Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.