GMMAT

Summary

GMMAT (Generalized linear Mixed Model Association Test) is a mixed-model GWAS method that controls for population structure and sample relatedness via a genetic relationship matrix (GRM) random effect, fit with a penalized quasi-likelihood approach and tested with a score test. It was originally developed for binary (case/control) traits, for which standard linear mixed models can be miscalibrated, and is used as a building block by newer ancestry-aware methods such as Tractor-Mix, which reuses GMMAT's null-model-fitting machinery to jointly model relatedness and local ancestry.

Citations

[1] Chen, H. et al. (2016). Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. American Journal of Human Genetics, 98(4), 653–666. DOI: 10.1016/j.ajhg.2016.02.012. Not independently inspected in full; description above draws on its publicly available abstract plus [2].

[2] Tan, T. et al. (2025). Extending Genome-Wide Association Studies to admixed cohorts with high degrees of relatedness. medRxiv preprint. DOI: 10.1101/2025.05.27.25328444. Source: 2025.05.27.25328444v1.full.md. Supports: GMMAT's role as the mixed-model basis reused by Tractor-Mix.