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Keep Garfagnina alive. An integrated study on patterns of homozygosity, genomic inbreeding, admixture and breed traceability of the Italian Garfagnina goat breed.

Christos DadousisFrancesca CecchiMichela AblondiMaria Chiara FabbriAlessandra StellaRiccardo Bozzi
Published in: PloS one (2021)
The objective of this study was to investigate the genetic diversity of the Garfagnina (GRF) goat, a breed that currently risks extinction. For this purpose, 48 goats were genotyped with the Illumina CaprineSNP50 BeadChip and analyzed together with 214 goats belonging to 9 other Italian breeds (~25 goats/breed), whose genotypes were available from the AdaptMap project [Argentata (ARG), Bionda dell'Adamello (BIO), Ciociara Grigia (CCG), Di Teramo (DIT), Garganica (GAR), Girgentana (GGT), Orobica (ORO), Valdostana (VAL) and Valpassiria (VSS)]. Comparative analyses were conducted on i) runs of homozygosity (ROH), ii) admixture ancestries and iii) the accuracy of breed traceability via discriminant analysis on principal components (DAPC) based on cross-validation. ROH analyses was used to assess the genetic diversity of GRF, while admixture and DAPC to evaluate its relationship to the other breeds. For GRF, common ROH (more than 45% in GRF samples) was detected on CHR 12 at, roughly 50.25-50.94Mbp (ARS1 assembly), which spans the CENPJ (centromere protein) and IL17D (interleukin 17D) genes. The same area of common ROH was also present in DIT, while a broader region (~49.25-51.94Mbp) was shared among the ARG, CCG, and GGT. Admixture analysis revealed a small region of common ancestry from GRF shared by BIO, VSS, ARG and CCG breeds. The DAPC model yielded 100% assignment success for GRF. Overall, our results support the identification of GRF as a distinct native Italian goat breed. This work can contribute to planning conservation programmes to save GRF from extinction and will improve the understanding of the socio-agro-economic factors related with the farming of GRF.
Keyphrases
  • genetic diversity
  • gene expression
  • dna methylation
  • quality improvement
  • pseudomonas aeruginosa
  • staphylococcus aureus
  • bioinformatics analysis