Rapid, Reference-Free human genotype imputation with denoising autoencoders
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2022-09-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/75600 |
| _version_ | 1811251290914684928 |
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| author | Raquel Dias Doug Evans Shang-Fu Chen Kai-Yu Chen Salvatore Loguercio Leslie Chan Ali Torkamani |
| author_facet | Raquel Dias Doug Evans Shang-Fu Chen Kai-Yu Chen Salvatore Loguercio Leslie Chan Ali Torkamani |
| author_sort | Raquel Dias |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T16:17:18Z |
| format | Article |
| id | doaj.art-6da6df6501724033943328bdb93d9bb0 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T16:17:18Z |
| publishDate | 2022-09-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-6da6df6501724033943328bdb93d9bb02022-12-22T03:25:41ZengeLife Sciences Publications LtdeLife2050-084X2022-09-011110.7554/eLife.75600Rapid, Reference-Free human genotype imputation with denoising autoencodersRaquel Dias0Doug Evans1Shang-Fu Chen2Kai-Yu Chen3Salvatore Loguercio4Leslie Chan5Ali Torkamani6https://orcid.org/0000-0003-0232-8053Scripps Research Translational Institute, Scripps Research Institute, La Jolla, United States; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United States; Department of Microbiology and Cell Science, University of Florida, Gainesville, United StatesScripps Research Translational Institute, Scripps Research Institute, La Jolla, United States; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United StatesScripps Research Translational Institute, Scripps Research Institute, La Jolla, United States; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United StatesScripps Research Translational Institute, Scripps Research Institute, La Jolla, United States; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United StatesScripps Research Translational Institute, Scripps Research Institute, La Jolla, United States; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United StatesScripps Research Translational Institute, Scripps Research Institute, La Jolla, United States; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United StatesScripps Research Translational Institute, Scripps Research Institute, La Jolla, United States; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United StatesGenotype 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.https://elifesciences.org/articles/75600imputationdeep learningartifitial intelligencepopulation geneticsgenomicsautoencoder |
| spellingShingle | Raquel Dias Doug Evans Shang-Fu Chen Kai-Yu Chen Salvatore Loguercio Leslie Chan Ali Torkamani Rapid, Reference-Free human genotype imputation with denoising autoencoders eLife imputation deep learning artifitial intelligence population genetics genomics autoencoder |
| title | Rapid, Reference-Free human genotype imputation with denoising autoencoders |
| title_full | Rapid, Reference-Free human genotype imputation with denoising autoencoders |
| title_fullStr | Rapid, Reference-Free human genotype imputation with denoising autoencoders |
| title_full_unstemmed | Rapid, Reference-Free human genotype imputation with denoising autoencoders |
| title_short | Rapid, Reference-Free human genotype imputation with denoising autoencoders |
| title_sort | rapid reference free human genotype imputation with denoising autoencoders |
| topic | imputation deep learning artifitial intelligence population genetics genomics autoencoder |
| url | https://elifesciences.org/articles/75600 |
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