Title |
A re-formulation of generalized linear mixed models to fit family data in genetic association studies
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Published in |
Frontiers in Genetics, March 2015
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DOI | 10.3389/fgene.2015.00120 |
Pubmed ID | |
Authors |
Tao Wang, Peng He, Kwang Woo Ahn, Xujing Wang, Soumitra Ghosh, Purushottam Laud |
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). |
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