油棕种壳厚度控制基因SHELL的SNP分子标记开发

油棕属棕榈科多年生木本油料作物,果实含油量高达50%,且单位面积产油量高,享有"世界油王"美誉。油棕果实由外果皮、中果皮、内果皮(种壳)、种子四个部分组成,产油部分主要是中果皮和种子,其中种壳厚度是影响果实含油量的重要因素。SHELL基因控制种壳厚度,是一类MADS-box同源基因,SHELL基因在厚壳种和无壳种中的变异主要是第一个外显子上的两个SNP位点。该研究根据两个SNP位点进行特异标记开发,根据已知的油棕SHELL基因的序列,设计了4对SNP引物。4对SNP引物以2个SNP位点设计,每个SNP位点设计2对SNP标记,并均在引物3'端第二位引入强错配碱基。以2份薄壳种油棕材料和2份厚壳种油棕材料DNA为模板,扩增筛选油棕SHELL基因SNP引物。通过PCR扩增发现,设计的SHELL基因特异SNP标记EgSh(N)-f/EgSh(SNP)-2r能够鉴别油棕厚壳种和薄壳种。再用24株油棕树进行特异性验证,发现该标记能较准确地判断油棕的厚薄壳。该研究结果表明SNP标记EgSh(N)-f/EgSh(SNP)-2r可用来进行油棕种质资源早期分子鉴定,为高产油棕品种选育提供了技术支撑。     

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。     

四引物PCR扩增反应的单管SNP快速测定法

建立一种在单管中进行单核苷酸多型性 (SNP)快速测定的高效廉价方法 .以人ABCA1基因中的I82 3M为研究对象 ,设计 4种引物进行PCR扩增 ,其中两种引物用于扩增一段含有SNP位点的DNA片段 ,另两种引物为SNP位点特异性引物 ,4种引物在单管中同时进行PCR扩增反应 ,根据延伸产物的长度确定SNP的类型 .为提高SNP测定的特异性 ,在特异性引物的 3′端倒数第 3个碱基引入了一个人为错配碱基 ,使引物的错误延伸率显著降低 ,大大提高了SNP分析的准确性 .实验结果表明 ,所建立的方法简单 ,操作简便 ,可在单管中完成SNP的测定反应 .     

双色荧光杂交芯片在近交系小鼠遗传监测中的应用

应用一种新的高通量SNP检测方法-双色荧光杂交芯片技术进行近交系小鼠遗传监测。应用双色荧光杂交芯片技术对4个品系近交系小鼠的多个基因组DNA样本进行SNP分型,整合6个SNP位点的芯片杂交信息,对样本所属品系进行判断。研究结果表明SNP检测方法-双色荧光杂交芯片技术能够对选定的6个SNP位点进行高准确率分型;双色荧光杂交芯片技术是一种高通量SNP检测的良好工具,适合于对少量近交系品系来源的大样本量小鼠进行遗传污染监测和品系鉴定,并具有扩大应用的潜力。     

双色荧光杂交芯片在近交系小鼠遗传监测中的应用

应用一种新的高通量SNP检测方法-双色荧光杂交芯片技术进行近交系小鼠遗传监测。应用双色荧光杂交芯片技术对4个品系近交系小鼠的多个基因组DNA 样本进行SNP分型,整合6个SNP位点的芯片杂交信息,对样本所属品系进行判断。研究结果表明SNP检测方法-双色荧光杂交芯片技术能够对选定的6个SNP位点进行高准确率分型;双色荧光杂交芯片技术是一种高通量SNP检测的良好工具,适合于对少量近交系品系来源的大样本量小鼠进行遗传污染监测和品系鉴定,并具有扩大应用的潜力。     

等位基因特异PCR技术的研究与应用

生物的单核苷酸多态性(Single-nucleotide polymorphism,SNP)具有数量多、分布广、易于分型、稳定性强等优点,很适合于用做分子标记.等位基因特异PCR(Allele-specific PCR,AS-PCR)是根据SNP位点设计3'末端与SNP位点碱基互补或错配的特异PCR引物,通过凝胶电泳等方法检测PCR扩增产物的有或无,从而检测基因型中SNP的一种技术.经过不断地改进与完善,基于SNP的等位基因特异PCR标记已逐渐成为一种快速、简便、低成本、可靠、高通量的检测基因型SNP的方法.本文应用等位基因特异PCR技术,根据小麦TaDREB1基因在旱选10和鲁麦14的120(C→A)SNP成功地开发了一个SNP分子标记,证明了该方法的有效性和可行性.     

