ArticleG3 (Bethesda, Md.)2014
Threshold models for genome-enabled prediction of ordinal categorical traits in plant breeding.
Article in G3 (Bethesda, Md.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Methods for joint genetic prediction of multiple ordinal categorical and continuous traits.Genetics · 2026Article
- Classification-based genomic prediction for early identification of high-yielding and stable soybean genotypes.Frontiers in plant science · 2026Article
- Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance toHorticulture research · 2024Article
- Integrating Parental Phenotypic Data Enhances Prediction Accuracy of Hybrids in Wheat Traits.Genes · 2023Article
- Genome-wide analysis of genetic diversity in a germplasm collection including wild relatives and interspecific clones of garden asparagus.Frontiers in plant science · 2023Article
- Improving power of genome-wide association studies via transforming ordinal phenotypes into continuous phenotypes.Frontiers in plant science · 2023Article
- Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding.The plant genome · 2022Article
- High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings.G3 (Bethesda, Md.) · 2022Article
- Integrating and optimizing genomic, weather, and secondary trait data for multiclass classification.Frontiers in genetics · 2022Article
- Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice.PloS one · 2022Article
- Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Frontiers in genetics · 2022Article
- Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population.Biology · 2021Article
- Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.PloS one · 2021Article
- MaximumG3 (Bethesda, Md.) · 2020Article
- Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.Genes · 2020Article
- 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
- Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants.Plant communications · 2020Review
- Using Genome-Wide Predictions to Assess the Phenotypic Variation of a Barley (Frontiers in plant science · 2020Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Categorical scores for disease susceptibility or resistance often are recorded in plant breeding. The aim of this study was to introduce genomic models for analyzing ordinal characters and to assess the predictive ability of genomic predictions for ordered categorical phenotypes using a threshold model counterpart of the Genomic Best Linear Unbiased Predictor (i.e., TGBLUP). The threshold model was used to relate a hypothetical underlying scale to the outward categorical response. We present an empirical application where a total of nine models, five without interaction and four with genomic × environment interaction (G×E) and genomic additive × additive × environment interaction (G×G×E), were used. We assessed the proposed models using data consisting of 278 maize lines genotyped with 46,347 single-nucleotide polymorphisms and evaluated for disease resistance [with ordinal scores from 1 (no disease) to 5 (complete infection)] in three environments (Colombia, Zimbabwe, and Mexico). Models with G×E captured a sizeable proportion of the total variability, which indicates the importance of introducing interaction to improve prediction accuracy. Relative to models based on main effects only, the models that included G×E achieved 9-14% gains in prediction accuracy; adding additive × additive interactions did not increase prediction accuracy consistently across locations.
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