5 research outputs found
High performance computation of landscape genomic models integrating local indices of spatial association
Since its introduction, landscape genomics has developed quickly with the
increasing availability of both molecular and topo-climatic data. The current
challenges of the field mainly 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 or by inferring
simultaneously environmental and demographic effects. Here we present
Samada, an integrated approach to study signatures of local adaptation,
providing rapid processing of whole genome data and enabling assessment of
spatial association using molecular markers. Specifically, candidate loci to
adaptation are identified by automatically assessing genome-environment
associations. In complement, measuring the Local Indicators of Spatial
Association (LISA) for these candidate loci allows to detect whether similar
genotypes tend to gather in space, which constitutes a useful indication of the
possible kinship relationship between individuals. In this paper, we also
analyze SNP data from Ugandan cattle to detect signatures of local adaptation
with Samada, BayEnv, LFMM and an outlier method (FDIST approach in
Arlequin) and compare their results. Samada is an open source software
for Windows, Linux and MacOS X available at \url{http://lasig.epfl.ch/sambada}Comment: 1 figure in text, 1 figure in supplementary material The structure of
the article was modified and some explanations were updated. The methods and
results presented are the same as in the previous versio
Landscape genomics dataset
Raw landscape genomics dataset in ped/map format is provided. Instructions and ancillary files are provided to obtain the clean dataset described in the main text
Landscape genomics analysis input files - K3 correction - R. appendiculatus association study
Samβada input files of the genotype-environment association study involving: 1) R. appendiculatus occurrence probability and 2) population structure predictors derived from the three-cluster solution of the ADMIXTURE analysis
Landscape genomics analysis input files - K16 correction - R. appendiculatus association study
Samβada input files of the genotype-environment association study involving: 1) R. appendiculatus occurrence probability and 2) population structure predictors derived from the sixteen-cluster solution of the ADMIXTURE analysis
Combining landscape genomics and ecological modelling to investigate local adaptation of indigenous ugandan cattle to East Coast fever
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