General Information
| Description | A fast and powerful method for GWAS of binary traits with risk covariates in related populations |
| Homepage | http://www.bioinformatics.org/~stanhope/GLOGS |
Publications
GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations
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.
Availability:
Source code and documentation are provided at http://www.bioinformatics.org/~stanhope/GLOGS.
Contact:
sstanhop@bsd.uchicago.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
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