請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16666
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林菀俞(WAN-YU LIN) | |
dc.contributor.author | Tsung-Hao Lee | en |
dc.contributor.author | 李宗澔 | zh_TW |
dc.date.accessioned | 2021-06-07T23:43:11Z | - |
dc.date.copyright | 2020-09-04 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-10 | |
dc.identifier.citation | Al-Goblan, A. S., Al-Alfi, M. A., Khan, M. Z. (2014). Mechanism linking diabetes mellitus and obesity. Diabetes, metabolic syndrome and obesity : targets and therapy, 7, 587-591. doi:10.2147/DMSO.S67400 Al-Sharbatti, S., Shaikh, R., Mathew, E., Sreedharan, J., Muttappallymyalil, J., Basha, S. (2011). The Use of Obesity Indicators for the Prediction of Hypertension Risk among Youth in the United Arab Emirates. Iranian journal of public health, 40(3), 33-40. Ali, O. (2013). Genetics of type 2 diabetes. World journal of diabetes, 4(4), 114-123. doi:10.4239/wjd.v4.i4.114 An, J., Gharahkhani, P., Law, M. H., Ong, J.-S., Han, X., Olsen, C. M., . . . andMe Research, T. (2019). Gastroesophageal reflux GWAS identifies risk loci that also associate with subsequent severe esophageal diseases. Nature Communications, 10(1), 4219. doi:10.1038/s41467-019-11968-2 Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., . . . Primary, I. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661-678. doi:10.1038/nature05911 Calabrò, M., Drago, A., Sidoti, A., Serretti, A., Crisafulli, C. (2015). Genes involved in pruning and inflammation are enriched in a large mega-sample of patients affected by Schizophrenia and Bipolar Disorder and controls. Psychiatry research, 228(3), 945-949. doi:10.1016/j.psychres.2015.06.013 Chen, C.-H., Yang, J.-H., Chiang, C. W. K., Hsiung, C.-N., Wu, P.-E., Chang, L.-C., . . . Shen, C.-Y. (2016). Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan Biobank project. Hum Mol Genet, 25(24), 5321-5331. doi:10.1093/hmg/ddw346 Chiang, C.-E., Wang, T.-D., Lin, T.-H., Yeh, H.-I., Liu, P.-Y., Cheng, H.-M., . . . Lin, J.-L. (2017). The 2017 Focused Update of the Guidelines of the Taiwan Society of Cardiology (TSOC) and the Taiwan Hypertension Society (THS) for the Management of Hypertension. Acta Cardiologica Sinica, 33(3), 213-225. doi:10.6515/acs20170421a Christie, S., Robiou-du-Pont, S., Anand, S. S., Morrison, K. M., McDonald, S. D., Paré, G., . . . Meyre, D. (2017). Genetic contribution to lipid levels in early life based on 158 loci validated in adults: the FAMILY study. Sci Rep, 7(1), 68. doi:10.1038/s41598-017-00102-1 Chyou, J. Y., Mega, J. L., Sabatine, M. S. (2013). Chapter 4 - Pharmacogenetics. In E. M. Antman M. S. Sabatine (Eds.), Cardiovascular Therapeutics: A Companion to Braunwald's Heart Disease (Fourth Edition) (pp. 53-66). Philadelphia: W.B. Saunders. Cole, C. B., Nikpay, M., Lau, P., Stewart, A. F. R., Davies, R. W., Wells, G. A., . . . McPherson, R. (2014). Adiposity significantly modifies genetic risk for dyslipidemia. Journal of lipid research, 55(11), 2416-2422. doi:10.1194/jlr.P052522 Das, S., Forer, L., Schönherr, S., Sidore, C., Locke, A. E., Kwong, A., . . . Fuchsberger, C. (2016). Next-generation genotype imputation service and methods. Nature Genetics, 48(10), 1284-1287. doi:10.1038/ng.3656 Dietrich, S., Jacobs, S., Zheng, J.-S., Meidtner, K., Schwingshackl, L., Schulze, M. B. (2019). Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review. Obesity Reviews, 20(11), 1557-1571. doi:10.1111/obr.12921 Doris, P. A. (2011). The genetics of blood pressure and hypertension: the role of rare variation. Cardiovascular therapeutics, 29(1), 37-45. doi:10.1111/j.1755-5922.2010.00246.x Duncan, L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., . . . Domingue, B. (2019). Analysis of polygenic risk score usage and performance in diverse human populations. Nature Communications, 10(1), 3328. doi:10.1038/s41467-019-11112-0 Dupuis, J., Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., Jackson, A. U., . . . the, M. i. (2010). New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature Genetics, 42(2), 105-116. doi:10.1038/ng.520 Duun-Henriksen, A. K., Schmidt, S., Nørgaard, K., Madsen, H. (2013). Clinical data for advanced glucose modeling. Technical University of Denmark, Lyngby. Ehret, G. B., Ferreira, T., Chasman, D. I., Jackson, A. U., Schmidt, E. M., Johnson, T., . . . Wellcome Trust Case Control, C. (2016). The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nature Genetics, 48(10), 1171-1184. doi:10.1038/ng.3667 Ehret, G. B., Munroe, P. B., Rice, K. M., Bochud, M., Johnson, A. D., Chasman, D. I., . . . consortium, C.-H. (2011). Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature, 478(7367), 103-109. doi:10.1038/nature10405 Hüls, A., Krämer, U., Carlsten, C., Schikowski, T., Ickstadt, K., Schwender, H. (2017). Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies. BMC Genetics, 18(1), 115. doi:10.1186/s12863-017-0586-3 Hoffmann, T. J., Ehret, G. B., Nandakumar, P., Ranatunga, D., Schaefer, C., Kwok, P.-Y., . . . Risch, N. (2017). Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nature Genetics, 49(1), 54-64. doi:10.1038/ng.3715 Hsu, C.-C., Tu, S.-T., Sheu, W. H.-H. (2019). 2019 Diabetes Atlas: Achievements and challenges in diabetes care in Taiwan. Journal of the Formosan Medical Association, 118, S130-S134. doi:https://doi.org/10.1016/j.jfma.2019.06.018 Husemoen, L. L., Fenger, M., Friedrich, N., Tolstrup, J. S., Beenfeldt Fredriksen, S., Linneberg, A. (2008). The association of ADH and ALDH gene variants with alcohol drinking habits and cardiovascular disease risk factors. Alcohol Clin Exp Res, 32(11), 1984-1991. doi:10.1111/j.1530-0277.2008.00780.x Hwang, J.-Y., Sim, X., Wu, Y., Liang, J., Tabara, Y., Hu, C., . . . Kim, B.-J. (2015). Genome-Wide Association Meta-analysis Identifies Novel Variants Associated With Fasting Plasma Glucose in East Asians. Diabetes, 64(1), 291. doi:10.2337/db14-0563 Ito, H., Nakasuga, K., Ohshima, A., Sakai, Y., Maruyama, T., Kaji, Y., . . . Sakamoto, M. (2004). Excess accumulation of body fat is related to dyslipidemia in normal-weight subjects. International Journal of Obesity, 28(2), 242-247. doi:10.1038/sj.ijo.0802528 Jamieson, M. J., Webster, J., Philips, S., Jeffers, T. A., Scott, A. K., Robb, O. J., . . . Petrie, J. C. (1990). The measurement of blood pressure: sitting or supine, once or twice? J Hypertens, 8(7), 635-640. doi:10.1097/00004872-199007000-00006 Jiang, S.-Z., Lu, W., Zong, X.-F., Ruan, H.-Y., Liu, Y. (2016). Obesity and hypertension. Experimental and therapeutic medicine, 12(4), 2395-2399. doi:10.3892/etm.2016.3667 Keller, M. C. (2014). Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution. Biol Psychiatry, 75(1), 18-24. doi:10.1016/j.biopsych.2013.09.006 Klop, B., Elte, J. W. F., Cabezas, M. C. (2013). Dyslipidemia in obesity: mechanisms and potential targets. Nutrients, 5(4), 1218-1240. doi:10.3390/nu5041218 Li, Y.-H., Ueng, K.-C., Jeng, J.-S., Charng, M.-J., Lin, T.-H., Chien, K.-L., . . . Yeh, H.-I. (2017). 2017 Taiwan lipid guidelines for high risk patients. Journal of the Formosan Medical Association, 116(4), 217-248. doi:https://doi.org/10.1016/j.jfma.2016.11.013 Lin, W.-Y., Chan, C.-C., Liu, Y.-L., Yang, A. C., Tsai, S.-J., Kuo, P.-H. (2019). Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants. PLOS Genetics, 15(8), e1008277. doi:10.1371/journal.pgen.1008277 Lin, W.-Y., Huang, C.-C., Liu, Y.-L., Tsai, S.-J., Kuo, P.-H. (2019). Polygenic approaches to detect gene–environment interactions when external information is unavailable. Briefings in Bioinformatics, 20(6), 2236-2252. doi:10.1093/bib/bby086 Liu, C., Kraja, A. T., Smith, J. A., Brody, J. A., Franceschini, N., Bis, J. C., . . . Consortium, C. K. (2016). Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nature Genetics, 48(10), 1162-1170. doi:10.1038/ng.3660 Luo, M., Zhang, Z., Peng, Y., Wang, S., Peng, D. (2018). The negative effect of ANGPTL8 on HDL-mediated cholesterol efflux capacity. Cardiovascular Diabetology, 17(1), 142. doi:10.1186/s12933-018-0785-x Manning, A. K., Hivert, M.-F., Scott, R. A., Grimsby, J. L., Bouatia-Naji, N., Chen, H., . . . The Multiple Tissue Human Expression Resource, C. (2012). A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nature Genetics, 44(6), 659-669. doi:10.1038/ng.2274 März, W., Kleber, M. E., Scharnagl, H., Speer, T., Zewinger, S., Ritsch, A., . . . Laufs, U. (2017). HDL cholesterol: reappraisal of its clinical relevance. Clinical Research in Cardiology, 106(9), 663-675. Oliveros, E., Somers, V. K., Sochor, O., Goel, K., Lopez-Jimenez, F. (2014). The Concept of Normal Weight Obesity. Progress in Cardiovascular Diseases, 56(4), 426-433. doi:https://doi.org/10.1016/j.pcad.2013.10.003 Onwe, P., Folawiyo, M., Anyigor-Ogah, C., Umahi, G., Okorocha, A., Afoke, A. (2015). Hyperlipidemia: etiology and possible control. IOSR-JDMS, 14(10), 93-100. Park, S. K., Ryoo, J.-H., Oh, C.-M., Choi, J.-M., Chung, P.-W., Jung, J. Y. (2019). Body fat percentage, obesity, and their relation to the incidental risk of hypertension. The Journal of Clinical Hypertension, 21(10), 1496-1504. doi:10.1111/jch.13667 Pippitt, K., Li, M., Gurgle, H. E. (2016). Diabetes Mellitus: Screening and Diagnosis. Am Fam Physician, 93(2), 103-109. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., . . . Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81(3), 559-575. doi:https://doi.org/10.1086/519795 Scott, R. A., Lagou, V., Welch, R. P., Wheeler, E., Montasser, M. E., Luan, J. a., . . . Meta-analysis, C. (2012). Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nature Genetics, 44(9), 991-1005. doi:10.1038/ng.2385 Sheen, Y.-J., Hsu, C.-C., Jiang, Y.-D., Huang, C.-N., Liu, J.-S., Sheu, W. H.-H. (2019). Trends in prevalence and incidence of diabetes mellitus from 2005 to 2014 in Taiwan. Journal of the Formosan Medical Association, 118, S66-S73. doi:https://doi.org/10.1016/j.jfma.2019.06.016 Sheikh, M. S., Shaikh, K., Budoff, M. J. (2018). Interaction of genetic risk scores and adiposity: a significant influence on triglyceride levels. Journal of Laboratory and Precision Medicine, 3. Soranzo, N., Sanna, S., Wheeler, E., Gieger, C., Radke, D., Dupuis, J., . . . Ricketts, S. L. (2010). Common Variants at 10 Genomic Loci Influence Hemoglobin A lt;sub gt;1C lt;/sub gt; Levels via Glycemic and Nonglycemic Pathways. Diabetes, 59(12), 3229. doi:10.2337/db10-0502 Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., . . . Kathiresan, S. (2010). Biological, clinical and population relevance of 95 loci for blood lipids. Nature, 466(7307), 707-713. doi:10.1038/nature09270 Tyrrell, J., Wood, A. R., Ames, R. M., Yaghootkar, H., Beaumont, R. N., Jones, S. E., . . . Frayling, T. M. (2017). Gene–obesogenic environment interactions in the UK Biobank study. International Journal of Epidemiology, 46(2), 559-575. doi:10.1093/ije/dyw337 Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., . . . Pontén, F. (2015). Tissue-based map of the human proteome. Science, 347(6220), 1260419. doi:10.1126/science.1260419 Wheeler, E., Leong, A., Liu, C.