Journal Article

Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits

Crawford, Lorin, Zeng, Ping, Zhou, Xiang, Mukherjee, Sayan

2017

Overview

Epistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects-the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the \"MArginal ePIstasis Test\", or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Biology and Life Sciences / Chromosome Mapping - statistics & numerical data / Computer applications / Computer Simulation / Consortia / DNA methylation / Epistasis / Epistasis, Genetic / Funding / Gene expression / Gene Expression Regulation / Gene mapping / Genetic diversity / Genetics / Genome-Wide Association Study - statistics & numerical data / Genomes / Genotype & phenotype / Humans / Models, Genetic / Observations / Phenotype / Phenotypic variations / Quantitative trait loci / Quantitative Trait Loci - genetics / Research and Analysis Methods / Software / Statistical methods / Statistics / Studies / Supervision / Variance