ArticleG3 (Bethesda, Md.)2017
Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (
Article in G3 (Bethesda, Md.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Bayesian networks and structural equation models reveal genetic causal relationships between productivity, defense, and climate-adaptability traits in interior lodgepole pine.G3 (Bethesda, Md.) · 2026Article
- Using Structural Equation Models to Interpret Genome-Wide Association Studies for Morphological and Productive Traits in Soybean [Plants (Basel, Switzerland) · 2025Article
- Multi-trait phenotypic modeling through factor analysis and bayesian network learning to develop latent reproductive, body conformational, and carcass-associated traits in admixed beef heifers.Frontiers in genetics · 2025Article
- Dissecting the effect of heat stress on durum wheat under field conditions.Frontiers in plant science · 2024Article
- Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions.Nature communications · 2022Article
- Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning.Plant direct · 2021Article
- Application of Bayesian networks to the prediction of the AMEn: a new methodology in broiler nutrition.Translational animal science · 2021Article
- Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes.Frontiers in genetics · 2021Review
- Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis.European heart journal supplements : journal of the European Society of Cardiology · 2020Article
- Article
- Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes.G3 (Bethesda, Md.) · 2019Article
- Carbon isotope composition, water use efficiency, and drought sensitivity are controlled by a common genomic segment in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies.Plant methods · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
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