Childhood Kidney Diseases

Korean Society of Pediatric Nephrology (대한소아신장학회)
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Genome-based medicine in Korea: the Korea National Institute of Health infrastructure for precision medicine

  • Received : 2026.01.13
  • Accepted : 2026.02.20
  • Published : 2026.02.28
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Abstract

Since the completion of the Human Genome Project, genome-based medicine has progressed from a predominantly research-driven endeavor to a field of increasing clinical relevance. In Korea, the Korea National Institute of Health (KNIH) has played a central role in the establishment of the necessary research infrastructure that supports the secure and responsible use of genomic and clinical data. These efforts have enabled the generation of comprehensive genomic datasets representative of the Korean population and, together with the Korea Biobank Array optimized for population-specific variants, have strengthened discovery-driven research and accelerated advances in disease gene identification and risk prediction. More recently, KNIH has expanded analyses based on whole-genome sequencing data to support clinical translation, enabling more comprehensive variant detection and facilitating the application of genomic information to disease diagnosis and precision medicine research. These national genomic resources provide an important foundation for improving the diagnosis and management of genetically mediated conditions, including pediatric kidney diseases, where early etiologic diagnosis can substantially influence clinical decision-making and long-term outcomes. Further strengthening of institutional and regulatory frameworks will be essential to support routine clinical implementation and maximize the public health impact of genomics in Korea.

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References

  1. Lu Y, Li M, Gao Z, Ma H, Chong Y, Hong J, et al. Advances in whole genome sequencing: methods, tools, and applications in population genomics. Int J Mol Sci 2025;26:372. https://doi.org/10.3390/ijms26010372
  2. Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 2016;17:333-51. https://doi.org/10.1038/nrg.2016.49
  3. Groopman EE, Rasouly HM, Gharavi AG. Genomic medicine for kidney disease. Nat Rev Nephrol 2018;14:83-104. https://doi.org/10.1038/nrneph.2017.167
  4. Arora V, Anand K, Chander Verma I. Genetic testing in pediatric kidney disease. Indian J Pediatr 2020;87:706-15. https://doi.org/10.1007/s12098-020-03198-y
  5. Leung EYL, Robbins HL, Zaman S, Lal N, Morton D, Dew L, et al. The potential clinical utility of whole genome sequencing for patients with cancer: evaluation of a regional implementation of the 100,000 Genomes Project. Br J Cancer 2024;131:1805-13.
  6. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203-9. https://doi.org/10.1038/s41586-018-0579-z
  7. Denny JC, Rutter JL, Goldstein DB, Philippakis A, Smoller JW, Jenkins G, et al. The "All of Us" research program. N Engl J Med 2019;381:668-76. https://doi.org/10.1056/NEJMsr1809937
  8. Kim Y, Han BG. Cohort profile: the Korean Genome and Epidemiology Study (KoGES) consortium. Int J Epidemiol 2017;46:e20. https://doi.org/10.1093/ije/dyv316
  9. Cho SY, Hong EJ, Nam JM, Han B, Chu C, Park O, et al. Opening of the National Biobank of Korea as the infrastructure of future biomedical science in Korea. Osong Public Health Res Perspect 2012;3:177-84. https://doi.org/10.1016/j.phrp.2012.07.004
  10. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12:e1001779. https://doi.org/10.1371/journal.pmed.1001779
  11. Nam Y, Kim J, Jung SH, Woerner J, Suh EH, Lee DG, et al. Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annu Rev Biomed Data Sci 2024;7:225-50. https://doi.org/10.1146/biodatasci.2024.7.issue-1
  12. Kim JS, Hong DU. Korea's Bio Big Data Project: importance and challenges of governance and data utilization. Healthc Inform Res 2025;31:226-34. https://doi.org/10.4258/hir.2025.31.3.226
  13. Dolle L, Bekaert S. High-quality biobanks: pivotal assets for reproducibility of OMICS-data in biomedical translational research. Proteomics 2019;19:e1800485. https://doi.org/10.1002/pmic.v19.21-22
  14. Lee JE, Kim JH, Hong EJ, Yoo HS, Nam HY, Park O, et al. National Biobank of Korea: quality control programs of collected-human biospecimens. Osong Public Health Res Perspect 2012;3:185-9. https://doi.org/10.1016/j.phrp.2012.07.007
  15. Geibel J, Reimer C, Weigend S, Weigend A, Pook T, Simianer H, et al. How array design creates SNP ascertainment bias. PLoS One 2021;16:e0245178. https://doi.org/10.1371/journal.pone.0245178
  16. Moon S, Kim YJ, Han S, Hwang MY, Shin DM, Park MY, et al. The Korea Biobank Array: design and identification of coding variants associated with blood biochemical traits. Sci Rep 2019;9:1382. https://doi.org/10.1038/s41598-018-37832-9
  17. Hwang MY, Choi NH, Won HH, Kim BJ, Kim YJ. Analyzing the Korean reference genome with meta-imputation increased the imputation accuracy and spectrum of rare variants in the Korean population. Front Genet 2022;13:1008646. https://doi.org/10.3389/fgene.2022.1008646
  18. Pagnamenta AT, Camps C, Giacopuzzi E, Taylor JM, Hashim M, Calpena E, et al. Structural and non-coding variants increase the diagnostic yield of clinical whole genome sequencing for rare diseases. Genome Med 2023;15:94. https://doi.org/10.1186/s13073-023-01240-0
  19. Clark MM, Hildreth A, Batalov S, Ding Y, Chowdhury S, Watkins K, et al. Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Sci Transl Med 2019;11:eaat6177. https://doi.org/10.1126/scitranslmed.aat6177
  20. Saunders CJ, Miller NA, Soden SE, Dinwiddie DL, Noll A, Alnadi NA, et al. Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci Transl Med 2012;4:154ra135. https://doi.org/10.1126/scitranslmed.3004041
  21. Wiencek JR. Genomic newborn screening: baby steps into the future. Clin Chem 2023;69:542-3. https://doi.org/10.1093/clinchem/hvad021
  22. Bros-Facer V, Taylor S, Patch C. Next-generation sequencing-based newborn screening initiatives in Europe: an overview. Rare Dis Orphan Drugs J 2023;2:21. https://doi.org/10.20517/rdodj.2023.26
  23. Lee S, Kim EY, Shin C. Longitudinal association between brain volume change and gait speed in a general population. Exp Gerontol 2019;118:26-30. https://doi.org/10.1016/j.exger.2019.01.004
  24. Kim JW, Choi EC, Lee KJ. Standardizing the approach to clinical-based human microbiome research: from clinical information collection to microbiome profiling and human resource utilization. Osong Public Health Res Perspect 2025;16:300-7. https://doi.org/10.24171/j.phrp.2024.0319
  25. Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med 2022;28:1773-84. https://doi.org/10.1038/s41591-022-01981-2
  26. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci 2021;14:86-93. https://doi.org/10.1111/cts.v14.1
  27. Knoppers BM, Thorogood AM. Ethics and Big Data in health. Curr Opin Syst Biol 2017;4:53-7.
  28. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018. https://doi.org/10.1038/sdata.2016.18