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Rapid, Reference-Free Human Genotype Imputation with Denoising Autoencoders

View ORCID ProfileRaquel Dias, Doug Evans, Shang-Fu Chen, Kai-Yu Chen, Leslie Chan, Ali Torkamani
doi: https://doi.org/10.1101/2021.12.01.470739
Raquel Dias
1Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
2Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
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  • ORCID record for Raquel Dias
Doug Evans
1Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
2Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
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Shang-Fu Chen
1Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
2Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
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Kai-Yu Chen
1Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
2Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
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Leslie Chan
1Scripps Research Translational Institute, Scripps Research, La Jolla, CA, 92037, USA
2Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
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Ali Torkamani
2Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
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  • For correspondence: atorkama@scripps.edu
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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 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 4-fold faster inference run time relative to standard imputation tools.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 02, 2021.
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Rapid, Reference-Free Human Genotype Imputation with Denoising Autoencoders
Raquel Dias, Doug Evans, Shang-Fu Chen, Kai-Yu Chen, Leslie Chan, Ali Torkamani
bioRxiv 2021.12.01.470739; doi: https://doi.org/10.1101/2021.12.01.470739
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Rapid, Reference-Free Human Genotype Imputation with Denoising Autoencoders
Raquel Dias, Doug Evans, Shang-Fu Chen, Kai-Yu Chen, Leslie Chan, Ali Torkamani
bioRxiv 2021.12.01.470739; doi: https://doi.org/10.1101/2021.12.01.470739

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