Investigation of FecB Mutation in Four Romanian Sheep Breeds

Authors

  • Sergiu-Emil Georgescu University of Bucharest, Faculty of Biology, Department of Biochemistry and Molecular Biology, 050095, Bucharest, Splaiul Independentei 91-95, Romania
  • Gheorghe Hrinca Popauti Research and Development Station for Sheeps and Goats Growth, 717310, Rachiti, Romania
  • Mariana Rebedea University of Bucharest, Faculty of Biology, Department of Biochemistry and Molecular Biology, 050095, Bucharest, Splaiul Independentei 91-95, Romania
  • Marieta Costache University of Bucharest, Faculty of Biology, Department of Biochemistry and Molecular Biology, 050095, Bucharest, Splaiul Independentei 91-95, Romania

Keywords:

FecB, PCR-RFLP, prolificacy, screening, sheep

Abstract

Hyperprolific phenotype of Booroola sheep was first discovered in the Australian Merino breed. This phenotype is due to the action of a single autosomal gene that influences the number of ovulations per estrogenic cycle. Recent discoveries have revealed that high prolificacy in Booroola Merino sheep is the result of a mutation (FecB) in the bone morphogenetic protein receptor 1B (BMPR-1B) gene. This mutation is located in the highly conserved kinase domain of the bone morphogenetic protein receptor IB, and is characterized by precocious differentiation of ovarian follicles, leading to the production of large numbers of ovulatory follicles. Our objective was to develop an easy method to identify the FecB mutation in order to screen sheep populations in terms of prolificacy. We designed primers to amplify a 190 bp fragment from the BMPR-1B gene containing or lacking the mutation. The PCR product was cut with AvaII endonuclease and the restriction products were analysed by agarose gel electrophoresis. Using the PCR-RFLP technique, we established an easy and efficient method that can be used to screen the FecB mutation. Therefore, these new methods increase the panel of molecular tools available for sheep breeders to choose the most prolific genotypes for improving artificial selection.

References

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Published

2023-11-01