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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/123456789/50730
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dc.contributor.advisor李文宗
dc.contributor.authorYi-Shan Suen
dc.contributor.author蘇翊善zh_TW
dc.date.accessioned2021-06-15T12:55:03Z-
dc.date.available2016-08-26
dc.date.copyright2016-08-26
dc.date.issued2015
dc.date.submitted2016-07-17
dc.identifier.citation1. Cordell HJ. Estimation and testing of gene-environment interactions in family-based association studies. Genomics 2009;93:5-9.
2. Hunter DJ. Gene-environment interactions in human diseases. Nat Rev Genet 2005;6:287-98.
3. Le Marchand L, Wilkens LR. Design considerations for genomic association studies: importance of gene-environment interactions. Cancer Epidemiol Biomarkers Prev 2008;17:263-7.
4. Lewis CM, Knight J. Introduction to genetic association studies. Cold Spring Harb Protoc 2012;2012:297-306.
5. Rava M, Ahmed I, Demenais F, Sanchez M, Tubert-Bitter P, Nadif R. Selection of genes for gene-environment interaction studies: a candidate pathway-based strategy using asthma as an example. Environ Health 2013;12:56.
6. Chatterjee N, Carroll RJ. Semiparametric maximum likelihood estimation exploiting gene-environment independence in case-control studies. Biometrika 2005;92:399-418.
7. Chui TT, Lee WC. Estimating risks and relative risks in case-base studies under the assumptions of gene-environment independence and Hardy-Weinberg equilibrium.PLoS One 2014;9:e105398.
8. Lee WC, Wang LY, Cheng KF. An easy-to-implement approach for analyzing case-control and case-only studies assuming gene-environment independence and Hardy-Weinberg equilibrium. Stat Med 2010;29:2557-67.
9. Lee WC. Testing for sufficient-cause gene-environment interactions under independence and Hardy-Weinberg equilibrium assumptions. Am J Epidemiol 2015
(in press).
10. Friedman DS, O'Colmain BJ, Munoz B, Tomany SC, McCarty C, de Jong PT, et al. Prevalence of age-related macular degeneration in the United States. Arch Ophthalmol 2004;122:564-72.
11. Giudice GL. Age-Related Macular Degeneration - Etiology, Diagnosis and Management - A Glance at the Future. Vienna: InTech; 2013.
12. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, et al. Complement factor H polymorphism in age-related macular degeneration. Science 2005;308:385-9.
13. Fraser-Bell S, Wu J, Klein R, Azen SP, Varma R. Smoking, alcohol intake, estrogen use, and age-related macular degeneration in Latinos: the Los Angeles Latino Eye Study. Am J Ophthalmol 2006;141:79-87.
14. Adams MK, Chong EW, Williamson E, Aung KZ, Makeyeva GA, Giles GG, et al. 20/20--Alcohol and age-related macular degeneration: the Melbourne Collaborative Cohort Study. Am J Epidemiol 2012;176:289-98.
15. Jamal A, Agaku IT, O'Connor E, King BA, Kenemer JB, Neff L. Current cigarette smoking among adults--United States, 2005-2013. MMWR Morb Mortal Wkly Rep 2014;63:1108-12.
16. De Leon J, Rendon DM, Baca-Garcia E, Aizpuru F, Gonzalez-Pinto A, Anitua C, et al. Association between smoking and alcohol use in the general population: stable and unstable odds ratios across two years in two different countries. Alcohol Alcohol 2007;42:252-7.
17. Greenland S. The effect of misclassification in the presence of covariates. Am J Epidemiol 1980;112:564-9.
18. Greenland S, Robins JM. Confounding and misclassification. Am J Epidemiol 1985;122:495-506.
19. Rothman KJ, Lash TL, Greenland S. Modern Epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2012.
20. Szklo M, Nieto J. Epidemiology: Beyond the Basics. 3rd ed. Burlington, MA: Jones & Bartlett Learning; 2012.
21. Zhang L, Mukherjee B, Ghosh M, Gruber S, Moreno V. Accounting for error due to misclassification of exposures in case-control studies of gene-environment interaction. Stat Med 2008;27:2756-83.
22. Cummings P. The relative merits of risk ratios and odds ratios. Arch Pediatr Adolesc Med 2009;163:438-45.
23. Doi M, Nakamura T, Yiimamoto E. Conservative tendency of the crude odds ratio. J Japan Statist Soc 2001;31:53-65.
24. Greenland S, Robins JM, Pearl J. Confounding and Collapsibility in Causal Inference. Stat Sci 1999;14:29-46.
25. Groenwold RH, Moons KG, Peelen LM, Knol MJ, Hoes AW. Reporting of treatment effects from randomized trials: a plea for multivariable risk ratios. Contemp Clin Trials 2011;32:399-402.
