Biology subjects

Edupalli, M.

Publications and source records attributed to Edupalli, M..

2 recordsLinked to original sources

bioRxiv2022

Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets

Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variants linked to health and disease1, 2. However, existing data sharing regulations limit the scope of such collaborations3. Although cryptographic tools for secure computation promise to enable collaborative analysis with formal privacy guarantees, existing approaches either are computationally impractical or support only simplified analyses4-7. We introduce secure federated genome-wide association studies (SF-GWAS), a novel combination of secure computation frameworks and distributed algorithms that empowers efficient and accurate GWAS on private data held by multiple entities while ensuring data confidentiality. SF-GWAS supports the most widely-used GWAS pipelines based on principal component analysis (PCA) or linear mixed models (LMMs). We demonstrate the accuracy and practical runtimes of SF-GWAS on five datasets, including a large UK Biobank cohort of 410K individuals, showcasing an order-of-magnitude improvement in runtime compared to previous work. Our work realizes the power of secure collaborative genomic studies at unprecedented scale.

bioinformatics↗

bioRxiv2022

Secure and federated linear mixed model association tests

Privacy-preserving algorithms for genome-wide association studies (GWAS) promise to facilitate data sharing across silos to accelerate new discoveries. 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. 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. 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. 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. Our work unlocks the promise of secure and distributed algorithms for collaborative genomic studies.1

bioinformatics↗