO identification and quantification. In this study, specific primers and TaqMan probes based on the revealed 5'-end junction sequence of GM cotton MON15985 were designed, and qualitative and quantitative polymerase chain reaction (PCR) assays were established employing the designed primers and probes. In the qualitative PCR assay, the limit of detection (LOD) was 0.5 g kg(-1) in 100 ng total cotton genomic DNA, corresponding to about 17 copies of haploid cotton genomic DNA, and the LOD and limit of quantification (LOQ) for quantitative PCR assay were 10 and 17 copies of haploid cotton genomic DNA, respectively. Furthermore, the developed quantitative PCR assays were validated in-house by five different researchers. Also, five practical samples with known GM contents were quantified using the developed PCR assay in in-house validation, and the bias between the true and quantification values ranged from 2.06% to 12.59%. This study shows that the developed qualitative and quantitative PCR methods are applicable for the identification and quantification of GM cotton MON15985 and its derivates.

Quantitative Systems Pharmacology: A Case for Disease Models.

PubMed

Musante, C J; Ramanujan, S; Schmidt, B J; Ghobrial, O G; Lu, J; Heatherington, A C

2017-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model-informed drug discovery and development, supporting program decisions from exploratory research through late-stage clinical trials. In this commentary, we discuss the unique value of disease-scale "platform" QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. © 2016 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of The American Society for Clinical Pharmacology and Therapeutics.

Additive Genetic Variability and the Bayesian Alphabet

PubMed Central

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

2009-01-01

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

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

USGS Publications Warehouse

Anderson, Ryan; Clegg, Samuel M.; Frydenvang, Jens; Wiens, Roger C.; McLennan, Scott M.; Morris, Richard V.; Ehlmann, Bethany L.; Dyar, M. Darby

2017-01-01

Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.

An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing.

PubMed

Wu, Zujian; Pang, Wei; Coghill, George M

2015-01-01

Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between reactants in the candidate models for a target biochemical system are evolved and eventually identified by the application of a qualitative model learning approach with an evolution strategy. Kinetic rates of the models generated from qualitative model learning are then further optimised by employing a quantitative approach with simulated annealing. Experimental results indicate that our proposed integrative framework is feasible to learn the relationships between biochemical reactants qualitatively and to make the model replicate the behaviours of the target system by optimising the kinetic rates quantitatively. Moreover, potential reactants of a target biochemical system can be discovered by hypothesising complex reactants in the synthetic models. Based on the biochemical models learned from the proposed framework, biologists can further perform experimental study in wet laboratory. In this way, natural biochemical systems can be better understood.

Qualitative, semi-quantitative, and quantitative simulation of the osmoregulation system in yeast

PubMed Central

Pang, Wei; Coghill, George M.

2015-01-01

In this paper we demonstrate how Morven, a computational framework which can perform qualitative, semi-quantitative, and quantitative simulation of dynamical systems using the same model formalism, is applied to study the osmotic stress response pathway in yeast. First the Morven framework itself is briefly introduced in terms of the model formalism employed and output format. We then built a qualitative model for the biophysical process of the osmoregulation in yeast, and a global qualitative-level picture was obtained through qualitative simulation of this model. Furthermore, we constructed a Morven model based on existing quantitative model of the osmoregulation system. This model was then simulated qualitatively, semi-quantitatively, and quantitatively. The obtained simulation results are presented with an analysis. Finally the future development of the Morven framework for modelling the dynamic biological systems is discussed. PMID:25864377

Real-Time PCR-Based Quantitation Method for the Genetically Modified Soybean Line GTS 40-3-2.

PubMed

Kitta, Kazumi; Takabatake, Reona; Mano, Junichi

2016-01-01

This chapter describes a real-time PCR-based method for quantitation of the relative amount of genetically modified (GM) soybean line GTS 40-3-2 [Roundup Ready(®) soybean (RRS)] contained in a batch. The method targets a taxon-specific soybean gene (lectin gene, Le1) and the specific DNA construct junction region between the Petunia hybrida chloroplast transit peptide sequence and the Agrobacterium 5-enolpyruvylshikimate-3-phosphate synthase gene (epsps) sequence present in GTS 40-3-2. The method employs plasmid pMulSL2 as a reference material in order to quantify the relative amount of GTS 40-3-2 in soybean samples using a conversion factor (Cf) equal to the ratio of the RRS-specific DNA to the taxon-specific DNA in representative genuine GTS 40-3-2 seeds.

