Large-scale Collaborative Imaging Genetics Studies of Risk Genetic Factors for Alzheimer's Disease Across Multiple Institutions

TL;DR

This paper introduces a novel distributed framework for collaborative GWAS of Alzheimer's disease that preserves data privacy, accelerates model selection, and identifies risk SNPs across multiple institutions with high efficiency.

Contribution

It proposes the Local Query Model and Distributed Enhanced Dual Polytope Projection methods for privacy-preserving, efficient, large-scale GWAS across institutions.

Findings

Achieved a 66-fold speed-up in feature screening.

Successfully identified risk SNPs without data sharing.

Enabled collaborative model selection across institutions.

Abstract

Genome-wide association studies (GWAS) offer new opportunities to identify genetic risk factors for Alzheimer's disease (AD). Recently, collaborative efforts across different institutions emerged that enhance the power of many existing techniques on individual institution data. However, a major barrier to collaborative studies of GWAS is that many institutions need to preserve individual data privacy. To address this challenge, we propose a novel distributed framework, termed Local Query Model (LQM) to detect risk SNPs for AD across multiple research institutions. To accelerate the learning process, we propose a Distributed Enhanced Dual Polytope Projection (D-EDPP) screening rule to identify irrelevant features and remove them from the optimization. To the best of our knowledge, this is the first successful run of the computationally intensive model selection procedure to learn a consistent model across different institutions without compromising their privacy while ranking the SNPs that may collectively affect AD. Empirical studies are conducted on 809 subjects with 5.9 million SNP features which are distributed across three individual institutions. D-EDPP achieved a 66-fold speed-up by effectively identifying irrelevant features.