ADIPOQ, PPAR, LEP, TCF7L2, FTO及PTPN1基因多型性與代謝症候群之關聯與基因-基因交互作用: 社區為基礎之病例對照研究

Individual Association and Gene-Gene Interaction among Genetic Variants of ADIPOQ, PPARG, LEP, TCF7L2, FTO and PTPN1 with Metabolic Syndrome in Taiwanese Adults: A Community-based Case-Control Study

陳瑩萍(Ying-Ping Chen)

指導教授 ： 陳為堅

國立臺灣大學/公共衛生學院/流行病學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.03271

摘要

背景：偵測複雜性疾病的基因-基因交互作用為目前基因研究較受注目的部分。一般多使用以基因多型性為基礎之方法--generalized multifactor dimensionality reduction (GMDR) 偵測，其對高維度之基因-基因交互作用有較好的檢定力。但在同一基因上之基因多型性的相關功能性可能會導致使用GMDR時有偽陰性的結果產生。而使用以基因為基礎之方法可以捕捉到在同一個基因內的遺傳變異之交互作用。
目標：1) 從六個與代謝症候群因子相關之基因 (ADIPOQ, LEP, PPARG, FTO, TCF7L2, 及 PTPN1)的基因多型性中，尋找單獨基因多型性與代謝症候群之相關；2) 並利用GMDR及以基因為基礎之方式尋找是否有基因-基因交互作用存在。
方法：樣本來自於桃園縣年度成人健康檢查收集而來的611名40歲以上之代謝症候群病例及1117名對照。我們選出ADIPOQ, PPARG, LEP, TCF7L2, FTO 及PTPN1六個與代謝症候群危險因子相關之基因中的19個SNPs,尋找它們與代謝症候群之相關性，並使用generalized multifactor dimensionality reduction (GMDR) 方法及透過(genotype-trait distortion score, GTD score)的計算並以基因為基礎的方式探討是否有基因-基因交互作用的存在。
結果：在獨立相關分析部分，沒有SNP與代謝症候群相關。使用以基因為基礎的方式分析基因-基因交互作用時，透過GMDR得到PTPN1與ADIPOQ及TCF7L2有交互作用(交互驗證一致性為 4/10，permutation P = 0.01)；而透過以基因為基礎之方法得到使用mean-ratio 方法時，可看到PTPN1分別和FTO及ADIPOQ有交互作用；而PTPN1和FTO的交互作用情形也可在quantile ratio看到。
結論： PTPN1在代謝症候群中是一個與ADIPOQ, TCF7L2和FTO有交互作用的重要因子，但這個潛在的生物機制仍須被探討並可能可以找出代謝症候群發生的新見解。

關鍵字

基因-基因交互作用 ； GMDR ； 以基因為基礎之方式 ； 代謝症候群

並列摘要

Background: A growing focus in genetic studies of complex diseases is how to detect the joint effects of susceptibility genes. A common method is generalized multifactor dimensionality reduction (GMDR), which is Single Nucleotide Polymorphism (SNP)-based and has good power in identifying high-order gene-gene interactions. However, the functional relevance among the SNPs in the same gene may result in false negatives in using the GMDR. Instead, the gene-based method has been proposed to capture interactions that may involve such functional variants within a gene.
Objective: 1) To investigate whether there exist associations with the metabolic syndrome for individual SNPs in each of six genes that have been implicated because of their association with some components of the metabolic syndrome, including ADIPOQ, LEP, PPARG, FTO, TCF7L2, and PTPN1; and 2) to search for gene-gene interactions among these genetic loci on the metabolic syndrome by means of the GMDR as well as the gene-based analysis.
Methods: A case-control study among participants of health check-up, aged 40 years or above, was carried out in Northern Taiwan, with a total of 611 cases with metabolic syndrome and 1117 controls. Among 19 SNPs of the six genes selected for this study, multivariable logistic regression analyses were used to estimate the association between individual SNPs and metabolic syndrome with adjustment for sex and age. Gene-gene interactions were then investigated using both the GMDR method and a gene-based approach that utilities the genotype-trait distortion score.
Results: Individual SNPs did not exhibit significant association with metabolic syndrome. The gene-gene interaction analyses using the GMDR showed that a combination of SNPs in ADIPOQ, TCF7L2, and PTPN1 was the best model, with a prediction accuracy of 0.54 and a cross-validation consistency of 4/10 (permutation P = 0.01). Meanwhile, the gene-based analysis indicated that PTPN1 interacted with FTO, regardless of the type of ratio statistics (mean-ratio or quantile ratio) or the method used in assessing significance level (curve method or rank method), or with ADIPOQ if the curve method was used for the quantile ratio.
Conclusions: Our results suggest that PTPN1 is an important gene that may interact with FTO, ADIPOQ, and TCF7L2 in exerting their influence on the metabolic syndrome. The underlying mechanism of the interaction warrants further investigation and may lead to new insights about the occurrence of the metabolic syndrome.

並列關鍵字

gene-gene interaction ； GMDR ； gene-based approach ； metabolic syndrome

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