Italian Goat Consortium: a first overview of genetic variability using a medium density SNP array

Paola Crepaldi*, Salvatore Bordonaro§, Stefania Chessa^, Beatrice Coizet*, Licia Colli#, Mariasilvia
D’Andrea°, Raffaele Mazza$, Sabrina Miari&, Salvatore Murru£, Letizia Nicoloso*, Giulio Pagnacco*,
Grazyna Ptak¥, Fabio Pilla°, Tiziana Sechi&, Alessio Valentini%
*Dipartimento di Scienze Veterinarie e Sanità Pubblica, Università degli Studi di Milano, Milano,
Italy; #Istituto di Zootecnica, Università Cattolica del Sacro Cuore, Piacenza, Italy; §Dipartimento di
Scienze delle Produzioni Agricole ed Alimentari – Sezione di Produzioni Animali, Università degli
Studi di Catania, Catania, Italy; &Settore Genetica e Biotecnologie, AGRIS Sardegna, Olmedo,
Sassari, Italy; ^Istituto di Biologia e Biotecnologia Agraria, Consiglio Nazionale delle Ricerche, UOS
di Lodi, Lodi, Italy; °Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise,
Campobasso, Italy; $ Associazione Italiana Allevatori, Cremona, Italy; £AssoNaPa, Roma Italy; ¥
Facoltà di Medicina Veterinaria, Università di Teramo; %Dipartimento per l’Innovazione nei sistemi
Biologici, Agroalimentari e Forestali, Università della Tuscia, Viterbo, Italy
The Italian Goat Consortium was established from the Innovagen Project to pool local efforts and
resources for the genomic characterisation of Italian goat breeds. Thirteen Italian goat breeds
(Valdostana, Saanen, Orobica, Bionda dell’Adamello, Val Passiria, Grigia Ciociara, Teramana,
Nicastrese, Aspromontana, Girgentana, Argentata dell’Etna, Sarda and Maltese – the latter sampled
in Sardinia and Sicily) for a total of 350 animals (15-32 per breed) were analysed using a medium
density SNP array. Data editing was based on the following thresholds: MAF <1%; Missing SNP
>5%; Missing animal >5%; HW within breed FDR >20%. After filtering, a total of 51136 SNPs were
retained, showing that the adopted SNP chip is highly informative for the characterization of Italian
goat gene pool. Population structure and breed distinctiveness were investigated with the software
Admixture, that revealed the best fitting solution at K=11 partitions. The software also highlighted
variable levels of admixture and an early differentiation of Val Passiria, Orobica, Valdostana,
Teramana, Girgentana and Sarda breeds. Within MDS plot, some breeds are clearly appreciable as
distinct: Girgentana, Teramana, Orobica and Maltese. PCA analysis revealed a gradient of diversity
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North (from alpine breeds) – South (to Sicilian breeds) and East (from Val Passiria breed) – West (to
Valdostana breed) in the Alps. The calculation of Fis index indicated little or no inbreeding, while LD
analysis in Chromosome 6 showed levels of disequilibrium much lower than cattle at short physical
The research was also supported by the project “INNOVAGEN” (Italian MIPAAF Ministery).


Abstract ASPA 2013

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