Development of GMDR-GPU for Gene-Gene Interaction Analysis and Its Application to WTCCC GWAS Data for Type 2 Diabetes
Zhixiang Zhu, Xiaoran Tong , Zhihong Zhu , Meimei Liang , Wenyan Cui , Kunkai Su , Ming D. Li, Jun Zhu,
Institute of Bioinformatics, Zhejiang University, Hangzhou, China
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 (G6G) is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR) approach for detecting G6G 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.
Zhu Z, Tong X, Zhu Z, et al. Development of GMDR-GPU for gene-gene interaction analysis and its application to WTCCC GWAS data for type 2 diabetes[J]. PloS one, 2013, 8(4): e61943.