Retargeting tensor accelerators for epistasis detection

Abstract

The substitution of nucleotides at specific positions in the genome of a population, known as single-nucleotide polymorphisms (SNPs), has been correlated with a number of important diseases. Complex conditions such as Alzheimer's disease or Crohn's disease are significantly linked to genetics when the impact of multiple SNPs is considered. SNPs often interact in an epistatic manner, where the joint effect of multiple SNPs may not be simply mapped to a linear additive combination of individual effects. Genome-wide association studies considering epistasis are computationally challenging, especially when performing triplet searches is required. Some contemporary computer architectures support fused XOR and population count as the highest throughput operations as part of tensor operations. This article presents a new approach for efficiently repurposing this capability to accelerate 2-way (pairs) and 3-way (triplets) epistasis detection searches. Experimental evaluation targeting the Turing GPU architecture resulted in previously unattainable levels of performance, with the proposal being able to evaluate up to 108.1 and 54.5 tera unique sets of SNPs per second, scaled to the sample size, in 2-way and 3-way searches, respectively.