ArticleHeredity2013
Bayesian methods for estimating GEBVs of threshold traits.
Article in Heredity, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- High-Resolution Genomic Resources for Trait Mapping and Precision Breeding for Adzuki Bean (Vigna angularis).Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Decoding hybrid origins and genetic architecture of leaf traits variation inHorticulture research · 2025Article
- Bayesian divergence-based approach for genomic multitrait ordinal selection.G3 (Bethesda, Md.) · 2025Article
- Consequences of ignoring dominance genetic effects from genomic selection model for discrete threshold traits.Scientific reports · 2025Article
- The statistical power of genome-wide association studies for threshold traits with different frequencies of causal variants.Genetica · 2022Article
- Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification.Frontiers in genetics · 2022Article
- Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- The theory on and software simulating large-scale genomic data for genotype-by-environment interactions.BMC genomics · 2021Article
- Validation of single-step GBLUP genomic predictions from threshold models using the linear regression method: An application in chicken mortality.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2021Article
- Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data.The plant genome · 2020Article
- Genomic Prediction of Pumpkin Hybrid Performance.The plant genome · 2019Article
- Bayesian methods for jointly estimating genomic breeding values of one continuous and one threshold trait.PloS one · 2017Article
- Article
- Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.G3 (Bethesda, Md.) · 2015Article
- GPOPSIM: a simulation tool for whole-genome genetic data.BMC genetics · 2015Article
- Accuracy and responses of genomic selection on key traits in apple breeding.Horticulture research · 2015Article
- Threshold models for genome-enabled prediction of ordinal categorical traits in plant breeding.G3 (Bethesda, Md.) · 2014Article
- Genome-enabled predictions for binomial traits in sugar beet populations.BMC genetics · 2014Article
- Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data.G3 (Bethesda, Md.) · 2013Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimation of genomic breeding values is the key step in genomic selection (GS). Many methods have been proposed for continuous traits, but methods for threshold traits are still scarce. Here we introduced threshold model to the framework of GS, and specifically, we extended the three Bayesian methods BayesA, BayesB and BayesCπ on the basis of threshold model for estimating genomic breeding values of threshold traits, and the extended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computing procedures of the three BayesT methods using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the benefit of the presented methods in accuracy with the genomic estimated breeding values (GEBVs) for threshold traits. Factors affecting the performance of the three BayesT methods were addressed. As expected, the three BayesT methods generally performed better than the corresponding normal Bayesian methods, in particular when the number of phenotypic categories was small. In the standard scenario (number of categories=2, incidence=30%, number of quantitative trait loci=50, h² = 0.3), the accuracies were improved by 30.4%, 2.4%, and 5.7% points, respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies and both performed better than BayesTA. In conclusion, our work proved that threshold model fits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be the method of choice for GS of threshold traits.
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