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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 | 林菀俞 | |
dc.contributor.author | Yu-Shun Lin | en |
dc.contributor.author | 林育昇 | zh_TW |
dc.date.accessioned | 2021-06-17T08:14:06Z | - |
dc.date.available | 2022-08-27 | |
dc.date.copyright | 2019-08-27 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-14 | |
dc.identifier.citation | Ahmad, S., Rukh, G., Varga, T. V., Ali, A., Kurbasic, A., Shungin, D., . . . Franks, P. W. (2013). Gene × Physical Activity Interactions in Obesity: Combined Analysis of 111,421 Individuals of European Ancestry. PLOS Genetics, 9(7), e1003607. doi:10.1371/journal.pgen.1003607
Albuquerque, D., Nóbrega, C., & Manco, L. (2013). Association of FTO Polymorphisms with Obesity and Obesity-Related Outcomes in Portuguese Children. PLoS ONE, 8(1), e54370. doi:10.1371/journal.pone.0054370 Andreasen, C. H., Stender-Petersen, K. L., Mogensen, M. S., Torekov, S. S., Wegner, L., Andersen, G., . . . Hansen, T. (2008). Low Physical Activity Accentuates the Effect of the <em>FTO</em> rs9939609 Polymorphism on Body Fat Accumulation. Diabetes, 57(1), 95-101. doi:10.2337/db07-0910 Aschard, H. (2016). A perspective on interaction effects in genetic association studies. Genetic epidemiology, 40(8), 678-688. doi:10.1002/gepi.21989 Cadoret, R. J., Cain, C. A., & Crowe, R. R. (1983). Evidence for gene-environment interaction in the development of adolescent antisocial behavior. Behavior Genetics, 13(3), 301-310. doi:10.1007/bf01071875 Cha, S. W., Choi, S. M., Kim, K. S., Park, B. L., Kim, J. R., Kim, J. Y., & Shin, H. D. (2012). Replication of Genetic Effects of FTO Polymorphisms on BMI in a Korean Population. Obesity, 16(9), 2187-2189. doi:10.1038/oby.2008.314 Chen, H., Meigs, J. B., & Dupuis, J. (2014). Incorporating gene-environment interaction in testing for association with rare genetic variants. Human heredity, 78(2), 81-90. doi:10.1159/000363347 Chuenta, W., Phonrat, B., Tungtrongchitr, A., Limwongse, C., Chongviriyaphan, N., Santiprabhob, J., & Tungtrongchitr, R. (2015). Common variations in the FTO gene and obesity in Thais: A family-based study. Gene, 558(1), 75-81. doi:https://doi.org/10.1016/j.gene.2014.12.050 Cronin, R. M., Field, J. R., Bradford, Y., Shaffer, C. M., Carroll, R. J., Mosley, J. D., . . . Denny, J. C. (2014). Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index. Frontiers in Genetics, 5(250). doi:10.3389/fgene.2014.00250 Dai, J. Y., Kooperberg, C., Leblanc, M., & Prentice, R. L. (2012). Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction. Biometrika, 99(4), 929-944. doi:10.1093/biomet/ass044 Dina, C., Meyre, D., Gallina, S., Durand, E., Körner, A., Jacobson, P., . . . Froguel, P. (2007). Variation in FTO contributes to childhood obesity and severe adult obesity. Nature Genetics, 39, 724. doi:10.1038/ng2048 https://www.nature.com/articles/ng2048#supplementary-information Franks, P. W., Pearson, E., & Florez, J. C. (2013). Gene-Environment and Gene-Treatment Interactions in Type 2 Diabetes. Progress, pitfalls, and prospects, 36(5), 1413-1421. doi:10.2337/dc12-2211 Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., . . . McCarthy, M. I. (2007). A Common Variant in the <em>FTO</em> Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity. Science, 316(5826), 889-894. doi:10.1126/science.1141634 Friedman, J., Hastie, T., & Tibshirani, R. (2009). glmnet: Lasso and elastic-net regularized generalized linear models. R package version, 1(4). Frost, H. R., Shen, L., Saykin, A. J., Williams, S. M., Moore, J. H., & Alzheimer's