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P ., et al. (2011). Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nature Biotechnology, 29(5), 411–414. https://www.nature.com/ articles/nbt.1837 25. Julkowska, D., Austin, C. P ., Cutillo, C. M., et al. (2017). The importance of international collaboration for rare diseases research: A European perspective. Gene Therapy, 24(9), 562–571. https://www.nature.com/articles/gt201729.pdf 26. Kayadjanian, N., Schwartz, L., Farrar, E., et al. (2018). High levels of caregiver burden in Prader-Willi syndrome. PLoS ONE, 13(3), e0194655. https://doi.org/10.1371/journal.pone.0194655 27. Foundation for Prader-Willi Research (FPWR). (2025, June 24). Top 10 learnings from the Global PWS Registry. https:// www.fpwr.org/blog/top-10-learnings-from-the-global-pws-registry 28. National Organization for Rare Disorders (NORD). (2025, June 5). First rare disease therapy developed from the NORD IAMRARE patient registry secures FDA approval. https://rarediseases.org/first-rare-disease-therapy-developed-from-the- nord-iamrare-patient-registry-secures-fda-approval/ 29. EveryLife Foundation for Rare Diseases. (2023, September 14). The cost of delayed diagnosis in rare disease: A health economic study. https://everylifefoundation.org/wp-content/uploads/2023/09/EveryLife-Cost-of-Delayed-Diagnosis-in- Rare-Disease_Final-Full-Study-Report_0914223.pdf 30. Thorpe, E., Williams, T., Shaw, C., et al. (2024). The impact of clinical genome sequencing in a global population with suspected rare genetic disease. American Journal of Human Genetics, 111(7), 1271–1281. https://doi.org/10.1016/j. ajhg.2024.05.006 31. Cederroth, H., Cederroth, M., Baynam, G., et al. (2025). Undiagnosed Hackathons: Ending the diagnostic odyssey for individuals with rare disease. Rare, 3, 100103. https://doi.org/10.1016/j.rare.2025.100103 32. Undiagnosed Diseases Network International ((UDNI). (2024). Undiagnosed day 2024. https://www.udninternational.org/ schede-202-the_undiagnosed_day_2024 33. Rohani-Montez, S. C., Reid, K., Yuan, M., & Cohen, J. (2025). Are paediatric neurologists who participate in rare disease education more likely to order genetic tests for rare diseases? Poster presented at the World Orphan Drug Congress Europe, 27–29 October 2025. Medscape Education Global, London 34. Kingsmore, S. F., Nofsinger, R., & Ellsworth, K. (2024). Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: A review. npj Genomic Medicine, 9(1), Article 17. https://doi.org/10.1038/s41525-024- 00404-0 35. World Economic Forum. (2020, February 26). Global data access for solving rare disease: A health economics value framework. https://www3.weforum.org/docs/WEF_Global_Data_Access_for_Solving_Rare_Disease_Report_2020.pdf 36. International Coalition of Medicines Regulatory Authorities (ICMRA). (2024). ICMRA rare diseases workshop report. https:// icmra.info/drupal/sites/default/files/2024-11/rare_event_report_lugano_16_17september2024.pdf 37. European Union. (2025, March 27). Regulation (EU) 2025/327 of the European Parliament and of the Council of 19 March 2025 on the European Health Data Space. Official Journal of the European Union, L 327. https://eur-lex.europa.eu/legal- content/EN/TXT/?uri=OJ:L_202500327 38. EURORDIS. (2024, January). Rare Barometer survey: Data sharing in rare diseases [Fact sheet]. https://download2. eurordis.org/rbv/20-01-24%20RB%20Data%20Survey%20Fact%20Sheet_Final.pdf 39. Smith, C., Boustred, C., & Campbell, C. (2024, September 11). The Clinical Variant Ark at Genomics England [Blog post]. Genomics England. https://www.genomicsengland.co.uk/blog/the-genomics-england-clinical-variant-ark 40. University College London. (2025, May 7). AI model trained on de-identified data from 57 million people. UCL News. https://www.ucl.ac.uk/news/2025/may/ai-model-trained-de-identified-data-57-million-people 41. American Diabetes Association. (2025, June 21). Novel artificial intelligence models detect type 1 diabetes risk in clinical settings [Press release]. https://diabetes.org/newsroom/press-releases/novel-artificial-intelligence-models-detect-type-1- diabetes-risk-clinical 42. Baynam, G., Hartman, A. L., Letinturier, M. C. V., et al. (2024). Global health for rare diseases through primary care. The Lancet Global Health, 12(7), e1192–e1199. https://doi.org/10.1016/S2214-109X(24)00134-7 43. Ibid. 44. Liu, P ., Gong, M., Li, J., et al. (2021). Innovation in informatics to improve clinical care and drug accessibility for rare diseases in China. Frontiers in Pharmacology, 12, Article 719415. https://doi.org/10.3389/fphar.2021.719415 Making Rare Diseases Count: How Better Data Can Unlock a Multitrillion-Dollar Opportunity 33 Making Rare Diseases Count: How Better Data Can Unlock a Multitrillion-Dollar Opportunity 33

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