26. Kent DM, Trikalinos TA, Hill MD. Are unadjusted analyses of clinical trials inappropriately biased toward the null? Stroke 2009;40:672-3.
27. Pang M, Kaufman JS, Platt RW. Studying noncollapsibility of the odds ratio with marginal structural and logistic regression models. Stat Methods Med Res 2013.
28. VanderWeele TJ, Knol MJ. A Tutorial on Interaction. Epidemiol Methods 2014;3:33-72.
29. Vanderweele TJ, Ko YA, Mukherjee B. Environmental confounding in gene-environment interaction studies. Am J Epidemiol 2013;178:144-52.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50730-
dc.description.abstract在基因環境獨立的假設下,未知或未測量的環境因子,不論其為何,皆不會干擾基因本身的效應。這使得許多研究者因此以為不同環境暴露下基因效應的異質性,即表示基因環境交互作用的存在─儘管仍有其他環境因子未被校正。本研究中,作者推導出在基因環境獨立假設下的交互作用誤差公式,進行電腦模擬,並用實例進行示範。結果顯示,即使感興趣的基因,與研究中及任何其他未測量的環境因子間皆未有交互作用,仍然可能會造成基因環境交互作用的假象。因此,我們建議研究者在進行基因相關研究時,應儘可能量測並且控制所有和疾病有強烈相關的環境因子─不論是獨立的危險因子、中介因子或是干擾因子。zh_TW
dc.description.abstractBackground: Under the assumption of gene-environment independence,unknown/unmeasured environmental factors, irrespective of what they may be, can notconfound the genetic effects. This has led many people to believe that genetic heterogeneity across different levels of the studied environmental exposure should only mean gene-environment interaction—even though other environmental factors are not adjusted for.
Methods: In this study, the authors derive formula for bias and conduct computer simulations under the gene-environment independence assumption. They use a real data example to demonstrate the methodologies.
Results: They show that apparent gene-environment interaction can and will arise, even if the gene of interest is not associated with, and is not interacting with, the environmental factor under study and any other unmeasured environmental factor.
Conclusions: It is recommended to measure and control as far as possible all strong environmental factors in genetic association studies, be they independent risk factors, mediators, or confounders.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T12:55:03Z (GMT). No. of bitstreams: 1
ntu-104-R01849040-1.pdf: 2117735 bytes, checksum: 65190eb1adafc29625202dfd8048cf0b (MD5)
Previous issue date: 2015
en
dc.description.tableofcontents口試委員會審定書........................................................................................ i
誌謝...............................................................................................................ii
中文摘要......................................................................................................iii
Abstract ........................................................................................................ iv
Contents......................................................................................................... v
Figure Contents ............................................................................................ vi
Introduction ................................................................................................... 1
Methods ......................................................................................................... 2
Results ........................................................................................................... 4
Discussion ..................................................................................................... 7
Reference....................................................................................................... 9
Appendix 1 .................................................................................................. 18
Appendix 2 .................................................................................................. 27
Appendix 3 .................................................................................................. 31
Appendix 4 .................................................................................................. 35
dc.language.isoen
dc.subject相對勝算比zh_TW
dc.subject基因環境交互作用zh_TW
dc.subject干擾作用zh_TW
dc.subject不可摺疊性zh_TW
dc.subject基因環境交互作用zh_TW
dc.subject干擾作用zh_TW
dc.subject不可摺疊性zh_TW
dc.subject相對勝算比zh_TW
dc.subjectgene–environment interactionen
dc.subjectgene–environment interactionen
dc.subjectodds ratioen
dc.subjectnon-collapsibilityen
dc.subjectconfoundingen
dc.subjectconfoundingen
dc.subjectnon-collapsibilityen
dc.subjectodds ratioen
dc.title基因相關研究中基因環境交互作用的假象zh_TW
dc.titleFalse Appearance of Gene-Environment Interactions in Genetic Association Studiesen
dc.typeThesis
dc.date.schoolyear104-2
dc.description.degree碩士
dc.contributor.oralexamcommittee林先和,廖勇柏
dc.subject.keyword基因環境交互作用,干擾作用,不可摺疊性,相對勝算比,zh_TW
dc.subject.keywordgene–environment interaction,confounding,non-collapsibility,odds ratio,en
dc.relation.page38
dc.identifier.doi10.6342/NTU201600988
dc.rights.note有償授權
dc.date.accepted2016-07-18
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
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