Collagene Enables Privacy-Aware Federated and Collaborative Genomic Data Analysis

Authors

Wentao Li
Miran Kim
Kai Zhang
Xiaoquian Jiang
Arif Harmanci

Publication Date

9-11-2023

Journal

Genome Biology

Abstract

Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935.

Keywords

Collaborative analysis, Federated model training, Genomic data privacy, Data security

Comments

Supplementary Materials

DOI

10.1186/s13059-023-03039-z

PMID

37697426

PMCID

PMC10496350

PubMedCentral® Posted Date

September 2023

PubMedCentral® Full Text Version

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