Disease Neuroimaging, I. (2016). Identifying significant gene-environment interactions using a combination of screening testing and hierarchical false discovery rate control. Genetic epidemiology, 40(7), 544-557. doi:10.1002/gepi.21997 Gauderman, W. J., Zhang, P., Morrison, J. L., & Lewinger, J. P. (2013). Finding Novel Genes by Testing G × E Interactions in a Genome-Wide Association Study. Genetic epidemiology, 37(6), 603-613. doi:10.1002/gepi.21748 Hüls, A., Ickstadt, K., Schikowski, T., & Krämer, U. (2017). Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression. BMC Genetics, 18(1), 55. doi:10.1186/s12863-017-0519-1 Hinney, A., Nguyen, T. T., Scherag, A., Friedel, S., Brönner, G., Müller, T. D., . . . Hebebrand, J. (2007). Genome Wide Association (GWA) Study for Early Onset Extreme Obesity Supports the Role of Fat Mass and Obesity Associated Gene (FTO) Variants. PLoS ONE, 2(12), e1361. doi:10.1371/journal.pone.0001361 Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67. Hubacek, J. A., Bohuslavova, R., Kuthanova, L., Kubinova, R., Peasey, A., Pikhart, H., . . . Bobak, M. (2008). The FTO Gene and Obesity in a Large Eastern European Population Sample: The HAPIEE Study. Obesity, 16(12), 2764-2766. doi:10.1038/oby.2008.421 Hunt, S. C., Stone, S., Xin, Y., Scherer, C. A., Magness, C. L., Iadonato, S. P., . . . Adams, T. D. (2012). Association of the FTO Gene With BMI. Obesity, 16(4), 902-904. doi:10.1038/oby.2007.126 Hunter, D. J. (2005). Gene–environment interactions in human diseases. Nature Reviews Genetics, 6(4), 287-298. doi:10.1038/nrg1578 Jiao, S., Hsu, L., Bézieau, S., Brenner, H., Chan, A. T., Chang-Claude, J., . . . Peters, U. (2013). SBERIA: set-based gene-environment interaction test for rare and common variants in complex diseases. Genetic epidemiology, 37(5), 452-464. doi:10.1002/gepi.21735 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, in press. Lin, W.-Y., Huang, C.-C., Liu, Y.-L., Tsai, S.-J., & Kuo, P.-H. (2018). Polygenic approaches to detect gene–environment interactions when external information is unavailable. Briefings in Bioinformatics. doi:10.1093/bib/bby086 Lin, W.-Y., Huang, C.-C., Liu, Y.-L., Tsai, S.-J., & Kuo, P.-H. (2019). Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests. Frontiers in genetics, 9(715). doi:10.3389/fgene.2018.00715 Lin, X., Lee, S., Christiani, D. C., & Lin, X. (2013). Test for interactions between a genetic marker set and environment in generalized linear models. Biostatistics (Oxford, England), 14(4), 667-681. doi:10.1093/biostatistics/kxt006 Lin, X., Lee, S., Wu, M. C., Wang, C., Chen, H., Li, Z., & Lin, X. (2016). Test for rare variants by environment interactions in sequencing association studies. Biometrics, 72(1), 156-164. doi:10.1111/biom.12368 Liu, J., Huang, J., Zhang, Y., Lan, Q., Rothman, N., Zheng, T., & Ma, S. (2013). Identification of gene–environment interactions in cancer studies using penalization. Genomics, 102(4), 189-194. doi:https://doi.org/10.1016/j.ygeno.2013.08.006 Liu, J. Z., Mcrae, A. F., Nyholt, D. R., Medland, S. E., Wray, N. R., Brown, K. M., . . . Investigators, A. (2010). A Versatile Gene-Based Test for Genome-wide Association Studies. Am J Hum Genet, 87(1), 139-145. doi:10.1016/j.ajhg.2010.06.009 Locke, A. E., Kahali, B., Berndt, S. I., Justice, A. E., Pers, T. H., Day, F. R., . . . Speliotes, E. K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518, 197. doi:10.1038/nature14177 https://www.nature.com/articles/nature14177#supplementary-information Maes, H. H., Neale, M. C., & Eaves, L. J. (1997). Genetic and environmental factors in relative body weight and human adiposity. Behavior Genetics, 27(4), 325-351. McAllister, K., Mechanic, L. E., Amos, C., Aschard, H., Blair, I. A., Chatterjee, N., . . . Witte, J. S. (2017). Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. American journal of epidemiology, 186(7), 753-761. doi:10.1093/aje/kwx227 Ottman, R. (1996). Gene–Environment Interaction: Definitions and Study Design. Preventive Medicine, 25(6), 764-770. doi:https://doi.org/10.1006/pmed.1996.0117 Price, R. A., Li, W.-D., & Zhao, H. (2008). FTO gene SNPs associated with extreme obesity in cases, controls and extremely discordant sister pairs. BMC Medical Genetics, 9(1), 4. doi:10.1186/1471-2350-9-4 Qi, L., & Cho, Y. A. (2008). Gene-environment interaction and obesity. Nutrition Reviews, 66(12), 684-694. doi:10.1111/j.1753-4887.2008.00128.x Rothman, N., Garcia-Closas, M., Chatterjee, N., Malats, N., Wu, X., Figueroa, J. D., . . . Chanock, S. J. (2010). A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci. Nature Genetics, 42, 978. doi:10.1038/ng.687 https://www.nature.com/articles/ng.687#supplementary-information Scuteri, A., Sanna, S., Chen, W. M., Uda, M., Albai, G., Strait, J., . . . Abecasis, G. R. (2007). Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet, 3(7), e115. doi:10.1371/journal.pgen.0030115 Tan, L.-J., Zhu, H., He, H., Wu, K.-H., Li, J., Chen, X.-D., . . . Deng, H.-W. (2014). Replication of 6 Obesity Genes in a Meta-Analysis of Genome-Wide Association Studies from Diverse Ancestries. PLoS ONE, 9(5), e96149. doi:10.1371/journal.pone.0096149 Thomas, D. (2010). Gene–environment-wide association studies: emerging approaches. Nature Reviews Genetics, 11, 259. doi:10.1038/nrg2764 https://www.nature.com/articles/nrg2764#supplementary-information Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267-288. Retrieved from http://www.jstor.org/stable/2346178 Wakefield, J. (2007). A Bayesian Measure of the Probability of False Discovery in Genetic Epidemiology Studies. The American Journal of Human Genetics, 81(2), 208-227. doi:https://doi.org/10.1086/519024 Wakefield, J. (2009). Bayes factors for genome-wide association studies: comparison with P-values. Genetic epidemiology, 33(1), 79-86. doi:10.1002/gepi.20359 Wang, K., Li, W.-D., Zhang, C. K., Wang, Z., Glessner, J. T., Grant, S. F. A., . . . Price, R. A. (2011). A Genome-Wide Association Study on Obesity and Obesity-Related Traits. PLoS ONE, 6(4), e18939. doi:10.1371/journal.pone.0018939 Winham, S. J., & Biernacka, J. M. (2013). Gene–environment interactions in genome-wide association studies: current approaches and new directions. Journal of Child Psychology and Psychiatry, 54(10), 1120-1134. doi:10.1111/jcpp.12114 Wu, C., Jiang, Y., Ren, J., Cui, Y., & Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures. Statistics in Medicine, 37(3), 437-456. doi:10.1002/sim.7518 Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: a tool for genome-wide complex trait analysis. American journal of human genetics, 88(1), 76-82. doi:10.1016/j.ajhg.2010.11.011 Yang, J., Loos, R. J. F., Powell, J. E., Medland, S. E., Speliotes, E. K., Chasman, D. I., . . . Visscher, P. M. (2012). FTO genotype is associated with phenotypic variability of body mass index. Nature, 490, 267. doi:10.1038/nature11401 https://www.nature.com/articles/nature11401#supplementary-information Zou, H., & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 67(2), 301-320. Retrieved from http://www.jstor.org/stable/3647580 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73931 | - |
