ArticleIEEE access : practical innovations, open solutions2021
Fast and Scalable Private Genotype Imputation Using Machine Learning and Partially Homomorphic Encryption.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 43 citations in OpenAlex.
- ALGI: Sparse Convolutional Denoising Autoencoder Utilizing Local Genomic Information for Genotype Imputation.Animals : an open access journal from MDPI · 2026Article
- A simple demonstration of a privacy-preserving de-centralised genotype imputation workflow.PloS one · 2026Article
- SURFBAT: a surrogate family based association test building on large imputation reference panels.G3 (Bethesda, Md.) · 2025Article
- Secure and scalable gene expression quantification with pQuant.Nature communications · 2025Article
- SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine.Genome biology · 2024Article
- Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data.Journal of biomedical informatics · 2024Article
- Ultra-secure storage and analysis of genetic data for the advancement of precision medicine.bioRxiv : the preprint server for biology · 2024Article
- A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review.Current genomics · 2023Review
- Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data.Molecular biology and evolution · 2023Article
- Privacy-preserving cancer type prediction with homomorphic encryption.Scientific reports · 2023Article
- Session Introduction: TOWARDS ETHICAL BIOMEDICAL INFORMATICS: LEARNING FROM OLELO NOEAU, HAWAIIAN PROVERBS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Article
- Storing and analyzing a genome on a blockchain.Genome biology · 2022Article
- Functional genomics data: privacy risk assessment and technological mitigation.Nature reviews. Genetics · 2022Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
The recent advances in genome sequencing technologies provide unprecedented opportunities to understand the relationship between human genetic variation and diseases. However, genotyping whole genomes from a large cohort of individuals is still cost prohibitive. Imputation methods to predict genotypes of missing genetic variants are widely used, especially for genome-wide association studies. Accurate genotype imputation requires complex statistical methods. Due to the data and computing-intensive nature of the problem, imputation is increasingly outsourced, raising serious privacy concerns. In this work, we investigate solutions for fast, scalable, and accurate privacy-preserving genotype imputation using Machine Learning (ML) and a standardized homomorphic encryption scheme, Paillier cryptosystem. ML-based privacy-preserving inference has been largely optimized for computation-heavy non-linear functions in a single-output multi-class classification setting. However, having a large number of multi-class outputs per genome per individual calls for further optimizations and/or approximations specific to this application. Here we explore the effectiveness of linear models for genotype imputation to convert them to privacy-preserving equivalents using standardized homomorphic encryption schemes. Our results show that performance of our privacy-preserving genotype imputation method is equivalent to the state-of-the-art plaintext solutions, achieving up to 99% micro area under curve score, even on real-world large-scale datasets up to 80,000 targets.
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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.