Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

Moore, Jason H.; Williams, Scott M.

doi:10.1002/bies.20236

Cited by 304 publications

(261 citation statements)

References 71 publications

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“…Since then, epistasis has been used in many different contexts, often with conflicting meanings, that led to several discussions about its formal definition (Phillips, 1998;Cordell, 2002;Strauch et al, 2003;Moore and Williams, 2005). As a consequence, many authors proposed different models to detect epistatic effects: additive models (Risch, 1990), multiplicative models (Hodge, 1981), and heterogeneity models (Risch, 1990;Vieland and Huang, 2003).…”

Section: Independent Action Models (Iams)mentioning

confidence: 99%

Allelic penetrance approach as a tool to model two-locus interaction in complex binary traits

et al. 2007

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Many binary phenotypes do not follow a classical Mendelian inheritance pattern. Interaction between genetic and environmental factors is thought to contribute to the incomplete penetrance phenomena often observed in these complex binary traits. Several two-locus models for penetrance have been proposed to aid the genetic dissection of binary traits. Such models assume linear genetic effects of both loci in different mathematical scales of penetrance, resembling the analytical framework of quantitative traits. However, changes in phenotypic scale are difficult to envisage in binary traits and limited genetic interpretation is extractable from current modeling of penetrance. To overcome this limitation, we derived an allelic penetrance approach that attributes incomplete penetrance to the stochastic expression of the alleles controlling the phenotype, the genetic background and environmental factors. We applied this approach to formulate dominance and recessiveness in a single diallelic locus and to model different genetic mechanisms for the joint action of two diallelic loci. We fit the models to data on the genetic susceptibility of mice following infections with Listeria monocytogenes and Plasmodium berghei. These models gain in genetic interpretation, because they specify the alleles that are responsible for the genetic (inter)action and their genetic nature (dominant or recessive), and predict genotypic combinations determining the phenotype. Further, we show via computer simulations that the proposed models produce penetrance patterns not captured by traditional two-locus models. This approach provides a new analysis framework for dissecting mechanisms of interlocus joint action in binary traits using genetic crosses.

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Section: Independent Action Models (Iams)mentioning

confidence: 99%

Allelic penetrance approach as a tool to model two-locus interaction in complex binary traits

et al. 2007

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“…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.…”

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confidence: 99%

Statistical Epistasis Is a Generic Feature of Gene Regulatory Networks

Hayes²,

et al. 2007

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Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation. M ANY, if not most, biologists are prone to believe that genetic interactions are common in the genetic architecture of complex traits. It is, however, more debatable how important these interactions are in contributing to the expression of phenotypes in individuals and in determining population responses to selection, maintenance of genetic variation, and speciation processes. Studies of genetic interactions, or epistasis, are commonly based on hierarchal genetic models with additivity as the main effect and dominance and epistasis modeled, if at all, as single-and multilocus deviations from the main effects. Using these models, hybridization experiments have shown an important overall contribution of epistasis to the phenotypic differences among (Doebley et al. 1995) and within (Hard et al. 1992;Lair et al. 1997;Carroll et al. 2001Carroll et al. , 2003 species. The same observations have been made in studies that dissect quantitative genetic variation into contributions from individual quantitative trait loci (QTL) using epistatic genetic models (Carlborg and Haley 2004). Phillips (1998) predicted that interaction between gene products that form molecular machines and signaling pathways would become increasingly important to genetic analysis and reinforce the concept of epistasis. His predictions are supported by the appearance of the first genomewide mapping studies of epistatic interactions underlying gene expression in yeast ...

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“…Interpretation of multi-locus models with interactions is always a difficult task due to the complex nature of gene-gene interactions plus the different meanings of interaction in statistics and genetics literatures [Cordell et al, 2001;Moore and Williams, 2005]. The ELA uses base learners, the importance measure of the base learners, and the importance measure of markers to help interpret the multi-marker model, that is, the final model of the ELA.…”

Section: Discussionmentioning

confidence: 99%

An ensemble learning approach jointly modeling main and interaction effects in genetic association studies

Zhang

Wong

et al. 2008

Genetic Epidemiology

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Complex diseases are presumed to be the results of interactions of several genes and environmental factors, with each gene only having a small effect on the disease. Thus, the methods that can account for gene-gene interactions to search for a set of marker loci in different genes or across genome and to analyze these loci jointly are critical. In this article, we propose an ensemble learning approach (ELA) to detect a set of loci whose main and interaction effects jointly have a significant association with the trait. In the ELA, we first search for "base learners" and then combine the effects of the base learners by a linear model. Each base learner represents a main effect or an interaction effect. The result of the ELA is easy to interpret. When the ELA is applied to analyze a data set, we can get a final model, an overall P-value of the association test between the set of loci involved in the final model and the trait, and an importance measure for each base learner and each marker involved in the final model. The final model is a linear combination of some base learners. We know which base learner represents a main effect and which one represents an interaction effect. The importance measure of each base learner or marker can tell us the relative importance of the base learner or marker in the final model. We used intensive simulation studies as well as a real data set to evaluate the performance of the ELA. Our simulation studies demonstrated that the ELA is more powerful than the single-marker test in all the simulation scenarios. The ELA also outperformed the other three existing multi-locus methods in almost all cases. In an application to a large-scale case-control study for Type 2 diabetes, the ELA identified 11 single nucleotide polymorphisms that have a significant multi-locus effect (Pvalue = 0.01), while none of the single nucleotide polymorphisms showed significant marginal effects and none of the two-locus combinations showed significant two-locus interaction effects.

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Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis

Cited by 304 publications

References 71 publications

Allelic penetrance approach as a tool to model two-locus interaction in complex binary traits

Allelic penetrance approach as a tool to model two-locus interaction in complex binary traits

Statistical Epistasis Is a Generic Feature of Gene Regulatory Networks

An ensemble learning approach jointly modeling main and interaction effects in genetic association studies

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