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dc.contributor.authorNaylor, Melissa G.
dc.contributor.authorLin, Xihong
dc.contributor.authorWeiss, Scott Tillman
dc.contributor.authorRaby, Benjamin Alexander
dc.contributor.authorLange, Christoph
dc.date.accessioned2012-01-08T21:37:42Z
dc.date.issued2010
dc.identifier.citationNaylor, Melissa G., Xihong Lin, Scott T. Weiss, Benjamin A. Raby, and Christoph Lange. 2010. Using canonical correlation analysis to discover genetic regulatory variants. PLoS ONE 5(5): e10395.en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:7349717
dc.description.abstractBackground: Discovering genetic associations between genetic markers and gene expression levels can provide insight into gene regulation and, potentially, mechanisms of disease. Such analyses typically involve a linkage or association analysis in which expression data are used as phenotypes. This approach leads to a large number of multiple comparisons and may therefore lack power. We assess the potential of applying canonical correlation analysis to partitioned genomewide data as a method for discovering regulatory variants. Methodology/Principal Findings: Simulations suggest that canonical correlation analysis has higher power than standard pairwise univariate regression to detect single nucleotide polymorphisms when the expression trait has low heritability. The increase in power is even greater under the recessive model. We demonstrate this approach using the Childhood Asthma Management Program data. Conclusions/Significance: Our approach reduces multiple comparisons and may provide insight into the complex relationships between genotype and gene expression.en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofdoi://10.1371/journal.pone.0010395en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2869348/pdf/en_US
dash.licenseLAA
dc.subjectgenetics and genomicsen_US
dc.subjectgene expressionen_US
dc.subjectgenomicsen_US
dc.titleUsing Canonical Correlation Analysis to Discover Genetic Regulatory Variantsen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalPLoS ONEen_US
dash.depositing.authorLin, Xihong
dc.date.available2012-01-08T21:37:42Z
dash.affiliation.otherSPH^Biostatisticsen_US
dash.affiliation.otherHMS^Medicine-Brigham and Women's Hospitalen_US
dash.affiliation.otherSPH^Biostatisticsen_US
dc.identifier.doi10.1371/journal.pone.0010395*
dash.contributor.affiliatedRaby, Benjamin
dash.contributor.affiliatedWeiss, Scott
dash.contributor.affiliatedLange, Christoph
dash.contributor.affiliatedLin, Xihong


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