SUNGKYUNKWAN UNIVERSITY SCHOOL OF MEDICINE
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Professor
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Hong-Hee Won
Hong-Hee Won PhD
| Graduate Program | Data Science and Personal Medicine |
| Research Area | Multi-omics and AI |
| Laboratory | Genomics & Digital Health Lab Laboratory |
| wonhh@skku.edu | |
| Tel | +82-2-2148-7566 |
Education & Careers
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> Education
2007 - 2011 Ph.D. in Bio and Brain Engineering, KAIST, South Korea
2002 - 2004 M.S. in Computer Science, Yonsei University, South Korea
1998 - 2002 B.S. in Computer Science, Yonsei University, South Korea
> Professional Experience
2020 - Pres. Associate Professor, SAIHST, Sungkyunkwan University, South Korea
2016 - 2020 Assistant Professor, SAIHST, Sungkyunkwan University, South Korea
2012 - 2015 Research Fellow, Massachusetts General Hospital, Harvard Medical School and Broad Institute of MIT and Harvard, USA
2004 - 2012 Research Scientist, Samsung Biomedical Research Institute and Samsung Medical Center
2002 - 2004 Researcher and Teaching Assistant, Soft Computing Laboratory, Yonsei University
2001 - 2002 Undergraduate Research Assistant, Soft Computing Laboratory, Yonsei University
Research Interest
> Medical & population genomics
Since the Human Genome Project, genome-wide association studies (GWAS) have been successful in identifying common genetic variants that are associated with diverse human diseases or traits. However, only a small portion of heritability of the diseases or traits has been explained by the discovered common variants. Among many possible factors for missing heritability, rare variants with large effects are expected to make a major contribution to the missing heritability. Our lab identifies disease-causing genes and treatment targets by utilizing advanced genomic technology (next-generation sequencing and statistical approaches for rare variants).
> Digital health informatics
Future of medicine is summarized as P4 medicine, a discipline that is predictive, personalized, preventive and participatory. P4 medicine will improve healthcare substantially and enable precision medicine. Recent advances in mobile and wearable devices and genomic technology generate unprecedentedly huge and digitized health information. Not only academic but also industrial organizations are currently working on digital healthcare. Digital healthcare informatics encompass genetic, environmental and behavioral components of personal health. Genomic and clinical data collected in Samsung Medical Center can be also utilized. Our lab members with various expertise collaborate together towards this goal.
> Machine learning, Artificial intelligence & Big data analysis
Data-driven medicine is a key aspect of future medicine. Machine learning techniques are very useful in generating data-driven hypotheses (ex., identifying unknown causal factors for disease) and prediction models of diagnosis and prognosis. In particular, deep neural network can be applied to unstructured data such as medical images, DNA sequences, etc. Our lab uses statistical and machine learning methods to address various biomedical questions.
Since the Human Genome Project, genome-wide association studies (GWAS) have been successful in identifying common genetic variants that are associated with diverse human diseases or traits. However, only a small portion of heritability of the diseases or traits has been explained by the discovered common variants. Among many possible factors for missing heritability, rare variants with large effects are expected to make a major contribution to the missing heritability. Our lab identifies disease-causing genes and treatment targets by utilizing advanced genomic technology (next-generation sequencing and statistical approaches for rare variants).
> Digital health informatics
Future of medicine is summarized as P4 medicine, a discipline that is predictive, personalized, preventive and participatory. P4 medicine will improve healthcare substantially and enable precision medicine. Recent advances in mobile and wearable devices and genomic technology generate unprecedentedly huge and digitized health information. Not only academic but also industrial organizations are currently working on digital healthcare. Digital healthcare informatics encompass genetic, environmental and behavioral components of personal health. Genomic and clinical data collected in Samsung Medical Center can be also utilized. Our lab members with various expertise collaborate together towards this goal.
> Machine learning, Artificial intelligence & Big data analysis
Data-driven medicine is a key aspect of future medicine. Machine learning techniques are very useful in generating data-driven hypotheses (ex., identifying unknown causal factors for disease) and prediction models of diagnosis and prognosis. In particular, deep neural network can be applied to unstructured data such as medical images, DNA sequences, etc. Our lab uses statistical and machine learning methods to address various biomedical questions.
Representative Research Achievements
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1. *Kim H, *Ahn Y, *Yoon J, Jung K, Kim S, Shim I, Park TH, Ko H, Jung SH, Kim JY, Park SH, Lee DJ, Choi S, Cha S, Kim B, Cho MY, Cho H, Kim DS, Jang Y, Ihm HK, Park WY, Bakhshi H, O Connell KS, Andreassen OA, Kendler KS, Myung W, Won HH (2024). Genome-wide association analyses using machine learning-based phenotyping reveal genetic architecture of occupational creativity and overlap with psychiatric disorders. Psychiatry Research, 333, 115753.
