ArticlePloS one2016
Inclusion of Dominance Effects in the Multivariate GBLUP Model.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Genomic prediction of agronomic traits in a switchgrass (Panicum virgatum L.) half-sib progeny panel evaluated across multiple environments.The plant genome · 2026Article
- Evaluating effect of different dominance genotype encodings on genome-wide association studies and genomic selection.Animal bioscience · 2025Article
- Comparative Study of Single-Trait and Multi-Trait Genomic Prediction Models.Animals : an open access journal from MDPI · 2024Article
- Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Scientific reports · 2024Article
- Univariate and multivariate genomic prediction for agronomic traits in durum wheat under two field conditions.PloS one · 2024Article
- Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Analysis of genetic dominance in the UK Biobank.Science (New York, N.Y.) · 2023Article
- Leveraging probability concepts for cultivar recommendation in multi-environment trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.Frontiers in genetics · 2022Article
- Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (G3 (Bethesda, Md.) · 2020Article
- Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.G3 (Bethesda, Md.) · 2020Article
- Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Article
- Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar.Frontiers in plant science · 2020Article
- Modeling copy number variation in the genomic prediction of maize hybrids.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis.Frontiers in plant science · 2019Article
- Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
- Article
- Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components.
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