ArticleeLife2022
Rapid, Reference-Free human genotype imputation with denoising autoencoders.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- ALGI: Sparse Convolutional Denoising Autoencoder Utilizing Local Genomic Information for Genotype Imputation.Animals : an open access journal from MDPI · 2026Article
- GS-Impute: A neural network framework for accurate imputation of low-density markers in across-population genomic selection.Plant communications · 2026Article
- Article
- BiU-Net: A Biologically Informed U-Net for Genotype Imputation.Research square · 2025Article
- STICI: Split-Transformer with integrated convolutions for genotype imputation.Nature communications · 2025Article
- Empirical versus estimated accuracy of imputation: optimising filtering thresholds for sequence imputation.Genetics, selection, evolution : GSE · 2024Article
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.Journal of human genetics · 2024Review
- Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.Journal of human genetics · 2024Review
- A rapid and reference-free imputation method for low-cost genotyping platforms.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.