PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies

Genome-wide association studies help uncover genetic influences on complex traits and diseases. Importantly, multi-site data collaborations enhance the statistical power of these studies but pose challenges due to the sensitivity of genomic data. Existing privacy-preserving approaches to performing multi-site genome-wide association studies rely on computationally expensive cryptographic techniques, which limit applicability. To address this, we present PP-GWAS, a privacy-preserving algorithm that improves efficiency and scalability while maintaining data privacy. Our method leverages randomized encoding within a distributed framework to perform stacked ridge regression on a linear mixed model, enabling robust analysis of quantitative phenotypes. We show experimentally using real-world and synthetic data that our approach achieves twice the computational speed of comparable methods while reducing resource consumption.

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Publication Details

Journal
Nature Communications
Published
2025-12-09
DOI
https://doi.org/10.1038/s41467-025-66771-z
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00

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article

PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies

Ali Burak Ünal, Mete Akgün, Nico Pfeifer, A. Hannemann et al.
Nature Communications
Privacy-Preserving Technologies in Data
article

PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies

Ali Burak Ünal, Mete Akgün, Nico Pfeifer, A. Hannemann, A. Swaminathan
article en

Abstract

Genome-wide association studies help uncover genetic influences on complex traits and diseases. Importantly, multi-site data collaborations enhance the statistical power of these studies but pose challenges due to the sensitivity of genomic data. Existing privacy-preserving approaches to performing multi-site genome-wide association studies rely on computationally expensive cryptographic techniques, which limit applicability. To address this, we present PP-GWAS, a privacy-preserving algorithm that improves efficiency and scalability while maintaining data privacy. Our method leverages randomized encoding within a distributed framework to perform stacked ridge regression on a linear mixed model, enabling robust analysis of quantitative phenotypes. We show experimentally using real-world and synthetic data that our approach achieves twice the computational speed of comparable methods while reducing resource consumption.

Nature CommunicationsVol. 16(1)
Leipzig University of Applied Sciences (DE), Zurich University of Applied Sciences in Business Administration (CH), ZHAW Zurich University of Applied Sciences (CH), University of Zurich (CH), Privy Council Office (CA), Natural and Medical Sciences Institute (DE), Zurich University of Teacher Education (CH), Center for Scalable Data Analytics and Artificial Intelligence (DE), University of Tübingen (DE), Leipzig University (DE), Delft University of Technology (NL)
Eberhard Karls Universität Tübingen, Bundesministerium für Bildung und Forschung, Forschungszentrum Jülich, German Network for Bioinformatics Infrastructure
Openalex Percentile: Top 79%
Privacy-Preserving Technologies in Data
DOIOpen Access PDFOpenAlex
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