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- Title
Efficient multivariate linear mixed model algorithms for genome-wide association studies.
- Authors
Zhou, Xiang; Stephens, Matthew
- 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.
- Subjects
GENETIC polymorphism research; MULTIVARIATE analysis; POPULATION dynamics; GENETIC algorithms; GENOTYPE-environment interaction; HUMAN biology
- Publication
Nature Methods, 2014, Vol 11, Issue 4, p407
- ISSN
1548-7091
- Publication type
Article
- DOI
10.1038/nmeth.2848