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 sm

SUBMITTER: Cheng R 

PROVIDER: S-EPMC5345711 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature