Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model.

ABSTRACT: Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation

SUBMITTER: Furlotte NA 

PROVIDER: S-EPMC4423381 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature