Abstract
Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. Here we describe a deep learning method, which we call Locator, to accomplish this task faster and more accurately than existing approaches. In simulations, Locator infers sample location to within 4.1 generations of dispersal and runs at least an order of magnitude faster than a recent model-based approach. We leverage Locator’s computational efficiency to predict locations separately in windows across the genome, which allows us to both quantify uncertainty and describe the mosaic ancestry and patterns of geographic mixing that characterize many populations. Applied to whole-genome sequence data from Plasmodium parasites, Anopheles mosquitoes, and global human populations, this approach yields median test errors of 16.9km, 5.7km, and 85km, respectively.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
In this revision we have added new analyses looking at centimorgan- vs fixed-length windowing and predictions around the EDAR locus in humans. We also expanded our discussion of the utility of machine learning in population genetics at the end of the discussion, made minor figure and syntax edits throughout, and added a table of mean and median losses for the comparison with SPASIBA.
