eLife (Sep 2022)

Rapid, Reference-Free human genotype imputation with denoising autoencoders

  • Raquel Dias,
  • Doug Evans,
  • Shang-Fu Chen,
  • Kai-Yu Chen,
  • Salvatore Loguercio,
  • Leslie Chan,
  • Ali Torkamani

DOI
https://doi.org/10.7554/eLife.75600
Journal volume & issue
Vol. 11

Abstract

Read online

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 panel, and spanning the entirety of human chromosome 22. Our autoencoder-based genotype imputation strategy achieved superior imputation accuracy across the allele-frequency spectrum and across genomes of diverse ancestry, while delivering at least fourfold faster inference run time relative to standard imputation tools.

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