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GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores

BACKGROUND: 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) also have hundreds of cores and have been recently...

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Detalles Bibliográficos
Autores principales: Chikkagoudar, Satish, Wang, Kai, Li, Mingyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3115877/
https://www.ncbi.nlm.nih.gov/pubmed/21615923
http://dx.doi.org/10.1186/1756-0500-4-158
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author Chikkagoudar, Satish
Wang, Kai
Li, Mingyao
author_facet Chikkagoudar, Satish
Wang, Kai
Li, Mingyao
author_sort Chikkagoudar, Satish
collection PubMed
description BACKGROUND: 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) 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. FINDINGS: Here 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. CONCLUSIONS: GENIE 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/.
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spelling pubmed-31158772011-06-16 GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores Chikkagoudar, Satish Wang, Kai Li, Mingyao BMC Res Notes Technical Note BACKGROUND: 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) 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. FINDINGS: Here 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. CONCLUSIONS: GENIE 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/. BioMed Central 2011-05-26 /pmc/articles/PMC3115877/ /pubmed/21615923 http://dx.doi.org/10.1186/1756-0500-4-158 Text en Copyright ©2011 Chikkagoudar et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Chikkagoudar, Satish
Wang, Kai
Li, Mingyao
GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores
title GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores
title_full GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores
title_fullStr GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores
title_full_unstemmed GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores
title_short GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores
title_sort genie: a software package for gene-gene interaction analysis in genetic association studies using multiple gpu or cpu cores
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3115877/
https://www.ncbi.nlm.nih.gov/pubmed/21615923
http://dx.doi.org/10.1186/1756-0500-4-158
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