Citations 2
Using a translational data platform to create clinical-grade genome-informed risk assessments
Morse, Jennifer L, He, Megan, Bangash, Hana , Cortopassi, Joshua, Forman, Sophie, Gordon, Adam S, Hamed, Marwan, Karlson, Elizabeth W. , Kiryluk, K, Kullo, Iftikhar J, Lewis, Adam, Limdi, Nita, Marathe, Priya, McGuffin, Kyle, Odgis, Jacqueline, Perez, Emma, Petrzelka, Jacob, Prows, Cynthia A. , Thomas, Ellis, Wiesner, Georgia L. , Wilson, Travis, Linder, Jodell E. , Liu, Cong , Rasmussen, Luke V , Morse, Jennifer L. , Chung, Wendy K. , Cortopassi, Josh , Gordon, Adam S, Harris, Paul A. , Kenny, Eimear E , Kiryluk, Krzysztof , Kullo, Iftikhar J. , Lewis, Adam J. , Liu, Cong , Marathe, Priya , McGuffin, Kyle, Odgis, Jacqueline A. , Perez, Emma , Rasmussen, Luke V. , Thomas, Ellis, Peterson, Josh F.
JAMIA Open | July 13, 2026 | Volume: 9 | Issue: 4 | Pages: No page available
Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.
Implementing integrated genomic risk assessments for breast cancer: lessons learned from the Electronic Medical Records and Genomics study
Liu, Cong , Morse, Jennifer, Linder, Jodell E. , Antoniou, Antonis C. , Carver, Tim , Cortopassi, Josh , Peterson, Josh F. , Ta, Casey , Hoell, Christin , Prows, Cynthia A. , Miller, Emily , Perez, Emma , Jarvik, Gail P, Odgis, Jacqueline A. , Bonini, Katherine E. , McGuffin, Kyle, Maradik, Mary, Limdi, Nita, Marathe, Priya , Suckiel, Sabrina A. , Aguilar, Sienna , Lewis, Toni J., Wei, Wei‐Qi, Freimuth, Robert R, Håkonarson, Håkon , Wiesner, Georgia L. , Jarvik, Gail P. , Carver, Tim , Ta, Casey N , Hoell, Christin , Bland, Sarah T , Kottyan, Leah C , Luo, Yuan , Weng, Chunhua , Hakonarson, Hakon , Jarvik, Gail P , Liu, Cong , Peterson, Josh F , Crew, Katherine D. , Morse, Jennifer, Kenny, Eimear E , Bland, Harris T. , Mittendorf, Kathleen F. , McGuffin, Kyle, Kottyan, Leah C. , Abul‐Husn, Noura S. , Luo, Yuan , Freimuth, Robert R. , Weng, Chunhua , Chung, Wendy K.
JAMIA Open | October 4, 2025 | Volume: 8 | Issue: 5 | Pages: No page available
Abstract Objectives To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions. Materials and Methods A five-stage process was executed at ten sites. Data streams from REDCap surveys, PRS and monogenic reports, and MeTree pedigrees were normalized and forwarded through a REDCap plug-in to the CanRisk API. Results Integrated risk was returned to >10 000 women; 3.6% were ≥25 % lifetime risk and 0.9% carried pathogenic variants. Pipeline generated score aligns well with manual generated ones. Major barriers such as heterogeneous pedigree formats, missing data, edge-case handling, and evolving model versions were identified and resolved through mapping rules, imputations, and iterative testing. Discussion Cross-platform data harmonization and stakeholder alignment were decisive for success. Borderline-risk communication and model-version drift remain open issues. Conclusion Large-scale PRS-integrated breast-cancer risk reporting is feasible but requires robust interoperability standards and iterative governance.