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 bi-directional gene-flow. Population divergence involved many short, widely dispersed regions across the genome. Analyses of allele frequency 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=0.0076). At the level of metabolic pathways, the overlap was Jcorr=0.2455, indicating increasing parallelism with increasing level of biological integration. The majority of pathways contained positively selected genes in both sulphide populations. Adaptation to sulphide habitats necessitated adjustments throughout the genome. 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 09/2015; DOI:10.1111/mec.13397 · 6.49 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
    • "this RIL population. Possible mechanisms underlying genetic epistasis in natural populations could refer to physical or functional interactions between gene products or gene and gene products that are connected within a biochemical or regulatory pathway (Phillips, 2008; Kliebenstein, 2009; Bassel et al., 2012). "
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    ABSTRACT: Deciphering the influence of genetics on primary metabolism in plants will provide insights useful for genetic improvement and enhance our fundamental understanding of plant growth and development. Although maize (Zea mays) is a major crop for food and feed worldwide, the genetic architecture of its primary metabolism is largely unknown. Here, we use high-density linkage mapping to dissect large-scale metabolic traits measured in three different tissues (leaf at seedling stage, leaf at reproductive stage, and kernel at 15 d after pollination [DAP]) of a maize recombinant inbred line population. We identify 297 quantitative trait loci (QTLs) with moderate (86.2% of the mapped QTL, R2 = 2.4 to 15%) to major effects (13.8% of the mapped QTL, R2 >15%) for 79 primary metabolites across three tissues. Pairwise epistatic interactions between these identified loci are detected for more than 25.9% metabolites explaining 6.6% of the phenotypic variance on average (ranging between 1.7 and 16.6%), which implies that epistasis may play an important role for some metabolites. Key candidate genes are highlighted and mapped to carbohydrate metabolism, the tricarboxylic acid cycle, and several important amino acid biosynthetic and catabolic pathways, with two of them being further validated using candidate gene association and expression profiling analysis. Our results reveal a metabolite-metabolite-agronomic trait network that, together with the genetic determinants of maize primary metabolism identified herein, promotes efficient utilization of metabolites in maize improvement
    The Plant Cell 07/2015; 27(7):1839-1856. DOI:10.1105/tpc.15.00208 · 9.34 Impact Factor
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