RT Journal Article
SR Electronic
T1 Predicting Geographic Location from Genetic Variation with Deep Neural Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2019.12.11.872051
DO 10.1101/2019.12.11.872051
A1 C.J. Battey
A1 Peter L. Ralph
A1 Andrew D. Kern
YR 2019
UL http://biorxiv.org/content/early/2019/12/12/2019.12.11.872051.abstract
AB 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.