Combining landscape genomics and ecological modelling to investigate local adaptation of indigenous ugandan cattle to East Coast fever

Licia Colli, Enrico Fabrizi, Riccardo Negrini, Paolo Ajmone Marsan, Mario Barbato, Marcello Del Corvo, Estelle Rochat, Christopher Mukasa, Charles Masembe, Vincent B. Muwanika, Fredrick Kabi, Tad Stewart Sonstegard, Heather Jay Huson, Stéphane Joost

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

10 Citazioni (Scopus)

Abstract

East Coast fever (ECF) is a fatal sickness affecting cattle populations of eastern, central, and southern Africa. The disease is transmitted by the tick Rhipicephalus appendiculatus, and caused by the protozoan Theileria parva parva, which invades host lymphocytes and promotes their clonal expansion. Importantly, indigenous cattle show tolerance to infection in ECF-endemically stable areas. Here, the putative genetic bases underlying ECF-tolerance were investigated using molecular data and epidemiological information from 823 indigenous cattle from Uganda. Vector distribution and host infection risk were estimated over the study area and subsequently tested as triggers of local adaptation by means of landscape genomics analysis. We identified 41 and seven candidate adaptive loci for tick resistance and infection tolerance, respectively. Among the genes associated with the candidate adaptive loci are PRKG1 and SLA2. PRKG1 was already described as associated with tick resistance in indigenous South African cattle, due to its role into inflammatory response. SLA2 is part of the regulatory pathways involved into lymphocytes' proliferation. Additionally, local ancestry analysis suggested the zebuine origin of the genomic region candidate for tick resistance.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
RivistaFrontiers in Genetics
Volume9
DOI
Stato di pubblicazionePubblicato - 2018

Keywords

  • East Coast fever
  • Genetics
  • Genetics (clinical)
  • Indigenous cattle
  • Landscape genomics
  • Local adaptation
  • Molecular Medicine
  • Species distribution modelling

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