A model selection approach for expression quantitative trait loci (eQTL) mapping.
Identifying the genetic basis of complex traits remains an important and challenging problem with the potential to affect a broad range of biological endeavors. A number of statistical methods are available for mapping quantitative trait loci (QTL), but their application to high-throughput phenotypes has been limited as most require user input and interaction. Recently, methods have been developed specifically for expression QTL (eQTL) mapping, but they too are limited in that they do not allow for interactions and QTL of moderate effect. We here propose an automated model-selection-based approach that identifies multiple eQTL in experimental populations, allowing for eQTL of moderate effect and interactions. Output can be used to identify groups of transcripts that are likely coregulated, as demonstrated in a study of diabetes in mouse.
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53726, USA.
Ping
P
Wang
John A
JA
Dawson
Mark P
MP
Keller
Brian S
BS
Yandell
Nancy A
NA
Thornberry
Bei B
BB
Zhang
I-Ming
IM
Wang
Eric E
EE
Schadt
Alan D
AD
Attie
C
C
Kendziorski
D000818:Animals; D002874:Chromosome Mapping; D019295:Computational Biology; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation / Q000235:genetics; D051379:Mice; D008810:Mice, Inbred C57BL; D008957:Models, Genetic; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics*