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Integration of georeferenced and genetic data for the management of biodiversity in sheep genetic resources in Brazil.

Concepta Margaret McManusPotira HermucheRenato Fontes GuimarãesOsmar Abílio de Carvalho JúniorBruno Stéfano Lima DallagoRenata Augusto VieiraDanielle Assis de FariaHarvey BlackburnJosé Carlos Ferrugem MoraesCarlos Hoff SouzaOlivardo FacóAdriana Mello AraújoHymerson Costa AzevedoPaulo Luiz Souza CarneiroSandra Aparecida SantosPaulo Sergio Ribeiro de MattosSamuel Rezende Paiva
Published in: Tropical animal health and production (2021)
There are few animal germplasm/gene bank collections in Brazil, and basic studies are needed to attend the future internal and external demands from international partners. The aim of this work was to validate a "proof of concept" that integrates spatial (georeferenced data) and genetic data regarding the local of origin from 3518 DNA samples from 17 different genetic groups or breeds of sheep in the Brazilian Germplasm bank. Spatialisation shows that not all genetic groups have samples in the bank, and collection is concentrated in the conservation nuclei spread nationwide. Only 21% of states with a specific breed have samples in the gene bank. The mean number of animals sampled per collection was 32, while the mean distance travelled to collect samples was 262 km from the conservation nuclei. For example, the Brazilian Somali were only collected in the conservation nucleus in Ceará State. No samples were collected to date for the Cariri breed, which is recognised by the Brazilian Ministry of Agriculture. Only two farms and one breed in the bank are from the northern region. Of the 27 states, there are samples in the gene bank of sheep from 13, so several states have no samples, requiring collection from herds outside the official system of conservation to make sure that studies using this germplasm realised are not biased. Significant genetic differences are seen above 332 km, which should guide future sampling efforts. Suggestions are given for improving the quantity, quality and diversity of samples in the gene bank.
Keyphrases
  • genome wide
  • copy number
  • dna methylation
  • climate change
  • machine learning
  • big data
  • data analysis
  • gene expression
  • deep learning
  • cell free
  • hepatitis c virus