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  1. NTU Theses and Dissertations Repository
  2. 公共衛生學院
  3. 流行病學與預防醫學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95033
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor馮嬿臻zh_TW
dc.contributor.advisorYen-Chen Fengen
dc.contributor.author郭羿君zh_TW
dc.contributor.authorYi-Chun Kuoen
dc.date.accessioned2024-08-26T16:21:38Z-
dc.date.available2024-08-27-
dc.date.copyright2024-08-26-
dc.date.issued2023-
dc.date.submitted2024-08-12-
dc.identifier.citationReferenesc
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2. Lewis, C.M. and E. Vassos, Polygenic risk scores: from research tools to clinical instruments. Genome medicine, 2020. 12(1): p. 44.
3. Forrest, I.S., et al., Genome-wide polygenic risk score for retinopathy of type 2 diabetes. Human Molecular Genetics, 2021. 30(10): p. 952-960.
4. Rawlik, K., O. Canela-Xandri, and A. Tenesa, Evidence for sex-specific genetic architectures across a spectrum of human complex traits. Genome biology, 2016. 17: p. 1-8.
5. Mars, N., et al., Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nature medicine, 2020. 26(4): p. 549-557.
6. Mars, N., et al., Systematic comparison of family history and polygenic risk across 24 common diseases. The American Journal of Human Genetics, 2022. 109(12): p. 2152-2162.
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10. Schaid, D.J., et al., Polygenic risk for prostate cancer: decreasing relative risk with age but little impact on absolute risk. The American Journal of Human Genetics, 2022. 109(5): p. 900-908.
11. Jukarainen, S., et al., Genetic risk factors have a substantial impact on healthy life years. Nature medicine, 2022. 28(9): p. 1893-1901.
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14. Thakur, J., et al., Tobacco use: a major risk factor for non communicable diseases in South-East Asia region. Indian journal of public health, 2011. 55(3): p. 155-160.
15. Zhang, K., et al., Smoking burden, MPOWER, future tobacco control and real-world challenges in China: reflections on the WHO report on the global tobacco epidemic 2021. Translational Lung Cancer Research, 2022. 11(1): p. 117.
16. Zhang, L., et al., Associations of socioeconomic factors with cause-specific Mortality and burden of cardiovascular diseases: findings from the vital registration in urban Shanghai, China, during 1974–2015. BMC Public Health, 2020. 20: p. 1-13.
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18. Afshin, A., et al., Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The lancet, 2019. 393(10184): p. 1958-1972.
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22. Tan, J.L. and K. Thakur, Systolic hypertension, in StatPearls [Internet]. 2023, StatPearls Publishing.
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24. Ruan, Y., et al., Improving polygenic prediction in ancestrally diverse populations. Nature genetics, 2022. 54(5): p. 573-580.
25. Bouaziz, O. and G. Nuel, L0 regularisation for the estimation of piecewise constant hazard rates in survival analysis. arXiv preprint arXiv:1609.04595, 2016.
26. Jermy, B., et al., A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk. Nature Communications, 2024. 15(1): p. 5007.
27. Collaborators, G.L.R.o.S., Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016. New England Journal of Medicine, 2018. 379(25): p. 2429-2437.
28. Witte, J.S., P.M. Visscher, and N.R. Wray, The contribution of genetic variants to disease depends on the ruler. Nature Reviews Genetics, 2014. 15(11): p. 765-776.
29. Care, D., Standards of Care in Diabetes—2023. Diabetes care, 2023. 46: p. S1-S267.
30. Fry, J., Peptic ulcer: a profile. British Medical Journal, 1964. 2(5412): p. 809.
31. Halldorsdottir, T., et al., Polygenic risk: predicting depression outcomes in clinical and epidemiological cohorts of youths. American Journal of Psychiatry, 2019. 176(8): p. 615-625.
32. Hsieh, C.-Y., et al., Taiwan’s national health insurance research database: past and future. Clinical epidemiology, 2019: p. 349-358.
