It is being increasingly accepted that traditional statistical Single
Nucleotide Polymorphism (SNP) analysis of Genome-Wide Association
Studies (GWAS) reveals just a small part of the heritability in complex
diseases. Study of SNPs interactions identify additional SNPs that contribute
to disease but that 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,
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. Here, we contribute with a new
entropy-based function to evaluate the interaction between SNPs
which does not compromise findings about the most significant SNP
interactions, but is more than 4000 times lighter in terms of computational time
when running on GPUs and provides more than 100x faster code than a CPU of similar cost.
We deploy a number of optimization techniques to tune the implementation of
this function using CUDA and show the way to enhance scalability on larger data sets.