Multiple quantitative trait analysis using Bayesian networks

Marco Scutari, Phil Howell, David J Balding, Ian Mackay

Research output: Contribution to journalArticleResearchpeer-review

28 Citations (Scopus)

Abstract

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.

Original languageEnglish
Pages (from-to)129-137
Number of pages9
JournalGenetics
Volume198
Issue number1
DOIs
Publication statusPrint publication - Sep 2014
Externally publishedYes

Fingerprint

Bayes Theorem
Bayesian theory
quantitative traits
Bayesian Networks
Best Linear Unbiased Prediction
Population
Genome-Wide Association Study
prediction
Winter Wheat
Elastic Net
Population Structure
Sample Size
Triticum
Confounding
Predictive Model
pleiotropy
animal genetics
Modeling
Phenotype
genetic traits

Keywords

  • Bayes Theorem
  • Plant genome
  • Genetic models
  • Quantitative trait
  • Triticum genetics

Cite this

Scutari, Marco ; Howell, Phil ; Balding, David J ; Mackay, Ian. / Multiple quantitative trait analysis using Bayesian networks. In: Genetics. 2014 ; Vol. 198, No. 1. pp. 129-137.
@article{b8eef0300d9d4f13830dbf20af059023,
title = "Multiple quantitative trait analysis using Bayesian networks",
abstract = "Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.",
keywords = "Bayes Theorem, Plant genome, Genetic models, Quantitative trait, Triticum genetics",
author = "Marco Scutari and Phil Howell and Balding, {David J} and Ian Mackay",
year = "2014",
month = "9",
doi = "10.1534/genetics.114.165704",
language = "English",
volume = "198",
pages = "129--137",
journal = "Genetics",
issn = "0016-6731",
publisher = "Genetics Society of America",
number = "1",

}

Multiple quantitative trait analysis using Bayesian networks. / Scutari, Marco; Howell, Phil; Balding, David J; Mackay, Ian.

In: Genetics, Vol. 198, No. 1, 09.2014, p. 129-137.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Multiple quantitative trait analysis using Bayesian networks

AU - Scutari, Marco

AU - Howell, Phil

AU - Balding, David J

AU - Mackay, Ian

PY - 2014/9

Y1 - 2014/9

N2 - Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.

AB - Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.

KW - Bayes Theorem

KW - Plant genome

KW - Genetic models

KW - Quantitative trait

KW - Triticum genetics

U2 - 10.1534/genetics.114.165704

DO - 10.1534/genetics.114.165704

M3 - Article

VL - 198

SP - 129

EP - 137

JO - Genetics

JF - Genetics

SN - 0016-6731

IS - 1

ER -