-T., Hivert, M.-F., Strawbridge, R. J., Podmore, C., . . . Meigs, J. B. (2017). Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis. PLOS Medicine, 14(9), e1002383. doi:10.1371/journal.pmed.1002383 Willer, C. J., Schmidt, E. M., Sengupta, S., Peloso, G. M., Gustafsson, S., Kanoni, S., . . . Global Lipids Genetics, C. (2013). Discovery and refinement of loci associated with lipid levels. Nature Genetics, 45(11), 1274-1283. doi:10.1038/ng.2797 Wu, H.-H., Liu, N.-J., Yang, Z., Tao, X.-M., Du, Y.-P., Wang, X.-C., . . . Wen, J. (2014). IGF2BP2 and obesity interaction analysis for type 2 diabetes mellitus in Chinese Han population. European Journal of Medical Research, 19(1), 40. doi:10.1186/2047-783X-19-40 Wu, Y., Marvelle, A. F., Li, J., Croteau-Chonka, D. C., Feranil, A. B., Kuzawa, C. W., . . . Mohlke, K. L. (2013). Genetic association with lipids in Filipinos: waist circumference modifies an APOA5 effect on triglyceride levels. Journal of lipid research, 54(11), 3198-3205. doi:10.1194/jlr.P042077 Xi, B., Zhao, X., Chandak, G. R., Shen, Y., Cheng, H., Hou, D., . . . Mi, J. (2013). Influence of Obesity on Association Between Genetic Variants Identified by Genome-Wide Association Studies and Hypertension Risk in Chinese Children. Am J Hypertens, 26(8), 990-996. doi:10.1093/ajh/hpt046 Zhao, T., Lin, Z., Zhu, H., Wang, C., Jia, W. (2017). Impact of body fat percentage change on future diabetes in subjects with normal glucose tolerance. IUBMB Life, 69(12), 947-955. doi:10.1002/iub.1693 Zhu, J., Sun, Q., Zong, G., Si, Y., Liu, C., Qi, Q., . . . Lin, X. (2013). Interaction between a common variant in FADS1 and erythrocyte polyunsaturated fatty acids on lipid profile in Chinese Hans. Journal of lipid research, 54(5), 1477-1483. doi:10.1194/jlr.P027516 Zubair, N., Mayer-Davis, E. J., Mendez, M. A., Mohlke, K. L., North, K. E., Adair, L. S. (2014). Genetic risk score and adiposity interact to influence triglyceride levels in a cohort of Filipino women. Nutrition Diabetes, 4(6), e118-e118. doi:10.1038/nutd.2014.16 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16666 | - |
dc.description.abstract | 糖尿病、高血壓與高血脂症 (簡稱「三高」) 已成為全球重要的健康議題。三高疾病同時受到遺傳因子與生活方式的影響,對糖尿病指標的遺傳度估計範圍位在20%到80%之間;對高血壓為30%到70%之間;對高血脂症為28%到78%之間。許多研究顯示三高疾病與肥胖相關,但尚不清楚哪種肥胖指標可能改變糖尿病、高血壓與高脂血症的遺傳風險,因此在本研究中,我們使用了五種肥胖指標來探討三高疾病中基因與肥胖的交互作用,五種肥胖指標包括身體質量指數(BMI)、體脂肪率(BFR)、腰圍(WC)、臀圍(HC)以及腰臀比(WHR)。 我們分析「臺灣人體生物資料庫」之資料,受試者由TWB1或TWB2晶片來定出基因型。研究中納入TWB1的25,460位個案作為檢測組與TWB2的58,774位個案作為驗證組,其中各包含了597,644與606,096個「單核苷酸多態性」(SNPs)。吾人使用空腹血糖值(FG)與糖化血色素(HbA1c)作為糖尿病指標;舒張壓(DBP)與收縮壓(SBP)作為高血壓指標;三酸甘油脂(TG)、總膽固醇(TC)、低密度脂蛋白膽固醇(LDL-C)以及高密度脂蛋白膽固醇(HDL-C)作為高血脂症指標。我們回顧了這些性狀的相關文獻,以文獻中的SNP主效應作為權重,建構各性狀的加權多基因分數(weighted polygenic score),以此分析五種肥胖指標與基因之間的交互作用。檢測組中的顯著水準設為0.00125 (即0.05/40,40為檢測組中檢定的交互作用總數),驗證組中的顯著水準設為0.00556 (即0.05/9,9為檢測組中交互作用達到顯著水準的個數)。 檢測組與驗證組均顯示全部肥胖指標皆與八種性狀顯著相關,其中體脂肪率(BFR)的主效應最為強烈。除此之外,在高血脂症的兩個性狀,三酸甘油脂(TG)與高密度脂蛋白膽固醇(HDL-C)中,發現5個能夠重複驗證的交互作用結果,這些結果表示肥胖與三酸甘油脂(TG)的基因風險上升有關,並與高密度脂蛋白膽固醇(HDL-C)的基因保護作用減弱有關。 | zh_TW |
