GWA-X: An Extensible GPU Accelerated Package for Permutation Testing in Genome-Wide Association Studies

Abstract

Genome-wide association studies (GWAS) aim to identify genetic variants that are associated with a trait or disease. The scale of genomic datasets has increased to millions of genetic variants and hundreds of thousands of individuals, opening the possibilities for GWAS discoveries. However, large-scale GWAS analyses are prone to high false positive rates because of the multiple testing problem. Permutation testing is the gold standard for maintaining false positive rates, yet it is impractical for large-scale GWAS because it demands vast computational resources. This paper presents GWA-X, a software package that can exploit GPU potential to accelerate permutation testing in GWAS. Unlike previous methods, GWA-X employs a novel whole-genome regression method and a permutation testing strategy to batch the computations of many genetic markers. It achieved a two-order magnitude speed-up compared with the existing CPU-based and GPU-based permutation methods. In addition, it provides an extensible package for GWAS permutation testing on GPUs.