AUTHOR=Wang Tao , He Peng , Ahn Kwang Woo , Wang Xujing , Ghosh Soumitra , Laud Purushottam 

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

JOURNAL=Frontiers in Genetics

VOLUME=6

YEAR=2015

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2015.00120

DOI=10.3389/fgene.2015.00120

ISSN=1664-8021

ABSTRACT=<p>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).</p>