ArticleGenetics2014
Multiple quantitative trait analysis using bayesian networks.
Article in Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed.
- A novel constraint-based structure learning algorithm using marginal causal prior knowledge.Scientific reports · 2024Article
- Integration of selective sweeps across the sheep genome: understanding the relationship between production and adaptation traits.Genetics, selection, evolution : GSE · 2024Article
- Wheat EARLY FLOWERING 3 affects heading date without disrupting circadian oscillations.Plant physiology · 2023Article
- Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions.Nature communications · 2022Article
- Crafting for a better MAGIC: systematic design and test for Multiparental Advanced Generation Inter-Cross population.G3 (Bethesda, Md.) · 2021Article
- Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks.G3 (Bethesda, Md.) · 2021Article
- Application of Bayesian networks to the prediction of the AMEn: a new methodology in broiler nutrition.Translational animal science · 2021Article
- Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding.Heredity · 2020Review
- TEOSINTE BRANCHED1 regulates height and stem internode length in bread wheat.Journal of experimental botany · 2020Article
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- Exploring genetic architecture of grain yield and quality traits in a 16-way indica by japonica rice MAGIC global population.Scientific reports · 2019Article
- Causal phenotypic networks for egg traits in an FMolecular genetics and genomics : MGG · 2019Article
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- High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering.Frontiers in genetics · 2019Article
- Single and multiple phenotype QTL analyses of downy mildew resistance in interspecific grapevines.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
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- Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (G3 (Bethesda, Md.) · 2017Article
- Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.Heredity · 2017Article
- Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.BioMed research international · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.