Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.

ABSTRACT: Widely used genotype imputation methods are based on the Li and Stephens model, which assumes that new haplotypes can be represented by modifying existing haplotypes in a reference panel through mutations and recombinations. These methods use genotypes from SNP arrays as inputs to estimate haplotypes that align with the input genotypes by analyzing recombination patterns within a reference panel, and then infer unobserved variants. While these methods require reference panels in an identifiable form, their public use is limited due to privacy and consent concerns. One strategy to overcome these limitations is to use de-identified haplotype information, such as summary statistics or model parameters. Advances in deep learning (DL) offer the potential to develop imputation methods that use h

SUBMITTER: Kojima K 

PROVIDER: S-EPMC11422160 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature