RT Journal Article
SR Electronic
T1 LIMIX: genetic analysis of multiple traits
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 003905
DO 10.1101/003905
A1 Christoph Lippert
A1 Franceso Paolo Casale
A1 Barbara Rakitsch
A1 Oliver Stegle
YR 2014
UL http://biorxiv.org/content/early/2014/05/21/003905.abstract
AB Multi-trait mixed models have emerged as a promising approach for joint analyses of multiple traits. In principle, the mixed model framework is remarkably general. However, current methods implement only a very specific range of tasks to optimize the necessary computations. Here, we present a multi-trait modeling framework that is versatile and fast: LIMIX enables to flexibly adapt mixed models for a broad range of applications with different observed and hidden covariates, and variable study designs. To highlight the novel modeling aspects of LIMIX we performed three vastly different genetic studies: joint GWAS of correlated blood lipid phenotypes, joint analysis of the expression levels of the multiple transcript-isoforms of a gene, and pathway-based modeling of molecular traits across environments. In these applications we show that LIMIX increases GWAS power and phenotype prediction accuracy, in particular when integrating stepwise multi-locus regression into multi-trait models, and when analyzing large numbers of traits. An open source implementation of LIMIX is freely available at: https://github.com/PMBio/limix.