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Rapid, Reference-Free human genotype imputation with denoising autoencoders
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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 fourfold faster inference run time relative to standard imputation tools.
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Metadata
- Authors
- Dias, R., Evans, D., Chen, S.-F., Chen, K.-Y., Loguercio, S., Chan, L., & Torkamani, A.
- Affiliations
- Scripps Research Institute (US)
- Scripps Research Translational Institute, Scripps Research Institute
- Corresponding author
- Torkamani, Ali
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2022-09-23
- Volume
- 11
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC9555874
- OpenAlex
- W4296808098
- Cited by
- 16
- References
- 1
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 7 · 2023: 4
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R01HG010881, KL2TR002552, U24TR002306, UL1TR002550)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- genotype imputation, denoising autoencoder, artificial neural networks, linkage disequilibrium, chromosome 22, allele frequency spectrum, ancestry diversity, inference speed, genetic privacy
- MeSH terms
- Gene Frequency, Genetics, Population, Genotype, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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