復(fù)雜性狀與基因組多位點(diǎn)的關(guān)聯(lián)分析方法研究
發(fā)布時(shí)間：2018-09-02 10:05
【摘要】： 影響復(fù)雜性狀的遺傳結(jié)構(gòu)包括很多基因,這些基因的順式及反式作用位置中的多個(gè)突變位點(diǎn)能夠相互互作共同影響復(fù)雜性狀。因此,多個(gè)位點(diǎn)的聯(lián)合分析比單個(gè)位點(diǎn)的分析能獲得更多的信息。單倍體型,即位于一條染色體上或某一區(qū)域內(nèi)的一組相關(guān)聯(lián)的SNP位點(diǎn)。顯然,對(duì)單倍體型進(jìn)行分析為運(yùn)用SNP信息探究遺傳性狀尤其是復(fù)雜性狀的遺傳機(jī)制提供了一條更加便捷、更加有效的途徑。因此本文研究單個(gè)基因的SNP互作的同時(shí),利用單倍體型從基因組水平考慮非連鎖區(qū)域基因與基因的共同作用。主要研究?jī)?nèi)容及其結(jié)果如下: 第一,基于半?yún)?shù)回歸模型的單區(qū)域多位點(diǎn)與復(fù)雜性狀關(guān)聯(lián)分析探討復(fù)雜性狀與某一 區(qū)域功能位點(diǎn)的關(guān)聯(lián)定位備受關(guān)注。目前通常用的方法是單獨(dú)分析每一個(gè)區(qū)域的單個(gè)SNP,但這樣可能會(huì)由于位點(diǎn)SNP與性狀標(biāo)記之間存在不完全的連鎖不平衡而導(dǎo)致分析結(jié)果效力降低。也有通過(guò)單倍體型等方法聯(lián)合分析這些位點(diǎn),但如果有許多單倍體型時(shí),可能會(huì)使單倍體型的分析方法的效能減弱。因此,我們基于Kwee等針對(duì)數(shù)量性狀基因座的半?yún)?shù)回歸模型,其模型能夠同時(shí)利用多個(gè)SNPs的信息且能考慮位點(diǎn)之間的連鎖不平衡,但相比現(xiàn)有多個(gè)位點(diǎn)聯(lián)合分析方法,維度能更低。針對(duì)Kwee等的模型缺陷,我們引入對(duì)缺失數(shù)據(jù)的處理。另外,通過(guò)逐步向下P值法篩選與數(shù)量性狀關(guān)聯(lián)的多個(gè)聯(lián)合SNP標(biāo)記。人的前列腺癌是個(gè)普發(fā)的疾病,威脅著很多人的生命,備受世界關(guān)注。本節(jié)我們利用HapMap的公共數(shù)據(jù),對(duì)人前列腺癌通路上具有淋巴細(xì)胞表達(dá)數(shù)據(jù)的67個(gè)基因與339個(gè)候選基因進(jìn)行順式與反式調(diào)控的分析,找出影響人前列腺癌通路上基因表達(dá)的順式和反式作用因子,并進(jìn)行通路分析。 第二,基于參數(shù)回歸模型的多區(qū)域單倍體型與復(fù)雜性狀關(guān)聯(lián)分析復(fù)雜性狀的遺傳基礎(chǔ)包 括很多基因,這些基因的聯(lián)合作用是很普遍的。因此同時(shí)考慮多個(gè)基因、多個(gè)區(qū)域是比較可取的。我們提出基于廣義線性回歸模型復(fù)雜性狀跟多個(gè)非連鎖區(qū)域的單倍體型聯(lián)合的關(guān)聯(lián)分析,通過(guò)打分統(tǒng)計(jì)來(lái)檢驗(yàn)單倍體型效應(yīng)的零假設(shè)。另外,通過(guò)多重檢驗(yàn)最小P值法獲得最好非連鎖區(qū)域多個(gè)位點(diǎn)的聯(lián)合。通過(guò)模擬研究檢驗(yàn)我們提出的方法的準(zhǔn)確性和檢出效能,證實(shí)模型的有效性和對(duì)單倍體型互作的關(guān)聯(lián)分析的檢出率。對(duì)于沒有考慮其他協(xié)變量的數(shù)據(jù),通過(guò)跟軟件FAMHAP的htr和hapcc模型比較得出,我們的方法在準(zhǔn)確性和有效檢出率能與htr和hapcc相當(dāng),甚至超過(guò)它們。另外,我們模型能考慮更多的性狀類型以及允許加入其他協(xié)變量。為了驗(yàn)證我們方法的有效性,我們應(yīng)用在有4個(gè)非連鎖侯選基因與豬肉質(zhì)的關(guān)聯(lián)分析中。 第三,基于半?yún)?shù)回歸模型的多區(qū)域單倍體型與復(fù)雜性狀關(guān)聯(lián)分析一般復(fù)雜性狀的遺傳 模式都包括多個(gè)基因及它們之間的相互作用。我們提出一種新的統(tǒng)計(jì)方法即基于單倍體型水平找出影響某一連續(xù)性狀變化的基因組多個(gè)區(qū)域。我們提出的方法是使用具有核函數(shù)的半?yún)?shù)回歸模型,能夠同時(shí)考慮大量基因。此種方法比現(xiàn)有的方法能夠有效地達(dá)到降維的作用。對(duì)于參數(shù)的估計(jì)和非參數(shù)函數(shù)檢驗(yàn)我們參照Liu等和Kwee等,即通過(guò)最小乘方核機(jī)器(LSKM)進(jìn)行參數(shù)估計(jì)和通過(guò)打分統(tǒng)計(jì)進(jìn)行非參數(shù)函數(shù)檢驗(yàn)。