2. *Chen TT., *Kim JY, Lam M, Chuang YF, Chiu YL, Lin SC, Jung SH, Kim B, Kim S, Cho C, Shim I, Park SH, Ahn Y, Okbay A, Jang H, Kim HJ, Seo SW, Park WY, Ge T, Huang H, Feng YA, Lin YF, Myung W, Chen CY, Won HH (2024). Shared genetic architectures of educational attainment in East Asian and European populations. Nature Human Behaviour, 8, 562-575.
3. *Kim MS, *Song M, Kim S, Kim B, Kang W, Kim JY, Myung W, Lee I, Do R, Khera AV, Won HH (2023). Causal effect of adiposity on the risk of 19 gastrointestinal diseases: a Mendelian randomization study. Obesity, 31(5), 1436-1444.
4. *Lee YC, *Jung SH, *Kumar A, Shim I, Song M, Kim MS, Kim K, Myung W, Park WY, Won HH (2023). ICD2Vec: Mathematical representation of diseases. Journal of Biomedical Informatics, 141, 104361.
5. *Kim MS, *Song M, Kim B, Shim I, Kim DS, Natarajan P, Do R, Won HH (2023). Prioritization of therapeutic targets for dyslipidemia using integrative multi-omics and multi-trait analysis. Cell Reports Medicine, 101112.
6. *Kim JY, *Song M, Kim MS, Natarajan P, Do R, Myung W, Won HH (2023). An atlas of associations between 14 micronutrients and 22 cancer outcomes: Mendelian randomization analyses. BMC medicine, 21(1), 316.
7. *Jung K, *Yoon J, *Ahn Y, Kim S, Shim I, Ko H, Jung SH, Kim J, Kim H, Lee DJ, Cha S, Lee H, Kim B, Cho MY, Cho H, Kim DS, Kim J, Park WY, Park TH, O Connell KS, Andreassen OA, Myung W, Won HH (2023). Leveraging genetic overlap between irritability and psychiatric disorders to identify genetic variants of major psychiatric disorders. Experimental & Molecular Medicine, 55(6), 1193-1202.
8. *Lee YC, *Cha J, Shim I, Park WY, Kang SW, Lim DH, Won HH (2023). Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction. NPJ digital medicine, 6(1), 14.
9. Jung SH, Kim HR, Chun MY, Jang H, Cho M, Kim B, Kim S, Jeong JH, Yoon SJ, Park KW, Kim EJ, Yoon B, Jang JW, Kim Y, Hong JY, Choi SH, Noh Y, Kim KW, Kim SE, Lee JS, Jung NY, Lee J, Lee AY, Kim BC, Cho SH, Cho H, Kim JH, Jung YH, Lee DY, Lee JH, Lee ES, Kim SJ, Moon SY, Son SJ, Hong CH, Bae JS, Lee S, Na DL, Seo SW, Cruchaga C, Kim HJ, Won HH (2022). Transferability of Alzheimer Disease Polygenic Risk Score Across Populations and Its Association With Alzheimer Disease-Related Phenotypes. JAMA network open, 5(12), e2247162.
10. *Kim S, *Kim K, *Hwang MY, Ko H, Jung SH, Shim I, Cha S, Lee H, Kim B, Yoon J, Ha TH, Kim DK, Kim J, Park WY, Okbay A, Kim BJ, Kim YJ, Myung W, Won HH (2022). Shared genetic architectures of subjective well-being in East Asian and European ancestry populations. Nature Human Behaviour, 1-13.
11. *Yun JS, *Jung SH, Shivakumar M, Xiao B, Khera AV, Won HH, Kim D (2022). Polygenic risk for type 2 diabetes, lifestyle, metabolic health, and cardiovascular disease: a prospective UK Biobank study. Cardiovascular Diabetology, 21(1), 1-11.
12. *Ko H, *Kim S, Kim K, Jung SH, Shim I, Cha S, Lee H, Kim B, Yoon J, Ha TH, Kwak S, Kang JM, Lee JY, Kim J, Park WY, Nho K, Kim DK, Myung W, Won HH (2022). Genome-wide association study of occupational attainment as a proxy for cognitive reserve. Brain, 145(4), 1436-1448.
13. Kim MS, Kim WJ, Khera AV, Kim JY, Yon DK, Lee SW, Shin JI, Won HH (2021). Association between adiposity and cardiovascular outcomes: an umbrella review and meta-analysis of observational and Mendelian randomization studies. European heart journal, 42(34), 3388-3403.
