Data from: Ancient trade routes shaped the genetic structure of horses in eastern Eurasia

Animal exchange networks have been shown to play an important role in determining gene flow among domestic animal populations. The Silk Road is one of the oldest continuous exchange networks in human history, yet its effectiveness in facilitating animal exchange across large geographic distances and topographically challenging landscapes has never been explicitly studied. Horses are known to have been traded along the Silk Roads; however, extensive movement of horses in connection with other human activities may have obscured the genetic signature of the Silk Roads. To investigate the role of the Silk Roads in shaping the genetic structure of horses in eastern Eurasia we analysed microsatellite genotyping data from 455 village horses sampled from 17 locations. Using least-cost path methods, we compared the performance of models containing the Silk Roads as corridors for gene flow with models containing single landscape features. We also determined whether the recent isolation of former Soviet Union countries from the rest of Eurasia has affected the genetic structure of our samples. The overall level of genetic differentiation was low, consistent with historically high levels of gene flow across the study region. The spatial genetic structure was characterised by a significant, albeit weak, pattern of isolation by distance across the continent with no evidence for the presence of distinct genetic clusters. Incorporating landscape features considerably improved the fit of the data; however, when we controlled for geographic distance, only the correlation between genetic differentiation and the Silk Roads remained significant, supporting the effectiveness of this ancient trade network in facilitating gene flow across large geographic distances in a topographically complex landscape.microsatellite genotyping data for 455 horses from 17 locations in eastern EurasiaDna was extracted from hair root samples collected in the field between 2006 and 2011 and amplified using 26 microsatellite markers. PCR products were run on an ABI 3730 Genetic Analyser (Applied Biosystems) and alleles were called using GENEMAPPER software v37 (Applied Biosystems).msat data.xlsxlandscape.dataThis folder contains RData objects and R scripts used to carry out the least-cost path analyses described in the m