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Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)

Relationships among traits were investigated on the genomic and residual levels using novel methodology. This included inference on these relationships via Bayesian networks and an assessment of the networks with structural equation models. The methodology employed three steps. First, a Bayesian mul...

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Autores principales: Töpner, Katrin, Rosa, Guilherme J. M., Gianola, Daniel, Schön, Chris-Carolin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5555481/
https://www.ncbi.nlm.nih.gov/pubmed/28637811
http://dx.doi.org/10.1534/g3.117.044263
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author Töpner, Katrin
Rosa, Guilherme J. M.
Gianola, Daniel
Schön, Chris-Carolin
author_facet Töpner, Katrin
Rosa, Guilherme J. M.
Gianola, Daniel
Schön, Chris-Carolin
author_sort Töpner, Katrin
collection PubMed
description Relationships among traits were investigated on the genomic and residual levels using novel methodology. This included inference on these relationships via Bayesian networks and an assessment of the networks with structural equation models. The methodology employed three steps. First, a Bayesian multiple-trait Gaussian model was fitted to the data to decompose phenotypic values into their genomic and residual components. Second, genomic and residual network structures among traits were learned from estimates of these two components. Network learning was performed using six different algorithmic settings for comparison, of which two were score-based and four were constraint-based approaches. Third, structural equation model analyses ranked the networks in terms of goodness of fit and predictive ability, and compared them with the standard multiple-trait fully recursive network. The methodology was applied to experimental data representing the European heterotic maize pools Dent and Flint (Zea mays L.). Inferences on genomic and residual trait connections were depicted separately as directed acyclic graphs. These graphs provide information beyond mere pairwise genetic or residual associations between traits, illustrating for example conditional independencies and hinting at potential causal links among traits. Network analysis suggested some genetic correlations as potentially spurious. Genomic and residual networks were compared between Dent and Flint.
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spelling pubmed-55554812017-08-17 Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.) Töpner, Katrin Rosa, Guilherme J. M. Gianola, Daniel Schön, Chris-Carolin G3 (Bethesda) Investigations Relationships among traits were investigated on the genomic and residual levels using novel methodology. This included inference on these relationships via Bayesian networks and an assessment of the networks with structural equation models. The methodology employed three steps. First, a Bayesian multiple-trait Gaussian model was fitted to the data to decompose phenotypic values into their genomic and residual components. Second, genomic and residual network structures among traits were learned from estimates of these two components. Network learning was performed using six different algorithmic settings for comparison, of which two were score-based and four were constraint-based approaches. Third, structural equation model analyses ranked the networks in terms of goodness of fit and predictive ability, and compared them with the standard multiple-trait fully recursive network. The methodology was applied to experimental data representing the European heterotic maize pools Dent and Flint (Zea mays L.). Inferences on genomic and residual trait connections were depicted separately as directed acyclic graphs. These graphs provide information beyond mere pairwise genetic or residual associations between traits, illustrating for example conditional independencies and hinting at potential causal links among traits. Network analysis suggested some genetic correlations as potentially spurious. Genomic and residual networks were compared between Dent and Flint. Genetics Society of America 2017-06-21 /pmc/articles/PMC5555481/ /pubmed/28637811 http://dx.doi.org/10.1534/g3.117.044263 Text en Copyright © 2017 Töpner et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Investigations
Töpner, Katrin
Rosa, Guilherme J. M.
Gianola, Daniel
Schön, Chris-Carolin
Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)
title Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)
title_full Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)
title_fullStr Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)
title_full_unstemmed Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)
title_short Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)
title_sort bayesian networks illustrate genomic and residual trait connections in maize (zea mays l.)
topic Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5555481/
https://www.ncbi.nlm.nih.gov/pubmed/28637811
http://dx.doi.org/10.1534/g3.117.044263
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