Computationally efficient demographic history inference from allele frequencies with supervised machine learning.

ABSTRACT: Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we developed donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks ca

SUBMITTER: Tran LN 

PROVIDER: S-EPMC10888863 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature