Abstract
The term epistasis refers to interactions between multiple genetic loci. Genetic epistasis is important in regulating biological function and is considered to explain part of the ‘missing heritability,’ which involves marginal genetic effects that cannot be accounted for in genome-wide association studies. Thus, the study of epistasis is of great interest to geneticists. However, estimating epistatic effects for quantitative traits is challenging due to the large number of interaction effects that must be estimated, thus significantly increasing computing demands. Here, we present a new web server-based tool, the Pipeline for estimating EPIStatic genetic effects (PEPIS), for analyzing polygenic epistatic effects. The PEPIS software package is based on a new linear mixed model that has been used to predict the performance of hybrid rice. The PEPIS includes two main sub-pipelines: the first for kinship matrix calculation, and the second for polygenic component analyses and genome scanning for main and epistatic effects. To accommodate the demand for high-performance computation, the PEPIS utilizes C/C++ for mathematical matrix computing. In addition, the modules for kinship matrix calculations and main and epistatic-effect genome scanning employ parallel computing technology that effectively utilizes multiple computer nodes across our networked cluster, thus significantly improving the computational speed. For example, when analyzing the same immortalized F2 rice population genotypic data examined in a previous study, the PEPIS returned identical results at each analysis step with the original prototype R code, but the computational time was reduced from more than one month to about five minutes. These advances will help overcome the bottleneck frequently encountered in genome wide epistatic genetic effect analysis and enable accommodation of the high computational demand. The PEPIS is publically available at http://bioinfo.noble.org/PolyGenic_QTL/.
Highlights
Epistasis, the interaction among multiple genetic loci, contributes significantly to phenotypic variation associated with the expression of polygenic complex traits
A new mixed-model was recently developed for mapping DNA regions that are associated with variations in observable traits, known as quantitative trait loci
In order to overcome these computational challenges, we developed a tool known as the PEPIS—Pipeline for estimating EPIStatic genetic effects in genome wide
Summary
The interaction among multiple genetic loci, contributes significantly to phenotypic variation associated with the expression of polygenic complex traits. A number of studies that have identified genetic susceptibility factors failed to account for the effects of interactions between multiple genetic loci [4,5], which may help explain why the genetic variability of individual genes by GWAS can only explain ~40% variation of psychiatric disorders [4]. This inability to provide complete genetic explanations gives support to the concept of missing heritability. Careful analyses of epistatic effects may help close the gap in our understanding of missing heritability [4,6]
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