Evidence map›Paper›PMID 39794122›Full record

ArticleGenome research2025

Proxy panels enable privacy-aware outsourcing of genotype imputation.

Degui Zhi, Xiaoqian Jiang, Arif Harmanci

Abstract read
In one paragraph

Article in Genome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Degui ZhiDepartment of Bioinformatics and Systems Medicine, D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas 77030, USA.ORCID 0000-0001-7754-1890
Xiaoqian JiangDepartment of Health Data Science and Artificial Intelligence, D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas 77030, USA.
Arif HarmanciDepartment of Bioinformatics and Systems Medicine, D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas 77030, USA; arif.o.harmanci@uth.tmc.edu.

Funding

Secure Outsourcing of Genotype Imputation for Privacy-aware Genomic Analysis (RO1HE21)R01HG012604 · NHGRI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Arif Ozgun Harmanci · 2023 to 2026
$2.2M
NHGRI NIH HHS R01 HG012604
6 · The paper itself

Abstract

One of the major challenges in genomic data sharing is protecting participants' privacy in collaborative studies and in cases when genomic data are outsourced to perform analysis tasks, for example, genotype imputation services and federated collaborations genomic analysis. Although numerous cryptographic methods have been developed, these methods may not yet be practical for population-scale tasks in terms of computational requirements, rely on high-level expertise in security, and require each algorithm to be implemented from scratch. In this study, we focus on outsourcing of genotype imputation, a fundamental task that utilizes population-level reference panels, and develop protocols that rely on using "proxy panels" to protect genotype panels, whereas the imputation task is being outsourced at servers. The proxy panels are generated through a series of protection mechanisms such as haplotype sampling, allele hashing, and coordinate anonymization to protect the underlying sensitive panel's genetic variant coordinates, genetic maps, and chromosome-wide haplotypes. Although the resulting proxy panels are almost distinct from the sensitive panels, they are valid panels that can be used as input to imputation methods such as Beagle. We demonstrate that proxy-based imputation protects against well-known attacks with a minor decrease in imputation accuracy for variants in a wide range of allele frequencies.

Indexed as

Genetic PrivacyGenomicsGenotypeOutsourced ServicesAlgorithmsHaplotypesHumans

Identifiers

PMID39794122
PMCPMC11874966

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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.