Breed assignment of Italian cattle using biallelic AFLP® markers

  1. R. Negrini1,
  2. E. Milanesi1,2,
  3. L. Colli1,
  4. M. Pellecchia1,
  5. L. Nicoloso2,
  6. P. Crepaldi2,
  7. J. A. Lenstra3,
  8. P. Ajmone-Marsan1

Article first published online: 27 FEB 2007

DOI: 10.1111/j.1365-2052.2007.01573.x

Issue

Animal Genetics

Animal Genetics

Volume 38, Issue 2, pages 147–153, April 2007

Additional Information

How to Cite

Negrini, R., Milanesi, E., Colli, L., Pellecchia, M., Nicoloso, L., Crepaldi, P., Lenstra, J. A. and Ajmone-Marsan, P. (2007), Breed assignment of Italian cattle using biallelic AFLP® markers. Animal Genetics, 38: 147–153. doi: 10.1111/j.1365-2052.2007.01573.x

Author Information

  1. 1

    Istituto di Zootecnica, Università Cattolica del S. Cuore, Piacenza, Italy

  2. 2

    Istituto di Zootecnia Generale, Università degli Studi di Milano, Italy

  3. 3

    Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands

*R. Negrini, Istituto di Zootecnica, Università Cattolica del S. Cuore, Piacenza, Italy. E-mail: riccardo.negrini@unicatt.it

Publication History

  1. Issue published online: 27 FEB 2007
  2. Article first published online: 27 FEB 2007
  3. Accepted for publication 26 December 2006

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Keywords:

  • AFLP;
  • Bayesian;
  • cattle;
  • likelihood;
  • traceability

Summary

The verification of the breed origin of animal products is relevant for food safety and authenticity. We assessed the suitability of AFLP molecular markers in the assignment of cattle individuals to their breed of origin. Three hundred and ninety-six animals belonging to 16 cattle breeds genotyped with 141 AFLP markers were used as reference data set. Assignment was performed with likelihood (aflpop) and Bayesian (structure) methods. The Bayesian approach was superior to the likelihood algorithm with respect to (i) the correct assignment of simulated individuals to their breed of origin (93% vs. 81% respectively), (ii) the correct assignment of 44 sampled Romagnola animals (91% vs. 45% respectively) and (iii) the correct classification of animals belonging to a breed that was not included within the reference dataset. Thus, AFLP profiling in combination with the Bayesian approach seems a useful tool for breed assignment.

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