dc.description.abstract | 基因-環境交互作用(G×E)已被發現影響許多複雜疾病。然而,由於多重檢定校正的嚴苛懲罰,迄今,許多G×E之效果仍無法被檢測出來。本研究探討二階段分析策略的候選基因與環境交互作用檢定方法。首先,以「脊迴歸」(RIDGE),「彈性網」(ENET)或「最小絕對值收斂與選擇算子」(LASSO)篩選具邊際效應的單核苷酸多型性(SNP),來建構出「基因風險分數」(GRS)。而後檢測GRS與E之間的交互作用。吾人以模擬來評估上述方法和常見的五種G×E檢測方法之統計檢定力。
在實際數據分析中,吾人將本法應用於臺灣人體生物資料庫中18,424位個案。針對每個SNP與身體質量指數(BMI)進行迴歸,調整性別、年齡(以年計)、教育程度、飲酒狀況、抽菸狀況、規律運動狀況及前10個代表祖源的主成分。最後檢測出達到全基因組顯著水準(即p值<5×〖10〗^(-8))的15個SNPs皆位於「脂肪質量與肥胖關聯基因」(FTO)中。 本文進一步探討FTO基因與三種環境因子間的交互作用,包括規律運動、抽菸與飲酒。檢測出FTO基因與規律運動存在交互作用(p值= 0.0039)。在不運動族群,GRS的增加對應到更高量的BMI上升。本研究的結果證明,規律運動可降低FTO基因對肥胖的不利影響。 | zh_TW |
dc.description.abstract | Gene-environment (GxE) interactions have been found to play a role in many complex diseases. However, due to the harsh penalty of multiple-testing correction, the detection of GxE is underpowered and many GxE interactions have remained hidden to date. The aim of this study is to explore powerful candidate-gene-based GxE interaction tests by using a two-stage analysis strategy. First, we constructed a genetic risk scores (GRS) by filtering the marginal effects of single-nucleotide polymorphisms (SNPs) with the ridge regression (RIDGE), elastic net (ENET), or the least absolute shrinkage and selection operator (LASSO). Second, we tested the interaction between the GRS and E. Moreover, statistical power of our methods and five existing gene-based GxE methods was evaluated with simulations.
In real data analysis, we applied our methods to 18,424 unrelated subjects in the Taiwan Biobank. Body mass index (BMI) was regressed on each SNP, while adjusting for sex, age (in years), educational attainment, drinking status, smoking status, regular exercise, and the first 10 ancestry principal components. A total of 15 SNPs located in the fat mass and obesity associated gene (FTO) reached the genome-wide significance level (i.e., p-value<5×〖10〗^(-8)). We further explored interactions between the variants in the FTO gene and three environmental factors, including performing regular exercise, cigarette smoking, and alcohol consumption. We found strong evidence that the FTO gene interacts with regular exercise (p = 0.0039). GRSs elevate more BMI in non-exercisers than in exercisers. Our results indicate that performing regular exercise can attenuate the adverse influence of the FTO variants on obesity. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:14:06Z (GMT). No. of bitstreams: 1 ntu-108-R06849029-1.pdf: 3532257 bytes, checksum: 63e17e512a8aa0fc2edd4aa11cbe6709 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii 英文摘要 iv 目錄 vi 表目錄 viii 圖目錄 ix 第一章 前言 1 第二章 文獻回顧 4 2.1 單標識基因分析法 4 2.2 十閾值邊際基因風險分數法 5 2.3 貝氏因子適性結合法 6 2.4 集合基礎基因環境交互作用法 7 2.5 交互作用序列核關聯法 8 第三章 材料與方法 10 第四章: 模擬與結果 13 4.1 型一錯誤率 15 4.2 統計檢定力 16 4.3 方向正確率 17 4.4 真陽性率 17 4.5 陽性預測率 18 第五章 臺灣人體生物資料庫分析 19 第六章 結論與討論 22 參考文獻 45 | |
dc.language.iso | zh-TW | |
dc.title | 以懲罰迴歸法來檢定基因環境交互作用 | zh_TW |
dc.title | Penalized regressions for testing gene-environment interactions | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李文宗,郭柏秀,楊欣洲,盧子彬 | |
dc.subject.keyword | 基因-環境交互作用,脊迴歸,彈性網,最小絕對值收斂與選擇算子,臺灣人體生物資料庫,身體質量指數,FTO基因, | zh_TW |
dc.subject.keyword | gene-environment interaction,ridge regression,elastic net,least absolute shrinkage and selection operator,Taiwan Biobank,body mass index,FTO gene, | en |
dc.relation.page | 49 | |
dc.identifier.doi | 10.6342/NTU201903703 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-15 | |
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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