Estimation of spatial demographic maps from polymorphism data using a neural network.

ABSTRACT: A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings

SUBMITTER: Smith CCR 

PROVIDER: S-EPMC10980082 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature