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Since its introduction, landscape genomics has developed quickly with theincreasing availability of both molecular and topo-climatic data. The currentchallenges of the field mainly involve processing large numbers of models anddisentangling selection from demography. Several methods address the latter,either by estimating a neutral model from population structure or by inferringsimultaneously environmental and demographic effects. Here we presentSam$\beta$ada, an integrated approach to study signatures of local adaptation,providing rapid processing of whole genome data and enabling assessment ofspatial association using molecular markers. Specifically, candidate loci toadaptation are identified by automatically assessing genome-environmentassociations. In complement, measuring the Local Indicators of SpatialAssociation (LISA) for these candidate loci allows to detect whether similargenotypes tend to gather in space, which constitutes a useful indication of thepossible kinship relationship between individuals. In this paper, we alsoanalyze SNP data from Ugandan cattle to detect signatures of local adaptationwith Sam$\beta$ada, BayEnv, LFMM and an outlier method (FDIST approach inArlequin) and compare their results. Sam$\beta$ada is an open source softwarefor Windows, Linux and MacOS X available at \url{http://lasig.epfl.ch/sambada}

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