Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies

Multivariate linear mixed models (mvLMMs) are powerful tools for testing SNP associations with multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present computationally-efficient algorithms for fitting mvLMMs and computing likelihood ratio tests that improve on existing approximate methods in i) computation speed, ii) power/p value calibration, iii) ability to deal with more than two phenotypes. We illustrate these features on real and simulated data.