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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 盧子彬(Tzu-Pin Lu) | |
dc.contributor.author | Chung-Yu Huang | en |
dc.contributor.author | 黃涱煜 | zh_TW |
dc.date.accessioned | 2021-06-15T11:28:22Z | - |
dc.date.available | 2021-12-31 | |
dc.date.copyright | 2020-08-27 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-13 | |
dc.identifier.citation | 1. Chiu HC. Risk Factors for Cardiovascular Disease in the Elderly in Taiwan Author. Kaohsiung Journal of Medical Sciences. 2004;20: 279-286. 2. Ralph B. D’Agostino, Sr RSV. General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation. 2008;117: 743-753. 3. Wood D, De Backer G, Faergeman O, Graham I, Mancia G, Pyorala K. Prevention of coronary heart disease in clinical practice. Summary of recommendations of the Second Joint Task Force of European and other Societies on Coronary Prevention. Blood Press. 1998;7: 262-269. 4. Liu J, Hong Y, D'Agostino RB, Sr., et al. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. JAMA. 2004;291: 2591-2599. 5. D'Agostino RB, Sr., Grundy S, Sullivan LM, Wilson P, Group CHDRP. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286: 180-187. 6. Thomsen TF, McGee D, Davidsen M, Jorgensen T. A cross-validation of risk-scores for coronary heart disease mortality based on data from the Glostrup Population Studies and Framingham Heart Study. Int J Epidemiol. 2002;31: 817-822. 7. Lu X, Huang J, Wang L, et al. Genetic predisposition to higher blood pressure increases risk of incident hypertension and cardiovascular diseases in Chinese. Hypertension. 2015;66: 786-792. 8. Vaarhorst AA, Lu Y, Heijmans BT, et al. Literature-based genetic risk scores for coronary heart disease: the Cardiovascular Registry Maastricht (CAREMA) prospective cohort study. Circ Cardiovasc Genet. 2012;5: 202-209. 9. Qi L, Ma J, Qi Q, Hartiala J, Allayee H, Campos H. Genetic risk score and risk of myocardial infarction in Hispanics. Circulation. 2011;123: 374-380. 10. Iribarren C, Lu M, Jorgenson E, et al. Weighted Multi-marker Genetic Risk Scores for Incident Coronary Heart Disease among Individuals of African, Latino and East-Asian Ancestry. Sci Rep. 2018;8: 6853. 11. International Consortium for Blood Pressure Genome-Wide Association S, Ehret GB, Munroe PB, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478: 103-109. 12. Havulinna AS, Kettunen J, Ukkola O, et al. A blood pressure genetic risk score is a significant predictor of incident cardiovascular events in 32,669 individuals. Hypertension. 2013;61: 987-994. 13. Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health. 1992;82: 816-820. 14. Consortium CAD, Deloukas P, Kanoni S, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45: 25-33. 15. X. Liu, J. Wu, Z. Zhou. Exploratory Undersampling for Class-Imbalance Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2008;39: 539-550. 16. Grundy SM. Obesity, metabolic syndrome, and cardiovascular disease. J Clin Endocrinol Metab. 2004;89: 2595-2600. 17. Barrett-Connor E, Khaw K. Family history of heart attack as an independent predictor of death due to cardiovascular disease. Circulation. 1984;69: 1065-1069. 18. Kato N, Takeuchi F, Tabara Y, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet. 2011;43: 531-538. 19. Lu X, Wang L, Lin X, et al. Genome-wide association study in Chinese identifies novel loci for blood pressure and hypertension. Hum Mol Genet. 2015;24: 865-874. 20. Gu D, Reynolds K, Wu X, Chen J, Duan X, Reynolds RF, Whelton PK, He J; InterASIA Collaborative Group. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet. 2005;365:1398–1405. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49431 | - |
dc.description.abstract | 全基因體關聯分析(genome-wide association studies; GWAS)已發現許多與心血管疾病相關的單核甘酸多型性 (single nucleotide polymorphism; SNP),然而大多數的研究集中在歐美,過去已有研究指出歐洲的基因風險分數 (genetic risk score; GRS) 與心血管疾病達顯著相關,因此現今預測模型的建立大多也會納入基因風險分數作為重要變數。然而由於種族的差異,目前尚無研究指出歐美的基因風險分數是否能有效地應用於台灣心血管疾病的病患上。因此本研究第一個目的是驗證歐美所發展的基因風險分數能否有效地應用於台灣資料,而第二個目的是建立台灣人專屬的心血管疾病預測模型。本研究之基因數據來自三個西方研究與一項中國研究,而該中國的研究均採用東亞全基因體關聯分析所發表的顯著位點作為分析標的,可視為亞洲研究之代表。在本研究中我們選取各研究分析的單核甘酸多型性標的並用相同的方法計算基因風險分數,再進行相關強度與區別能力的比較,區別能力則是以曲線下面積(Area Under Curve; AUC) 做判別。進行完驗證後,會根據台灣人體資料庫(Taiwan Biobank; TWB)挑選這些外部的單核甘酸多型性並且以風險等位基因頻率之倒數(inversed risk allele frequency; inversed RAF)為內部的權重做加權,而後以平均混和模型 (Average blending model) 搭配傳統危險因子 (traditional risk factors; TRFs) 來建立心血管疾病的預測模型,並評估其與外部基因風險分數的表現差異。在相關強度的比較結果中,唯有東亞的單核甘酸多型性組成之基因風險分數與台灣心血管疾病達統計上顯著,而其風險比(odds ratio; OR)為1.5 (95% 信賴區間,1.20 – 1.87),代表東亞基因風險分數可做為台灣心血管疾病的危險預測因子,但在曲線下面積的比較中發現東亞與歐洲的基因風險分數均無法顯著地改善模型區別能力。在模型表現比較中,東亞基因風險分數的F1分數(F1 score)改善程度優於其他歐洲的基因風險分數,說明東亞發表之顯著單核甘酸多型性相較於歐洲更適合於台灣人。若使用內部權重加權之基因風險分數,其改善模型的程度優於外部的東亞基因風險分數,且其改善程度並非隨機發生,這些結果說明了即使應用了外部發表的顯著之單核甘酸多型性,也須使用台灣人的內部權重來建立模型。然而,加入基因數據的改善程度有限,顯示出本模型尚無法應用於實際臨床端,仍有賴後續研究改善。 | zh_TW |
