Haplotype and population structure inference using neural networks in whole-genome sequencing data.

ABSTRACT: Accurate inference of population structure is important in many studies of population genetics. Here we present HaploNet, a method for performing dimensionality reduction and clustering of genetic data. The method is based on local clustering of phased haplotypes using neural networks from whole-genome sequencing or dense genotype data. By using Gaussian mixtures in a variational autoencoder framework, we are able to learn a low-dimensional latent space in which we cluster haplotypes along the genome in a highly scalable manner. We show that we can use haplotype clusters in the latent space to infer global population structure using haplotype information by exploiting the generative properties of our framework. Based on fitted neural networks and their latent haplotype clusters, we can per

SUBMITTER: Meisner J 

PROVIDER: S-EPMC9435741 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature