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Genetics, selection, evolution : GSE2016; 48; 13; doi: 10.1186/s12711-016-0192-2

Genomic prediction of unordered categorical traits: an application to subpopulation assignment in German Warmblood horses.

Abstract: Categorical 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. Results: Prediction 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. Conclusions: Genomic 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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Publication Date: 2016-02-11 PubMed ID: 26867647PubMed Central: PMC4751658DOI: 10.1186/s12711-016-0192-2Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The study explores the use of genomic prediction of unordered categorical traits to successfully assign German Warmblood horses into subpopulations. The method employed can be seen as a straightforward extension of existing binary threshold models for the same.

Introduction and Methodology

  • The research presents an examination of categorical traits that do not have an ordinal representation of classes, hence threshold models cannot be applied.
  • To navigate this issue, the authors propose an alternative, in the form of a sequence of independent contrasts using one-vs-all or one-vs-one schemes.
  • Class probabilities can be reached using normalization or pair-wise coupling strategies. The main focus of the study was to assess the predictive ability of whole-genome regression models and support vector machines for horse classification into four German Warmblood breeds.

Results

  • The prediction accuracies obtained through leave-one-out cross-validation were high, with a range of 0.75 to 0.97. These values were dependent on the breed involved in the training and the chosen binary classifier.
  • The researchers also performed an analysis of the population structure, using eigenvectors of the genomic relationship matrix, and found that the clustering of individuals extended beyond the given breed labels.
  • It was evident that an admixture, or genetic blend, between two breeds significantly impacted the prediction accuracies between those two breeds and influenced contrasts with other breeds.

Conclusions

  • The research managed to perform genomic prediction of unordered categorical traits and successfully apply it to the subpopulation assignment of German Warmblood horses.
  • The study thereby demonstrates a practical extension of the existing binary threshold models for genomic prediction—employing a series of independent binary contrasts for categorical traits that do not have ordinal representation.

Cite This Article

APA
Heuer C, Scheel C, Tetens J, Kühn C, Thaller G. (2016). Genomic prediction of unordered categorical traits: an application to subpopulation assignment in German Warmblood horses. Genet Sel Evol, 48, 13. https://doi.org/10.1186/s12711-016-0192-2

Publication

Genetics, selection, evolution : GSE
ISSN: 1297-9686
NlmUniqueID: 9114088
Country: France
Language: English
Volume: 48
Pages: 13
PII: 13

Researcher Affiliations

Heuer, Claas
  • Institute of Animal Breeding and Husbandry, University of Kiel, Hermann-Rodewald-Strasse 6, 24098, Kiel, Germany. cheuer@tierzucht.uni-kiel.de.
Scheel, Christoph
  • Institute of Animal Breeding and Husbandry, University of Kiel, Hermann-Rodewald-Strasse 6, 24098, Kiel, Germany. cscheel@tierzucht.uni-kiel.de.
Tetens, Jens
  • Institute of Animal Breeding and Husbandry, University of Kiel, Hermann-Rodewald-Strasse 6, 24098, Kiel, Germany. jtetens@tierzucht.uni-kiel.de.
Kühn, Christa
  • Institute for Genome Biology, Leibniz Institute for Farm Animal Biology, Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany. kuehn@fbn-dummerstorf.de.
  • Faculty of Agricultural and Environmental Sciences, University Rostock, Justus-von-Liebig-Weg 6, 18059, Rostock, Germany. kuehn@fbn-dummerstorf.de.
Thaller, Georg
  • Institute of Animal Breeding and Husbandry, University of Kiel, Hermann-Rodewald-Strasse 6, 24098, Kiel, Germany. gthaller@tierzucht.uni-kiel.de.

MeSH Terms

  • Animals
  • Bayes Theorem
  • Breeding
  • Genetics, Population
  • Genome
  • Genomics / methods
  • Genotype
  • Germany
  • Horses / genetics
  • Models, Genetic
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Quantitative Trait, Heritable
  • Regression Analysis
  • Support Vector Machine

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Citations

This article has been cited 7 times.
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