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

Genome-wide association studies (GWAS) aim to identify associations of genetic variants 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 discoveries from GWAS. 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 requires vast computational resources.

This paper presents GWA-X, a software package that can fully benefit from GPUs and accelerate permutation testing in GWAS. In contrast to previous methods, GWA-X employs a novel whole-genome regression method 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 methods and more than one-order magnitude speed-up compared with the current state-of-the-art GPU-based library. In addition, GWA-X provides an extensible framework for conducting permutation tests in GWAS.