title: GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  Source code and documentation are provided at http://www.bioinformatics.org/~stanhope/GLOGS.
authors.affiliation: Department of Human Genetics, University of Chicago, 920 E. 58th St., Chicago, IL 60637, USA. sstanhop@bsd.uchicago.edu
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Stanhope
authors.forename: Mark
authors.initials: M
authors.lastname: Abney
mesh_terms: D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D010641:Phenotype; D012306:Risk