纳米金在基因突变检测及SNP分析中的应用

对特异核苷酸序列的高选择性检测在生物医学研究和临床检测中日趋重要. 纳米金特殊的光学性质、电学性质、化学性质、以及良好的生物相容性,使之成为检测生物大分子的首选工具.本文介绍了几种典型的基因突变检测及单核苷酸多态性(SNP)分析系统：基因芯片、生物传感器和光学检测系统.综述了多种颇有新意的检测方法和原理,详细阐明了它们的检测机制和研究进展,分析并比较了纳米金不同的作用方式,为纳米金在突变检测上的进一步研究提供了一定思路和参考.     

单核苷酸多态性分析在近交系大鼠遗传检测中的应用

目的研究SNP在近交系大鼠遗传检测中的应用。方法 选取大鼠20号染色体MHC所在P12区上的9个SNP位点,应用新建立的高保真酶特异性检测SNP基因分型技术对五种常用近交系大鼠(BN、F344、WKY、LEW、SHR)和两种新培育近交系大鼠(MIJ和HFJ)进行SNP多态性分析。结果五种常用近交系的SNP检测结果与Rat Genome Database网站提供的基因型数据一致,并检测确立了新品系的SNP基因型。同时绘制出七种近交系大鼠在该9个SNP位点的遗传扩增图谱。结论运用所筛选的9个SNP位点进行大鼠多态性分析,能够快速、可靠地对BN、F344、WKY、LEW、SHR及MIJ、HFJ进行遗传监测。     

基于贝叶斯网潜类模型的高维SNPs分析

采用贝叶斯(Bayesian)网的潜类模型对GAW17高维SNPs数据进行分析,为复杂性状疾病遗传以及基因定位等方面的研究提供新的方法支持。本研究从GAW17提供的包含697个个体22条常染色体的上万个SNP中,随机挑选出1号染色体上12个基因的29个SNPs作为研究对象。按照累计信息贡献率达到95%的原则,应用贝叶斯网潜变量模型选出C1S11408,C1S3201,C1S1786等15个与X0互信息量大的SNPs位点来对研究人群进行分类与解释。结果表明697个个体总的被分为2个潜在类别,各类别的概率分别为0.68和0.32。对两类人群的疾病分布状况进行分析,结果表明二者不一致,第二个类别人群患病率(38.64%)明显高于第一个类别人群(25.99%)(χ2=11.46,P=0.001)。由此可见,两类人群疾病患病率的差别正是由选出的15个SNPs造成的,从而有理由认为这些SNPs为可疑致病位点,为进一步的研究提供明确的思路。     

基于生物信息学的SNP候选位点搜寻方法

单核苷酸多态性(Single Nucleotide Polymorphism,SNP)是人类基因组中最常见的遗传多态,在遗传学研究的很多方面具有重要的作用。它的搜寻正受到广泛关注。近年来,国际上出现了一种基于生物信息学的发掘SNP新方法。本文对该方法的两种策略及其各自所存在的问题作一介绍。 Abstract：Single Nucleotide Polymorphism (SNP), the most common form of human genetic variation, represents a valuable resources for a variety of genetic research. There is considerable interest in the discovery of it. Recently, a new method based on bioinfomatics has been developed for the discovery of SNP. In this paper, the two strategy of this method and their respective problem are discussed.     

基因结构变异检测方法综述

在人类基因组中结构变异(SVs),拷贝数变化(CNVs),单核苷酸多态性(SNP)是非常普遍的,而且和人类健康与疾病密切相关,因此检测这些结构变异对于人类生命健康非常重要。基于第二代基因测序平台,目前已经有很多结构变异检测算法,这些算法主要分为五大类:微阵列方法、读对方法、读深方法、分裂读取方法、序列组装方法。本文系统地阐述了这五类方法的基本原理、优缺点以及使用范围,并简要介绍了每一种方法的经典检测算法及应用范围、检测性能等,并对未来检测算法的研究提出了展望。     

