Efficient multivariate linear mixed model algorithms for genome-wide association studies - Zhou, Xiang; Stephens, Matthew (Nature methods, 2014)
Multivariate linear mixed models implemented in the GEMMA software package add speed, power and the ability to test for genome-wide associations between genetic polymorphisms and multiple correlated phenotypes. Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P -value calibration over existing methods, and can deal with more than two phenotypes.