A multimetric approach to analysis of genome-wide association by single markers and composite likelihood.

A multimetric approach to analysis of genome-wide association by single markers and composite likelihood. Research Abstract Details 

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A multimetric approach to analysis of genome-wide association by single markers and composite likelihood. Abstract Text:

Jane Gibson,William Tapper,David Cox,Weihua Zhang,Arne Pfeufer,Christian Gieger,H-Erich Wichmann,Stefan Kääb,Andrew R Collins,Thomas Meitinger,Newton Morton,

Two case/control studies with different phenotypes, marker densities, and microarrays were examined for the most significant single markers in defined regions. They show a pronounced bias toward exaggerated significance that increases with the number of observed markers and would increase further with imputed markers. This bias is eliminated by Bonferroni adjustment, thereby allowing combination by principal component analysis with a Malecot model composite likelihood evaluated by a permutation procedure to allow for multiple dependent markers. This intermediate value identifies the only demonstrated causal locus as most significant even in the preliminary analysis and clearly recognizes the strongest candidate in the other sample. Because the three metrics (most significant single marker, composite likelihood, and their principal component) are correlated, choice of the n smallest P values by each test gives <3n regions for follow-up in the next stage. In this way, methods with different response to marker selection and density are given approximately equal weight and economically compared, without expressing an untested prejudice or sacrificing the most significant results for any of them. Large numbers of cases, controls, and markers are by themselves insufficient to control type 1 and 2 errors, and so efficient use of multiple metrics with Bonferroni adjustment promises to be valuable in identifying causal variants and optimal design simultaneously.

A multimetric approach to analysis of genome-wide association by single markers and composite likelihood. Publishing Authors By Initials

J Gibson,W Tapper,D Cox,W Zhang,A Pfeufer,C Gieger,HE Wichmann,S Kääb,AR Collins,T Meitinger,N Morton,

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A multimetric approach to analysis of genome-wide association by single markers and composite likelihood. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Proceedings of the National Academy of Sciences of

VOLUME: 105

Page Numbers: 2592-7

Journal Abbreviation: Proc. Natl. Acad. Sci. U.S.A.

ISSN: 1091-6490

DAY: 11

MONTH: 02

YEAR: 2008

A multimetric approach to analysis of genome-wide association by single markers and composite likelihood. Information

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LANGUAGE: eng

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Grant and Affiliation Information for A multimetric approach to analysis of genome-wide association by single markers and composite likelihood.

AFFILIATION: Human Genetics Division, School of Medicine, University of Southampton, Southampton SO16 6YD, United Kingdom.

Country: United States

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MEDLINETA: Proc Natl Acad Sci U S A

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