基于珙桐SNP位点的dCAPS标记开发

本研究收集了珙桐、喜树等19份植物材料,运用分子生物学和生物信息学手段比对分析了rpoB,rpoC,matK等8个基因碱基序列上的单核苷酸多态性(SNP)位点,在atpF-atpH基因上找到了合适的SNP位点并设计了dCAPS引物,经PCR扩增和酶切验证后,将SNP转化为衍生型酶切扩增多态性序列(dCAPS)标记,可促进SNP标记在珙桐的遗传图谱构建、基因定位、种质资源鉴定、遗传多样性研究等领域的应用。     

山地虎耳草和棒腺虎耳草转录组SSR和SNP分析

利用Illumina HiSeqTM2000对山地虎耳草和棒腺虎耳草进行转录组测序,分析和比较其SSR和SNP特征。结果表明:山地虎耳草63 763条Unigene序列中含有4 622个SSR,发生频率为7.25%,有110种重复基元,平均每10.00kb出现一个SSR位点;棒腺虎耳草60 972条Unigene序列中含有4 542个SSR,发生频率为7.45%,有85种重复基元,平均每10.40kb出现一个SSR位点,略低于山地虎耳草。山地虎耳草和棒腺虎耳草转录组序列的SSR优势基元均为三核苷酸重复。2个物种的转录组SSR以5~10次的较低重复次数为主,长度主要集中于12~30bp,具有较高的多态性。山地虎耳草和棒腺虎耳草中分别获得118 424个和112 006个SNP位点,编码区的SNP位点分别占30.40%和28.59%,且在编码SNP中同义突变所占比例(30.27%、28.48%)远高于非同义突变(0.13%、0.11%)。比较发现,2个物种的各项检索结果基本一致,推测与选取的组织部位、组织的发育阶段以及物种的亲缘关系有关。     

四引物扩增受阻突变体系PCR技术及其在动植物遗传育种研究中的应用

四引物扩增受阻突变体系PCR(Tetra-primer amplification refractory mutation system PCR,Tetra-primer ARMS PCR)技术是一种在普通PCR基础上发展起来的单核苷酸多态性(SNP)分型技术。该项技术综合了扩增受阻突变体系(Amplification refractory mutation system,ARMS)和四引物PCR(tetra-primer PCR)技术的优点,是对等位基因特异性PCR法的改良。它具有操作简便、分型快速、费用低廉等特点,在国内外生命科学领域尤其是遗传育种领域的应用越来越广泛。本文介绍了四引物扩增受阻突变体系PCR的技术原理及优势、结果检测手段和反应体系改进方法,并在此基础上对该技术在遗传育种研究中的应用进行综述。     

结构方程模型及其在生态学中的应用

基于多变量统计方法同时研究自然系统内多个因子之间的相互关系, 是阐释复杂的自然系统的一个重要手段。相比传统的多变量统计法, 结构方程模型基于研究者的先验知识预先设定系统内因子间的依赖关系, 不仅能够判别各因子之间的关系强度(路径系数), 还能对整体模型进行拟合和判断, 从而能更全面地了解自然系统。由于结构方程模型只在近年才被应用到生态学的数据分析中, 因此该文试图对其作一简略介绍, 包括结构方程模型的定义和变量类型, 结合事例研究展现结构方程模型分析的一般步骤、在生态学中的应用以及相关软件的介绍等。望能为相关研究人员提供直观的认识, 加强结构方程模型在生态学数据分析中的应用。     

基于R的结构方程模型在生态学中的应用

结构方程模型已经成为当前生态数据分析的主要方法之一。与其他多变量统计方法不同,结构方程模型的建模过程由理论假设驱动,且可以同时量化多个变量间的直接和间接因果关系。然而,由于结构方程模型引入国内生态学领域的时间相对较短,研究者经常在实际应用中遇到各种问题,各种使用错误也屡见不鲜。对此,本文系统阐述了结构方程模型的建模原理、建模流程、模型评价、模型修正等方面内容,并且结合具体研究案例介绍了结构方程模型分析的两个主流R包—lavaan和piecewiseSEM。其中,lavaan可以分析纳入了潜变量的结构方程模型,piecewiseSEM则可以解决各观测数据不独立以及响应变量残差不满足多元正态分布等问题。本文将有助于研究者准确理解结构方程模型并能扩大其在生态学中的应用。     

