GMMAT (Generalized Linear Mixed Model Association Test)

Update (10/29/2018)

There is an updated version of GMMAT here. This code is no longer being maintained. Please use the updated version.

Description

GMMAT is an R package for performing genetic association tests for outcomes with distribution in the exponential family (e.g. binary outcomes) based on the generalized linear mixed model. It can be used to analyze genetic data from individuals with population structure and relatedness. GMMAT fits a generalized linear mixed model under the null hypothesis of no genetic association, and then performs a score test for each individual genetic variant. See user manual here. A light version of GMMAT which does not depend on the C++ library boost is available here (it cannot take .gz and .bz2 compressed genotype files).

References

• Breslow NE and Clayton DG. (1993) Approximate Inference in Generalized Linear Mixed Models. Journal of the American Statistical Association 88: 9-25. doi:10.1080/01621459.1993.10594284.
• Chen H, Wang C, Conomos MP, Stilp AM, Li Z, Sofer T, Szpiro AA, Chen W, Brehm JM, Celedon JC, Redline S, Papanicolaou GJ, Thornton TA, Laurie CC, Rice K and Lin X. (2016) Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies Using Logistic Mixed Models. The American Journal of Human Genetics, 98(4): 653-666. doi:10.1016/j.ajhg.2016.02.012.

License

This software is licensed under GPL-3.