GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations

Mixed model-based approaches to genome-wide association studies (GWAS) of binary traits in related individuals can account for non-genetic risk factors in an integrated manner. However, they are technically challenging. GLOGS (Genome-wide LOGistic mixed model/Score test) addresses such challenges wi...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2012-06, Vol.28 (11), p.1553-1554
Main Authors: STANHOPE, Stephen A, ABNEY, Mark
Format: Article
Language:English
Subjects:
Applications Note
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
General aspects
Genome-Wide Association Study - methods
Humans
Logistic Models
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Phenotype
Risk
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Summary:Mixed model-based approaches to genome-wide association studies (GWAS) of binary traits in related individuals can account for non-genetic risk factors in an integrated manner. However, they are technically challenging. GLOGS (Genome-wide LOGistic mixed model/Score test) addresses such challenges with efficient statistical procedures and a parallel implementation. GLOGS has high power relative to alternative approaches as risk covariate effects increase, and can complete a GWAS in minutes. Source code and documentation are provided at http://www.bioinformatics.org/~stanhope/GLOGS.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/bts190