CAPL: an efficient association software package using family and case-control data and accounting for population stratification

<p>Abstract</p> <p>Background</p> <p>With many genome-wide association study (GWAS) datasets available, it is critical that we have statistical tools that are both flexible to accommodate different study designs and fast. We recently proposed the combined APL (CAPL) met...

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Main Authors: Martin Eden R, Schmidt Michael A, Chung Ren-Hua
Format: Article
Language:English
Published: BMC 2011-05-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/12/201
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spelling doaj-c7892cef8122433f970160bc0fce49032020-11-24T23:28:39ZengBMCBMC Bioinformatics1471-21052011-05-0112120110.1186/1471-2105-12-201CAPL: an efficient association software package using family and case-control data and accounting for population stratificationMartin Eden RSchmidt Michael AChung Ren-Hua<p>Abstract</p> <p>Background</p> <p>With many genome-wide association study (GWAS) datasets available, it is critical that we have statistical tools that are both flexible to accommodate different study designs and fast. We recently proposed the combined APL (CAPL) method, which can use family and case-control datasets and can account for population stratification in the data. Because computationally intensive algorithms are used in CAPL, implementing CAPL with efficient parallel algorithms is essential.</p> <p>Results</p> <p>We used a hybrid of open message passing interface (open MPI) and POSIX threads to parallelize CAPL, which enable the program to operate in a cluster environment. We used simulations to demonstrate that the parallel implementation of CAPL can analyze a large GWAS dataset in a reasonable time frame when a parallel computing resource is available.</p> <p>Conclusions</p> <p>As many GWAS datasets based on both family and case-control designs are available, a flexible and efficient tool such as CAPL will be very helpful to combine the datasets to greatly increase statistical power and finish the analysis in a reasonable time frame.</p> http://www.biomedcentral.com/1471-2105/12/201
collection DOAJ
language English
format Article
sources DOAJ
author Martin Eden R
Schmidt Michael A
Chung Ren-Hua
spellingShingle Martin Eden R
Schmidt Michael A
Chung Ren-Hua
CAPL: an efficient association software package using family and case-control data and accounting for population stratification
BMC Bioinformatics
author_facet Martin Eden R
Schmidt Michael A
Chung Ren-Hua
author_sort Martin Eden R
title CAPL: an efficient association software package using family and case-control data and accounting for population stratification
title_short CAPL: an efficient association software package using family and case-control data and accounting for population stratification
title_full CAPL: an efficient association software package using family and case-control data and accounting for population stratification
title_fullStr CAPL: an efficient association software package using family and case-control data and accounting for population stratification
title_full_unstemmed CAPL: an efficient association software package using family and case-control data and accounting for population stratification
title_sort capl: an efficient association software package using family and case-control data and accounting for population stratification
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2011-05-01
description <p>Abstract</p> <p>Background</p> <p>With many genome-wide association study (GWAS) datasets available, it is critical that we have statistical tools that are both flexible to accommodate different study designs and fast. We recently proposed the combined APL (CAPL) method, which can use family and case-control datasets and can account for population stratification in the data. Because computationally intensive algorithms are used in CAPL, implementing CAPL with efficient parallel algorithms is essential.</p> <p>Results</p> <p>We used a hybrid of open message passing interface (open MPI) and POSIX threads to parallelize CAPL, which enable the program to operate in a cluster environment. We used simulations to demonstrate that the parallel implementation of CAPL can analyze a large GWAS dataset in a reasonable time frame when a parallel computing resource is available.</p> <p>Conclusions</p> <p>As many GWAS datasets based on both family and case-control designs are available, a flexible and efficient tool such as CAPL will be very helpful to combine the datasets to greatly increase statistical power and finish the analysis in a reasonable time frame.</p>
url http://www.biomedcentral.com/1471-2105/12/201
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