Rodent Models of Genetic Contributions to Motivation to Abuse Alcohol

PubMed Central

Crabbe, John C.

2016-01-01

The distinction between alcohol use (normative) and abuse (unfortunately common) implies dysregulation of motivation directed toward the drug. Genetic contributions to abuse risk are mediated through personality differences, other predispositions to drink excessively, and differences in sensitivity to the acute and chronic consequences of the drug. How to assess motivation in laboratory animals is not straightforward but risk factors for and consequences of alcohol abuse can be modeled with reasonable fidelity in laboratory rodents. Remarkably few rodent studies focus on the genetic contributions to alcohol’s reinforcing value: almost all examine preferential drinking of unflavored alcohol over water. Such studies will likely never avoid the confounding role of taste preferences and most often yield intake levels insufficient to yield a pharmacologically significant blood alcohol level. Genotypes that avoid alcohol probably do so based on pre-ingestive sensory cues; however, post-ingestive consequences are also important. Thus, the quest for improved measures of reinforcing value continues. We have genetic differences aplenty, but still lack evidence that any genotype will readily self-administer alcohol to the devastating extent that many alcoholics will. Encouraging results that are emerging include improved behavioral methods for elevating alcohol intake and inferring alcohol reinforcement, as well as new genetic animal models. Several ingenious assays to index alcohol’s motivational effects have been used extensively. Alcoholic drinking that attempts to prevent or to alleviate withdrawal symptoms has been modeled. Another characteristic of alcoholic drinking is its persistence despite abundant evidence to the drinker of the damaging effects of the excessive drinking on work, relationships, and/or health. Modeling such persistence in rodents has been uncommon to date. New genetic animal models include lines of mice selectively bred for chronic high drinking

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

Investigating genetic loci that encode plant-derived paleoclimate proxies

NASA Astrophysics Data System (ADS)

Bender, A. L. D.; Suess, M.; Chitwood, D. H.; Bradley, A. S.

2016-12-01

Long chain (>C25) n-alkanes in sediments predominantly derive from terrestrial plant waxes. Hydrogen isotope ratios (δD) of leaf wax hydrocarbons correlate with δDH2O of precipitation and are commonly used as paleoclimate proxies. However, biological variability in the isotopic fractionations between water and plant materials also affects the n-alkane δD values. Correct interpretation of this paleoclimate proxy requires that we resolve genetic and environmental effects. Genetic variability underlying differences in leaf wax structure and isotopic composition can be quantitatively determined through the use of model organisms. Interfertile Solanum sect. Lycopersicon (tomato) species provide an ideal model species complex for this approach. We used a set of 76 precisely defined near-isogenic lines (introgression lines [ILs]) in which small genomic regions from the wild tomato relative Solanum pennellii have been introduced into the genome of the domestic tomato, S. lycopersicum. By characterizing quantitative traits of these ILs (leaf wax structure and isotopic composition), we can resolve the degree to which each trait is regulated by genetic versus environmental factors. We present data from two growth experiments conducted with all 76 ILs. In this study, we quantify leaf wax traits, including δD values, δ13C values, and structural metrics including the methylation index (a variable that describes the ratio of iso­- and anteiso- to n-alkanes). Among ILs, δD values vary by up to 35‰ and 60‰ for C31 and C33 n-alkanes, respectively. Many ILs have methylation indices that are discernably different from the parent domesticated tomato (p < 0.001), which suggests that methylation is a highly polygenic trait. This pattern is similar to the genetics that control leaf shape, another trait commonly used as a paleoclimate proxy. Based on our preliminary analysis, we propose candidate genes that control aspects of plant physiology that affect these quantitative

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

PubMed Central

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

2016-01-01

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

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