LinkedNN: a neural model of linkage disequilibrium decay for recent effective population size inference

TL;DR

LinkedNN is a neural network-based tool that estimates recent effective population size from linkage disequilibrium data, outperforming existing methods especially with limited and unphased genomic data.

Contribution

It introduces a novel neural network approach for inferring population size using linkage disequilibrium, effective with sparse and unphased data.

Findings

Outperforms existing deep learning methods

Effective with small sample sizes and unphased data

Available as an easy-to-use Python package

Abstract

Summary: A bioinformatics tool is presented for estimating recent effective population size by using a neural network to automatically compute linkage disequilibrium-related features as a function of genomic distance between polymorphisms. The new method outperforms existing deep learning and summary statistic-based approaches using relatively few sequenced individuals and variant sites, making it particularly valuable for molecular ecology applications with sparse, unphased data. Availability and implementation: The program is available as an easily installable Python package with documentation here: https://pypi.org/project/linkedNN/. The open source code is available from: https://github.com/the-smith-lab/LinkedNN.