Journal Article

Detecting epistasis in human complex traits

Wei, Wen-Hua, Hemani, Gibran, Haley, Chris S.

2014

Overview

Key Points Tremendous activity in the development of methodology has now rendered the exhaustive search for pairwise genetic interactions computationally routine, but addressing the statistical problems of detecting epistasis remains a big challenge. Most reports of epistasis influencing human complex traits that exist in the literature raise concerns regarding their validity and do not follow the same strict protocols that are in place for reporting additive effects. There is mounting evidence against the existence of pairwise epistatic effects influencing human complex traits that are sufficiently large for detection in standard single-sample genome-wide association studies (GWASs). If epistatic effects do influence complex traits, then each interaction effect will probably be small, as is observed with additive effects. The majority of robust additive effects are only found when GWASs are carried out using huge sample sizes and good single-nucleotide polymorphism coverage, often as a result of multistudy meta-analyses. Similar approaches are necessary if epistatic effects are also to be robustly detected, although methodology or attempts at implementation are yet to surface. Methods have emerged for estimating the total contribution of additive effects across the whole genome; similar methods for estimating the total contribution of genetic interactions would be valuable but have not yet been developed. Genome-wide association studies have been extensively used to uncover genetic variants that independently influence complex traits, including diseases. This Review describes advances in computational approaches to detect interactions (epistasis) between genetic variants underlying complex traits, including the different promises and pitfalls of the methods. Additionally, the authors summarize current empirical evidence on how pervasive epistasis is in complex traits and its wider biological implications. Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 631/208/205/2138 / 631/208/2490/1472 / 631/208/480 / 631/208/729/743 / Agriculture / Algorithms / Animal Genetics and Genomics / Biomedicine / Cancer Research / Epistasis, Genetic - genetics / Gene Function / Gene loci / Genetic epistasis / Genome, Human - genetics / Genome-wide association studies / Genome-Wide Association Study - methods / Genome-Wide Association Study - statistics & numerical data / Genomes / Genotype / Genotype & phenotype / Human Genetics / Humans / Identification and classification / Linkage Disequilibrium / Models, Biological / Phenotype / Polymorphism / Polymorphism, Single Nucleotide - genetics / Quantitative trait loci / review-article