Structural analysis of proinsulin hexamer assembly by hydroxyl radical footprinting and computational modeling

Mutations in the insulin gene can impair proinsulin folding and cause diabetes mellitus. Although crystal structures of insulin dimers and hexamers are well established, proinsulin is refractory to crystallization. Although an NMR structure of an engineered proinsulin monomer has been reported, structures of the wild-type monomer and hexamer remain undetermined. We have utilized hydroxyl radical footprinting and molecular modeling to characterize these structures. Differences between the footprints of insulin and proinsulin, defining a "shadow" of the connecting (C) domain, were employed to refine the model. Our results demonstrate that in its monomeric form, (i) proinsulin contains a native-like insulin moiety and (ii) the C-domain footprint resides within an adjoining segment (residues B23-B29) that is accessible to modification in insulin but not proinsulin. Corresponding oxidation rates were observed within core insulin moieties of insulin and proinsulin hexamers, suggesting that the proinsulin hexamer retains an A/B structure similar to that of insulin. Further similarities in rates of oxidation between the respective C-domains of proinsulin monomers and hexamers suggest that this loop in each case flexibly projects from an outer surface. Although dimerization or hexamer assembly would not be impaired, an ensemble of predicted C-domain positions would block hexamer-hexamer stacking as visualized in classical crystal lattices. We anticipate that protein footprinting in combination with modeling, as illustrated here, will enable comparative studies of diabetes-associated mutant proinsulins and their aberrant modes of aggregation.     

Generalized spatial structural equation models

It is common in public health research to have high-dimensional, multivariate, spatially referenced data representing summaries of geographic regions. Often, it is desirable to examine relationships among these variables both within and across regions. An existing modeling technique called spatial factor analysis has been used and assumes that a common spatial factor underlies all the variables and causes them to be related to one another. An extension of this technique considers that there may be more than one underlying factor, and that relationships among the underlying latent variables are of primary interest. However, due to the complicated nature of the covariance structure of this type of data, existing methods are not satisfactory. We thus propose a generalized spatial structural equation model. In the first level of the model, we assume that the observed variables are related to particular underlying factors. In the second level of the model, we use the structural equation method to model the relationship among the underlying factors and use parametric spatial distributions on the covariance structure of the underlying factors. We apply the model to county-level cancer mortality and census summary data for Minnesota, including socioeconomic status and access to public utilities.     

水稻单核苷酸多态性及其应用现状

单核苷酸多态性（single nucleotide polymorphisms, SNPs）在水稻中数量多,分布密度高,遗传稳定性高。水稻SNPs的发现方法主要有对样本DNA的PCR产物直接测序、从SSR区段检测SNPs和从基因组序列直接搜索等。目前已有多种基因分型技术运用到了水稻SNPs检测,SNPs检测的高度自动化使水稻SNPs基因分型非常方便。单核苷酸多态性在水稻遗传图谱的构建、基因克隆和功能基因组学研究、标记辅助选择育种、遗传资源分类及物种进化等方面的应用具有巨大潜力。     

Identification of SNP markers for common CNV regions and association analysis of risk of subarachnoid aneurysmal hemorrhage in Japanese population

