title: Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping.
abstract: Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations andArabidopsis thalianatranscript data.
authors.affiliation: Research School of Biology, The Australian National University, Acton, Australian Capital Territory 2601, Australia, ARC Center of Excellence in Plant Energy Biology, The Australian National University, Acton, ACT 2601, Australia riyan.cheng@anu.edu.au.
authors.forename: Riyan
authors.initials: R
authors.lastname: Cheng
authors.affiliation: Department of Statistics, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213.
authors.forename: R W
authors.initials: RW
authors.lastname: Doerge
authors.affiliation: Research School of Biology, The Australian National University, Acton, Australian Capital Territory 2601, Australia, ARC Center of Excellence in Plant Energy Biology, The Australian National University, Acton, ACT 2601, Australia.
authors.forename: Justin
authors.initials: J
authors.lastname: Borevitz
mesh_terms: D002874:Chromosome Mapping; D003198:Computer Simulation; D005819:Genetic Markers; D008957:Models, Genetic; D040641:Quantitative Trait Loci / Q000235:genetics; D019655:Quantitative Trait, Heritable; D013223:Statistics as Topic
chemical_list: D005819:Genetic Markers
keywords: multiple-trait mapping; quantitative trait locus (QTL); resampling; statistical power