Regional Heritability Advanced Complex Trait Analysis for GPU and Traditional Parallel Architectures

L Cebamanos; Alan Gray; I Stewart; A Tenesa

doi:10.1093/bioinformatics/btt754

Regional Heritability Advanced Complex Trait Analysis for GPU and Traditional Parallel Architectures

L Cebamanos, Alan Gray, I Stewart, A Tenesa

Research output: Contribution to journal › Article › peer-review

Abstract

Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of SNPs and individuals. To meet the challenges in making feasible large scale studies, we present the REACTA software. Adapted from ACTA (and, in turn, GCTA), it is tailored to exploit the parallelism present in modern traditional and GPU-accelerated machines, from workstations to supercomputers.

Original language	English
Pages (from-to)	1177-1179
Journal	Bioinformatics
Volume	30
Issue number	8
DOIs	https://doi.org/10.1093/bioinformatics/btt754
Publication status	Published - 7 Jan 2014

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10.1093/bioinformatics/btt754

Development of gpgpu tools for modelling complex phenotypes
Gray, A. (Principal Investigator) & Tenesa, A. (Co-investigator)
BBSRC
1/07/12 → 31/08/13
Project: Research
Statistical and computational methods for population stratification and prediction of cancer risk
Tenesa, A. (Principal Investigator)
UK-based charities
1/10/11 → 31/05/15
Project: Research

Cite this

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title = "Regional Heritability Advanced Complex Trait Analysis for GPU and Traditional Parallel Architectures",

abstract = "Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of SNPs and individuals. To meet the challenges in making feasible large scale studies, we present the REACTA software. Adapted from ACTA (and, in turn, GCTA), it is tailored to exploit the parallelism present in modern traditional and GPU-accelerated machines, from workstations to supercomputers.",

author = "L Cebamanos and Alan Gray and I Stewart and A Tenesa",

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AB - Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of SNPs and individuals. To meet the challenges in making feasible large scale studies, we present the REACTA software. Adapted from ACTA (and, in turn, GCTA), it is tailored to exploit the parallelism present in modern traditional and GPU-accelerated machines, from workstations to supercomputers.

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DO - 10.1093/bioinformatics/btt754

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JO - Bioinformatics

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Regional Heritability Advanced Complex Trait Analysis for GPU and Traditional Parallel Architectures

Abstract

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Projects

Development of gpgpu tools for modelling complex phenotypes

Statistical and computational methods for population stratification and prediction of cancer risk

Cite this