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Efficient multivariate linear mixed model algorithms for genome-wide association studies

2014; Nature Portfolio; Volume: 11; Issue: 4 Linguagem: Inglês

10.1038/nmeth.2848

1548-7105

Xiang Zhou, Matthew Stephens,

Genetics and Plant Breeding

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.