Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks.

ABSTRACT: Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs of individuals and of the age of genomic variants is key in several population genetic analyses. We developed a likelihood-free approach, called CoalNN, which uses a convolutional neural network to predict pairwise TMRCAs and allele ages from sequencing or SNP array data. CoalNN is trained through simulation and can be adapted to varying parameters, such as demographic history, using transfer learning. Across several simulated scenarios, CoalNN matched or outperformed the accuracy of model-based approaches for pairwise TMRCA and allele age prediction. We applied CoalNN to settings for which model-based approaches are under-developed and performed analyses to gain insights into the set of features it use

SUBMITTER: Nait Saada J 

PROVIDER: S-EPMC10581698 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature