TY - JOUR T1 - LIMIX: genetic analysis of multiple traits JF - bioRxiv DO - 10.1101/003905 SP - 003905 AU - Christoph Lippert AU - Franceso Paolo Casale AU - Barbara Rakitsch AU - Oliver Stegle Y1 - 2014/01/01 UR - http://biorxiv.org/content/early/2014/05/21/003905.abstract N2 - 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. ER -