dc.description.abstract | Diabetes, hypertension, and hyperlipidemia have been important health issues around the world. Genetic and lifestyle factors are responsible for these diseases. The heritability values were estimated to be ~20% to 80% for diabetes; ~30% to 70% for hypertension; and ~28% to 78% for hyperlipidemia. Many studies have shown that these chronic diseases are related to obesity, but it is not clear which obesity measure may modify the genetic risk of diabetes, hypertension, and hyperlipidemia. In this study, we used five obesity measures to investigate the gene-by-obesity interactions on these diseases. The five obesity measures included body mass index (BMI), body fat rate (BFR), waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR). We analyzed data from the Taiwan Biobank (TWB), where subjects were genotyped by TWB1 or TWB2 array. This study includes 25,460 TWB1 participants as discovery cohort and 58,774 TWB2 participants as replication cohort, each with around 600,000 genotyped single-nucleotide polymorphisms (SNPs). In this study, fasting glucose and glycated hemoglobin (HbA1c) were used as indicators for diabetes; diastolic blood pressure (DBP) and systolic blood pressure (SBP) for hypertension; and triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) for hyperlipidemia. We reviewed previous studies related to these phenotypes, and used SNP main effects in those studies as weights of our weighted polygenic score (PS). Then we analyzed PS-by-obesity interactions on these eight phenotypes. The significance level in the discovery cohort was set at 0.000125 (i.e., 0.05/40, where 40 is the number of interaction tests performed in the discovery cohort), whereas the significance level in replication cohort was set at 0.00556 (i.e., 0.05/9, where 9 is the number of significant PS-by-obesity interactions detected from the discovery cohort). The discovery cohort and the replication cohort both show that all the five obesity measures are significantly associated with the eight phenotypes, where body fat rate (BFR) provides the strongest effects among all the five obesity measures. Moreover, 5 significant PS-by-obesity interactions were discovered from 2 phenotypes, TG and HDL-C. These results indicate that obesity is associated with an exacerbation of the detrimental genetic effects of TG and an attenuation of the beneficial genetic effects of HDL-C. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T23:43:11Z (GMT). No. of bitstreams: 1 U0001-1008202018063800.pdf: 3564828 bytes, checksum: 9b2f6752007287efdad8023277bb8eb1 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 目錄 誌謝 I 中文摘要 II ABSTRACT IV 目錄 VI 圖目錄 VIII 表目錄 IX 第一章. 前言 1 第二章. 材料與方法 3 2.1 研究資料 3 2.1.1 台灣人體生物資料庫 3 2.1.2 品質控制 3 2.1.3 基因型插補 4 2.1.4 三高相關性狀 4 2.1.5 校正項定義 5 2.2 肥胖指標主效應分析 5 2.3 基因與肥胖指標交互作用分析 6 2.3.1 建構多基因分數 6 2.3.2 多基因分數交互作用分析 7 2.3.3 單點交互作用分析 7 第三章. 結果 9 3.1 資料特徵 9 3.2 肥胖指標主效應分析結果 9 3.3 基因與肥胖指標交互作用分析結果 10 3.3.1 多基因分數交互作用結果 10 3.3.2 單點交互作用分析結果 11 第四章. 討論 13 第五章. 圖 16 第六章. 表 20 第七章. 參考文獻 28 第八章. 附錄 34 附錄一 空腹血糖值多基因分數(FGPS)組成 34 附錄二 糖化血色素多基因分數(HBA1CPS)組成 37 附錄三 舒張壓多基因分數(DBPPS)組成 40 附錄四 收縮壓多基因分數(SBPPS)組成 46 附錄五 三酸甘油脂多基因分數(TGPS)組成 51 附錄六 總膽固醇多基因分數(TCPS)組成 54 附錄七 低密度脂蛋白膽固醇多基因分數(LDL-CPS)組成 58 附錄八 高密度脂蛋白膽固醇多基因分數(HDL-CPS)組成 61 | |
dc.language.iso | zh-TW | |
dc.title | 肥胖指標與糖尿病、高血壓與高血脂症遺傳傾向之關聯性 | zh_TW |
dc.title | The association of obesity indicators with the genetic predisposition to diabetes, hypertension, and hyperlipidemia | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 楊欣洲(Hsin-Chou Yang),李文宗(WEN-CHUNG LEE),盧子彬(TZU-PIN LU) | |
dc.subject.keyword | 基因-肥胖交互作用,臺灣人體生物資料庫,肥胖指標,加權多基因分數,單核苷酸多態性, | zh_TW |
dc.subject.keyword | Gene-by-obesity interaction,Taiwan biobank,Obesity measures,Weighted polygenic score,Single nucleotide polymorphism, | en |
dc.relation.page | 64 | |
dc.identifier.doi | 10.6342/NTU202002854 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2020-08-11 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-1008202018063800.pdf 目前未授權公開取用 | 3.48 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。