- THIS ARTICLE
- Full Text
- Full Text (PDF)
- Data Supplement
-
All Versions of this Article:
genetics.107.071407v1
177/2/761 most recent - Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Email this article to a friend
- Related articles in Genetics
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- CITING ARTICLES
- Citing Articles via HighWire
- Citing Articles via Google Scholar
- GOOGLE SCHOLAR
- Articles by Chen, M.
- Articles by Kendziorski, C.
- Search for Related Content
- PUBMED
- PubMed Citation
- Articles by Chen, M.
- Articles by Kendziorski, C.
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
A Statistical Framework for Expression Quantitative Trait Loci Mapping
Meng Chen* and
Christina Kendziorski
,1
* Pfizer Global Research and Development, Groton, Connecticut 06340 and 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.
Related articles in Genetics:
ISSUE HIGHLIGHTS
Genetics 2007 177: NP.
This article has been cited by other articles:
 |
|
 |
|
  H. Chun and S. Keles Expression Quantitative Trait Loci Mapping With Multivariate Sparse Partial Least Squares Regression Genetics, May 1, 2009; 182(1): 79 - 90. [Abstract] [Full Text] [PDF]
|
|
|