Medical College of Wisconsin
CTSICores SearchResearch InformaticsREDCap

A re-formulation of generalized linear mixed models to fit family data in genetic association studies. Front Genet 2015;6:120 PMID: 25873936 PMCID: PMC4379931

Pubmed ID

25873936

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).

Author List

Wang T, He P, Ahn KW, Wang X, Ghosh S, Laud P

Authors

Kwang Woo Ahn PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Purushottam W. Laud PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Tao Wang PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin




jenkins-FCD Prod-299 9ef562391eceb2b8f95265c767fbba1ce5a52fd6