Fast search of third-order epistatic interactions on CPU and GPU clusters. (January 2020)

• Record Type: Journal Article
• Authors: Ponte-Fernández, Christian; González-Domínguez, Jorge; Martín, María J
• Other Names: Cecilia José M guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 34:Number 1(2020)
• Page Start: 20
• Page End: 29
• Publication Date: 2020-01
• Subjects: Bioinformatics -- epistasis -- genetic interaction -- GPU -- GWAS -- high performance computing -- MPI -- mutual information; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342019852128
• Languages: English
• ISSNs: 1094-3420

Abstract

Genome-Wide Association Studies (GWASs), analyses that try to find a link between a given phenotype (such as a disease) and genetic markers, have been growing in popularity in the recent years. Relations between phenotypes and genotypes are not easy to identify, as most of the phenotypes are a product of the interaction between multiple genes, a phenomenon known as epistasis. Many authors have resorted to different approaches and hardware architectures in order to mitigate the exponential time complexity of the problem. However, these studies make some compromises in order to keep a reasonable execution time, such as limiting the number of genetic markers involved in the interaction, or discarding some of these markers in an initial filtering stage. This work presents MPI3SNP, a tool that implements a three-way exhaustive search for cluster architectures with the aim of mitigating the exponential growth of the run-time. Modern cluster solutions usually incorporate GPUs. Thus, MPI3SNP includes implementations for both multi-CPU and multi-GPU clusters. To contextualize the performance achieved, MPI3SNP is able to analyze an input of 6300 genetic markers and 3200 samples in less than 6 min using 768 CPU cores or 4 min using 8 NVIDIA K80 GPUs. The source code is available athttps://github.com/chponte/mpi3snp .