【摘要】：在后基因組時(shí)代，單核苷酸多態(tài)性（single-nucleotidepolymorphisms，SNPs）研究已成為生物醫(yī)學(xué)研究的熱點(diǎn)這是因?yàn)镾NPs是最常見(jiàn)的人類(lèi)序列變異，廣泛分布在人類(lèi)DNA中，且SNPs的檢測(cè)已自動(dòng)化目前，與SNPs相適應(yīng)的統(tǒng)計(jì)學(xué)方法，已成為統(tǒng)計(jì)遺傳學(xué)領(lǐng)域研究的熱點(diǎn)有學(xué)者將潛在結(jié)構(gòu)模型（latent structural model）或潛變量模型（latentvariable model）引入單體型或高維SNPs整體效應(yīng)的關(guān)聯(lián)分析及其相關(guān)的推斷性研究但潛變量模型要求觀測(cè)變量與潛變量服從正態(tài)分布，SNPs數(shù)據(jù)無(wú)論以何種遺傳模式量化，都違背其正態(tài)假定為此，本文針對(duì)SNPs數(shù)據(jù)不服從正態(tài)分布的情況，擬采用S-B估計(jì)方法擬合驗(yàn)證性因子模型，進(jìn)行SNPs整體效應(yīng)和關(guān)聯(lián)性分析 本文詳細(xì)介紹了驗(yàn)證性因子模型的有關(guān)理論，包括模型概述模型參數(shù)估計(jì)模型擬合評(píng)價(jià)及模型修正的相關(guān)內(nèi)容其中著重介紹模型參數(shù)估計(jì)的幾種方法：最大似然估計(jì)Browne’s漸近任意分布方法S-B測(cè)度調(diào)整（scaled）估計(jì)并對(duì)幾種方法方法做比較，得出S-B估計(jì)方法為最適合處理SNPs數(shù)據(jù)的參數(shù)估計(jì)方法 在此理論的基礎(chǔ)上，用GAW17提供的SNPs數(shù)據(jù)進(jìn)行實(shí)例分析本次研究隨機(jī)選取2號(hào)染色體上，分布在6個(gè)基因之中的13個(gè)SNPs作為研究對(duì)象，結(jié)果顯示：ML估計(jì)方法卡方自由度比2/ df=3.59，S-B調(diào)整估計(jì)方法卡方自由度=2.89，ML估計(jì)法RMSEA=0.061，S-B調(diào)整估計(jì)法RMSEA=0.052此結(jié)果表示使用S-B調(diào)整方法得到的擬合指標(biāo)較ML法好，說(shuō)明在處理SNPs數(shù)據(jù)時(shí)，使用S-B估計(jì)能得到更好的擬合模型此外，，由于6個(gè)基因之間的相關(guān)系數(shù)很大，所以將這6個(gè)基因作為初階因子，做二階驗(yàn)證性因子分析，能得到一個(gè)擬合很好簡(jiǎn)潔的二階模型該實(shí)例通過(guò)GAW17提供的模擬數(shù)據(jù)，對(duì)選取的6個(gè)基因做潛變量得分，然后對(duì)基因和疾病感染做t檢驗(yàn),得出6個(gè)基因?qū)υ摳腥径加杏绊�，可以推測(cè)這6個(gè)基因下的13個(gè)SNP位點(diǎn)可能是感染的致病位點(diǎn)對(duì)二階因子和疾病感染做檢驗(yàn)，得到基因A對(duì)感染有影響( t 3 .657, P 0.001) 本文的討論部分簡(jiǎn)要介紹了本次研究的主要內(nèi)容，并對(duì)ML參數(shù)估計(jì)與S-B調(diào)整估計(jì)方法高階驗(yàn)證性因子模型與驗(yàn)證性因子模型分別做了比較，此外，在討論部分，本次研究的優(yōu)缺點(diǎn)及研究展望也作了闡述
[Abstract]:In the post-genome era, single nucleotide polymorphism (single-nucleotidepolymorphisms,SNPs) research has become a hot topic in biomedical research because SNPs is the most common human sequence variation, widely distributed in human DNA, and the detection of SNPs has been automated. Statistical methods adapted to SNPs, It has become a hot topic in the field of statistical genetics that scholars introduce latent structural model (latent structural model) or latent variable model (latentvariable model) into the correlation analysis of haplotype or high-dimensional SNPs global effect and its correlation inference, but the latent variable model The observed variables and latent variables should be applied to normal distribution, and the SNPs data should be quantified by any genetic model. In this paper, the S-B estimation method is used to fit the confirmatory factor model in view of the fact that the SNPs data are dissatisfied with the normal distribution. In this paper, the theory of confirmatory factor model is introduced in detail. Including model overview, model parameter estimation, model fitting evaluation and model modification. Several methods of model parameter estimation are emphatically introduced: maximum likelihood estimation (Browne's) asymptotic arbitrary distribution method S-B measure adjusted (scaled) estimation. And compare several methods, It is concluded that S-B estimation method is the most suitable parameter estimation method for processing SNPs data. On the basis of this theory, an example of SNPs data provided by GAW17 is used to analyze the random selection of chromosome 2 in this study. Thirteen SNPs distributed in 6 genes were used as research subjects. The results show that the chi-square degree of freedom of the ML estimation method is better than that of the 2 / df=3.59,S-B adjustment estimation method, the chi-square degree of freedom = 2.89 RMSEA=0.061,S-B adjustment estimation method RMSEA=0.052. The result shows that the fitting index obtained by using S-B adjustment method is better than that by ML method, which shows that when SNPs data are processed, A better fitting model can be obtained by using S-B estimation. In addition, because of the large correlation coefficient among the six genes, the six genes are used as the first order factor and the second order confirmatory factor analysis is done. We can get a second-order model that fits well and succinctly. The simulation data provided by GAW17 can be used to score the latent variables of the selected six genes, and then t test the gene and disease infection, and find that the six genes have an effect on the infection. It can be inferred that 13 SNP loci under these 6 genes may be the pathogenic sites of infection to test the second order factor and disease infection. The main contents of this study are briefly introduced in the discussion part of this paper. The gene A has an effect on infection (t 3.657, P 0.001). In addition, in the discussion part, the advantages and disadvantages of this study and the prospect of the research are also discussed and compared with the high-order confirmatory factor model and the confirmatory factor model of the ML parameter estimation and the S-B adjustment estimation method.
【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：R346