為了獲得最好的基因或區(qū)域組合,通過(guò)逐步向下P值法篩選。模擬研究證明這種方法的準(zhǔn)確性以及檢驗(yàn)多個(gè)基因的檢出效能。我們把這種方法應(yīng)用到人前列腺癌通路的KLK3表達(dá)與339個(gè)候選基因的關(guān)聯(lián)分析中,找到影響KLK3表達(dá)的基因群,比上節(jié)單個(gè)基因的分析獲得更多的信息。另外,我們應(yīng)用這種方法研究豬肉質(zhì)的遺傳機(jī)制。 第四,基于半?yún)?shù)邏輯斯蒂核模型的多區(qū)域單倍體型與二類性狀關(guān)聯(lián)分析尋找新的統(tǒng)計(jì) 方法來(lái)檢驗(yàn)疾病的遺傳通路越來(lái)越受到關(guān)注。原因是一個(gè)通路中的基因傾向于彼此相互作用,如果使用傳統(tǒng)的參數(shù)估計(jì)由于維數(shù)太大而不可行,使得用非參數(shù)方法更可取。通過(guò)核機(jī)器函數(shù)對(duì)高維基因組單倍體型信息擬合,我們提出了高效靈活的分析和檢驗(yàn)基因組基因與疾病關(guān)聯(lián)的遺傳通路的半?yún)?shù)邏輯斯蒂模型。按照Liu等,我們把我們半?yún)?shù)模型轉(zhuǎn)化成邏輯斯蒂混合模型來(lái)表達(dá),利用現(xiàn)有的統(tǒng)計(jì)軟件進(jìn)行參數(shù)估計(jì),對(duì)非參數(shù)函數(shù)檢驗(yàn)采用打分統(tǒng)計(jì)。通過(guò)模擬研究證明這種方法準(zhǔn)確性以及檢驗(yàn)疾病遺傳通路的效能。這個(gè)方法應(yīng)用在磷酸鹽治療下的多種骨髓瘤下巴骨壞死病人數(shù)據(jù)的通路分析中。 第五,基于半?yún)?shù)回歸模型的多區(qū)域單倍體型與縱向性狀關(guān)聯(lián)分析對(duì)于具有多次記錄的 縱向數(shù)據(jù)研究中,能夠同時(shí)考慮影響性狀的時(shí)間及其他協(xié)變量是很重要的�；赯hang等研究縱向數(shù)據(jù)的半?yún)?shù)模型,我們把模型的參數(shù)固定效應(yīng)用來(lái)擬合單倍體型和其他固定協(xié)變量效應(yīng),參數(shù)的估計(jì)按照Z(yǔ)hang等的方法,采用似然比檢驗(yàn)來(lái)檢驗(yàn)單倍體型效應(yīng)。通過(guò)對(duì)我們改進(jìn)的方法與一般的混合模型Haplo.stats和FAMHAP的htr模型進(jìn)行模擬比較,證實(shí)對(duì)動(dòng)態(tài)性狀通過(guò)考慮多次采樣數(shù)據(jù)的時(shí)間效應(yīng)比對(duì)單次采樣更能提高單倍體型效應(yīng)的檢出率。我們通過(guò)這種半?yún)?shù)回歸模型研究豬具有多胎的繁殖記錄與MMP1和MMP10基因的單倍體型分析中。 綜上所述,本論文針對(duì)基因組研究中存在的問(wèn)題,建立了基于廣義線性模型研究復(fù)雜性狀與多個(gè)非連鎖區(qū)域單倍體型聯(lián)合的關(guān)聯(lián)分析、基于核函數(shù)的半?yún)?shù)回歸模型分析靜態(tài)與動(dòng)態(tài)數(shù)據(jù)的遺傳模式。通過(guò)模擬研究證實(shí)了模型的可靠性,并將我們的模型系統(tǒng)應(yīng)用到多個(gè)實(shí)際的例子中。本研究結(jié)果不僅能推進(jìn)復(fù)雜性狀候選基因研究,而且為從基因組層面上進(jìn)行復(fù)雜性狀遺傳通路等研究的實(shí)施奠定了理論基礎(chǔ)。同時(shí),這些算法都開發(fā)出相應(yīng)的軟件程序并可自由下載,為科研工作者提供更全面準(zhǔn)確的進(jìn)行基因組關(guān)聯(lián)分析。
[Abstract]:The genetic structure that affects complex traits involves many genes. Mutations in the cis-and Trans-position of these genes can interact with each other to influence complex traits. Therefore, joint analysis of multiple loci yields more information than single locus analysis. Haploidy is located on a chromosome or in a region. Obviously, the analysis of haplotypes provides a more convenient and effective way to explore the genetic mechanism of genetic traits, especially complex traits, by using SNP information. The main research contents and results are as follows:
