Dispersal inference from population genetic variation using a convolutional neural network.

ABSTRACT: The geographic nature of biological dispersal shapes patterns of genetic variation over landscapes, making it possible to infer properties of dispersal from genetic variation data. Here, we present an inference tool that uses geographically distributed genotype data in combination with a convolutional neural network to estimate a critical population parameter: the mean per-generation dispersal distance. Using extensive simulation, we show that our deep learning approach is competitive with or outperforms state-of-the-art methods, particularly at small sample sizes. In addition, we evaluate varying nuisance parameters during training-including population density, demographic history, habitat size, and sampling area-and show that this strategy is effective for estimating dispersal distance w

SUBMITTER: Smith CCR 

PROVIDER: S-EPMC10213498 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature