Browsing by Author "Joost, Stéphane"

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    Combining Landscape Genomics and Ecological Modelling to Investigate Local Adaptation of Indigenous Ugandan Cattle to East Coast Fever
    (Frontiers in Genetics, 2018) Vajana, Elia; Barbato, Mario; Colli, Licia; Milanesi, Marco; Rochat, Estelle; Fabrizi, Enrico; Mukasa, Christopher; Del Corvo, Marcello; Masembe, Charles; Muwanika, Vincent B.; Kabi, Fredrick; Stewart Sonstegard, Tad; Huson, Heather Jay; Negrini, Riccardo; Consortium, NextGen; Joost, Stéphane; Ajmone-Marsan, Paolo
    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.
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    SamBada in Uganda: landscape genomics study of traditional cattle breeds with a large SNP dataset
    (n The IALE 2013 European Congress, 2013) Sylvie, Stucki; Orozco-terWengel, Pablo; Colli, Licia; Kabi, Fredrick; Masembe, Charles; Negrini, Riccardo; Bruford, Michael W.; NEXTGEN, Consortium; Joost, Stéphane
    Since its introduction [9], landscape genomics has developed quickly with the increasing availability of both molecular and topoclimatic data. The current challenges involve processing large numbers of models and disentangling selection from demography. Several methods address the latter, either by estimating a neutral model from population structure [3] or by inferring simultaneously environmental and demographic effects [6]. Here we present Sam ada, an integrated software for landscape genomic analysis of large datasets. This tool was developed in the framework of NextGen with the objective of characterising traditional Ugandan cattle breeds using single nucleotide polymorphisms (SNPs) data.