Using canonical correlation analysis to discover genetic regulatory variants.
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.


RESULTS
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
Our approach reduces multiple comparisons and may provide insight into the complex relationships between genotype and gene expression.
Naylor
Melissa G
MG
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America. mnaylor@hsph.harvard.edu
Lin
Xihong
X
Weiss
Scott T
ST
Raby
Benjamin A
BA
Lange
Christoph
C
D001249:Asthma / Q000235:genetics; D002648:Child; D003198:Computer Simulation; D053263:Gene Regulatory Networks / Q000235:genetics*; D056726:Genetic Association Studies; D006801:Humans; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D013223:Statistics as Topic