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A re-formulation of generalized linear mixed models to fit family data in genetic association studies.

ABSTRACT: 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).

SUBMITTER: Wang T

PROVIDER: S-EPMC4379931 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A re-formulation of generalized linear mixed models to fit family data in genetic association studies.

Wang Tao T He Peng P Ahn Kwang Woo KW Wang Xujing X Ghosh Soumitra S Laud Purushottam P

Frontiers in genetics 20150331

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 ...[more]

PMID: 25873936

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