GBOOST 2.0: A GPU-based tool for detecting gene-gene interactions with covariates adjustment in genome-wide association studies

Abstract

Detecting gene-gene interaction patterns is important to reveal associations between genotype and complex diseases. This task, however, is computationally challenging. For example, in order to exhaustively detect interactions of 1,000,000 single nucleotide polymorphisms (SNPs) genotyped from thousands of individuals, we need to carry out 5×1011 statistical tests. To address the computational challenge, Wan et. al. [1] proposed a fast method named BOOST to exhaustively detect interactions of all SNP pairs. BOOST completes pairwise analysis of 360,000 SNPs in 60 hours on a standard desktop PC. As the interaction tests of SNP pairs are highly parallel, Yung et. al. [2] implemented the BOOST method in GPU and named it GBOOST. GBOOST usually takes about one and a half hours to finish genome-wide interaction analysis of a data set containing about 350,000 SNPs and 5,000 samples using Nvidia GeForce GTX 285 dispaly card.