Extended Bayesian model averaging in generalized linear mixed models applied to schizophrenia family data

Miao-Yu Tsai, Chuhsing K. Hsiao, Wei J. Chen

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)62-77
Number of pages16
JournalAnnals of Human Genetics
Volume75
Issue number1
DOIs
Publication statusPublished - 2011 Jan 1

Fingerprint

Linear Models
Schizophrenia
Genes
Gene-Environment Interaction
Genetic Structures
Genetic Models
Ethnic Groups
Uncertainty

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

Cite this

@article{deac13e0e5a54f659a14f4d0f2e7d2dd,
title = "Extended Bayesian model averaging in generalized linear mixed models applied to schizophrenia family data",
abstract = "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.",
author = "Miao-Yu Tsai and Hsiao, {Chuhsing K.} and Chen, {Wei J.}",
year = "2011",
month = "1",
day = "1",
doi = "10.1111/j.1469-1809.2010.00592.x",
language = "English",
volume = "75",
pages = "62--77",
journal = "Annals of Human Genetics",
issn = "0003-4800",
publisher = "Wiley-Blackwell",
number = "1",

}

Extended Bayesian model averaging in generalized linear mixed models applied to schizophrenia family data. / Tsai, Miao-Yu; Hsiao, Chuhsing K.; Chen, Wei J.

In: Annals of Human Genetics, Vol. 75, No. 1, 01.01.2011, p. 62-77.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Extended Bayesian model averaging in generalized linear mixed models applied to schizophrenia family data

AU - Tsai, Miao-Yu

AU - Hsiao, Chuhsing K.

AU - Chen, Wei J.

PY - 2011/1/1

Y1 - 2011/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78650166818&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650166818&partnerID=8YFLogxK

U2 - 10.1111/j.1469-1809.2010.00592.x

DO - 10.1111/j.1469-1809.2010.00592.x

M3 - Article

VL - 75

SP - 62

EP - 77

JO - Annals of Human Genetics

JF - Annals of Human Genetics

SN - 0003-4800

IS - 1

ER -