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Your search keyword '"Re-parameterization"' showing total 2 results
Start Over Descriptor "Re-parameterization" Journal frontiers in genetics
2 results on '"Re-parameterization"'

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

Author

Tao eWang, Peng eHe, Kwang Woo eAhn, Xujing eWang, Soumitra eGhosh, and Purushottam eLaud

Subjects
genetic correlation, family data, genetic variance components, Cholesky decomposition, Re-parameterization, Bayesian methods., Genetics, QH426-470
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).

Published
2015
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2. A re-formulation of generalized linear mixed models to fit family data in genetic association studies

Author

Kwang Woo Ahn, Xujing Wang, Tao Wang, Soumitra Ghosh, Purushottam W. Laud, and Peng He

Subjects
genetic variance components, Bayesian methods, generalized linear mixed models (GLMM), lcsh:QH426-470, Computer science, Bayesian probability, family data, computer.software_genre, 01 natural sciences, Genetic correlation, Generalized linear mixed model, Correlation, re-parameterization, 010104 statistics & probability, 03 medical and health sciences, Genetics, 0101 mathematics, Genetics (clinical), random genetic effects, 030304 developmental biology, Genetic association, Original Research, 0303 health sciences, genetic correlation, Data set, R package, lcsh:Genetics, Molecular Medicine, Data mining, computer, Cholesky decomposition
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).

Published
2014

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