The introduction of livestock species in Europe has been followed by various genetic events, which created a complex spatial pattern of genetic differentiation. Spatial principal component (sPCA) analysis and spatial metric multidimensional scaling (sMDS) incorporate geography in multivariate analysis. This method was applied to three microsatellite data sets for 45 goat breeds, 46 sheep breeds, and 101 cattle breeds from Europe, Southwest Asia, and India. The first two sPCA coordinates for goat and cattle, and the first sPCA coordinate of sheep, correspond to the coordinates of ordinary PCA analysis. However, higher sPCA coordinates suggest, for all three species, additional spatial structuring. The goat is the most geographically structured species, followed by cattle. For all three species, the main genetic cline is from southeast to northwest, but other geographic patterns depend on the species. We propose sPCA and sMDS to be useful tools for describing the correlation of genetic variation with geography.
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