Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants

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Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants

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Title: Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants
Author: Naylor, Melissa G.; Lin, Xihong; Weiss, Scott Tillman; Raby, Benjamin Alexander; Lange, Christoph

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Citation: Naylor, 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.
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Abstract: Background: 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.
Published Version: doi://10.1371/journal.pone.0010395
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2869348/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:7349717

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