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Extended Bayesian Model Averaging in Generalized Linear Mixed Models Applied to Schizophrenia Family Data
Author(s) -
Tsai MiaoYu,
Hsiao Chuhsing K.,
Chen Wei J.
Publication year - 2011
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2010.00592.x
Subject(s) - covariate , generalized linear mixed model , heritability , schizophrenia (object oriented programming) , mixed model , linear model , generalized linear model , bayesian probability , random effects model , family aggregation , permutation (music) , bayesian inference , mathematics , econometrics , statistics , computer science , genetics , disease , biology , medicine , meta analysis , physics , pathology , acoustics , programming language
Summary The study of disease etiology and the search for susceptible genes of schizophrenia have attracted scientists’ attention for decades. Many findings however are inconsistent, possibly due to the higher order interactions involving multi‐dimensional genetic and environmental factors or due to the commingling of different ethnic groups. Several studies applied generalized linear mixed models (GLMMs) with family data to identify the genetic contribution to, and environmental influence on, schizophrenia, and to clarify the existence and sources of familial aggregation. Based on an extended Bayesian model averaging (EBMA) procedure, here we estimate the gene‐gene (GG) and gene‐environment (GE) interactions, and heritability of schizophrenia via variance components of random‐effects in GLMMs. Our proposal takes into account the uncertainty in covariates and in genetic model structures, where each competing model includes environmental and genetic covariates, and GE and GG interactions. Simulation studies are conducted to compare the performance of the EBMA approach, permutation test procedure and GEE method. We also illustrate this approach with data from singleton and multiplex schizophrenia families. The results indicate that EBMA is a flexible and stable tool in exploring true candidate genes, and GE and GG interactions, after adjusting for explanatory variables and correlation structures within family members.