Greater power and computational efficiency for kernel-based association testing of sets of genetic variants

Motivation: Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compar...

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Main Authors:	Lippert, Christoph, Xiang, Jing, Horta, Danilo, Widmer, Christian, Kadie, Carl, Heckerman, David, Listgarten, Jennifer
Format:	Online
Language:	English
Published:	Oxford University Press 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221116/

id	pubmed-4221116
recordtype	oai_dc
spelling	pubmed-42211162014-11-10 Greater power and computational efficiency for kernel-based association testing of sets of genetic variants Lippert, Christoph Xiang, Jing Horta, Danilo Widmer, Christian Kadie, Carl Heckerman, David Listgarten, Jennifer Original Papers Motivation: Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test—a score test—with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene–gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods. Oxford University Press 2014-11-15 2014-07-29 /pmc/articles/PMC4221116/ /pubmed/25075117 http://dx.doi.org/10.1093/bioinformatics/btu504 Text en © The Author 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Lippert, Christoph Xiang, Jing Horta, Danilo Widmer, Christian Kadie, Carl Heckerman, David Listgarten, Jennifer
spellingShingle	Lippert, Christoph Xiang, Jing Horta, Danilo Widmer, Christian Kadie, Carl Heckerman, David Listgarten, Jennifer Greater power and computational efficiency for kernel-based association testing of sets of genetic variants
author_facet	Lippert, Christoph Xiang, Jing Horta, Danilo Widmer, Christian Kadie, Carl Heckerman, David Listgarten, Jennifer
author_sort	Lippert, Christoph
title	Greater power and computational efficiency for kernel-based association testing of sets of genetic variants
title_short	Greater power and computational efficiency for kernel-based association testing of sets of genetic variants
title_full	Greater power and computational efficiency for kernel-based association testing of sets of genetic variants
title_fullStr	Greater power and computational efficiency for kernel-based association testing of sets of genetic variants
title_full_unstemmed	Greater power and computational efficiency for kernel-based association testing of sets of genetic variants
title_sort	greater power and computational efficiency for kernel-based association testing of sets of genetic variants
description	Motivation: Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test—a score test—with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene–gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods.
publisher	Oxford University Press
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221116/
_version_	1613152609468678144

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants

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