Privacy-preserving GWAS

Privacy-preserving framework for multi-site GWAS on quantitative traits using a distributed linear mixed model and randomized encoding so servers never see raw genotypes or phenotypes—only obfuscated intermediates—while improving speed versus several cryptographic baselines.

Privacy-preserving GWAS, multi-site, quantitative traits, federated analysis

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.