Using canonical correlation analysis to discover genetic regulatory variants

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...

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Bibliographic Details
Published in:PloS one 2010-05, Vol.5 (5), p.e10395-e10395
Main Authors: Naylor, Melissa G, Lin, Xihong, Weiss, Scott T, Raby, Benjamin A, Lange, Christoph
Format: Article
Language:English
Subjects:
Analysis
Association analysis
Asthma
Asthma - genetics
Brain
Child
Childhood asthma
Children
Computer Simulation
Correlation
Correlation analysis
Deoxyribonucleic acid
DNA
DNA damage
Eigen values
Eigenvalues
Gene expression
Gene regulation
Gene Regulatory Networks - genetics
Genes
Genetic aspects
Genetic Association Studies
Genetic markers
Genetics and Genomics
Genetics and Genomics/Gene Expression
Genetics and Genomics/Genomics
Genomes
Haplotypes
Heritability
Humans
Laboratories
Linkage analysis
Models, Statistical
Nuclear electric power generation
Polymorphism, Single Nucleotide - genetics
Public health
Regression analysis
Single nucleotide polymorphisms
Single-nucleotide polymorphism
Statistical analysis
Statistics as Topic
Womens health
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Description
Summary: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. 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. Our approach reduces multiple comparisons and may provide insight into the complex relationships between genotype and gene expression.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0010395