![]() Genome-wide association studies (GWAS) have been a major driving force behind genomics research. Our work unlocks the promise of secure and distributed algorithms for collaborative genomic studies. ![]() Our results show that SafeGENIE obtains accurate association test results comparable to a state-of-the-art centralized algorithm (REGENIE), and achieves practical runtimes even for large datasets of up to 100K individuals. We overcome the computational burden of LMMs by leveraging recent advances in LMMs and secure computation, as well as a novel scalable dimensionality reduction technique. Here we introduce SafeGENIE, an efficient and provably secure algorithm for LMM-based association studies, which allows multiple entities to securely share their data to jointly compute association statistics without leaking any intermediary results. However, existing approaches do not support an important, prevalent class of methods known as linear mixed model (LMM) association tests or would provide limited privacy protection, due to the high computational burden of LMMs under existing secure computation frameworks. Privacy-preserving algorithms for genome-wide association studies (GWAS) promise to facilitate data sharing across silos to accelerate new discoveries.
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