Regional heritability advanced complex trait analysis for GPU and traditional parallel architectures

Motivation: Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of single-nucleotide polymorphisms and individuals. To meet the challenges in making feasible large-scale studies, we...

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Vydáno v:Bioinformatics Ročník 30; číslo 8; s. 1177 - 1179
Hlavní autoři: Cebamanos, L., Gray, A., Stewart, I., Tenesa, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: England International Society for Computational Biology - Oxford University Press 15.04.2014
Témata:
Algorithms
BASIC BIOLOGICAL SCIENCES
biochemistry & molecular biology
Bioinformatics
biotechnology & applied microbiology
Computational Biology
Computer programs
computer science
Genetics
Graphics processing units
Likelihood Functions
mathematical & computational biology
mathematics
Phenotype
Polymorphism
Polymorphism, Single Nucleotide
Regional
Software
Supercomputers
ISSN:1367-4803, 1367-4811, 1367-4811, 1460-2059
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Shrnutí:Motivation: Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of single-nucleotide polymorphisms and individuals. To meet the challenges in making feasible large-scale studies, we present the REgional heritability advanced complex trait analysis software. Adapted from advanced complex trait analysis (and, in turn, genome-wide complex trait analysis), it is tailored to exploit the parallelism present in modern traditional and graphics processing unit (GPU)-accelerated machines, from workstations to supercomputers. Results: We adapt the genetic relationship matrix estimation algorithm to remove limitations on memory, allowing the analysis of large datasets. We build on this to develop a version of the code able to efficiently exploit GPU-accelerated systems for both the genetic relationship matrix and REstricted maximum likelihood (REML) parts of the analysis, offering substantial speedup over the traditional central processing unit version. We develop the ability to analyze multiple small regions of the genome across multiple compute nodes in parallel, following the ‘regional heritability’ approach. We demonstrate the new software using 1024 GPUs in parallel on one of the world’s fastest supercomputers. Availability: The code is freely available at http://www.epcc.ed.ac.uk/software-products Contact:  a.gray@ed.ac.uk
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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USDOE Office of Science (SC)
AC05-00OR22725
None
Biotechnology and Biological Sciences Research Council (BBSRC)
ISSN:1367-4803
1367-4811
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btt754