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Last Posted: Apr 25, 2024
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Secondary use of genomic data: patients’ decisions at point of testing and perspectives to inform international data sharing
M Martyn et al, EJHG, March 25, 2024

From the abstract: "International sharing of genomic data files arising from clinical testing of patients is essential to further improve genomic medicine. Whilst the general public are reluctant to donate DNA for research, the choices patients actually make about sharing their clinical genomic data for future re-use (research or clinical) are unknown. We ascertained the data-sharing choices of 1515 patients having genomic testing for inherited conditions or cancer treatment from clinical consent forms. "

Next-generation sequencing and bioinformatics in rare movement disorders.
Michael Zech et al. Nat Rev Neurol 2024 1

From the abstract: "In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype–phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling. "

Organizational Factors in Clinical Data Sharing for Artificial Intelligence in Health Care.
Alaa Youssef et al. JAMA Netw Open 2023 12 (12) e2348422

From the abstract: "Are organizational factors associated with the motivation of health organizations to share clinical data for artificial intelligence (AI) development? In this qualitative study, 27 leaders from 18 health organizations were interviewed, and a predominant concern among them was data privacy risks. Most stakeholders viewed these as a substantial barrier for public health data sharing due to potential liability and reputational consequences; however, they identified external incentives as key factors for enhancing organizational motivation and fostering both within and across-sector data-sharing collaborations for AI development. The findings of this study suggest that data-sharing policies should be rooted in feasibility and incentivization strategies to promote responsible and equitable AI development in the health care sector. "

Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer.
James Casaletto et al. Annu Rev Genomics Hum Genet 2023 5

Continued advances in precision medicine rely on the widespread sharing of data that relate human genetic variation to disease. However, data sharing is severely limited by legal, regulatory, and ethical restrictions that safeguard patient privacy. Federated analysis addresses this problem by transferring the code to the data—providing the technical and legal capability to analyze the data within their secure home environment rather than transferring the data to another institution for analysis.

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.