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

ABSTRACT: 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.

SUBMITTER: Zhou X

PROVIDER: S-EPMC4211878 | biostudies-other | 2014 Apr

REPOSITORIES: biostudies-other

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

Zhou Xiang X Stephens Matthew M

Nature methods 20140216 4

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 m ...[more]

PMID: 24531419

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