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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 馮嬿臻 | zh_TW |
| dc.contributor.advisor | Yen-Chen Anne Feng | en |
| dc.contributor.author | 柯怡瑄 | zh_TW |
| dc.contributor.author | Yi-Syuan Ke | en |
| dc.date.accessioned | 2025-09-19T16:19:32Z | - |
| dc.date.available | 2025-09-20 | - |
| dc.date.copyright | 2025-09-19 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-04 | - |
| dc.identifier.citation | 1. Devinsky, O., et al., Epilepsy. Nature Reviews Disease Primers, 2018. 4(1): p. 18024.
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Abou-Khalil, B., et al., Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. Nature Communications, 2018. 9(1): p. 5269. 16. Kjeldsen, M.J., et al., Epileptic seizures and syndromes in twins: the importance of genetic factors. Epilepsy Research, 2003. 55(1): p. 137-146. 17. Stevelink, R., et al., GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture. Nature Genetics, 2023. 55(9): p. 1471-1482. 18. Choi, S.W., T.S.-H. Mak, and P.F. O’Reilly, Tutorial: a guide to performing polygenic risk score analyses. Nature Protocols, 2020. 15(9): p. 2759-2772. 19. Lewis, C.M. and E. Vassos, Polygenic risk scores: from research tools to clinical instruments. Genome Medicine, 2020. 12(1): p. 44. 20. Dudbridge, F., Power and predictive accuracy of polygenic risk scores. PLoS Genet, 2013. 9(3): p. e1003348. 21. Leu, C., et al., Polygenic burden in focal and generalized epilepsies. Brain, 2019. 142(11): p. 3473-3481. 22. Heyne, H.O., et al., Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events. Nature Communications, 2024. 15(1): p. 6277. 23. Moreau, C., et al., Polygenic risk scores of several subtypes of epilepsies in a founder population. Neurology Genetics, 2020. 6(3): p. e416. 24. Nagai, A., et al., Overview of the BioBank Japan Project: Study design and profile. Journal of Epidemiology, 2017. 27(3, Supplement): p. S2-S8. 25. Chen, C.Y., et al., Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits. Cell Genom, 2023. 3(12): p. 100436. 26. Feng, Y.A., et al., Taiwan Biobank: A rich biomedical research database of the Taiwanese population. Cell Genom, 2022. 2(11): p. 100197. 27. Lin, L.Y., et al., Data resource profile: the National Health Insurance Research Database (NHIRD). Epidemiol Health, 2018. 40: p. e2018062. 28. Yang, H.-C., et al., The Taiwan Precision Medicine Initiative: A Cohort for Large-Scale Studies. bioRxiv, 2024: p. 2024.10.14.616932. 29. Chen, H.-H., et al., Population-Specific Polygenic Risk Scores Developed for the Han Chinese. medRxiv, 2024: p. 2024.10.14.24315279. 30. Ultra-Rare Genetic Variation in the Epilepsies: A Whole-Exome Sequencing Study of 17,606 Individuals. Am J Hum Genet, 2019. 105(2): p. 267-282. 31. Purcell, S., et al., PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007. 81(3): p. 559-75. 32. Das, S., et al., Next-generation genotype imputation service and methods. Nat Genet, 2016. 48(10): p. 1284-1287. 33. Auton, A., et al., A global reference for human genetic variation. Nature, 2015. 526(7571): p. 68-74. 34. Browning, B.L., et al., Fast two-stage phasing of large-scale sequence data. The American Journal of Human Genetics, 2021. 108(10): p. 1880-1890. 35. Ge, T., et al., Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature Communications, 2019. 10(1): p. 1776. 36. Lee, C.H., et al., Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores. Genomics Inform, 2016. 14(4): p. 173-180. 37. Lee, S.H., et al., A better coefficient of determination for genetic profile analysis. Genet Epidemiol, 2012. 36(3): p. 214-24. 