ArticleG3 (Bethesda, Md.)2015
Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.
Article in G3 (Bethesda, Md.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Integrating genomic prediction into crop DUS testing: new approaches in support of reference collection management and distinctness assessment.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Bayesian divergence-based approach for genomic multitrait ordinal selection.G3 (Bethesda, Md.) · 2025Article
- A semi-parametric Bayesian model for semi-continuous longitudinal data.Statistics in medicine · 2022Article
- Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (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
- Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification.Frontiers in genetics · 2022Article
- Application of Genomic Big Data in Plant Breeding:Past, Present, and Future.Plants (Basel, Switzerland) · 2020Review
- Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data.The plant genome · 2020Article
- Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry.GigaScience · 2020Article
- An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction.G3 (Bethesda, Md.) · 2019Article
- A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.G3 (Bethesda, Md.) · 2019Article
- Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction.G3 (Bethesda, Md.) · 2016Article
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Authors and funding
5 authors.
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Abstract
Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.
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