Firstly, the association analysis between complex traits and complex traits based on semi-parametric regression model
Localization of functional loci in a region has attracted considerable attention. Currently, the common method is to analyze a single SNP in each region separately. However, incomplete linkage imbalance between SNPs and trait markers may result in a decrease in the validity of the analysis results. Therefore, based on the semi-parametric regression model for quantitative trait loci such as Kwee, the model can utilize the information of multiple SNPs at the same time and can consider the linkage imbalance between loci, but compared with the existing multi-locus joint analysis method, the dimension of the model can be reduced. In this section, we use HapMap's public data to filter out multiple combined SNP markers associated with quantitative traits. Sixty-seven genes with lymphocyte expression data in prostate cancer pathway and 339 candidate genes were analyzed for cis-and trans-regulation, and cis-and trans-acting factors affecting gene expression in prostate cancer pathway were identified.
Secondly, the genetic basis package of complex traits for multiregional haplotype and complex traits association analysis based on parametric regression model
It is preferable to consider multiple genes and multiple regions at the same time. We propose a generalized linear regression model based association analysis between complex traits and haplotypes of multiple non-linked regions to test the zero hypothesis of haplotype effect by scoring statistics. The validity of the model and the detection rate of haplotype interaction were verified by simulating the accuracy and detection efficiency of the proposed method. For data without considering other covariates, FAMHAP was used. Comparing the HTR and hapcc models, we found that the accuracy and effective detection rate of our method were comparable to those of HTR and hapcc, and even exceeded them. In addition, our model could consider more types of traits and allow other covariates to be added. In order to verify the effectiveness of our method, we applied it to 4 non * chain candidate genes and pig meat quality. Correlation analysis.
