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Updated: May 23, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations
Stephen A Stanhope1, Mark Abney
1Department of Human Genetics, University of Chicago, 920 E. 58th St., Chicago, IL 60637, USA. sstanhop@bsd.uchicago.edu
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.
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