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

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Abstract

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. © The Author 2012. Published by Oxford University Press. All rights reserved.

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Stanhope, S. A., & Abney, M. (2012). GLOGS: A fast and powerful method for GWAS of binary traits with risk covariates in related populations. Bioinformatics, 28(11), 1553–1554. https://doi.org/10.1093/bioinformatics/bts190

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