1. Rapid, Reference-Free human genotype imputation with denoising autoencoders
- Author
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Raquel Dias, Doug Evans, Shang-Fu Chen, Kai-Yu Chen, Salvatore Loguercio, Leslie Chan, and Ali Torkamani
- Subjects
imputation ,deep learning ,artifitial intelligence ,population genetics ,genomics ,autoencoder ,Medicine ,Science ,Biology (General) ,QH301-705.5 - 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.
- Published
- 2022
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