TY - JOUR
T1 - Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping
JF - G3: Genes|Genomes|Genetics
SP - 813
LP - 822
DO - 10.1534/g3.116.037531
VL - 7
IS - 3
AU - Cheng, Riyan
AU - Doerge, R. W.
AU - Borevitz, Justin
Y1 - 2017/03/01
UR - http://www.g3journal.org/content/7/3/813.abstract
N2 - 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 and Arabidopsis thaliana transcript data.
ER -