GPU Acceleration of an Entroy-Based Model to Quantify Epistatic Interactions Between SNPs

摘要

The process of characterizing naturally occurring variations in the human genome has captivated the high performance computation community over the past few years. Changes known as biallelic Single-Nucleotide Polymorphisms (SNPs) have become essential biomarkers both in evolutionary relationships and propensity to degenerative diseases. It is being increasingly accepted that traditional statistical SNP analysis of Genome-Wide Association Studies (GWAS) reveals just a small part of the heritability in complex diseases. Study of interactions among SNPs has been suggested as a plausible approach to identify further SNPs that contribute to disease but either do not reach genome-wide significance or exhibit only epistatic effects. We have introduced a methodology for genome-wide screening of epistatic interactions which is feasible to be handled by state-of-art high performance computing technology. Unlike standard software [1], our method computes all Boolean binary interactions between SNPs across the whole genome without assuming a particular model of interaction. Our extensive search for epistasis comes at the expense of higher computational complexity, which we tackled using graphics processors (GPUs) to reduce the computational time from several months in a cluster of CPUs to 3-4 days on a multi-GPU platform [2].