A re-formulation of generalized linear mixed models to fit family data in genetic association studies - Wang, Tao; He, Peng; Ahn, Kwang Woo; Wang, Xujing; Ghosh, Soumitra; Laud, Purushottam (Frontiers in Genetics, 2015)
The generalized linear mixed model (GLMM) is a useful tool for modeling genetic correlation among family data in genetic association studies. However, when dealing with families of varied sizes and diverse genetic relatedness, the GLMM has a special correlation structure which often makes it difficult to be specified using standard statistical software. In this study, we propose a Cholesky decomposition based re-formulation of the GLMM so that the re-formulated GLMM can be specified conveniently via `proc nlmixed' and `proc glimmix' in SAS, or OpenBUGS via R package BRugs. Performances of these procedures in fitting the re-formulated GLMM are examined through simulation studies. We also apply this re-formulated GLMM to analyze a real data set from Type 1 Diabetes Genetics Consortium (T1DGC).