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Locus minimization in breed prediction using artificial neural network approach.

M A Iquebal1, M S Ansari, Sarika

  • 1Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, Library Avenue, PUSA, New Delhi, 110012, India.

Animal Genetics
|September 4, 2014
PubMed
Summary

Artificial neural networks (ANNs) reduce costs for animal breed identification by minimizing molecular markers. This machine learning approach offers accurate, web-accessible breed identification, aiding conservation and intellectual property protection.

Keywords:
DNA markersbreed assignmentgoat breedwebserver

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Area of Science:

  • Animal genetics
  • Bioinformatics
  • Machine learning

Background:

  • Molecular markers like microsatellites and SNPs are crucial for identifying animal breeds from small biological samples.
  • Traditional methods face limitations including data unavailability, high costs, and complex analysis.

Purpose of the Study:

  • To develop and demonstrate an artificial neural network (ANN) model for cost-effective and accurate breed identification.
  • To create a webserver for accessible reference breed data, reducing the need for repeated genotyping.

Main Methods:

  • Utilized microsatellite-based DNA fingerprinting data from 51,850 samples across 22 Indian goat breeds.
  • Employed a multilayer perceptron model (a type of ANN) for locus minimization and breed identification.
  • Developed a freely accessible webserver (http://nabg.iasri.res.in/bisgoat) for the research community.

Main Results:

  • Achieved 96.63% training accuracy by minimizing the number of analyzed loci to nine.
  • Demonstrated significant cost reduction through locus minimization.
  • Established a web-based resource for reference breed data.

Conclusions:

  • The ANN approach provides a cost-effective and accurate method for animal breed identification.
  • The developed webserver serves as a valuable tool for identifying existing and new breeds, protecting intellectual property.
  • This model offers a scalable solution for cost reduction in breed identification for various species, supporting conservation and improvement programs.