38. Arain, M., et al., Maturation of the adolescent brain. Neuropsychiatr Dis Treat, 2013. 9: p. 449-61. 39. Wei, W.-Q., et al., Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLOS ONE, 2017. 12(7): p. e0175508. 40. Wu, P., et al., Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation. JMIR Med Inform, 2019. 7(4): p. e14325. 41. Carroll, R.J., L. Bastarache, and J.C. Denny, R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment. Bioinformatics, 2014. 30(16): p. 2375-2376. 42. Yu, T., et al., Clinical characteristics of post-traumatic epilepsy and the factors affecting the latency of PTE. BMC Neurology, 2021. 21(1): p. 301. 43. Maloney, E.M., É.J. O'Reilly, and D.J. Costello, Causes and classification of first unprovoked seizures and newly-diagnosed epilepsy in a defined geographical area- an all-comers analysis. Seizure, 2021. 92: p. 118-127. 44. Ioannou, P., et al., The burden of epilepsy and unmet need in people with focal seizures. Brain and Behavior, 2022. 12(9): p. e2589. 45. Kotsopoulos, I.A.W., et al., Systematic Review and Meta-analysis of Incidence Studies of Epilepsy and Unprovoked Seizures. Epilepsia, 2002. 43(11): p. 1402-1409. 46. Dussault, P.M. and A.A. Lazzari, Epilepsy and osteoporosis risk. Curr Opin Endocrinol Diabetes Obes, 2017. 24(6): p. 395-401. 47. Chen, C.C., et al., Geographic variation in the age- and gender-specific prevalence and incidence of epilepsy: analysis of Taiwanese National Health Insurance-based data. Epilepsia, 2012. 53(2): p. 283-90. 48. Hsieh, C.Y., et al., Taiwan's National Health Insurance Research Database: past and future. Clin Epidemiol, 2019. 11: p. 349-358. 49. Martin, A.R., et al., Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 2019. 51(4): p. 584-591. 50. Kachuri, L., et al., Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet, 2024. 25(1): p. 8-25. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99928 | - |
| dc.description.abstract | 癲癇影響全球逾五千萬人，癲癇因其病因多樣、臨床症狀複雜，導致診斷過程充滿挑戰。儘管全基因體關聯研究 (GWAS) 已識別出多個與常見癲癇相關的基因座，基於這些變異建構的多基因風險分數 (PRS) 逐漸被視為潛在的輔助診斷工具。然而，現有癲癇PRS研究主要集中於歐洲族群，其在非歐洲族群的預測效能仍缺乏驗證。
本研究評估了癲癇 PRS 在三個招募方式與表型定義各異的台灣人群資料 中之預測與分層效能。這些目標樣本分別是：台灣人體生物資料庫 (TWB) 連結全民健康保險研究資料庫 (NHIRD) 的104,680名參與者，其中依ICD診斷碼與抗癲癇藥物紀錄判定915例癲癇病例；台灣精準醫療計畫 (TPMI) 的464,596名參與者，其中依相同標準定義識別5,609例癲癇病例；以及Epi25聯盟之Epi25-TWN樣本的1,140名參與者，內含699例由臨床醫師精細診斷之癲癇個案。接著利用國際抗癲癇組織 (ILAE) 最新的GWAS數據 (族群組成：85% 歐洲、6% 亞洲、9% 非洲)，計算了所有癲癇 (All Epilepsy)、遺傳性全面性癲癇 (GGE) 及局灶性癲癇 (FE) 在這三個樣本中的PRS。隨後，透過終身風險模型、發病年齡分層分析及全表型關聯分析 (PheWAS)，全面評估其預測與風險分層能力。 研究結果顯示GGE PRS在Epi25-TWN樣本中效應最強 (每標準差OR = 1.65, R^2 = 3.2%)，與GGE具高遺傳度 (~40%) 的預期相符，但在兩個大型人口型樣本（TWB-NHIRD與TPMI）中卻僅有相對微弱的效應 (OR = 1.09¬¬–1.13 , R^2 = 0.14–0.22%)。相較之下，FE PRS在三個世代中的效應較¬為一致，但整體屬中 (OR = 1.02–¬1.16, R^2 = 0.01–0.31%）。在風險分層方面，Epi25-TWN中的效果亦最為顯著，GGE PRS排名前5% 高的個體，其患GGE風險為其餘個體的四倍以上，然而相同效應在 TWB-NHIRD 和 TPMI中則較為溫和，風險為其餘個體的不到1.5倍。此外，研究結果支持較高的PRS分數與發病年齡呈負相關，此現象在GGE中尤為顯著，進一步分析發現，將分析限制於較早發病的患者時，GGE PRS在PheWAS中呈現出與癲癇相關的特異訊號。 這項研究是目前針對非歐洲族群規模最大的癲癇 PRS 研究，發現目標樣本研究設計與表型精確度對於PRS預測力的顯著影響，並突顯未來需整合更全面的表型數據與更多元族群的癲癇GWAS，方能顯著提升PRS在跨族群的解釋力與臨床應用潛力。 | zh_TW |
| dc.description.abstract | Epilepsy affects over 50 million people worldwide, and its diagnosis remains challenging due to heterogeneous etiologies and diverse clinical presentations. Genome-wide association studies (GWAS) have identified dozens of loci for common epilepsies, and polygenic risk score (PRS) derived from these findings have been proposed as tools to estimate genetic susceptibility and support diagnosis. However, most epilepsy PRS studies have focused on European populations, and their performance in non-European cohorts is largely unexplored.