33. Wu, C.-S., et al., Validation of self-reported medical condition in the Taiwan Biobank. Journal of Epidemiology, 2024: p. JE20240110.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95033-
dc.description.abstract全基因組關聯研究揭示了常見基因變異在複雜疾病中的顯著作用。多基因風險分數透過加總多個基因變異的效應來估計個體對疾病的遺傳易感性,已成為預測疾病風險和制定個性化預防和治療策略的有用工具。先前的多基因風險分數研究主要關注疾病的相對風險,而針對個體層面的絕對風險和遺傳風險因素對疾病負擔的分析在非歐洲人群中尤其有限,而這些對評估風險和指導公共衛生干預非常有幫助。因此,本研究旨在量化遺傳因子對台灣常見疾病的終身風險和疾病負擔的貢獻程度。
利用台灣人體生物資料庫和2019年全球疾病負擔研究的數據,我們選出三種在台灣具有顯著負擔的常見疾病,包含第二型糖尿病、高血壓以及消化性潰瘍。我們使用PRS-CSx計算每種疾病的多基因風險評分,通過Cox比例風險模型估計多基因風險分數對疾病的影響。接著,我們利用遺傳效應和2019年全球疾病負擔研究的估計值,計算出多基因風險分數對疾病絕對風險和疾病負擔的貢獻,並以傷殘調整生命年作為疾病負擔的衡量方法。此外,我們亦比較了遺傳因素與這些疾病的常見風險因素對疾病負擔的貢獻程度,以更好地理解遺傳的相對重要性。
我們的研究結果顯示,遺傳風險因素對此三種疾病之風險有顯著的效應,並在性別、年齡和家庭史上存在差異。對於第二型糖尿病和高血壓,多基因風險分數高(前10%)之於低(後90%)的相對效應為男性小於女性,而消化性潰瘍則相反。隨著年齡的增加,多基因風險分數的效應會減弱,而比起具有家族史的人群,遺傳效應在不具家庭史的組別來得更高。將相對風險轉換成疾病絕對風險則顯示,對於第二型糖尿病,介於30至40歲且多基因風險分數在前10%的人在約40歲時就可達到45歲的篩檢閾值,而女性的估計值大於男性;對於高血壓,30至40歲且多基因風險評分在前10%的人在65至70歲時的發病率比多基因風險分數在後10%的人高出8.22倍,此估計值則男性略高於女性;對於消化性潰瘍,介於30至40歲且多基因風險評分在前10%的人在約57歲時達到建議的篩檢閾值。最後,我們計算了高多基因風險分數的可歸因傷殘調整生命年,結果顯示,前10%第二型糖尿病的多基因風險分數可導致1.61年的健康壽命損失,此效應和高身體質量指數對疾病負擔的貢獻相當;而高血壓之相對應數值為0.22年,和高度飲酒的影響相似;消化性潰瘍的遺傳影響相較可改變風險因素則較低。
通過基於個體化基因特徵的分層風險和負擔估計,這些發現突顯了遺傳風險因素對常見疾病的重要性,可能有助於制定針對台灣人群的個人化疾病干預和預防策略。
zh_TW
dc.description.abstractGenome-wide association studies have revealed a significant role of common genetic variations in common complex disorders. Polygenic risk score (PRS), which aggregates the effects of many genetic variants to estimate an individual's genetic predisposition to a disease, has been developed as a useful tool to predict disease risk and inform personalized prevention and treatment strategies. Previous studies of PRS have mostly examined relative risks on disease, while analyses of absolute risk and burden of disease attributable to genetic risk factors at the individual level—both informative for evaluating risk and guiding public health interventions—have been limited, particularly in non-European populations. This study aims to quantify the degree of genetic contribution to lifetime risk and disease burden for selected common diseases in Taiwan.
Leveraging data in the Taiwan Biobank and the 2019 Global Burden of Disease (GBD) study, we identified three common diseases in Taiwan with nontrivial burden as measured in disability-adjusted life years (DALYs), including type 2 diabetes (T2D), hypertension, and peptic ulcer disease (PUD). We calculated polygenic risk scores (PRS) for each disease with PRS-CSx and estimated the effect of PRS on disease in hazard ratios via Cox proportional hazards models. We then transformed the genetic effects into its contribution to absolute risk and individual DALYs, using estimates derived from the 2019 GBD study. Additionally, we compared the burden of genetic factors with those from common risk factors of the diseases to better understand the relative importance of genetics.
Our findings demonstrated a prominent effect of genetic risk factors on the three diseases, with variations observed across sex, age, and family history. Comparing those in the top 10% PRS to the bottom 90%, the relative risk of disease was smaller in males than in females for T2D and hypertension, while the opposite was true for PUD. These effects declined when age increased and were higher in those without than with family history. Transforming relative risk to absolute risk, for T2D, individuals in the top 10% PRS could reach the screening threshold of cumulative incidence for a 45-year-old by age 40, and the risk was higher in females than in males; for hypertension, those aged 30-40 in the top 10% PRS had 8.22 times higher incidence at ages 65-70 than those in the bottom 90%, slightly larger for males than in females; for PUD, individuals in the top 10% PRS could reach the threshold for a 60-year-old by around 57-year-old. Estimation of individual DALYs suggested that the top 10% PRS for T2D contributed to a 1.61-year loss of healthy life years, similar to that attributable to high BMI, while for hypertension, it was 0.22 years, akin to high alcohol use. The genetic impact on peptic ulcer disease (PUD) was comparatively low relative to modifiable risk factors.
