Are Genetic Interactions Influencing Gene Expression Evidence for Biological Epistasis or Statistical Artifacts?

The importance of epistasis - or statistical interactions between genetic variants - to the development of complex disease in humans has long been controversial. Genome-wide association studies of statistical interactions influencing human traits have recently become computationally feasible and have identified many putative interactions. However, several factors that are difficult to address confound the statistical models used to detect interactions and make it unclear whether statistical interactions are evidence for true molecular epistasis. In this study, we investigate whether there is evidence for epistasis regulating gene expression after accounting for technical, statistical, and biological confounding factors that affect interaction studies. We identified 1,119 (FDR=5%) interactions within cis-regulatory regions that regulate gene expression in human lymphoblastoid cell lines, a tightly controlled, largely genetically determined phenotype. Approximately half of these interactions replicated in an independent dataset (363 of 803 tested). We then performed an exhaustive analysis of both known and novel confounders, including ceiling/floor effects, missing genotype combinations, haplotype effects, single variants tagged through linkage disequilibrium, and population stratification. Every replicated interaction could be explained by at least one of these confounders, and replication in independent datasets did not protect against this issue. Assuming the confounding factors provide a more parsimonious explanation for each interaction, we find it unlikely that cis-regulatory interactions contribute strongly to human gene expression. As this calls into question the relevance of interactions for other human phenotypes, the analytic framework used here will be useful for protecting future studies of epistasis against confounding.