Evidence map›Paper›PMID 41512862›Full record

ArticleCell reports methods2026

Homomorphic encryption enables privacy preserving polygenic risk scores.

Elizabeth Knight, Jiaqi Li, Matthew Jensen, Israel Yolou, Can Kockan, Mark Gerstein

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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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

6 authors.

Elizabeth KnightProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06520, USA.
Jiaqi LiProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06520, USA.
Matthew JensenProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06520, USA.
Israel YolouProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06520, USA.
Can KockanBroad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Mark GersteinProgram in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA; Department of Computer Science, Yale University, New Haven, CT 06511, USA; Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT 06511, USA; Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA. Electronic address: mark@gersteinlab.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polygenic risk score models (PRSs) are important tools in precision medicine, enabling personalized risk prediction; however, they raise privacy concerns. Fully homomorphic encryption (FHE) provides a potential solution, allowing computation on encrypted genomic data. Here, we develop an open-source implementation of FHE for PRS (HEPRS), available online. HEPRS involves a three party system: clients (clinicians handling sensitive genetic data), modelers developing a PRS (academics), and evaluators (a local hospital running the models while maintaining confidentiality). We apply HEPRS to synthetic datasets and a 110,000 single-nucleotide-polymorphism (SNP) model for schizophrenia and show that encrypted and plaintext PRSs agree closely. We investigate encryption parameters that influence computational accuracy, memory, and time, demonstrating that HEPRS is practical to use on a single CPU. These results show that FHE enables realistic, privacy-preserving PRSs with negligible accuracy loss, supporting secure and scalable genomic analytics.

Indexed as

Computer SecurityMultifactorial InheritancePrivacyGenetic Risk ScoreHumansPolymorphism, Single NucleotideSchizophreniaCP: computational biologyCP: geneticsgenomic privacygenomicshomomorphic encryptionpolygenic risk score

Identifiers

PMID41512862
PMCPMC12853174

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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.