By providing stratified risk and burden estimates given individualized genetic profiles, these findings highlight the importance of genetic risk factors to common diseases that may inform tailored disease intervention and prevention strategies in the Taiwanese population.
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dc.description.tableofcontents口試委員會審定書 i
誌謝 ii
中文摘要 iii
Abstract v
Contents vii
List of Figures x
Chapter 1 Introduction 1
Chapter 2 Materials and Methods 3
2.1 Study participants 3
2.2 2019 Global Burden of Disease study 3
2.3 Disease selection 4
2.4 Genome-wide association study (GWAS) summary statistic 5
2.5 Polygenic risk score (PRS) 6
2.6 Survival analysis 7
2.7 Lifetime risk estimation 7
2.8 Individual attributable DALYs 8
2.9 Modifiable risk factors 10
Chapter 3 Results 12
3.1 Descriptive Statistics 12
3.2 Overall survival analysis results 13
3.3 Stratified analysis of PRS: age, sex, and family history 14
3.4 Lifetime risk associated with PRS 15
3.5 Individual-attributable DALYs for PRS 17
3.6 Comparison with modifiable risk factors 17
Chapter 4 Discussion 19
Figures 22
References 26
Supplementary Figures 28
Figure S1. Visualizing descriptive statistics. 28
Figure S2. Piece-wise relative risk models by age for disease with continuous PRS. 29
Supplementary Tables 30
Table S1. The complex diseases ranked by prevalence in TWB (N = 122,065) and the corresponding DALYs reported in the 2019 GBD study. 30
Table S2. Descriptive statistics of the five selected diseases for TWB participants. 31
Table S3. Proportion of having family history for five selected diseases among TWB participants. 32
Table S4. GWAS summary statistics utilized for PRS calculation in the study. 33
Table S5. Population attributable DALYs (Per 100000) of risk factors in the 2019 GBD study. 34
Table S6. The definition of risk factors in GBD study. 35
Table S7. Hazard ratios (HRs) for each disease from Cox proportional hazards models in the TWB cohort. 36
Table S8. Sex-specific HRs of genetic risk factors. 37
Table S9. Family history-specific HRs of genetic risk factors. 38
Table S10. Age-specific HRs of genetic risk factors. 39
Table S11. Absolute risk estimates based on PRS for the age 30-40 group. 40
Table S12. Absolute risk estimates based on PRS for males of age 30-40. 46
Table S13. Absolute risk estimates based on PRS for females of age 30-40. 49
Table S14. Individual attributable DALYs for top 10% PRS. 52
Table S15. Population attributable DALYs for PRS and selected modifiable risk factors. 53
Table S16. Population attributable DALYs for male. 54
Table S17. Population attributable DALYs for female. 55
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dc.language.isoen-
dc.subject複雜性疾病zh_TW
dc.subject疾病負擔zh_TW
dc.subject多基因風險分數zh_TW
dc.subjectCox比例風險模型zh_TW
dc.subject絕對風險zh_TW
dc.subject失能調整生命年zh_TW
dc.subjectpolygenic risk scoreen
dc.subjectComplex disorderen
dc.subjectdisability-adjusted life yearsen
dc.subjectabsolute risken
dc.subjectcox proportional hazards modelen
dc.subjectdisease burdenen
dc.title台灣人口中多基因遺傳對複雜性疾病之負擔與終身風險的影響zh_TW
dc.titlePolygenic contribution to disease risk and burden for complex disorders in the Taiwanese populationen
dc.typeThesis-
dc.date.schoolyear112-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee蕭朱杏;林先和;陳弘昕zh_TW
dc.contributor.oralexamcommitteeChu-Hsing Hsiao;Hsien-Ho Lin;Hung-Hsin Chenen
dc.subject.keyword複雜性疾病,疾病負擔,多基因風險分數,Cox比例風險模型,絕對風險,失能調整生命年,zh_TW
dc.subject.keywordComplex disorder,disease burden,polygenic risk score,cox proportional hazards model,absolute risk,disability-adjusted life years,en
dc.relation.page55-
dc.identifier.doi10.6342/NTU202404207-
dc.rights.note未授權-
dc.date.accepted2024-08-12-
dc.contributor.author-college公共衛生學院-
dc.contributor.author-dept流行病學與預防醫學研究所-
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