Thirdly, the association analysis between haplotypes and complex traits based on semi-parametric regression model for inheritance of general complex traits
We propose a new statistical method based on haplotype level to identify multiple regions of the genome that affect a continuous trait change. Our method is to use a semi-parametric regression model with kernel function, which can consider a large number of genes at the same time. For parameter estimation and nonparametric function testing, we refer to Liu et al. and Kwee et al., that is, we estimate the parameters by the Least Square Kernel Machine (LSKM) and test the nonparametric function by scoring statistics. Screening. Simulation studies demonstrate the accuracy of this method and the detection efficiency of multiple genes. We applied this method to the association analysis of KLK3 expression in the human prostate cancer pathway with 339 candidate genes, and found the gene groups that affect the expression of KLK3. More information was obtained than the analysis of single gene in the previous section. This method * studies the genetic mechanism of pig meat quality.
Fourthly, based on the semi-parametric logistic kernel model, the association analysis of haplotypes and second-class traits in multiple regions is conducted to find new statistics.
The reason is that genes in one pathway tend to interact with each other. If the traditional parameter estimation is too large to be feasible, it is preferable to use non-parametric methods. According to Liu et al, we transformed our semi-parametric model into a logistic mixed model to express it. We used existing statistical software to estimate the parameters, and used scoring statistics to test non-parametric functions. To determine the accuracy of this method and the effectiveness of examining the genetic pathways of disease. This method was applied to pathway analysis of data from multiple myeloma patients with chin osteonecrosis treated with phosphate.
Fifth, the association analysis of haplotypes and longitudinal traits based on semi-parametric regression model for multiple records
In longitudinal data study, it is important to consider both the time and other covariates that affect the traits. Based on the semi-parametric model of longitudinal data studied by Zhang et al, we use the fixed effects of the parameters of the model to fit the haploid type and other fixed covariate effects. The estimation of the parameters is based on Zhang et al. The likelihood ratio test is used to test the parameters. By comparing our improved method with the general mixed model Haplo. stats and FAMHAP's HTR model, it is proved that the detection rate of haplotype effect can be improved by considering the time effect of multiple sampling data than by single sampling. * the reproductive records of pigs with multiple births and the haplotype analysis of MMP1 and MMP10 genes were studied.
In summary, aiming at the problems existing in genome research, this paper established a generalized linear model to study the association between complex traits and haplotypes of multiple non-linked regions, and a semi-parametric regression model based on kernel function to analyze the genetic model of static and dynamic data. The results of this study not only promote the study of candidate genes for complex traits, but also lay a theoretical foundation for the study of genetic pathways for complex traits at the genome level. Researchers provide more comprehensive and accurate analysis of genome association.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2009
【分類號(hào)】：R346
本文編號(hào)：2219030
[Abstract]:The genetic structure that affects complex traits involves many genes. Mutations in the cis-and Trans-position of these genes can interact with each other to influence complex traits. Therefore, joint analysis of multiple loci yields more information than single locus analysis. Haploidy is located on a chromosome or in a region. Obviously, the analysis of haplotypes provides a more convenient and effective way to explore the genetic mechanism of genetic traits, especially complex traits, by using SNP information. The main research contents and results are as follows:
Firstly, the association analysis between complex traits and complex traits based on semi-parametric regression model
Localization of functional loci in a region has attracted considerable attention. Currently, the common method is to analyze a single SNP in each region separately. However, incomplete linkage imbalance between SNPs and trait markers may result in a decrease in the validity of the analysis results. Therefore, based on the semi-parametric regression model for quantitative trait loci such as Kwee, the model can utilize the information of multiple SNPs at the same time and can consider the linkage imbalance between loci, but compared with the existing multi-locus joint analysis method, the dimension of the model can be reduced. In this section, we use HapMap's public data to filter out multiple combined SNP markers associated with quantitative traits. Sixty-seven genes with lymphocyte expression data in prostate cancer pathway and 339 candidate genes were analyzed for cis-and trans-regulation, and cis-and trans-acting factors affecting gene expression in prostate cancer pathway were identified.
