ArticleFrontiers in genetics2015
A re-formulation of generalized linear mixed models to fit family data in genetic association studies.
Article in Frontiers in genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Transforming Growth Factor-β1 Decreases βAmerican journal of respiratory cell and molecular biology · 2019Article
- An Efficient Test for Gene-Environment Interaction in Generalized Linear Mixed Models with Family Data.International journal of environmental research and public health · 2017Article
- Estimating genetic effect sizes under joint disease-endophenotype models in presence of gene-environment interactions.Frontiers in genetics · 2015Article
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Authors and funding
6 authors.
Funding
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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