title: 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).
authors: Division of Biostatistics, Medical College of Wisconsin Milwaukee, WI, USA.
authors: Tao
authors: T
authors: Wang
authors: Division of Biostatistics, Medical College of Wisconsin Milwaukee, WI, USA ; Global Biostatistical Science, Amgen Inc. Thousand Oaks, CA, USA.
authors: Peng
authors: P
authors: He
authors: Division of Biostatistics, Medical College of Wisconsin Milwaukee, WI, USA.
authors: Kwang Woo
authors: KW
authors: Ahn
authors: Bioinformatics and Systems Biology Core, National Heart, Lung, and Blood Institute Bethesda, MD, USA.
authors: Xujing
authors: X
authors: Wang
authors: Department of Genetics, Quantitative Sciences, GlaxoSmithKline King of Prussia, PA, USA.
authors: Soumitra
authors: S
authors: Ghosh
authors: Division of Biostatistics, Medical College of Wisconsin Milwaukee, WI, USA.
authors: Purushottam
authors: P
authors: Laud
keywords: Bayesian methods; Cholesky decomposition; family data; generalized linear mixed models (GLMM); genetic correlation; genetic variance components; random genetic effects; re-parameterization