Re-use of patients' health records can provide tremendous benefits for clinical research. Yet, when researchers need to access sensitive/identifying data, such as genomic data, in order to compile cohorts of well-characterized patients for specific studies, privacy and security concerns represent major obstacles that make such a procedure extremely difficult if not impossible. In this paper, we address the challenge of designing and deploying in a real operational setting an efficient privacy-preserving explorer for genetic cohorts. Our solution is built on top of the i2b2 (Informatics for Integrating Biology and the Bedside) framework and leverages cutting-edge privacy-enhancing technologies such as homomorphic encryption and differential privacy. Solutions involving homomorphic encryption are often believed to be costly and immature for use in operational environments. Here, we show that, for specific applications, homomorphic encryption is actually a very efficient enabler. Indeed, our solution outperforms prior work by enabling a researcher to securely compute simple statistics on more than 3,000 encrypted genetic variants simultaneously for a cohort of 5,000 individuals in less than 5 seconds with commodity hardware. To the best of our knowledge, our privacy-preserving solution is the first to also be successfully deployed and tested in a operation setting (Lausanne University Hospital).