期刊
NATURE METHODS
卷 11, 期 4, 页码 407-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.2848
关键词
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资金
- US National Institutes of Health (NIH) [HL092206]
- NIH [HG02585]
- National Heart, Lung, and Blood Institute (NHLBI)
- Broad Institute, University of California Los Angeles
- University of Oulu
- National Institute for Health and Welfare in Finland
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.
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