We assessed the predictive and stratification performance of epilepsy PRSs across three Taiwanese cohorts with distinct recruitment and phenotyping strategies. These cohorts included: 104,680 individuals from the community-based Taiwan Biobank (TWB) linked to 20 years of National Health Insurance Research Database (NHIRD), with 915 epilepsy cases identified using ICD codes and antiseizure medication usage; 464,596 individuals from the multi-hospital-based Taiwan Precision Medicine Initiative (TPMI), including 5,609 cases defined using the same criteria; and 1,140 individuals (700 cases) from the Epi25-TWN cohort (Epi25 consortium) with detailed clinical phenotyping. PRSs for all epilepsy, generalized epilepsy (GGE), and focal epilepsy (FE) were derived from the most recent ILAE GWAS summary statistics (85% European, 6% Asian, 9% African). Predictive performance was evaluated using lifetime risk models, age-of-onset stratification analyses and phenome-wide association study (PheWAS). The GGE PRS showed the strongest effect in Epi25-TWN (OR = 1.65 per SD, R^2 = 3.2%) but weaker effects in the two population-based cohorts, TWB-NHIRD and TPMI (OR = 1.09–1.13, R^2 = 0.14–0.22%). In contrast, the FE PRS yielded more consistent but modest associations across cohorts (OR = 1.02–1.16, R^2 = 0.01–0.31%). Risk stratification was most effective in Epi25-TWN, where individuals in the top 5% of GGE PRS showed over fourfold higher odds of GGE. However, enrichment in the TWB-NHIRD and TPMI was modest, less than 1.5-fold. Furthermore, higher PRS was associated with an earlier age of onset across all three cohorts, especially for GGE, with epilepsy-specific signals for GGE PRS emerging in PheWAS when analyses were restricted to earlier-onset patients. This study represents the largest evaluation of epilepsy PRS performance in non-European populations to date. Our findings highlight how cohort design and phenotyping accuracy significantly influence PRS predictive utility, underscoring the critical need for richer phenotypic data and more diverse epilepsy GWAS to enhance cross-population portability and clinical applicability. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-19T16:19:32Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-19T16:19:32Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iv Contents vi LIST OF FIGURES x LIST OF TABLES xi Chapter 1 Introduction 1 Chapter 2 Methods 4 2.1 Study cohorts 4 2.1.1 TWB-NHIRD 4 2.1.2 TPMI 5 2.1.3 Epi25-TWN 6 2.2 Calculation of polygenic risk scores 8 2.3 Statistical analysis 9 2.3.1 Association between PRS and epilepsy susceptibility 9 2.3.2 Association between PRS and epilepsy age of onset 10 2.3.3 PheWAS analysis of epilepsy PRS 11 2.4 Sensitivity analysis 12 2.4.1 ICD-based phenotype refinement: overlap between FE and GGE 12 2.4.2 ICD-based phenotype refinement: removal of non-genetic epilepsy 13 Chapter 3 Results 14 3.1 Summary of study cohort characteristics 14 3.2 Variation in PRS distribution and its effects on epilepsy risk across cohorts 15 3.3 PRS stratification and case enrichment analysis 18 3.4 Sensitivity analysis in TWB-NHIRD and TPMI 19 3.5 Association between epilepsy PRS and age of onset 20 3.6 PheWAS 22 Chapter 4 Discussion 24 4.1 Main findings 24 4.2 Strengths and Limitations 27 4.3 Future directions 28 REFERENCES 29 Figures 32 Figure 1. Overview of study design and analysis workflow 32 Figure 2. Effect estimates of three epilepsy PRSs on all epilepsy and the two common subtypes across different cohorts 33 Figure 3. Risk stratification based on GGE PRS across the three target cohorts 34 Figure 4. PRS effects on epilepsy across different age-of-onset thresholds 35 Figure 5. PheWAS results for the GGE PRS in individuals with epilepsy onset before age 40 in the TPMI cohort 36 Tables 37 Table 1. Descriptive statistics of three target cohorts 37 Table 2. R2 and AUC