Efficient multivariate linear mixed model algorithms for genome-wide association studies
Multivariate linear mixed models implemented in the GEMMA software package add speed, power and the ability to test for genome-wide associations between genetic polymorphisms and multiple correlated phenotypes. Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations bet...
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| Published in | Nature methods Vol. 11; no. 4; pp. 407 - 409 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.04.2014
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Multivariate linear mixed models implemented in the GEMMA software package add speed, power and the ability to test for genome-wide associations between genetic polymorphisms and multiple correlated phenotypes.
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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| AbstractList | 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. Multivariate linear mixed models implemented in the GEMMA software package add speed, power and the ability to test for genome-wide associations between genetic polymorphisms and multiple correlated phenotypes. 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. 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.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. |
| Author | Zhou, Xiang Stephens, Matthew |
| Author_xml | – sequence: 1 givenname: Xiang surname: Zhou fullname: Zhou, Xiang email: xz7@uchicago.edu organization: Department of Human Genetics, University of Chicago, Department of Statistics, University of Chicago – sequence: 2 givenname: Matthew surname: Stephens fullname: Stephens, Matthew email: mstephens@uchicago.edu organization: Department of Human Genetics, University of Chicago, Department of Statistics, University of Chicago |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24531419$$D View this record in MEDLINE/PubMed |
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| Snippet | Multivariate linear mixed models implemented in the GEMMA software package add speed, power and the ability to test for genome-wide associations between... Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated... |
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| SubjectTerms | 45/43 49/23 631/114/2415 631/208/480 Algorithms Bioinformatics Biological Microscopy Biological Techniques Biomedical Engineering/Biotechnology brief-communication Calibration Genome Genomes Life Sciences Likelihood Functions Linear Models Mathematical models Multivariate Analysis Polymorphism, Single Nucleotide - genetics Proteomics Software |
| Title | Efficient multivariate linear mixed model algorithms for genome-wide association studies |
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