Abstract
Various methods have been developed for identifying gene–gene interactions in genome-wide association studies (GWAS). However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene–gene interaction (GGG) tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein–protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.
Highlights
Genome-wide association studies (GWAS) have identified over six thousand single-nucleotide polymorphisms (SNPs) associated with complex human diseases or traits [1]
We further present an empirical application of the novel methods, jointly with a curated human protein-protein interactions (PPI) network, to test for gene-level interactions underlying lipid levels in genome-wide association studies (GWAS) data from the Atherosclerosis Risk in Communities (ARIC) study [50]
To ensure the type I error rates were not affected by the number of interactions combined into a gene-based gene–gene interaction (GGG) test, we conducted another set of simulations using more SNPs (30 and 20 randomly selected SNPs in each locus respectively) and still observed type I error rates consistent with the nominal significance levels (Table S1)
Summary
Genome-wide association studies (GWAS) have identified over six thousand single-nucleotide polymorphisms (SNPs) associated with complex human diseases or traits [1]. Most of these SNPs have small effect sizes, and for most traits collectively explain only a small fraction of heritable genetic variance [2,3,4]. We aim to improve the power of gene-gene interaction testing by moving beyond testing between a pair (or a group) of individual SNPs, which is the case in conventional marker-based testing, and instead considering all pairs of SNPs from each of a pair of genes in a single gene-based test of interaction
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