Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes

Abstract

Although genome-wide association studies (GWAS) have identified a significant number of single-nucleotide polymorphisms (SNPs) associated with many complex human traits, the susceptibility loci identified so far can explain only a small fraction of the genetic risk. Among other possible explanations, the lack of a comprehensive examination of gene-gene interaction (G×G) is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR) approach for detecting G×G in both dichotomous and quantitative phenotypes. However, the computational burden and less efficient implementation of the original programs make them impossible to use for GWAS. In this study, we developed a graphics processing unit (GPU)-based GMDR program (named GWAS-GPU), which is able not only to analyze GWAS data but also to run much faster than the earlier version of the GMDR program. As a demonstration of the program, we used the GMDR-GPU software to analyze a publicly available GWAS dataset on type 2 diabetes (T2D) from the Wellcome Trust Case Control Consortium. Through an exhaustive search of pair-wise interactions and a selected search of three- to five-way interactions conditioned on significant pair-wise results, we identified 24 core SNPs in six genes (FTO: rs9939973, rs9940128, rs9922047, rs1121980, rs9939609, rs9930506; TSPAN8: rs1495377; TCF7L2: rs4074720, rs7901695, rs4506565, rs4132670, rs10787472, rs11196205, rs10885409, rs11196208; L3MBTL3: rs10485400, rs4897366; CELF4: rs2852373, rs608489; RUNX1: rs445984, rs1040328, rs990074, rs2223046, rs2834970) that appear to be important for T2D. Of these core SNPs, 11 in FTO, TSPAN8, and TCF7L2 have been reported to be associated with T2D, obesity, or both, providing an independent replication of previously reported SNPs. Importantly, we identified three new susceptibility genes; i.e., L3MBTL3, CELF4, and RUNX1, for T2D, a finding that warrants further investigation with independent samples.

Figure 1. Working process of the GMDR-GPU program for conducting a two-dimensional interaction search on a sample consisting of four SNPs, two covariates, and a quantitative phenotype.

Figure 2. Determination of core SNPs for T2D through two-way to five-way interaction analysis using GMDR-GPU.

Each SNP is represented by a red dot and each interaction by a vertical line. The red dots in the same horizontal line correspond to the same unique SNP. Different colors of vertical lines represent different interaction dimensions (green: two-way; blue: three-way; pink: four-way; yellow: five-way). All identified core SNPs are indicated with their IDs.

Figure 3. Interaction/association network among the six genes containing at least one core SNPs identified by analyzing WTCCC T2D data with GMDR-GPU.

Beyond the six susceptibility genes identified in this work, three other genes, CTNNB1, RUNX3, and CBFB, were found by a literature search. Although these genes have been associated with a number of human disorders, only two closely related diseases, i.e., T2D and colorectal cancer, are shown.