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Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP

Changheng Zhao1, Dan Wang1, Jun Teng1

  • 1Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China.

Journal of Animal Science and Biotechnology
|May 31, 2023
PubMed
Summary

The optimal strategy for cattle breed identification combines integrated breed-informative SNP detection (DFI) and machine learning (KSR) methods, achieving over 99% accuracy. SNP chip data offers a cost-effective alternative to sequence data with minimal accuracy loss.

Keywords:
Breed identificationBreed-informative SNPsGenomic breed compositionMachine learningWhole genome sequence data

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Area of Science:

  • Genomics and Animal Breeding
  • Bioinformatics and Computational Biology
  • Quantitative Genetics

Background:

  • Accurate breed identification is crucial for various biological applications.
  • Existing breed identification methods involve SNP detection and breed assignment, but optimal combinations are unclear.
  • Whole genome sequence data from 13 cattle breeds were utilized.

Purpose of the Study:

  • To compare combinations of SNP detection and machine learning methods for cattle breed identification.
  • To identify the most accurate and robust strategy for breed identification.
  • To evaluate the impact of reference population size and SNP density on identification accuracy.

Main Methods:

  • Compared three breed-informative SNP detection methods (Delta, FST, In) and five machine learning classifiers (KNN, SVM, RF, NB, ANN).
  • Integrated top-performing SNP detection methods into DFI and machine learning methods into KSR.
  • Evaluated performance using whole genome sequence data and SNP chip data of varying densities.

Main Results:

  • All tested combinations achieved over 95% accuracy.
  • The integrated DFI and KSR methods demonstrated superior performance, exceeding 99% accuracy in most scenarios.
  • SNP chip data yielded accuracies only slightly lower than sequence data, indicating its viability.

Conclusions:

  • The combination of DFI and KSR represents the optimal strategy for cattle breed identification.
  • While sequence data offers higher accuracy, SNP chip data provides a cost-effective alternative with comparable results.
  • This study provides a robust framework for accurate and efficient cattle breed identification.