ArticleGenetics, selection, evolution : GSE2016
Genomic prediction of unordered categorical traits: an application to subpopulation assignment in German Warmblood horses.
Article in Genetics, selection, evolution : GSE, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Polygenic Prediction of Equestrian Sport Discipline Among Horses Bred for Jumping and Dressage.Animal genetics · 2026Article
- Sport predispositions in warmblood horses: insights from Grand Prix competitions.Journal of equine science · 2026Article
- Using visual scores for genomic prediction of complex traits in breeding programs.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genetic characterisation of the Connemara pony and the Warmblood horse using a within-breed clustering approach.Genetics, selection, evolution : GSE · 2023Article
- Using Genome-Wide Predictions to Assess the Phenotypic Variation of a Barley (Frontiers in plant science · 2020Article
- An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction.G3 (Bethesda, Md.) · 2019Article
- Selection signatures in four German warmblood horse breeds: Tracing breeding history in the modern sport horse.PloS one · 2019Article
- Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study.International journal of nephrology and renovascular disease · 2019Article
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5 authors.
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Abstract
backgroundCategorical traits without ordinal representation of classes do not qualify for threshold models. Alternatively, the multinomial problem can be assessed by a sequence of independent binary contrasts using schemes such as one-vs-all or one-vs-one. Class probabilities can be arrived at by normalization or pair-wise coupling strategies. We assessed the predictive ability of whole-genome regression models and support vector machines for the classification of horses into four German Warmblood breeds.
resultsPrediction accuracies of leave-one-out cross-validation were high and ranged from 0.75 to 0.97 depending on the binary classifier and breeds incorporated in the training. An analysis of the population structure using eigenvectors of the genomic relationship matrix revealed clustering of individuals beyond the given breed labels. Admixture between two breeds became apparent which had substantial impact on the prediction accuracies between those two breeds and also influenced the contrasts between other breeds.
conclusionsGenomic prediction of unordered categorical traits was successfully applied to subpopulation assignment of German Warmblood horses. The applied methodology is a straightforward extension of existing binary threshold models for genomic prediction.
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