A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

ABSTRACT: Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, includin

SUBMITTER: Hejase HA 

PROVIDER: S-EPMC8789311 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature