Rapid, Reference-Free human genotype imputation with denoising autoencoders.

ABSTRACT: Genotype imputation is a foundational tool for population genetics. Standard statistical imputation approaches rely on the co-location of large whole-genome sequencing-based reference panels, powerful computing environments, and potentially sensitive genetic study data. This results in computational resource and privacy-risk barriers to access to cutting-edge imputation techniques. Moreover, the accuracy of current statistical approaches is known to degrade in regions of low and complex linkage disequilibrium. Artificial neural network-based imputation approaches may overcome these limitations by encoding complex genotype relationships in easily portable inference models. Here, we demonstrate an autoencoder-based approach for genotype imputation, using a large, commonly used reference pane

SUBMITTER: Dias R 

PROVIDER: S-EPMC9555874 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature