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Comparing assignment-based approaches to breed identification within a large set of horses

  • Lenka PutnováEmail author
  • Radek Štohl
Animal Genetics • Original Paper

Abstract

Considering the extensive data sets and statistical techniques, animal breeding embodies a branch of machine learning that has a constantly increasing impact on breeding. In our study, information regarding the potential of machine learning and data mining within a large set of horses and breeds is presented. The individual assignment methods and factors influencing the success rate of the procedure are compared at the Czech population scale. The fixation index values ranged from 0.057 (HMS1) to 0.144 (HTG6), and the overall genetic differentiation amounted to 8.9% among the breeds. The highest genetic divergence (FST = 0.378) was established between the Friesian and Equus przewalskii; the highest degree of gene migration was obtained between the Czech and Bavarian Warmblood (Nm = 14,302); and the overall global heterozygote deficit across the populations was 10.4%. The eight standard methods (Bayesian, frequency, and distance) using GeneClass software and almost all mainstream classification algorithms (Bayes Net, Naive Bayes, IB1, IB5, KStar, JRip, J48, Random Forest, Random Tree, PART, MLP, and SVM) from the WEKA machine learning workbench were compared by utilizing 314,874 real allelic data sets. The Bayesian method (GeneClass, 89.9%) and Bayesian network algorithm (WEKA, 84.8%) outperformed the other techniques. The breed genomic prediction accuracy reached the highest value in the cold-blooded horses. The overall proportion of individuals correctly assigned to a population depended mainly on the breed number and genetic divergence. These statistical tools could be used to assess breed traceability systems, and they exhibit the potential to assist managers in decision-making as regards breeding and registration.

Keywords

Assignment success Horse breeds Genetic differentiation Microsatellite variability Machine learning 

Notes

Acknowledgments

The authors would like to thank Professor Petr Hořín (Department of Animal Genetics, VFU Brno) for providing samples of the Camargue, Murgese, and Icelandic horses. This section would be incomplete without quoting Irena Vrtková, PhD (Laboratory of Agrogenomics) and her unwavering support over the years.

Funding information

The research was funded by a project (NAZV QH92277) of the National Agency for Agricultural Research of the Ministry of Agriculture of the Czech Republic, utilizing the institutional support for the development of Mendel University in Brno. Furthermore, the research was supported by the Ministry of Education, Youth and Sports under project No. LO1210 solved at the Centre for Research and Utilization of Renewable Energy.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical statement

All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted.

Supplementary material

13353_2019_495_MOESM1_ESM.docx (49 kb)
Table S1 The multilocus Nm (below the diagonal) and FST values (above the diagonal) between pairs of 43 populations studied across all loci (n = 9261). (DOCX 48 kb)
13353_2019_495_MOESM2_ESM.docx (35 kb)
Table S2 The numbers of animals sampled per population and correctly assigned, and the individual assignment success rates for each population achieved using different assignment methods and numbers of microsatellite markers (GeneClass). (DOCX 35 kb)
13353_2019_495_MOESM3_ESM.docx (23 kb)
Table S3 The individual assignment success as calculated by GeneClass using the Bayesian method (Rannala & Mountain) for each horse breed (n = 2879). (DOCX 22 kb)
13353_2019_495_MOESM4_ESM.docx (36 kb)
Table S4 The numbers of animals sampled per population and correctly assigned, and the individual assignment success rates for each population achieved using different assignment methods and numbers of microsatellite markers (the WEKA software). (DOCX 36 kb)
13353_2019_495_MOESM5_ESM.docx (22 kb)
Table S5 The performance of the Bayes Net classification model tested for breed identification as the confusion matrix (the average accuracy of 84.8%). (DOCX 21 kb)

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Copyright information

© Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2019

Authors and Affiliations

  1. 1.Laboratory of Agrogenomics, Department of Morphology, Physiology and Animal Genetics, Faculty of AgronomyMendel University in BrnoBrnoCzech Republic
  2. 2.Department of Control and Instrumentation, Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrnoCzech Republic

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