Accurate pedigrees are essential to optimize genetic improvement and conservation of animal genetic resources. In goats, the use of mating groups and kidding management procedures hamper the identification of parentage. Small panels of single nucleotide polymorphisms (SNP) have been proposed in other species to substitute microsatellites for parentage assessment. Using data from the current GoatSNP50 chip, we developed a new 3-step procedure to identify a low-density SNP panel for highly accurate parentage assessment. Methodologies for SNP selection used in other species are less suitable in the goat because of uncertainties in the genome assembly. The procedure developed in this study is based on parent–offspring identification and on estimation of Mendelian errors, followed by canonical discriminant analysis identification and stepwise regres- sion reduction. Starting from a reference sample of 109 Alpine goats with known pedigree relationships, we first identified a panel of 200 SNP that was further reduced to 2 final panels of 130 and 114 SNP with random coincidental match inclusion of 1.51 × 10−57 and 2.94 × 10−34, respectively. In our reference data set, all panels correctly identified all parent–offspring combinations, revealing a 40% pedigree error rate in the information provided by breeders. All reference trios were confirmed by official tests based on microsatellites. Panels were also tested on Saanen and Teramana breeds. Although the testing on a larger set of breeds in the reference population is still needed to validate these results, our findings suggest that our procedure could identify SNP panels for accurate parentage assessment in goats or in other species with unreliable marker positioning.

A method for single nucleotide polymorphism selection for parentage assessment in goats

Chessa S.;Moretti R.;
2016-01-01

Abstract

Accurate pedigrees are essential to optimize genetic improvement and conservation of animal genetic resources. In goats, the use of mating groups and kidding management procedures hamper the identification of parentage. Small panels of single nucleotide polymorphisms (SNP) have been proposed in other species to substitute microsatellites for parentage assessment. Using data from the current GoatSNP50 chip, we developed a new 3-step procedure to identify a low-density SNP panel for highly accurate parentage assessment. Methodologies for SNP selection used in other species are less suitable in the goat because of uncertainties in the genome assembly. The procedure developed in this study is based on parent–offspring identification and on estimation of Mendelian errors, followed by canonical discriminant analysis identification and stepwise regres- sion reduction. Starting from a reference sample of 109 Alpine goats with known pedigree relationships, we first identified a panel of 200 SNP that was further reduced to 2 final panels of 130 and 114 SNP with random coincidental match inclusion of 1.51 × 10−57 and 2.94 × 10−34, respectively. In our reference data set, all panels correctly identified all parent–offspring combinations, revealing a 40% pedigree error rate in the information provided by breeders. All reference trios were confirmed by official tests based on microsatellites. Panels were also tested on Saanen and Teramana breeds. Although the testing on a larger set of breeds in the reference population is still needed to validate these results, our findings suggest that our procedure could identify SNP panels for accurate parentage assessment in goats or in other species with unreliable marker positioning.
2016
99
5
3646
3653
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959888341&doi=10.3168%2fjds.2015-10077&partnerID=40&md5=4dd3daf2f45df883b385664f7b768fe0
parentage analysis, goat, single nucleotide polymorphism
Talenti A., Nicolazzi E.L., Chessa S., Frattini S., Moretti R., Coizet B., Nicoloso L., Colli L., Pagnacco G., Stella A., Ajmone-Marsan P., Ptak G., Crepaldi P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1688979
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