Reggiana is an autochthonous cattle breed, mainly reared in the North of Italy. Milk of Reggiana cows is almost exclusively used for the production of a mono-breed branded Protected Designation of Origin (PDO) Parmigiano-Reggiano cheese. Reggiana population size experienced a dramatic reduction in the past, which was then reverted after 1980’. As in many local breeds, Reggiana pedigree records are not always complete and partial recording may increase error rate in several applications, such as inbreeding coefficient calculation and definition of selection plans. Highthroughput genotyping data could help to identify and then correct pedigree errors. A few approaches have been proposed to identify pedigree inconsistencies using DNA markers. Opposing homozygotes (OH) define parentage inconsistencies in candidate parent-offspring pairs based on homozygous single nucleotide polymorphisms (SNPs) for alternative alleles. In this work, available Reggiana pedigree information has been used as a starting point for reconstructing problematic records using OH evaluation. A total of 2730 Reggiana cattle, including commonly used 50 sires, have been genotyped with the GGP Bovine 150K SNP Chip. PLINK software and ad hoc scripts have been used to filter and to obtain all pairwise comparisons of the genotyped cattle based on OH. The genotyping error rate was estimated and then used to define the threshold of the number of inconsistent OH SNPs to declare an incorrect pedigree. Parentage information from pedigree records was not correct for about 10% of the animals. The routine application of this method will improve both the managing and conservation of Reggiana cattle breed and will improve accuracy of estimated breeding values.

Giuseppina Schiavo, S.B. (2023). Reconstructing pedigree information in the Reggiana cattle breed based on high density SNP data.

Reconstructing pedigree information in the Reggiana cattle breed based on high density SNP data

Giuseppina Schiavo;Samuele Bovo;Francesca Bertolini;Matteo Bolner;Anisa Ribani;Valeria Taurisano;Stefania Dall'olio;Luca Fontanesi
2023

Abstract

Reggiana is an autochthonous cattle breed, mainly reared in the North of Italy. Milk of Reggiana cows is almost exclusively used for the production of a mono-breed branded Protected Designation of Origin (PDO) Parmigiano-Reggiano cheese. Reggiana population size experienced a dramatic reduction in the past, which was then reverted after 1980’. As in many local breeds, Reggiana pedigree records are not always complete and partial recording may increase error rate in several applications, such as inbreeding coefficient calculation and definition of selection plans. Highthroughput genotyping data could help to identify and then correct pedigree errors. A few approaches have been proposed to identify pedigree inconsistencies using DNA markers. Opposing homozygotes (OH) define parentage inconsistencies in candidate parent-offspring pairs based on homozygous single nucleotide polymorphisms (SNPs) for alternative alleles. In this work, available Reggiana pedigree information has been used as a starting point for reconstructing problematic records using OH evaluation. A total of 2730 Reggiana cattle, including commonly used 50 sires, have been genotyped with the GGP Bovine 150K SNP Chip. PLINK software and ad hoc scripts have been used to filter and to obtain all pairwise comparisons of the genotyped cattle based on OH. The genotyping error rate was estimated and then used to define the threshold of the number of inconsistent OH SNPs to declare an incorrect pedigree. Parentage information from pedigree records was not correct for about 10% of the animals. The routine application of this method will improve both the managing and conservation of Reggiana cattle breed and will improve accuracy of estimated breeding values.
2023
ASPA 25th Congress Book of Abstract
45
45
Giuseppina Schiavo, S.B. (2023). Reconstructing pedigree information in the Reggiana cattle breed based on high density SNP data.
Giuseppina Schiavo, Samuele Bovo, Francesca Bertolini, Matteo Bolner, Anisa Ribani, Valeria Taurisano, Stefania Dall'olio, Massimo Bonacini, Luca Font...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/996707
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