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• W3214897572 abstract "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." @default.
• W3214897572 created "2021-12-06" @default.
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• W3214897572 date "2021-12-02" @default.
• W3214897572 modified "2023-10-16" @default.
• W3214897572 title "Rapid, Reference-Free Human Genotype Imputation with Denoising Autoencoders" @default.
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• W3214897572 doi "https://doi.org/10.1101/2021.12.01.470739" @default.
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