DESCRIPTION (provided by applicant): The rapid development of Next Generation Sequencing (NGS) technologies significantly reduces the cost for producing DNA data. As a result, genome sequencing may soon become a routine tool for clinical diagnosis and therapy selection. In the meantime, the demand for large-scale meta-analysis of human genomic data from patients with various diseases is expected to grow substantially in the near future. However, the effort to meet such a demand has not benefited from the progress in sequencing technologies, due to the massive amount of computational resources needed for storing and analyzing the NGS data and the complicated procedures for researchers to get access to the data, which are put in place to protect the privacy of human subjects. To address such challenges and facilitate secure and also convenient DNA data sharing, we propose to study and develop a suite of innovative and transformative techniques aimed at achieving practical and cost-effective genomic data protection. Using these techniques, NIH data center can offer a centralized analysis service on the genome data it hosts; execute the analysis programs submitted by the data users, and control release of analysis outcomes to ensure the privacy of DNA donors. Our techniques will also help the center outsource the computation tasks it does not have sufficient resources to handle to the computing systems rented locally and remotely in a highly privacy-preserving manner. The proposed research will be conducted in a close collaboration with iDASH, a National Center for Biomedical Computing for "integrating Data for Analysis, Anonymization and Sharing", using its data to evaluate our techniques and its infrastructure to deploy them.

Collaborating with iDASH, a National Center for Biomedical Computing for "integrating Data for Analysis, Anonymization and Sharing", we will develop innovative and practical techniques for protecting the privacy of human subjects in the large-scale analysis of human genome sequencing data. These techniques will significantly reduce the cost for human genome research, help overcome the barrier to data access, and ultimately accelerate the translational research in human genomics and discovery of novel diagnosis tools using genomic techniques.