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 張巖波;;基于潛變量構(gòu)建高維單核苷酸多態(tài)性基因關(guān)聯(lián)模型[J];鄭州大學(xué)學(xué)報(bào)(醫(yī)學(xué)版);2011年03期

2 王穎,褚迅,黃薇;單核苷酸多態(tài)性研究及其對(duì)人類(lèi)醫(yī)學(xué)的影響[J];基礎(chǔ)醫(yī)學(xué)與臨床;2004年06期

3 孫連榮;結(jié)構(gòu)方程模型(SEM)的原理及操作[J];寧波大學(xué)學(xué)報(bào)(教育科學(xué)版);2005年02期

4 李婧,潘玉春,李亦學(xué),石鐵流;人類(lèi)基因組單核苷酸多態(tài)性和單體型的分析及應(yīng)用[J];遺傳學(xué)報(bào);2005年08期

5 陳民志;程廣輝;馬龍;王慧;邱熔芳;薛付忠;劉奇跡;;TNFSF4基因與冠心病的關(guān)聯(lián)研究[J];遺傳;2011年03期

6 方敏;黃正峰;;結(jié)構(gòu)方程模型下非正態(tài)數(shù)據(jù)的處理[J];中國(guó)衛(wèi)生統(tǒng)計(jì);2010年01期

相關(guān)博士學(xué)位論文 前1條

1 張巖波;醫(yī)護(hù)人員職業(yè)緊張的統(tǒng)計(jì)建模研究[D];山西醫(yī)科大學(xué);2004年

相關(guān)碩士學(xué)位論文 前5條

1 張韶凱;基于貝葉斯網(wǎng)的潛類(lèi)分析在基因關(guān)聯(lián)分析中的應(yīng)用[D];山西醫(yī)科大學(xué);2011年

2 徐秀娟;結(jié)構(gòu)方程模型及其在醫(yī)學(xué)研究中的應(yīng)用[D];山西醫(yī)科大學(xué);2004年

3 沈亞;基于芯片方法的非綜合征性唇腭裂與IRF6、TGFA的SNPs相關(guān)性分析[D];南京醫(yī)科大學(xué);2007年

4 郝彥斌;小樣本非正態(tài)數(shù)據(jù)結(jié)構(gòu)方程模型估計(jì)方法研究與醫(yī)學(xué)應(yīng)用[D];山西醫(yī)科大學(xué);2007年

5 裴磊磊;抑郁患者單核苷酸多態(tài)性（SNPs）分布特征的潛在類(lèi)別分析[D];山西醫(yī)科大學(xué);2009年

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