The Selective Values of Alleles in a Molecular Network Model Are Context Dependent

Abstract

Classical quantitative genetics has applied linear modeling to the problem of mapping genotypic to phenotypic variation. Much of this theory was developed prior to the availability of molecular biology. The current understanding of the mechanisms of gene expression indicates the importance of nonlinear effects resulting from gene interactions. We provide a bridge between genetics and gene network theories by relating key concepts from quantitative genetics to the parameters, variables, and performance functions of genetic networks. We illustrate this methodology by simulating the genetic switch controlling galactose metabolism in yeast and its response to selection for a population of individuals. Results indicate that genes have heterogeneous contributions to phenotypes and that additive and nonadditive effects are context dependent. Early cycles of selection suggest strong additive effects attributed to some genes. Later cycles suggest the presence of strong context-dependent nonadditive effects that are conditional on the outcomes of earlier selection cycles. A single favorable allele cannot be consistently identified for most loci. These results highlight the complications that can arise with the presence of nonlinear effects associated with genes acting in networks when selection is conducted on a population of individuals segregating for the genes contributing to the network. R ECENTLY there has been interest in interpretinganalyze the epistatic interactions between the genes conthe quantitative genetic properties of gene nettrolling this pathway and their impact on the selection works at the population level (Frank 1999; Omholt et process. al. 2000). This is warranted on at least three grounds:Fundamental to genetics is the relationship between (i) much of the molecular genetic evidence points to the genotype of an individual, the environment where the roles of genes in nonlinear networks in the determiit lives, and its resulting phenotype. This relationship is nation of gene-to-phenotype relationships, (ii) we have often referred to as genotype-to-phenotype (GP) mapa growing body of data on the structural and functional ping. Since the true mechanisms of gene expression properties of the genomes of organisms and as this pool have historically been poorly understood, geneticists of data continues to expand it is becoming more feasible have derived such mappings from the joint distributions to construct models of gene networks, and (iii) for many of genotypic and phenotypic data. The simplest mapaspects of basic and applied genetics it is necessary to ping, Mendelian genetics, considers traits that are deterstudy the properties of allelic variation for genes at the mined completely by individual genes. Many traits, howlevel of phenotypic effects and variation within populaever, are more complex than that; they are quantitative tions. Bridging the molecular and population-level views in nature and are influenced by contributions from of gene-to-phenotype relationships is a challenging area alleles...

Statistical Epistasis Is a Generic Feature of Gene Regulatory Networks

“…Attempts to refine the concept of epistasis have been made [e.g., ''physiological epistasis'' (Cheverud and Routman 1995) and ''functional epistasis'' (Hansen and Wagner 2001)] and studies have addressed the genetics of biological network models (Wagner 1994;Frank 1999;Omholt et al 2000;You and Yin 2002;Peccoud et al 2004;Cooper et al 2005;Moore and Williams 2005;Segre et al 2005;Welch et al 2005;Azevedo et al 2006;Omholt 2006). In this work we study the relationship between statistical epistasis and functional dependency by doing quantitative genetic analysis of synthetic data sets obtained from genotype-phenotype models where phenotypic variation at the level of gene expression arises from allelic variation in model parameters.…”

Towards causally cohesive genotype–phenotype modelling for characterization of the soft-tissue mechanics of the heart in normal and pathological geometries

“…Genetics offers many concepts for describing context-dependence at various levels, such as epistasis and genotype-by-environment interactions, but few tools for understanding the underlying mechanisms and including them in prediction models [5]. As demonstrated here and in earlier studies [39,50], cGP modelling is very well suited for integrating different types of variation, revealing context-dependent effects and identifying the underlying mechanisms. A key step for application in personalized medicine will be new phenomics technologies and experimental programmes aimed at characterizing the genotype-parameter map in specific populations [5,51].…”

“…Given that biological systems are inherently nonlinear, the assumption of an additive mapping from one gene to one parameter is clearly unrealistic. However, additive genotype-to-parameter maps is a natural starting point for most exploratory cGP studies [3,39,40], because then any nonlinearity that emerges must be due to the modelled physiology. But, when the biomechanical model is clearly nonlinear, why do we not observe a more nonlinear GP map?…”

A Critical Analysis of IQ Studies of Adopted Children

“…Accordingly, there is now much experimental evidence of gene-environment interactions in behavioural traits in animals [see, for example, Newman et al, 2005;Suomi, 2003]. In such systems many allelic variants can be rendered functionally equivalent; many deviant environmental eff ects can be compensated for, and additive sources of variance are much reduced [Peccoud, Venden, Podlich, Winkler, Arthur, & Cooper, 2004]. In addition there is strong evidence of other kinds of geneenvironment and environment-environment interactions arising within the special dynamics of the adopted state (discussed in some detail below).…”