results for the predictive performance of epilepsy PRSs 38 Table 3. Association between PRSs and age of onset across cohorts 39 Supplementary Figures 40 Figure S1. Distribution of epilepsy PRSs in TWB-NHIRD (batch 2) 42 Figure S2. Distribution of epilepsy PRSs in TPMI 43 Figure S3. Distribution of epilepsy PRSs in Epi25-TWN 44 Figure S4. Effect estimates of three epilepsy PRSs on all epilepsy and the two common subtypes across different case definition tiers in TWB-NHIRD 45 Figure S5. Comparison of epilepsy PRS estimates from Cox proportional hazards models and logistic regression across population cohorts 46 Figure S6. Effect estimates for PRSs predicting unclassified seizures across TWB-NHIRD and TPMI 47 Figure S7. Effect estimates for PRSs predicting syncope across TWB-NHIRD and TPMI 47 Figure S8. Effect estimates for PRSs predicting unclassified epilepsy to specified epilepsy in TWB-NHIRD 48 Figure S9. Stratified analysis of PRS across three cohorts 49 Figure S10. Sensitivity analysis of epilepsy PRS performance under refined subtype case definitions 51 Figure S11. Sensitivity analysis of epilepsy PRSs after excluding individuals with brain injury or related diagnoses 52 Figure S12. Association between epilepsy PRSs and earlier-onset versus later-onset epilepsy 53 Figure S13. Risk stratification by PRS with and without earlier-onset restriction across epilepsy subtypes and cohorts 54 Figure S14. PheWAS results testing the specificity of the epilepsy PRS across 1,361 phenotypes in the TWB-NHIRD 56 Figure S15. PheWAS results testing the specificity of the epilepsy PRS across 1,733 phenotypes in the TPMI 58 Figure S16. PheWAS results for the epilepsy PRS in individuals with epilepsy onset before age 40 in the TWB-NHIRD 60 Figure S17. PheWAS results for the epilepsy PRS in individuals with epilepsy onset before age 40 in the TPMI 61 Supplementary Tables 63 Table S1. Definition of cases and controls in population cohorts 64 Table S2. NHI codes for anti-seizure medications (ASMs) 65 Table S3. ICD codes used to exclude individuals with brain injury or related conditions 68 Table S4. Effects of PRS on epilepsy risk across cohorts 69 Table S5. Sample sizes of refined epilepsy subtypes across strategies and cohorts 70 Table S6. Sample sizes after excluding cases with brain injury or related conditions across cohort 71 Table S7. Sample sizes by onset age thresholds (25 and 40 years) across cohorts 72 | - |
| dc.language.iso | en | - |
| dc.subject | 癲癇 | zh_TW |
| dc.subject | 多基因風險分數 | zh_TW |
| dc.subject | 台灣族群 | zh_TW |
| dc.subject | 台灣人體生物資料庫 | zh_TW |
| dc.subject | 台灣健保資料庫 | zh_TW |
| dc.subject | 台灣精準醫療計畫 | zh_TW |
| dc.subject | National Health Insurance Research Database | en |
| dc.subject | Taiwanese population | en |
| dc.subject | Taiwan Biobank | en |
| dc.subject | Taiwan Precision Medicine Initiative | en |
| dc.subject | Epilepsy | en |
| dc.subject | Polygenic Risk Score | en |
| dc.title | 評估癲癇多基因風險分數在台灣族群與臨床樣本中預測及風險分層之表現 | zh_TW |
| dc.title | Evaluating the Predictive and Stratification Performance of Epilepsy Polygenic Risk Scores in Taiwanese Population and Clinical Cohorts | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 郭柏秀;陳弘昕;蔡孟翰 | zh_TW |
| dc.contributor.oralexamcommittee | Po-Hsiu Kuo;Hung-Hsin Chen;Meng-Han Tsai | en |
| dc.subject.keyword | 癲癇,多基因風險分數,台灣族群,台灣人體生物資料庫,台灣健保資料庫,台灣精準醫療計畫, | zh_TW |
| dc.subject.keyword | Epilepsy,Polygenic Risk Score,Taiwanese population,Taiwan Biobank,National Health Insurance Research Database,Taiwan Precision Medicine Initiative, | en |
| dc.relation.page | 72 | - |
| dc.identifier.doi | 10.6342/NTU202503145 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-04 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 健康數據拓析統計研究所 | - |
| dc.date.embargo-lift | 2030-08-01 | - |
| 顯示於系所單位： | 健康數據拓析統計研究所 | |
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