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Nat Methods. 2014 Apr;11(4):407-9. doi: 10.1038/nmeth.2848. Epub 2014 Feb 16.

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

Author information

1
1] Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. [2] Department of Statistics, University of Chicago, Chicago, Illinois, USA.

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.

PMID:
24531419
PMCID:
PMC4211878
DOI:
10.1038/nmeth.2848
[Indexed for MEDLINE]
Free PMC Article

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