Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown.

ABSTRACT: Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics. Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators.

SUBMITTER: Burger KE 

PROVIDER: S-EPMC9377634 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature