Detecting epistasis in human complex traits

Abstract

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.

Thyroid cancer GWAS identifies 10q26.12 and 6q14.1 as novel susceptibility loci and reveals genetic heterogeneity among populations

“…Tumor histology was assessed by pathologists at each participating institution. The median time from diagnosis to recruitment was 4 years (range [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Personal and clinical data, such as family history of thyroid cancer and What's new?…”

“…[4][5][6][7][8] Therefore, it has become generally accepted that genetic predisposition to DTC is likely due to common lowpenetrance or rare moderate-penetrance variants. 9 Moreover, there is evidence that gene-gene 10,11 and gene-environment interactions 12,13 modulate individual susceptibility. In this scenario, both carefully designed candidate-gene association studies [14][15][16] and genome-wide association studies (GWAS) [17][18][19][20] have been used to identify susceptibility variants for DTC.…”

Use of Information Measures and Their Approximations to Detect Predictive Gene-Gene Interaction

“…into four terms (see Equation (9) in [23]). Han in [18] proved that for the distribution of P X 1 ,X 2 ,Y close to independence, i.e., when all three variables X 1 , X 2 and Y are approximately independent, interaction information II(X 1 ; X 2 ; Y) and 2 −1 χ 2 X 1 ;X 2 ;Y are approximately equal.…”

A model of compound heterozygous, loss-of-function alleles is broadly consistent with observations from complex-disease GWAS datasets

“…Genetic variance due to epistatic or gene-by-environment interactions is difficult to identify statistically because of, among other reasons, increased multiple hypothesis testing burden [6,7], and could artificially inflate estimates of broad-sense heritability [8]. Well-tagged intermediate frequency variants may not reach genome-wide significance in an association study if they have smaller effect sizes [9,10].…”