GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations.
other · Level V
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- Record sourced from PubMed, PMID 22522135.
- Also identified by DOI 10.1093/bioinformatics/bts190 and PMC identifier 3356846.
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Abstract
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.
Medical subject headings
- Genome-Wide Association Study
- Logistic Models
- Models, Genetic