Extended bayesian model averaging for heritability in twin studies

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Family studies are often conducted to examine the existence of familial aggregation. Particularly, twin studies can model separately the genetic and environmental contribution. Here we estimate the heritability of quantitative traits via variance components of random-effects in linear mixed models (LMMs). The motivating example was a myopia twin study containing complex nesting data structures: twins and siblings in the same family and observations on both eyes for each individual. Three models are considered for this nesting structure. Our proposal takes into account the model uncertainty in both covariates and model structures via an extended Bayesian model averaging (EBMA) procedure. We estimate the heritability using EBMA under three suggested model structures. When compared with the results under the model with the highest posterior model probability, the EBMA estimate has smaller variation and is slightly conservative. Simulation studies are conducted to evaluate the performance of variance-components estimates, as well as the selections of risk factors, under the correct or incorrect structure. The results indicate that EBMA, with consideration of uncertainties in both covariates and model structures, is robust in model misspecification than the usual Bayesian model averaging (BMA) that considers only uncertainty in covariates selection.

Original languageEnglish
Pages (from-to)1043-1058
Number of pages16
JournalJournal of Applied Statistics
Volume37
Issue number6
DOIs
Publication statusPublished - 2010 Jun 1

Fingerprint

Bayesian Model Averaging
Heritability
Covariates
Components of Variance
Estimate
Model
Uncertainty
Linear Mixed Model
Model Misspecification
Probability Model
Model Uncertainty
Risk Factors
Random Effects
Bayesian model averaging
Aggregation
Data Structures
Simulation Study
Evaluate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

@article{033291a81fd54945a8b53dc862b13358,
title = "Extended bayesian model averaging for heritability in twin studies",
abstract = "Family studies are often conducted to examine the existence of familial aggregation. Particularly, twin studies can model separately the genetic and environmental contribution. Here we estimate the heritability of quantitative traits via variance components of random-effects in linear mixed models (LMMs). The motivating example was a myopia twin study containing complex nesting data structures: twins and siblings in the same family and observations on both eyes for each individual. Three models are considered for this nesting structure. Our proposal takes into account the model uncertainty in both covariates and model structures via an extended Bayesian model averaging (EBMA) procedure. We estimate the heritability using EBMA under three suggested model structures. When compared with the results under the model with the highest posterior model probability, the EBMA estimate has smaller variation and is slightly conservative. Simulation studies are conducted to evaluate the performance of variance-components estimates, as well as the selections of risk factors, under the correct or incorrect structure. The results indicate that EBMA, with consideration of uncertainties in both covariates and model structures, is robust in model misspecification than the usual Bayesian model averaging (BMA) that considers only uncertainty in covariates selection.",
author = "Miao-Yu Tsai",
year = "2010",
month = "6",
day = "1",
doi = "10.1080/02664760903093625",
language = "English",
volume = "37",
pages = "1043--1058",
journal = "Journal of Applied Statistics",
issn = "0266-4763",
publisher = "Routledge",
number = "6",

}

Extended bayesian model averaging for heritability in twin studies. / Tsai, Miao-Yu.

In: Journal of Applied Statistics, Vol. 37, No. 6, 01.06.2010, p. 1043-1058.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Extended bayesian model averaging for heritability in twin studies

AU - Tsai, Miao-Yu

PY - 2010/6/1

Y1 - 2010/6/1

N2 - Family studies are often conducted to examine the existence of familial aggregation. Particularly, twin studies can model separately the genetic and environmental contribution. Here we estimate the heritability of quantitative traits via variance components of random-effects in linear mixed models (LMMs). The motivating example was a myopia twin study containing complex nesting data structures: twins and siblings in the same family and observations on both eyes for each individual. Three models are considered for this nesting structure. Our proposal takes into account the model uncertainty in both covariates and model structures via an extended Bayesian model averaging (EBMA) procedure. We estimate the heritability using EBMA under three suggested model structures. When compared with the results under the model with the highest posterior model probability, the EBMA estimate has smaller variation and is slightly conservative. Simulation studies are conducted to evaluate the performance of variance-components estimates, as well as the selections of risk factors, under the correct or incorrect structure. The results indicate that EBMA, with consideration of uncertainties in both covariates and model structures, is robust in model misspecification than the usual Bayesian model averaging (BMA) that considers only uncertainty in covariates selection.

AB - Family studies are often conducted to examine the existence of familial aggregation. Particularly, twin studies can model separately the genetic and environmental contribution. Here we estimate the heritability of quantitative traits via variance components of random-effects in linear mixed models (LMMs). The motivating example was a myopia twin study containing complex nesting data structures: twins and siblings in the same family and observations on both eyes for each individual. Three models are considered for this nesting structure. Our proposal takes into account the model uncertainty in both covariates and model structures via an extended Bayesian model averaging (EBMA) procedure. We estimate the heritability using EBMA under three suggested model structures. When compared with the results under the model with the highest posterior model probability, the EBMA estimate has smaller variation and is slightly conservative. Simulation studies are conducted to evaluate the performance of variance-components estimates, as well as the selections of risk factors, under the correct or incorrect structure. The results indicate that EBMA, with consideration of uncertainties in both covariates and model structures, is robust in model misspecification than the usual Bayesian model averaging (BMA) that considers only uncertainty in covariates selection.

UR - http://www.scopus.com/inward/record.url?scp=77952376678&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77952376678&partnerID=8YFLogxK

U2 - 10.1080/02664760903093625

DO - 10.1080/02664760903093625

M3 - Article

AN - SCOPUS:77952376678

VL - 37

SP - 1043

EP - 1058

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

SN - 0266-4763

IS - 6

ER -