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

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 mouse. The quantitative trait loci (QTL) mapping framework developed by Sen and Churchill (2001), referred to hereinafter as the Sen-Churchill framework, unifies many methods for QTL mapping in inbred line crosses. The seminal work of Lander and Botstein (1989) and subsequent methods including Haley-Knott regression (1992), composite interval mapping and multiple QTL mapping (Jansen, 1993; Jansen and Stam, 1994; Zeng, 1993, 1994), are all represented, at least approximately, as special cases of the framework. The framework also accounts for covariates, nonstandard cross designs, missing genotype data, genotyping errors, multiple interacting QTL, and nonnormal as well as multivariate phenotypes. As a result, it provides a powerful approach to localize the genetic basis of quantitative traits. There has been much interest recently in identifying the genetic basis of thousands of gene expression traits measured via microarrays (Brem et al., 2002; Schadt et al., 2003;