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Abstract
BackgroundGene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits.FindingsHere we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run.ConclusionsGENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/.
Highlights
Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved
Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster Graphics Processing Units (GPUs) cards
Speedup was calculated as the ratio of total time taken by the interaction module of Central Processing Units (CPUs)-GENIE and the total time taken by the interaction module of GPU-GENIE
Summary
Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) have hundreds of cores and have been recently used to implement faster scientific software. Most GWAS focus on single marker-based analysis in which each marker is analyzed individually, ignoring the dependence or interactions between markers This approach has led to the discovery of disease susceptibility genes for many diseases, the identified markers often only explain a small fraction of the phenotypic variation, suggesting a large number of disease variants are yet to be discovered. It is gene-gene interaction analysis is parallelizable in nature. Schupbach et al [8] developed a GPU-based software package that greatly speeds up gene-gene interaction analysis of quantitative traits
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