Secondly, the genetic basis package of complex traits for multiregional haplotype and complex traits association analysis based on parametric regression model
It is preferable to consider multiple genes and multiple regions at the same time. We propose a generalized linear regression model based association analysis between complex traits and haplotypes of multiple non-linked regions to test the zero hypothesis of haplotype effect by scoring statistics. The validity of the model and the detection rate of haplotype interaction were verified by simulating the accuracy and detection efficiency of the proposed method. For data without considering other covariates, FAMHAP was used. Comparing the HTR and hapcc models, we found that the accuracy and effective detection rate of our method were comparable to those of HTR and hapcc, and even exceeded them. In addition, our model could consider more types of traits and allow other covariates to be added. In order to verify the effectiveness of our method, we applied it to 4 non * chain candidate genes and pig meat quality. Correlation analysis.
Thirdly, the association analysis between haplotypes and complex traits based on semi-parametric regression model for inheritance of general complex traits
We propose a new statistical method based on haplotype level to identify multiple regions of the genome that affect a continuous trait change. Our method is to use a semi-parametric regression model with kernel function, which can consider a large number of genes at the same time. For parameter estimation and nonparametric function testing, we refer to Liu et al. and Kwee et al., that is, we estimate the parameters by the Least Square Kernel Machine (LSKM) and test the nonparametric function by scoring statistics. Screening. Simulation studies demonstrate the accuracy of this method and the detection efficiency of multiple genes. We applied this method to the association analysis of KLK3 expression in the human prostate cancer pathway with 339 candidate genes, and found the gene groups that affect the expression of KLK3. More information was obtained than the analysis of single gene in the previous section. This method * studies the genetic mechanism of pig meat quality.
Fourthly, based on the semi-parametric logistic kernel model, the association analysis of haplotypes and second-class traits in multiple regions is conducted to find new statistics.
The reason is that genes in one pathway tend to interact with each other. If the traditional parameter estimation is too large to be feasible, it is preferable to use non-parametric methods. According to Liu et al, we transformed our semi-parametric model into a logistic mixed model to express it. We used existing statistical software to estimate the parameters, and used scoring statistics to test non-parametric functions. To determine the accuracy of this method and the effectiveness of examining the genetic pathways of disease. This method was applied to pathway analysis of data from multiple myeloma patients with chin osteonecrosis treated with phosphate.
Fifth, the association analysis of haplotypes and longitudinal traits based on semi-parametric regression model for multiple records
In longitudinal data study, it is important to consider both the time and other covariates that affect the traits. Based on the semi-parametric model of longitudinal data studied by Zhang et al, we use the fixed effects of the parameters of the model to fit the haploid type and other fixed covariate effects. The estimation of the parameters is based on Zhang et al. The likelihood ratio test is used to test the parameters. By comparing our improved method with the general mixed model Haplo. stats and FAMHAP's HTR model, it is proved that the detection rate of haplotype effect can be improved by considering the time effect of multiple sampling data than by single sampling. * the reproductive records of pigs with multiple births and the haplotype analysis of MMP1 and MMP10 genes were studied.
In summary, aiming at the problems existing in genome research, this paper established a generalized linear model to study the association between complex traits and haplotypes of multiple non-linked regions, and a semi-parametric regression model based on kernel function to analyze the genetic model of static and dynamic data. The results of this study not only promote the study of candidate genes for complex traits, but also lay a theoretical foundation for the study of genetic pathways for complex traits at the genome level. Researchers provide more comprehensive and accurate analysis of genome association.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2009
【分類號(hào)】：R346
【引證文獻(xiàn)】
相關(guān)期刊論文 前1條
1 張雁明;邢國(guó)芳;劉美桃;劉曉東;韓淵懷;;全基因組關(guān)聯(lián)分析:基因組學(xué)研究的機(jī)遇與挑戰(zhàn)[J];生物技術(shù)通報(bào);2013年06期
，本文編號(hào)：2219030
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