本文選題：肥胖　切入點：受試者操作特征曲線　出處：《湖南師范大學(xué)》2010年博士論文

【摘要】： 最近的研究數(shù)據(jù)表明,白種人群的肥胖診斷標(biāo)準(zhǔn)可能不適用于亞洲人。雖然在白種人群中已經(jīng)開展了許多關(guān)于肥胖相關(guān)表型的全基因組關(guān)聯(lián)研究,但是在中國人群中至今沒有成功運用全基因組關(guān)聯(lián)研究定位肥胖基因的報道。在統(tǒng)計遺傳學(xué)領(lǐng)域,已有大量關(guān)于單個性狀的關(guān)聯(lián)分析方法研究,但是對于兩個相關(guān)性狀的關(guān)聯(lián)分析方法研究,還少有開展。 本文首先使用全身脂肪含量(PBF)作為金標(biāo)準(zhǔn),在1109個男性個體和879個女性個體中采用受試者操作特征曲線(ROC)分析估計了三個簡易測量指標(biāo),即體重指數(shù)(BMI)、腰圍(WC)、腰髖比(WHR)預(yù)測肥胖的能力。分析表明,BMI、WC、WHR都與PBF高度相關(guān),相關(guān)系數(shù)為0.45-0.75。真陽性率在男性中是82.4%到94.1%,在女性中是68.8%到86.3%。真陰性率在男性中是64.1%到84.7%,在女性中是56.9%到79.0%。BMI和WC兩個指標(biāo)在男性和女性不同年齡組中曲線下的面積比較高(0.76-0.92),而WHR這個指標(biāo)稍微低-些(0.74-0.88)。研究表明,BMI和WC是兩個很好的預(yù)測肥胖的指標(biāo),WHR次之。該研究同時發(fā)現(xiàn),用BMI在中國人群中預(yù)測肥胖的標(biāo)準(zhǔn)要明顯低于白種人群。 接下來我們在597個中國北方人群中運用Affymetrix 500k芯片開展了全基因組關(guān)聯(lián)研究定位影響B(tài)MI變異的新基因,此研究進(jìn)一步在2955個中國南方人中進(jìn)行了重復(fù)驗證。通過一系列的質(zhì)量控制程序后,281,533個單核苷酸多態(tài)性(SNP)可以進(jìn)行關(guān)聯(lián)分析。經(jīng)過FDR方法進(jìn)行多重校正后,8個SNP仍然與BMI關(guān)聯(lián)(FDRq=0.033-0.048)。最顯著的SNP是rs4633,位于catechol-0-methyltransferase (COMT)基因的外顯子上(p=5.45×10-7,FDRq=0.033)。另外,位于EIF2AK4(eukaryotic translation initiation factor 2 alpha kinase 4)基因內(nèi)的兩個臨近的SNP rs4432245和rs711906也與BMI顯著關(guān)聯(lián)(?)值分別為4.38×10-6，6.39×10-6,FDRq=0.048)。在南方人群的驗證研究中,這兩個位點與BMI的關(guān)聯(lián)獲得了證實(p=0.03和0.01)。 除此之外,我們通過大量的計算機(jī)模擬,運用廣義估計方程2(GEE2)對兩個相關(guān)性狀的關(guān)聯(lián)定位方法進(jìn)行研究。通過與單個性狀關(guān)聯(lián)研究的功效比較,發(fā)現(xiàn)雙變量模型比單變量模型統(tǒng)計功效更高。而且雙變量關(guān)聯(lián)分析的假陽性率與單變量分析相比沒有差別,都在設(shè)定的顯著性水平0.05左右。所以我們提出的雙變量分析模型是合理的。進(jìn)一步對肥胖和骨質(zhì)疏松兩個相關(guān)性狀的實驗數(shù)據(jù)進(jìn)行的雙變量關(guān)聯(lián)分析驗證了該方法的優(yōu)越性。 本研究的創(chuàng)新性在于：1)第一次在中國南方人群中對衡量全身肥胖的簡易指標(biāo)進(jìn)行評估；2)第一次在中國人群中運用全基因組關(guān)聯(lián)分析方法定位影響B(tài)MI變異的新基因；3)第一次嘗試運用廣義估計方程2擬合基于群體設(shè)計的兩個數(shù)量性狀的關(guān)聯(lián)定位研究。
[Abstract]:Recent research data suggest that criteria for the diagnosis of obesity in white populations may not be applicable to Asians.Although many genome-wide association studies have been carried out on obesity related phenotypes in white populations, there have been no reports on the use of genome-wide association studies to locate obesity genes in Chinese population.In the field of statistical genetics, there have been a lot of studies on the correlation analysis of single traits, but few studies have been carried out on the correlation analysis of two related traits.In this paper, the body fat content (PBF) was first used as the gold standard, and three simple measurements were estimated by using the operating characteristic curve of subjects in 1109 male and 879 female individuals.Body mass index (BMI), waist circumference (WCC) and waist hip ratio (WHR) can predict obesity.The analysis showed that the WHR were highly correlated with PBF, and the correlation coefficient was 0.45-0.75.The true positive rate was 82.4% to 94.1% for males and 68.8% to 86.3% for females.The true negative rate was 64.1% to 84.7in males and 56.9% to 79.0%.BMI and WC in females. The area under the curve was higher in males and females in different age groups, but WHR was slightly lower (0.74-0.88g).Studies show that BMI and WC are two good predictors of obesity, followed by WHR.The study also found that BMI was significantly lower in predicting obesity in Chinese than in white populations.Then we used Affymetrix 500k chip in 597 northern Chinese population to carry out the whole genome association study to locate the new gene that affected the BMI mutation. This study was further verified in 2955 southerners.After a series of quality control procedures, 281533 single nucleotide polymorphisms (SNPs) can be analyzed.After multiple correction by FDR, 8 SNP are still associated with BMI 0.033-0.048.The most significant SNP is rs4633, which is located on the exon of catechol-0-methyltransferase mott gene.In addition, two adjacent SNP rs4432245 and rs711906 located in the EIF2AK4(eukaryotic translation initiation factor 2 alpha kinase 4 gene were also significantly associated with BMI.The values were 4.38 脳 10-6 and 6.39 脳 10-6 respectively.The correlation between the two loci and BMI was confirmed in the southern population.In addition, through a large number of computer simulations, we use the generalized estimation equation 2 / GEE2) to study the correlation mapping of two related traits.By comparing the efficacy with the single trait correlation study, it is found that the bivariate model is more effective than the univariate model.The false positive rate of bivariate correlation analysis was not different from that of univariate analysis.So the bivariate analysis model proposed by us is reasonable.The bivariate correlation analysis of two related traits, obesity and osteoporosis, proved the superiority of this method.The innovation of this study lies in: (1) for the first time in southern China, we evaluated a simple index of general obesity) for the first time, we used a genome-wide association analysis method to locate a new gene affecting BMI mutation in Chinese population.3) for the first time, the generalized estimation equation 2 is used to fit the correlation mapping of two quantitative traits based on population design.
【學(xué)位授予單位】：湖南師范大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2010
【分類號】：R589.2;R181.3

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