Evolution of inbreeding: a gaze into five Italian beef cattle breeds history

Author:

Rovelli Giacomo12,Luigi-Sierra Maria Gracia2,Guan Dailu23,Sbarra Fiorella4,Quaglia Andrea4,Sarti Francesca Maria1,Amills Marcel25,Lasagna Emiliano1

Affiliation:

1. Department of Agricultural, Food and Environmental Sciences (DSA3), University of Perugia, Perugia, Italy

2. Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Universitat Autónoma de Barcelona, Bellaterra, Barcelona, Spain

3. Department of Animal Science, University of California, Davis, Davis, CA, United States of America

4. National Association of Italian Beef-Cattle Breeders (ANABIC), San Martino in Colle, Perugia, Italy

5. Departament de Ciència Animal i dels Aliments, Universitat Autónoma de Barcelona, Bellaterra, Barcelona, Spain

Abstract

In the last decades, intensive selection programs have led to sustained increases of inbreeding in dairy cattle, a feature that might have adverse consequences on the viability and phenotypic performance of their offspring. This study aimed to determine the evolution of inbreeding of five Italian beef cattle breeds (Marchigiana, Chianina, Romagnola, Maremmana, and Podolica) during a period of almost 20 years (2002–2019). The estimates of Ho, He, Fhat2, and Fped averaged across years (2002–2019) in the studied breeds fluctuated between 0.340–0.401, 0.348–0.392, –0.121–0.072, and 0.000–0.068, respectively. Moreover, annual rates of increase of the estimated inbreeding coefficients have been very low (Fhat2 = 0.01–0.02%; Fped = 0.003–0.004%). The use of a high number of bulls combined with strategies implemented by the Association of Italian Beef Cattle Breeders ANABIC to minimize inbreeding might explain these results. Despite the fact that diversity and inbreeding have remained quite stable during the last two decades, we have detected a sustained decrease of the population effective size of these five breeds. Such results should be interpreted with caution due to the inherent difficulty of estimating Ne from SNPs data in a reliable manner.

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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