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Article: Adjusting for Covariates in Variance Components QTL Linkage Analysis

TitleAdjusting for Covariates in Variance Components QTL Linkage Analysis
Authors
Issue Date2004
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0001-8244
Citation
Behavior Genetics, 2004, v. 34 n. 2, p. 127-133 How to Cite?
AbstractVariance components modeling has emerged as a powerful method for quantitative trait loci (QTL) linkage analysis. However, the power to detect a gene of minor effect is low. Many complex traits are affected by environmental as well as genetic factors, and one strategy to increase power is to reduce nongenetic phenotypic variance by adjusting for environmental covariates. In this paper, we investigate the power of three approaches to covariate adjustment in variance components linkage analysis: (i) incorporate covariates in the means model, (ii) incorporate covariates in the covariance matrix, and (iii) perform analysis on residual statistics. These approaches are compared to an analysis without adjustment. The results show that in the absence of correlation between the covariate and the QTL effect, adjusting for covariates indeed increases power to detect an underlying QTL. As this correlation increases, however, the power decreases. In the presence of a causal association between QTL and covariates, not adjusting for covariates appeared to be more powerful. The three approaches for adjusting for covariate: residual statistics, the means model, and the covariance model, had equal power to detect a QTL.
Persistent Identifierhttp://hdl.handle.net/10722/175906
ISSN
2015 Impact Factor: 3.268
2015 SCImago Journal Rankings: 1.457
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZeegers, Men_US
dc.contributor.authorRijsdijk, Fen_US
dc.contributor.authorSham, Pen_US
dc.date.accessioned2012-11-26T09:02:22Z-
dc.date.available2012-11-26T09:02:22Z-
dc.date.issued2004en_US
dc.identifier.citationBehavior Genetics, 2004, v. 34 n. 2, p. 127-133en_US
dc.identifier.issn0001-8244en_US
dc.identifier.urihttp://hdl.handle.net/10722/175906-
dc.description.abstractVariance components modeling has emerged as a powerful method for quantitative trait loci (QTL) linkage analysis. However, the power to detect a gene of minor effect is low. Many complex traits are affected by environmental as well as genetic factors, and one strategy to increase power is to reduce nongenetic phenotypic variance by adjusting for environmental covariates. In this paper, we investigate the power of three approaches to covariate adjustment in variance components linkage analysis: (i) incorporate covariates in the means model, (ii) incorporate covariates in the covariance matrix, and (iii) perform analysis on residual statistics. These approaches are compared to an analysis without adjustment. The results show that in the absence of correlation between the covariate and the QTL effect, adjusting for covariates indeed increases power to detect an underlying QTL. As this correlation increases, however, the power decreases. In the presence of a causal association between QTL and covariates, not adjusting for covariates appeared to be more powerful. The three approaches for adjusting for covariate: residual statistics, the means model, and the covariance model, had equal power to detect a QTL.en_US
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0001-8244en_US
dc.relation.ispartofBehavior Geneticsen_US
dc.subject.meshAnalysis Of Varianceen_US
dc.subject.meshChromosome Mapping - Statistics & Numerical Dataen_US
dc.subject.meshHumansen_US
dc.subject.meshMathematical Computingen_US
dc.subject.meshModels, Geneticen_US
dc.subject.meshModels, Statisticalen_US
dc.subject.meshPersonality - Geneticsen_US
dc.subject.meshQuantitative Trait Loci - Geneticsen_US
dc.subject.meshSiblingsen_US
dc.subject.meshSocial Environmenten_US
dc.titleAdjusting for Covariates in Variance Components QTL Linkage Analysisen_US
dc.typeArticleen_US
dc.identifier.emailSham, P: pcsham@hku.hken_US
dc.identifier.authoritySham, P=rp00459en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1023/B:BEGE.0000013726.65708.c2en_US
dc.identifier.pmid14755177-
dc.identifier.scopuseid_2-s2.0-1442283620en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1442283620&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume34en_US
dc.identifier.issue2en_US
dc.identifier.spage127en_US
dc.identifier.epage133en_US
dc.identifier.isiWOS:000189303800002-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridZeegers, M=7003691618en_US
dc.identifier.scopusauthoridRijsdijk, F=6701830835en_US
dc.identifier.scopusauthoridSham, P=34573429300en_US

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