Copy number variation (CNV) is emerging as a new tool for understanding human genomic variation, but its relationship with human disease is not yet fully understood. The data for a total of 317,503 genotypes were collected for a genome-wide association study of subarachnoid aneurismal hemorrhage (SAH) in a Japanese population (cases and controls, n = 497) using Illumina HumanHap300 BeadChip^®. To identify multi-allelic CNV markers, we visually inspected all genotype clusters of 317,503 SNP markers covering the whole genome using Illumina’s BeadStudio 3.0^® software. As a result, we identified 597 multi-allelic CNV markers for common (copy loss frequency > 0.05) CNV regions in a Japanese population (n = 497). The identified CNV markers shared the following characteristics: enrichment of Hardy–Weinberg disequilibria, Mendelian inconsistency among families, and high missing genotype rate. All annotated information for those markers is summarized in our database (http://www.snp-genetics.com/user/srch.htm). In addition, we performed case-control association analyses of identified multi-allelic CNV markers with the risk of subarachnoid aneurysmal hemorrhage. One SNP marker (rs1242541) within a CNV region neighboring the Sel-1 suppressor of lin-12-like protein (SEL1L) was significantly associated with a risk of SAH (P = 0.0006). We also validated the CNV around rs1242541 using real-time quantitative polymerase chain reaction (PCR). Information and methods used in this study would be helpful for accurate genotyping of SNPs on CNV regions, which could be used for association analysis of SNP markers within CNV regions.     

长白落叶松群体4CL基因单核苷酸多态性分析

以黑龙江省林口县青山林场21年生长白落叶松（Larix olgensis Henry）异地保存种为材料,利用SNP标记方法和DNAMAN软件分析白刀山种源子代林的单核甘酸多态性情况。从分子水平证明长白落叶松具有丰富的遗传多样性,用4CL-A引物检测到178个SNP多态位点。共测序240条EST序列,测序成功193条,测序成功率为80.42%。本研究178个SNP变异中,有126个属于转换类型,占总变异的70.79%;有52个属于颠换类型,占总变异的29.21%,变异类型符合,转换∶颠换=7∶3。在转换类型中,A/G和C/T转换分别占37.08%和33.71%;颠换类型中G/C、A/C、A/T、和G/T分别在占7.87%、8.99%、7.87%和4.99%。但每个家系内的转换和颠换的比例相差较大,没有规律。转换和颠换的比例最大的是554号家系,其比值为7∶1,最小的家系是855号家系,其比值为3∶4。     

A composite likelihood approach to latent multivariate Gaussian modeling of SNP data with application to genetic association testing

Many statistical tests have been proposed for case-control data to detect disease association with multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium. The main reason for the existence of so many tests is that each test aims to detect one or two aspects of many possible distributional differences between cases and controls, largely due to the lack of a general and yet simple model for discrete genotype data. Here we propose a latent variable model to represent SNP data: the observed SNP data are assumed to be obtained by discretizing a latent multivariate Gaussian variate. Because the latent variate is multivariate Gaussian, its distribution is completely characterized by its mean vector and covariance matrix, in contrast to much more complex forms of a general distribution for discrete multivariate SNP data. We propose a composite likelihood approach for parameter estimation. A direct application of this latent variable model is to association testing with multiple SNPs in a candidate gene or region. In contrast to many existing tests that aim to detect only one or two aspects of many possible distributional differences of discrete SNP data, we can exclusively focus on testing the mean and covariance parameters of the latent Gaussian distributions for cases and controls. Our simulation results demonstrate potential power gains of the proposed approach over some existing methods.     

Search for and analysis of single nucleotide polymorphisms (SNPs) in rice (Oryza sativa, Oryza rufipogon) and establishment of SNP markers.

We searched for SNPs in 417 regions distributed throughout the genome of three Oryza sativa ssp. japonica cultivars, two indica cultivars, and a wild rice (O. rufipogon). We found 2800 SNPs in approximately 250,000 aligned bases for an average of one SNP every 89 bp, or one SNP every 232 bp between two randomly selected strains. Graphic representation of the frequency of SNPs along each chromosome showed uneven distribution of polymorphism-rich and -poor regions, but little obvious association with the centromere or telomere. The 94 SNPs that we found between the closely related cultivars 'Nipponbare' and 'Koshihikari' can be converted into molecular markers. Our establishment of 213 co-dominant SNP markers distributed throughout the genome illustrates the immense potential of SNPs as molecular markers not only for genome research, but also for molecular breeding of rice.