Dryad
Published October 4, 2018 | Version v1
Dataset Open

Data from: Combining landscape genomics and ecological modelling to investigate local adaptation of indigenous Ugandan cattle to East Coast fever

Creators

  • 1. Catholic University of the Sacred Heart
  • 2. École Polytechnique Fédérale de Lausanne
  • 3. National Animal Genetic Resource Centre and Data Bank, Uganda*
  • 4. Makerere University
  • 5. National Agricultural Research Organisation
  • 6. Recombinetics (United States)
  • 7. Cornell University

Description

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.

Notes

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.
Population structure dataset
Raw population structure dataset in ped/map format is provided. Instructions and ancillary files are provided to obtain the clean dataset described in the main text.
Rhipicephalus appendiculatus distribution model
Data and analyses (R code) used to obtain the discussed Rhipicephalus appendiculatus model are provided. README information is provided in the archive.
Syncerus caffer distribution model
Data and analyses (R code) used to obtain the discussed Syncerus caffer model are provided. README information is provided in the archive.
Theileria parva parva infection risk model
Data and analyses (R code) used to obtain the discussed Theileria parva parva infection risk model are provided. README information is provided in the archive.
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 - K4 correction - R. appendiculatus association study
Samβada inputs files of the genotype-environment association study involving: 1) R. appendiculatus occurrence probability and 2) population structure predictors derived from the four-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.
Landscape genomics analysis input files - K3 correction - T. parva parva association study
Samβada input file of the genotype-environment association study involving: 1) T. parva parva infection risk and 2) population structure predictors derived from the three-cluster solution of the ADMIXTURE analysis.
Landscape genomics analysis input files - K4 correction - T. parva parva association study
Samβada input files of the genotype-environment association study involving: 1) T. parva parva infection risk and 2) population structure predictors derived from the four-cluster solution of the ADMIXTURE analysis.
Landscape genomics analysis input files - K16 correction - T. parva parva association study
Samβada input files of the genotype-environment association study involving: 1) T. parva parva infection risk and 2) population structure predictors derived from the sixteen-cluster solution of the ADMIXTURE analysis.
Hereford vs. Lohani test - Target pop. Uganda
Local ancestry results for the comparison Hereford vs. Lohani are provided together with the R code used for beta regression analysis. Pipeline is explained in the README file within the archive.
Hereford vs. Tharparkar test - Target pop. Uganda
Local ancestry results for the comparison Hereford vs. Tharparkar are provided together with the R code used for beta regression analysis. Pipeline is explained in the README file within the archive.
Muturu vs. Lohani test - Target pop. Uganda
Local ancestry results for the comparison Muturu vs. Lohani are provided together with the R code used for beta regression analysis. Pipeline is explained in the README file within the archive.
Muturu vs. Tharparkar test - Target pop. Uganda
Local ancestry results for the comparison Muturu vs. Tharparkar are provided together with the R code used for beta regression analysis. Pipeline is explained in the README file within the archive.
NDVI - eaststm01-03
Normalized Difference Vegetation Index (NDVI) from the MODIS collection 5 (C5) data (see S1 Text for explanation). Average NDVI values in East Africa in the time span 2001-2010. Data are composed by 72 ten-day overlapping periods (composites), which were distributed in twelve files (eaststm_XX). Here: eaststm01, eaststm02, eaststm03
eaststm01-03.7z
NDVI - eaststm04-06
Normalized Difference Vegetation Index (NDVI) from the MODIS collection 5 (C5) data (see S1 Text for explanation). Average NDVI values in East Africa in the time span 2001-2010. Data are composed by 72 ten-day overlapping periods (composites), which were distributed in twelve files (eaststm_XX). Here: eaststm04, eaststm05, eaststm06
eaststm04-06.7z
NDVI - eaststm07-09
Normalized Difference Vegetation Index (NDVI) from the MODIS collection 5 (C5) data (see S1 Text for explanation). Average NDVI values in East Africa in the time span 2001-2010. Data are composed by 72 ten-day overlapping periods (composites), which were distributed in twelve files (eaststm_XX). Here: eaststm07, eaststm08, eaststm09
eaststm07-09.7z
NDVI - eaststm10-12
Normalized Difference Vegetation Index (NDVI) from the MODIS collection 5 (C5) data (see S1 Text for explanation). Average NDVI values in East Africa in the time span 2001-2010. Data are composed by 72 ten-day overlapping periods (composites), which were distributed in twelve files (eaststm_XX). Here: eaststm10, eaststm11, eaststm12
eaststm10-12.7z
Supplementary Figure 4
Additional ADMIXTURE analysis: results.

Files

Supplementary Figure 4.pdf

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Additional details

Related works

Is cited by
10.3389/fgene.2018.00385 (DOI)