dc.description.abstract | In spite the fact that multiple single nucleotide polymorphisms (SNPs) associated with cardiovascular disease (CVD) have been identified by genome-wide association studies (GWAS) in European populations, there were no evidences showing that the external genetic risk scores (GRSs) developed in European studies could be properly used in Taiwanese. This study aimed to validate the GRSs from European and East Asian, and build the predictive models with these GRSs. The data was obtained from Taiwan Biobank(TWB) and there were 924 cases and 13671 controls in this study. We selected three western studies and one Chinese study for GRSs validation. The effect sizes and AUCs were used for comparison. After validation, we used average blending algorithm to construct the predictive models with different external GRSs. In addition, we selected the SNPs in TWB with F1 scores and p-value to compute the GRSs with internal coefficients, which were the inversed risk allele frequencies (RAF). The results showed that only East Asian GRS was significantly associated with CVDs in Taiwanese and the quintile OR was 1.50 (95%CI, 1.20 – 1.87) after adjusted for traditional risk factors (TRFs). Besides, the performance of predictive model built with East Asian GRS was better than the other external GRSs, which suggested that East Asian GRS was the predictor of CVDs. Furthermore, the GRSs selected in TWB and weighted by internal coefficients were better in improvement, which suggested that external associated SNPs should be weighted by internal weights. However, the modest improvements in discrimination illustrated that the clinical application was limited at present. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T11:28:22Z (GMT). No. of bitstreams: 1 U0001-1208202015113100.pdf: 1557771 bytes, checksum: 5db6c53e92b48905e7b9ea45144669ab (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 口試委員會審定書 i 中文摘要 ii Abstract iv Chapter 1 Introduction 1 Chapter 2 Method 3 2.1 Study Population 3 2.2 Quality Control 4 2.3 Referenced Studies and selection of SNPs Selection 4 2.4 Genetic Risk Score (GRS) computation 6 2.5 Association test of TRFs 7 2.6 Data separation and undersampling 7 2.7 GRSs validation 8 2.8 Predictive modeling 8 2.9 Null simulation 11 2.10 The difference of GRSs between cases and controls 11 Chapter 3 Results 13 3.1 Associations Between the TRFs and CVD 13 3.2 Effect sizes of the GRSs from different ethnics 13 3.3 Discriminated improvement of GRSs 14 3.4 Performance of models 15 3.5 The difference of GRSs between cases and controls 16 Chapter 4 Discussion 17 4.1 Interpretation of results 17 4.2 Limitations 18 4.3 Application 19 4.4 Conclusion 20 Reference 21 Supplements 40 Table S1. The performance of each model in training set 40 Table S2. The performance of each average blending combination in training set 41 Table S3. The F1 scores of each average blending combinations in testing set 42 The website of cardiovascular disease predictor 43 List of tables Table 1. Associations between traditional risk factors and CVD 24 Table 2. Associations between traditional risk factors and CVD 25 Table 3. Comparison of the effect sizes of European GRSs between CAREMA and TWB 26 Table 4. Comparison of the effect sizes of European GRSs between Hispanic and TWB 27 Table 5. Comparison of the effect sizes of European GRSs between GERA and TWB 28 Table 6. Comparison of the effect sizes of East AGRSs between Chinese and TWB 29 Table 7. Comparison of the AUCs of European GRSs between CAREMA and TWB 30 Table 8. Comparison of the AUCs of European GRSs between Hispanic and TWB 31 Table 9. Comparison of the AUCs of European GRSs between Hispanic and TWB 32 Table 10. Comparison of the AUCs of East Asian GRSs between Chinese and TWB 33 Table 11. Performance of each predictive model 34 List of figures Figure 1. The change of F1 score with multiple of control increasing 35 Figure 2. The distribution of the SNPs’ difference of mean F1 scores 36 Figure 3. The distribution of the SNPs’ p-values 37 Figure 4. The workflow of method in this study 38 | |
dc.language.iso | en | |
dc.title | 建立台灣人心血管疾病預測模型並驗證不同種族的基因風險分數之應用 | zh_TW |
dc.title | Predictive modeling of Cardiovascular Diseases for Taiwanese and Validating the Genetic Risk Scores(GRSs) Derived from Different Ethnics | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 郭柏秀(Po-Hsiu Kuo),蕭朱杏(Chuhsing Kate Hsiao),方啟泰(Chi-Tai Fang),蕭自宏(Tzu-Hung Hsiao) | |
dc.subject.keyword | 心血管疾病,單核甘酸多型性,基因風險分數,傳統危險因子,平均混和模型,F1分數, | zh_TW |
dc.subject.keyword | single nucleotide polymorphisms (SNPs),cardiovascular disease (CVD),genetic risk scores (GRS),average blending model,F1-score, | en |
dc.relation.page | 43 | |
dc.identifier.doi | 10.6342/NTU202003097 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-14 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
顯示於系所單位: | 流行病學與預防醫學研究所 |
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