Phillips PC. Epistasis-the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9: 855-867

Center for Ecology and Evolution, University of Oregon, Eugene, Oregon 97403 USA.
Nature Reviews Genetics (Impact Factor: 36.98). 11/2008; 9(11):855-67. DOI: 10.1038/nrg2452
Source: PubMed


Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.

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    • "Furthermore, parallel changes at the nucleotide level appear more probable if there is little epistasis, i.e. when the fitness effects of individual mutations is independent of the particular order in which they appear (Lobkovsky & Koonin 2012). However, epistasis is pervasive in all genetic systems and consequently also in complex eukaryotes (Phillips 2008). Therefore, it appears more likely that early beneficial mutations have a particularly large influence on the further evolutionary trajectory via pleiotropic interactions, priority effects etc. (Lenormand et al. 2008). "
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    ABSTRACT: Replicated ecological gradients are prime systems to study processes of molecular evolution underlying ecological divergence. Here, we investigated the repeated adaptation of the neotropical fish Poecilia mexicana to habitats containing toxic hydrogen sulphide (H2S) and compared two population pairs of sulphide-adapted and ancestral fish by sequencing population pools of >200 individuals (Pool-Seq). We inferred the evolutionary processes shaping divergence and tested the hypothesis of increase of parallelism from SNPs to molecular pathways. Coalescence analyses showed that the divergence occurred in the face of substantial bidirectional gene flow. Population divergence involved many short, widely dispersed regions across the genome. Analyses of allele fre- quency spectra suggest that differentiation at most loci was driven by divergent selection, followed by a selection-mediated reduction of gene flow. Reconstructing allelic state changes suggested that selection acted mainly upon de novo mutations in the sulphide-adapted populations. Using a corrected Jaccard index to quantify parallel evolution, we found a negligible proportion of statistically significant parallel evolution of Jcorr = 0.0032 at the level of SNPs, divergent genome regions (Jcorr = 0.0061) and genes therein (Jcorr = 0.0091). At the level of metabolic pathways, the overlap was Jcorr = 0.2545, indicating increasing parallelism with increasing level of biological integration. The majority of pathways contained positively selected genes in both sulphide populations. Hence, adaptation to sulphidic habitats necessitated adjustments throughout the gen- ome. The largely unique evolutionary trajectories may be explained by a high proportion of de novo mutations driving the divergence. Our findings favour Gould’s view that evolution is often the unrepeatable result of stochastic events with highly contingent effects.
    Molecular Ecology 10/2015; 24(21):5446-5459. DOI:10.1111/mec.13397 · 6.49 Impact Factor
    • "KEYWORDS epistasis; genomic selection; genomic best linear unbiased prediction (G-BLUP); extended G-BLUP (EG-BLUP); reproducing kernel Hilbert space regression (RKHS); GenPred; shared data resource E PISTASIS has long been recognized as an important component in dissecting genetic pathways and understanding the evolution of complex genetic systems (Phillips 2008). It is hence a biologically influential component contributing to the genetic architecture of quantitative traits (Mackay 2014). "
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    ABSTRACT: Modelling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the reproducing kernel Hilbert space regression (RKHS) are two attractive approaches reducing the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches explicitly modelling epistatic effects. Moreover, we have shown why the RKHS model based on a Gaussian kernel captures epistatic effects among markers. Using experimental data sets in wheat and maize, we compared different genomic selection approaches and concluded that prediction accuracy can be improved by modelling epistasis for selfing species but may not for out-crossing species. Copyright © 2015, The Genetics Society of America.
    Genetics 07/2015; 201:759-768. DOI:10.1534/genetics.115.177907 · 5.96 Impact Factor
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    • "In classical GWAS each genetic marker is analyzed separately in order to identify markers showing differences in genotype frequencies between cases and controls. Unfortunately, this approach is generally not powerful enough to model complex traits for which the detection of joint genetic effects (epistasis) needs to be considered [1], [2], [3]. For instance, 2-SNPs analyses try to find pairs of SNPs whose joint genotype frequencies show a statistically significant difference between cases and controls which potentially explains the effect of the genetic variation leading to disease. "
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    ABSTRACT: Development of new methods to detect pairwise epistasis, such as SNP-SNP interactions, in Genome-Wide Association Studies is an important task in bioinformatics as they can help to explain genetic influences on diseases. As these studies are time consuming operations, some tools exploit the characteristics of different hardware accelerators (such as GPUs and Xeon Phi coprocessors) to reduce the runtime. Nevertheless, all these approaches are not able to efficiently exploit the whole computational capacity of modern clusters that contain both GPUs and Xeon Phi coprocessors. In this paper we investigate approaches to map pairwise epistasic detection on heterogeneous clusters using both types of accelerators. The runtimes to analyze the well-known WTCCC dataset consisting of about 500K SNPs and 5K samples on one and two NVIDIA K20m are reduced by 27% thanks to the use of a hybrid approach with one additional single Xeon Phi coprocessor.
    IEEE Transactions on Parallel and Distributed Systems 07/2015; DOI:10.1109/TPDS.2015.2460247 · 2.17 Impact Factor
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