Advanced Complex Trait Analysis

Alan Gray; I Stewart; A Tenesa

doi:10.1093/bioinformatics/bts571

Advanced Complex Trait Analysis

Alan Gray, I Stewart, A Tenesa

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

Abstract

MOTIVATION: The Genome-wide Complex Trait Analysis (GCTA) software package can quantify the contribution of genetic variation to phenotypic variation for complex traits. However, as those data sets of interest continue to increase in size, GCTA becomes increasingly computationally prohibitive. We present an adapted version, Advanced Complex Trait Analysis (ACTA), demonstrating dramatically improved performance. RESULTS: We restructure the GRM estimation phase of the code and introduce the highly optimized parallel BLAS library combined with manual parallelization and optimization. We introduce the LAPACK library into the REML analysis stage. For a testcase with 8999 individuals and 279,435 SNPs we reduce the total runtime, using a compute node with 2 multi-core Intel Nehalem CPUs, from around 17 hours to around 11 minutes. AVAILABILITY: The source code is fully available under the GNU Public License, along with Linux binaries. For more information see http://www.epcc.ed.ac.uk/software-products/acta. CONTACT: a.gray@ed.ac.uk.

Original language	English
Pages (from-to)	3134-3136
Journal	Bioinformatics
Volume	28
Issue number	23
Early online date	27 Sep 2012
DOIs	https://doi.org/10.1093/bioinformatics/bts571
Publication status	Published - 2012

Access to Document

10.1093/bioinformatics/bts571

http://bioinformatics.oxfordjournals.org/content/early/2012/09/27/bioinformatics.bts571

2 Finished

ISP1: Analysis and prediction in complex animal systems
Tenesa, A., Archibald, A., Beard, P., Bishop, S., Bronsvoort, M., Burt, D., Freeman, T., Haley, C., Hocking, P., Houston, R., Hume, D., Joshi, A., Law, A., Michoel, T., Summers, K., Vernimmen, D., Watson, M., Wiener, P., Wilson, A., Woolliams, J., Ait-Ali, T., Barnett, M., Carlisle, A., Finlayson, H., Haga, I., Karavolos, M., Matika, O., Paterson, T., Paton, B., Pong-Wong, R., Robert, C. & Robertson, G.
BBSRC
1/04/12 → 31/03/17
Project: Research
Statistical and computational methods for population stratification and prediction of cancer risk
Tenesa, A.
UK-based charities
1/10/11 → 31/05/15
Project: Research

Cite this

@article{43064c57e1c543848ec080e2ada0545e,

title = "Advanced Complex Trait Analysis",

abstract = "MOTIVATION: The Genome-wide Complex Trait Analysis (GCTA) software package can quantify the contribution of genetic variation to phenotypic variation for complex traits. However, as those data sets of interest continue to increase in size, GCTA becomes increasingly computationally prohibitive. We present an adapted version, Advanced Complex Trait Analysis (ACTA), demonstrating dramatically improved performance. RESULTS: We restructure the GRM estimation phase of the code and introduce the highly optimized parallel BLAS library combined with manual parallelization and optimization. We introduce the LAPACK library into the REML analysis stage. For a testcase with 8999 individuals and 279,435 SNPs we reduce the total runtime, using a compute node with 2 multi-core Intel Nehalem CPUs, from around 17 hours to around 11 minutes. AVAILABILITY: The source code is fully available under the GNU Public License, along with Linux binaries. For more information see http://www.epcc.ed.ac.uk/software-products/acta. CONTACT: a.gray@ed.ac.uk.",

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

year = "2012",

doi = "10.1093/bioinformatics/bts571",

language = "English",

volume = "28",

pages = "3134--3136",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "23",

}

TY - JOUR

T1 - Advanced Complex Trait Analysis

AU - Gray, Alan

AU - Stewart, I

AU - Tenesa, A

PY - 2012

Y1 - 2012

N2 - MOTIVATION: The Genome-wide Complex Trait Analysis (GCTA) software package can quantify the contribution of genetic variation to phenotypic variation for complex traits. However, as those data sets of interest continue to increase in size, GCTA becomes increasingly computationally prohibitive. We present an adapted version, Advanced Complex Trait Analysis (ACTA), demonstrating dramatically improved performance. RESULTS: We restructure the GRM estimation phase of the code and introduce the highly optimized parallel BLAS library combined with manual parallelization and optimization. We introduce the LAPACK library into the REML analysis stage. For a testcase with 8999 individuals and 279,435 SNPs we reduce the total runtime, using a compute node with 2 multi-core Intel Nehalem CPUs, from around 17 hours to around 11 minutes. AVAILABILITY: The source code is fully available under the GNU Public License, along with Linux binaries. For more information see http://www.epcc.ed.ac.uk/software-products/acta. CONTACT: a.gray@ed.ac.uk.

AB - MOTIVATION: The Genome-wide Complex Trait Analysis (GCTA) software package can quantify the contribution of genetic variation to phenotypic variation for complex traits. However, as those data sets of interest continue to increase in size, GCTA becomes increasingly computationally prohibitive. We present an adapted version, Advanced Complex Trait Analysis (ACTA), demonstrating dramatically improved performance. RESULTS: We restructure the GRM estimation phase of the code and introduce the highly optimized parallel BLAS library combined with manual parallelization and optimization. We introduce the LAPACK library into the REML analysis stage. For a testcase with 8999 individuals and 279,435 SNPs we reduce the total runtime, using a compute node with 2 multi-core Intel Nehalem CPUs, from around 17 hours to around 11 minutes. AVAILABILITY: The source code is fully available under the GNU Public License, along with Linux binaries. For more information see http://www.epcc.ed.ac.uk/software-products/acta. CONTACT: a.gray@ed.ac.uk.

U2 - 10.1093/bioinformatics/bts571

DO - 10.1093/bioinformatics/bts571

M3 - Article

C2 - 23023980

VL - 28

SP - 3134

EP - 3136

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 23

ER -

Advanced Complex Trait Analysis

Abstract

Access to Document

Fingerprint

Projects

ISP1: Analysis and prediction in complex animal systems

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

Cite this