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. Available here.


An R package for testing gene-environment interaction for rare genetic variants. Use interaction tests to test interaction effects only, and use the joint test to test genetic association allowing for effect modification by the environmental variable. See paper for details. Available on CRAN.


An R function for testing genetic association with quantitative traits using the sequence kernel association test (SKAT) in family samples. See paper for details. See the code here.


An R package for performing multivariate meta-analysis using the fixed-effects model and the random-effects model. Between-study covariance matrix in the random-effects model is estimated using a multivariate extension of DerSimonian and Laird’s estimator for between-study variance (method of moments). See paper for details. Available on CRAN.