Genetics. Published Articles Ahead of Print: July 29, 2007, Copyright © 2007
doi:10.1534/genetics.107.071407


A more recent version of this article appeared on October 1, 2007.

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A Statistical Framework for Expression Quantitative Trait Loci (eQTL) Mapping

Meng Chen 1 and Christina Kendziorski 2*

1 Pfizer
2 Univeristy of Wisconsin

* To whom correspondence should be addressed. E-mail: kendzior{at}biostat.wisc.edu.

Submitted on January 31, 2007
Revised on March 15, 2007
Accepted on 21 July 2007


Abstract

In 2001, Sen and Churchill reported a general 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 mouse.

Key Words: expression quantitative trait loci (eQTL) mapping, gene expression, microarray, quantitative trait loci (QTL) mapping