Spatial Areas of Genotype Probability of Cattle Genomic Variants Involved in the Resistance to East Coast Fever: A Tool to Predict Future Disease-Vulnerable Geographical Regions

East Coast Fever (ECF) is a livestock disease caused by Theileria parva, a protozoan transmitted by the vector tick Rhipicephalus appendiculatus. This disease causes high mortality in cattle populations of Central and Eastern Africa, especially in exotic breeds. Here, we highlight genomic regions likely involved into tolerance/resistance mechanisms against ECF, and we introduce the estimation of their Spatial Area of Genotype Probability (SPAG) to delimit areas where the concerned genotypes are predicted to be present. During the NEXTGEN project, 803 Ugandan cattle were geo-referenced and genotyped (54K SNPs), while 532 tick occurrences were retrieved from a published database. To get a proxy of the parasite selective pressure, we used WorldClim bioclimatic variables to model vector ecological niche. Landscape genomics models were then used to detect cattle genotypes associated with vector probability of presence, and to estimate their SPAGs. Finally, climate change scenarios for 2070 were considered to compare the predicted shift in the vector niche with the estimated current SPAG. The analysis revealed two main areas of presence of possibly resistance-related genotypes, one South and one East of Lake Victoria. Climate change will probably shift tick niche southwards in the Eastern regions of Lake Victoria, inducing a critical area that currently does not show the candidate genotypes, but where disease will likely spread in the future. The combined use of SPAGs and niche maps could therefore facilitate the identification of regions of concern and to direct future targeted breeding schemes.

Effect of climate change on the spatial distribution of genomic variants involved in the resistance to East Coast Fever in Ugandan cattle

Stéphane Joost, Estelle Rochat, Elia Vajana

East Coast Fever (ECF) is a major livestock disease caused by Theileria parva Theiler, 1904, an emo-parasite protozoan transmitted by the tick Rhipicephalus appendiculatus Neumann, 1901. This disease provokes high mortality in cattle populations of East and Central Africa, especially in exotic breeds and crossbreds (Olwoch et al., 2008). Here, we use landscape genomics (Joost et al., 2007) to highlight genomic regions likely involved into tolerance/resistance mechanisms against ECF, and we introduce Spatial Areas of Genotype Probability (SPAG) to delimit territories where favourable allelic variants are predicted to be present. Between 2010 and 2012, the NEXTGEN project (nextgen.epfl.ch) carried out the geo-referencing and genotyping (54K SNPs) of 803 Ugandan cattle, among which 496 were tested for T. parva presence. Moreover, 532 additional R. appendiculatus occurrences were obtained from a published database (Cumming, 1998). Current and future values of 19 bioclimatic variables were also retrieved from the WorldClim database (www.worldclim.org/). In order to evaluate the selective pressure of the parasite, we used MAXENT (Phillips et al., 2006; Muscarella et al., 2014) and a mixed logistic regression (Bates et al., 2014) to model and map the ecological niches of both T. parva and R. appendiculatus. Then, we used a correlative approach (Stucki et al., 2014) to detect molecular markers positively associated with the resulting probabilities of presence and built the corresponding SPAG. Finally, we considered bioclimatic predictors representing two different climate change scenarios for 2070 - one moderate and one severe - to forecast the simultaneous shift of both SPAG and vector/pathogen niches. While suitable ecological conditions for T. parva are predicted to remain constant, the best environment for the vector is predicted around Lake Victoria. However, when considering future conditions, parasite occurrence is expected to decrease because of the contraction of suitable environments for the tick in both scenarios. Landscape genomics’ analyses revealed several markers significantly associated with a high probability of presence of the tick and of the parasite. Among them, we found the marker ARS-BFGL-NGS-113888, whose heterozygous genotype AG showed a positive association. Interestingly, this marker is located close to the gene IRAK-M, an essential component of the Toll-like receptors involved in the immune response against pathogens (Kobayashi et al., 2002). If the implication of this gene into resistance mechanisms against ECF is confirmed, the corresponding SPAG (Figure 1) represents either areas where the variant of interest shows a high probability to exist now, or areas where ecological characteristics are the most favorable to induce its presence under future climatic conditions. Beyond the results presented here, the combined use of SPAG and niche maps could help identifying critical geographical regions that do not present the favourable genetic variant in the present, but where a parasite is likely to expand its range in the future. This may represent a valuable tool to support the identification of current resistant populations and to direct future targeted crossbreeding schemes.

2015

GEOME : A web-based landscape genomics geocomputation platform

Stéphane Joost, Michaël Kalbermatten

Landscape ecologists and resource conservation managers increasingly need geo-referenced data but are not trained to efficiently use Geographical Information Systems together with appropriate geo-environmental information and spatial analysis approaches. In the present context of rapid global climate change, they show a renewed interest to study adaptation in species of interest (wildlife, livestock, plants) with the help of landscape genomics. This emerging research field is at the interface of genome sciences, environmental resources analysis, bioinformatics, geocomputation and spatial statistics. Their combination permits to assess the level of association between specific genomic regions of living organisms and environmental factors, to better understand the genetic basis of local adaptation. Landscape genomics is thus able to provide objective criteria to prioritize species which are the most worthwhile preserving. In livestock science for instance, husbandry based on adapted breeds is of key importance to emerging countries. Consequently, to facilitate the use of landscape genomics, the GEOME project proposes a WebGIS-based platform for the integrated analysis of environmental, ecological and molecular data through the implementation of an original set of combined geocomputation, databases, spatial analysis and population genetics tools. The platform will gather two existing software to identify genomic regions under selection (BayeScan and MatSAM) using a WebGIS solution named MapIntera. The latter is a multiple views exploratory visualization interface allowing users to interactively browse and query multiple dynamic layouts (e.g. graphs, maps). It is based on a SVG graphical interface coupled to Javascript interactivity and AJAX technology for the retrieval of information from a spatial database. The platform will be connected to a High Performance Computing infrastructure, able to handle and process very large genome and environmental data sets

International Society for Photogrammetry and Remote Sensing2010

Effect of climate change on the spatial distribution of genomic variants involved in the resistance to East Coast Fever in Ugandan cattle

Stéphane Joost, Estelle Rochat, Elia Vajana

2015

GEOME : A web-based landscape genomics geocomputation platform

Stéphane Joost, Michaël Kalbermatten

International Society for Photogrammetry and Remote Sensing2010