14. Cha S, Lee E, Won HH (2021). Comprehensive characterization of distinct genetic alterations in metastatic breast cancer across various metastatic sites. NPJ breast cancer, 7(1), 93.
15. Kim HR, Jung SH, Kim J, Jang H, Kang SH, Hwangbo S, Kim JP, Kim SY, Kim B, Kim S, Jeong JH, Yoon SJ, Park KW, Kim EJ, Yoon B, Jang JW, Hong JY, Choi SH, Noh Y, Kim KW, Kim SE, Lee JS, Jung NY, Lee J, Kim BC, Son SJ, Hong CH, Na DL, Seo SW, Won HH, Kim HJ (2021). Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population. Alzheimers research & therapy, 13(1), 117.
16. Shin JG, Leem S, Kim B, Kim Y, Lee SG, Song HJ, Seo JY, Park SG, Won HH, Kang NG (2021). GWAS Analysis of 17,019 Korean Women Identifies the Variants Associated with Facial Pigmented Spots. The Journal of investigative dermatology, 141(3), 555&8211562.
17. Duffy A, Verbanck M, Dobbyn A, Won HH, et al. (2020). Tissue-specific genetic features inform prediction of drug side effects in clinical trials. Sci Adv. 6(37):eabb6242.
18. Jordan DM, Choi HK, Verbanck M, Topless R, Won HH, et al. (2019). No causal effects of serum urate levels on the risk of chronic kidney disease: A Mendelian randomization study. PLoS Med. 16(1):e1002725.
19. *Lee YC, *Jung SH, Won HH (2018). WonDerM: Skin Lesion Classification with Fine-tuned Neural Networks arXiv:1808.03426 [cs.CV]. [pdf] leaderboard link (ranked 8th of 77 teams / 13th of 141 models in ISIC Challenge 2018 task 3: lesion diagnosis)
20. Webb TR, Erdmann J, Stirrups KE, et al. (2017). Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease. Journal of the American college of cardiology JACC 69(7), 823-36.
21. *Khera A, *Won HH, Peloso GM, et al. (2017). Association of rare and common variation in the lipoprotein lipase gene with coronary artery disease. the Journal of the American Medical Association JAMA 317(9), 937-46.
22. Saleheen D, Natarajan P, Armean IM, Zhao W, Rasheed A, Khetarpal SA, Won HH, et al. (2017). Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature 544(7649), 235-9.
23. Nomura A, Won HH, Khera AV, et al. (2017). Protein-Truncating Variants at the Cholesteryl Ester Transfer Protein Gene and Risk for Coronary Heart Disease. Circ Res, 121(1):81-88.
24. Emdin CA, Khera AV, Natarajan P, Klarin D, Won HH, et al. (2016). Phenotypic Characterization of Genetically Lowered Human Lipoprotein(a) Levels. Journal of the American college of cardiology JACC 68(25):2761-2772.
25. Exome Aggregation Consortium (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285-291.
26. *Khera A, *Won HH, *Peloso GM, et al. (2016). Diagnostic yield of sequencing familial hypercholesterolemia genes in severe hypercholesterolemia. Journal of the American college of cardiology JACC 67(22), 2578-89.
27. Stitziel NO, Stirrups KE, Masca NGD, Erdmann J, Ferrario PG, Konig IR, Weeke PE, Webb TR, Auer PL, Schick UM, Lu Y, Zhang H, Dube MP, Goel A, Farrall M, Peloso GM, Won HH, et al. (2016) Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. New England journal of medicine NEJM 374(12), 1134-44.
28. Won HH, Natarajan P, Dobbyn A, et al. (2015). Disproportionate contributions of select genomic compartments and cell types to genetic risk for coronary artery disease. PloS genetics doi:10.1371/journal.pgen.1005622.
29. *Nikpay M, *Goel A, *Won HH, et al. (2015). A comprehensive 1000 Genomes-based GWAS metaanalysis of coronary artery disease. Nature genetics 47(10), 1121-30.
30. *Kim J, *Won HH, Kim Y, et al. (2015). Breakpoint mapping by whole genome sequencing identifies PTH2R gene disruption in a patient with midline craniosynostosis and a de novo balanced chromosomal rearrangement. Journal of medical genetics 52(10), 706-9. (Featured on the Cover page)
31. Thormaehlen AS, Schuberth C, Won HH, et al. (2015). Systematic cell-based phenotyping of missense alleles empowers rare variant association studies: a case for LDLR and myocardial infarction. PloS genetics 11(2), e1004855.
32. *Do R, *Stitziel NO, *Won HH, et al. (2015). Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102-106.