Cloud computing in population-scale genomics: Strategies, challenges, and future directions

초록

Advances in next-generation sequencing technologies have enabled population-scale genomic studies, transforming biomedical research and precision medicine. However, the resulting exponential growth in sequencing data has resulted in significant computational, storage, and governance challenges. Cloud computing has emerged as a critical infrastructure to address these issues by providing elastic computing resources, scalable storage, and standardized workflow management. This review evaluates the role of cloud computing in population-scale genomics and examines the technological foundations of cloudnative analysis, including containerization, workflow engines, and multi-cloud integration. We also discuss the regulatory frameworks such as Health Insurance Portability and Accountability Act, General Data Protection Regulation, and Personal Information Protection Act that govern the secure use of data, as well as Findable, Accessible, Interoperable, and Reusable (FAIR) and Secure and Authorized FAIR Environment principles that emphasize the responsible data sharing. Economic considerations, including Financial Operations-driven strategies for cost optimization, are explored alongside emerging technologies such as federated learning, quantum computing, and blockchain that are poised to reshape genomic research. Despite substantial progress, challenges persist in vendor lock-in, data migration, and cross-platform interoperability. Addressing these barriers will be essential to achieving scalable, secure, and reproducible cloud-based genomics. Ultimately, integrating cloud infrastructure into population-scale genomics will accelerate discovery and enable real-time clinical applications, inspiring a new era of precision medicine.

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