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Originally published as Genetics Published Articles Ahead of Print on April 3, 2007.
Genetics, Vol. 176, 675-683, May 2007, Copyright © 2007
doi:10.1534/genetics.106.066241
Genomewide Association Analysis in Diverse Inbred Mice: Power and Population Structure
Phillip McClurg*,1,
Jeff Janes*,1,
Chunlei Wu*,
David L. Delano*,
John R. Walker*,
Serge Batalov*,
Joseph S. Takahashi
,
,
Kazuhiro Shimomura,,
Akira Kohsaka,
,
Joseph Bass,,**,
Tim Wiltshire* and
Andrew I. Su*,2
* Genomics Institute of the Novartis Research Foundation, San Diego, California 92121, Department of Neurobiology and Physiology, Howard Hughes Medical Institute, Northwestern University, Evanston, Illinois 60208, Evanston Northwestern Healthcare Research Institute, Evanston, Illinois 60208 and ** Department of Medicine, Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208
2 Corresponding author: Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Dr., San Diego, CA 92121.
E-mail: asu{at}gnf.org
The discovery of quantitative trait loci (QTL) in model organisms has relied heavily on the ability to perform controlled breeding to generate genotypic and phenotypic diversity. Recently, we and others have demonstrated the use of an existing set of diverse inbred mice (referred to here as the mouse diversity panel, MDP) as a QTL mapping population. The use of the MDP population has many advantages relative to traditional F2 mapping populations, including increased phenotypic diversity, a higher recombination frequency, and the ability to collect genotype and phenotype data in community databases. However, these methods are complicated by population structure inherent in the MDP and the lack of an analytical framework to assess statistical power. To address these issues, we measured gene expression levels in hypothalamus across the MDP. We then mapped these phenotypes as quantitative traits with our association algorithm, resulting in a large set of expression QTL (eQTL). We utilized these eQTL, and specifically cis-eQTL, to develop a novel nonparametric method for association analysis in structured populations like the MDP. These eQTL data confirmed that the MDP is a suitable mapping population for QTL discovery and that eQTL results can serve as a gold standard for relative measures of statistical power.
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Genetics 2007 176: NP.
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