Bayesian mixture structural equation modelling in multiple-trait QTL mapping

Xiaojuan Mi, Kent Eskridge, Dong Wang, P. Stephen Baenziger, B. Todd Campbell, Kulvinder S. Gill, Ismail Dweikat

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Quantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

Original languageEnglish (US)
Pages (from-to)239-250
Number of pages12
JournalGenetics Research
Volume92
Issue number3
DOIs
StatePublished - Jun 1 2010

Fingerprint

Quantitative Trait Loci
Monte Carlo Method
Markov Chains
Bayes Theorem
Structural Models
Triticum
Research Personnel
Genes

ASJC Scopus subject areas

  • Genetics

Cite this

Mi, X., Eskridge, K., Wang, D., Baenziger, P. S., Campbell, B. T., Gill, K. S., & Dweikat, I. (2010). Bayesian mixture structural equation modelling in multiple-trait QTL mapping. Genetics Research, 92(3), 239-250. https://doi.org/10.1017/S0016672310000236

Bayesian mixture structural equation modelling in multiple-trait QTL mapping. / Mi, Xiaojuan; Eskridge, Kent; Wang, Dong; Baenziger, P. Stephen; Campbell, B. Todd; Gill, Kulvinder S.; Dweikat, Ismail.

In: Genetics Research, Vol. 92, No. 3, 01.06.2010, p. 239-250.

Research output: Contribution to journalArticle

Mi, X, Eskridge, K, Wang, D, Baenziger, PS, Campbell, BT, Gill, KS & Dweikat, I 2010, 'Bayesian mixture structural equation modelling in multiple-trait QTL mapping', Genetics Research, vol. 92, no. 3, pp. 239-250. https://doi.org/10.1017/S0016672310000236
Mi, Xiaojuan ; Eskridge, Kent ; Wang, Dong ; Baenziger, P. Stephen ; Campbell, B. Todd ; Gill, Kulvinder S. ; Dweikat, Ismail. / Bayesian mixture structural equation modelling in multiple-trait QTL mapping. In: Genetics Research. 2010 ; Vol. 92, No. 3. pp. 239-250.
@article{c4bc1d0df912485098fb870a5d7a0aa4,
title = "Bayesian mixture structural equation modelling in multiple-trait QTL mapping",
abstract = "Quantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.",
author = "Xiaojuan Mi and Kent Eskridge and Dong Wang and Baenziger, {P. Stephen} and Campbell, {B. Todd} and Gill, {Kulvinder S.} and Ismail Dweikat",
year = "2010",
month = "6",
day = "1",
doi = "10.1017/S0016672310000236",
language = "English (US)",
volume = "92",
pages = "239--250",
journal = "Genetics Research",
issn = "0016-6723",
publisher = "Cambridge University Press",
number = "3",

}

TY - JOUR

T1 - Bayesian mixture structural equation modelling in multiple-trait QTL mapping

AU - Mi, Xiaojuan

AU - Eskridge, Kent

AU - Wang, Dong

AU - Baenziger, P. Stephen

AU - Campbell, B. Todd

AU - Gill, Kulvinder S.

AU - Dweikat, Ismail

PY - 2010/6/1

Y1 - 2010/6/1

N2 - Quantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

AB - Quantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

UR - http://www.scopus.com/inward/record.url?scp=77958472026&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77958472026&partnerID=8YFLogxK

U2 - 10.1017/S0016672310000236

DO - 10.1017/S0016672310000236

M3 - Article

C2 - 20667167

AN - SCOPUS:77958472026

VL - 92

SP - 239

EP - 250

JO - Genetics Research

JF - Genetics Research

SN - 0016-6723

IS - 3

ER -