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Originally published as Genetics Published Articles Ahead of Print on July 29, 2007.

Genetics, Vol. 177, 761-771, October 2007, Copyright © 2007
doi:10.1534/genetics.107.071407

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A Statistical Framework for Expression Quantitative Trait Loci Mapping

Meng Chen* and Christina Kendziorski{dagger},1

* Pfizer Global Research and Development, Groton, Connecticut 06340 and {dagger} Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706

1 Corresponding author: Department of Biostatistics and Medical Informatics, 6729 Medical Sciences Center, 1300 University Ave., Madison, WI 53706.
E-mail: kendzior{at}biostat.wisc.edu

In 2001, Sen and Churchill reported a general Bayesian framework for quantitative trait loci (QTL) mapping in inbred line crosses. The framework is a powerful one, as many QTL mapping methods can be represented as special cases and many important considerations are accommodated. These considerations include accounting for covariates, nonstandard crosses, missing genotypes, genotyping errors, multiple interacting QTL, and nonnormal as well as multivariate phenotypes. The dimension of a multivariate phenotype easily handled within the framework is bounded by the number of subjects, as a full-rank covariance matrix describing correlations across the phenotypes is required. We address this limitation and extend the Sen–Churchill framework to accommodate expression quantitative trait loci (eQTL) mapping studies, where high-dimensional gene-expression phenotypes are obtained via microarrays. Doing so allows for the precise comparison of existing eQTL mapping approaches and facilitates the development of an eQTL interval-mapping approach that shares information across transcripts and improves localization of eQTL. Evaluations are based on simulation studies and a study of diabetes in mice.


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