Selecting informative traits for multivariate quantitative trait locus mapping helps to gain optimal power.

ABSTRACT: A major consideration in multitrait analysis is which traits should be jointly analyzed. As a common strategy, multitrait analysis is performed either on pairs of traits or on all of traits. To fully exploit the power of multitrait analysis, we propose variable selection to choose a subset of informative traits for multitrait quantitative trait locus (QTL) mapping. The proposed method is very useful for achieving optimal statistical power for QTL identification and for disclosing the most relevant traits. It is also a practical strategy to effectively take advantage of multitrait analysis when the number of traits under consideration is too large, making the usual multivariate analysis of all traits challenging. We study the impact of selection bias and the usage of permutation tests in th

SUBMITTER: Cheng R 

PROVIDER: S-EPMC3813856 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature