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 Sam$\beta$ada, 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 Sam$\beta$ada, BayEnv, LFMM and an outlier method (FDIST approach in Arlequin) and compare their results. Sam$\beta$ada 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

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

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

SamBada in Uganda: landscape genomics study of traditional cattle breeds with a large SNP dataset

Since its introduction, landscape genomics has developed quickly with the increasing availability of both molecular and topo-climatic data. 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 or by inferring simultaneously environmental and demographic effects. 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

High performance computation of landscape genomic models including local indicators of spatial association

With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics — i.e. the combination of landscape ecology with population genomics — include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present Samβada, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large-scale genetic and environmental datasets. Samβada identifies candidate loci using genotype-environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models in order to lower the occurrences of spurious genotype-environment associations. In addition, Samβada calculates Local Indicators of Spatial Association (LISA) for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a dataset from Ugandan cattle to detect signatures of local adaptation with Samβada, BayEnv, LFMM and an FST outlier method (FDIST approach in Arlequin) and compare their results. Samβada — an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada — outperforms other approaches and better suits whole genome sequence data processing

Genomic diversity and Population Structure of Ugandan Taurine and Zebuine Cattle Breeds

An extensive sampling of Ugandan cattle was carried out in the course of the European project Nextgen to identify possible associations between genotypes, livestock endemic diseases and environmental variables. As a prior to the GWAS and selection signatures analyses planned within the project, we analyzed the population structure of Ugandan cattle genotyped with both 54K and 800K HD SNP panels in the context of the worldwide cattle genomic diversity

Modeling the spatial distribution of Theileria parva (Theiler 1904), causative agent of East Coast Fever disease in cattle

Theileria parva is a protozoan emo-parasite affecting sub-Saharan Bos taurus and Bos indicus populations. It is the causative agent of East Coast Fever, a major cattle disease causing the death of ~1.1∙106 animals per year and an annual loss of ~168∙106 USD (Norval et. al., 1992). T. parva geographical occurrence is bound to the presence of susceptible bovine host populations, the main tick vector Rhipicephalus appendiculatus (Neumann 1901), as well as suitable ecological conditions for the survival of both the vector and the parasite. While tick habitat requirements have been extensively investigated (see e.g. Cumming, 2002), studies focusing solely on the environmental conditions determining the parasite occurrence are still lacking. The goal of the study is to define T. parva ecological fundamental niche, thus fostering our understanding of the environmental requirements needed to maintain the parasite-vector-host biological system

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 - 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

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