title: 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.
authors: 1] Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. [2] Department of Statistics, University of Chicago, Chicago, Illinois, USA.
authors: Xiang
authors: X
authors: Zhou
authors: 1] Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. [2] Department of Statistics, University of Chicago, Chicago, Illinois, USA.
authors: Matthew
authors: M
authors: Stephens
mesh_terms: D000465:Algorithms; D016678:Genome; D016013:Likelihood Functions; D016014:Linear Models; D015999:Multivariate Analysis; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012984:Software