一种基于适配器连接介导的等位基因特异性扩增法测定多重SNP

建立了一种基于DNA适配器连接介导的等位基因特异性扩增法测定多重SNP。以CYP2D6基因中的5个SNP位点（100C>T,1661G>C,1758G>T,2470T>C和2850C>T）为例,用PCR法预扩增得一段含所有待测SNP位点的长片段,然后用限制性内切酶将其消化成短片段,在连接酶的作用下与设计的DNA适配器（adapter）相连;该适配器的一端与限制性内切酶降解后留下的粘性末端相同,另一端带有一段公共序列。在两管中加入与适配器连接的片段作为PCR扩增模板,并分别加入SNP特异性引物和一种适配器特异性的通用引物进行PCR扩增,最后用凝胶电泳法分离PCR扩增产物。由于每管与SNP的两种特异性引物中的一种对应,可以根据每管中扩增片段的大小判断SNP的类型。通过凝胶电泳法可以一次分离与5种SNP类型相对应的引物特异性延伸反应产物;采用该法成功测定了20名健康中国人的CYP2D6基因中5个SNP位点的基因多态性,与限制性片段长度多态性法（RFLP）测定结果完全一致。该方法采用n+1种引物（n种SNP特异性引物和一种通用引物）进行n重PCR反应,极大提高了PCR反应的特异性,结果准确,可用于同时测定多个SNP位点。

     

牛SNP芯片分型检出率和分型错误率对基因型填充准确率的影响

SNP芯片已被广泛应用于动植物的遗传研究和生产实践,其基因分型的准确性至关重要。但在实际应用中,常有一定数量的基因型因缺失而需要去估计(填充)。此外,由于各种原因,又常常需要在不同芯片的基因型之间相互填充彼此没有的SNP基因型,或从低密度SNP填充到高密度SNP基因型。因此,基因型填充准确率直接影响后续数据分析的准确性和可靠性。为深入了解基因型填充准确率的影响因素,本研究利用20 116头美国荷斯坦牛的50K SNP芯片基因分型数据,在SNP分型检出率与错误率存在相关和没有相关两种情形下,分别评估了上述两个因素对下游基因型填充准确率的影响。当两者不相关时,模拟的SNP分型检出率从100%降低到50%,SNP分型错误率由0%提升到50%。当两者存在相关时,基因分型的检出率和错误率之间的关系是基于一个实际数据中这两个变量之间的线性回归方程来确定,即模拟的SNP分型检出率从100%降低到50%,SNP分型错误率从0%升高到13.35%。最后,采用5折交叉验证的方法评估基因型填充的准确率。结果表明,当原始数据的SNP分型检出率与错误率彼此独立发生时,基因型填充的错误率受原始SNP分型检出率影响不大(P0.05),却随着原始SNP分型错误率的升高而显著提高(P0.01)。当原始数据的SNP分型检出率与错误率存在负相关时,基因型填充的错误率随着原始SNP分型检出率的降低而显著提高(P0.01)。在这两种情形下,建议SNP分型检出率应在90%以上,基因型填充准确率才能不低于98%。该结果可为提升实际的SNP分型和下游数据分析的质控提供参考依据。     

基于地黄转录组数据的SNP标记开发与地黄指纹图谱构建

地黄(Rehmannia glutinosa)是一种具有较高药用价值和经济价值的植物。准确的品种鉴定对地黄种质管理和育种至关重要,使用SNP分子标记来鉴定地黄种质和构建指纹图谱对地黄分子标记育种提供了新方法。利用地黄转录组数据和SRA数据库中的3个地黄转录组数据比对,寻找候选SNP,用PCR技术扩增和序列分析研究28个地黄品种候选SNP变异。从地黄转录组数据中获得了102 075条Unigenes,其中共有SNP位点35 339个,发生频率为0.51/kb;从中随机选取40个候选SNP位点,设计引物39对,用PCR和序列分析从中筛选出7对好的SNP引物,包含8个多态性好的SNP位点;利用最终筛选出的8个SNP位点构建地黄指纹图谱,可以将17个不同地黄种质区分开,可用于地黄种间和种内品种的鉴定。     

大规模单核苷酸多态性分析与肿瘤易感性

单核苷酸多态性(SNPs)是人类基因组中最常见的变异形式。作为第三代遗传标记,SNP在基因定位、克隆、遗传多态性方面具有广泛应用,特别是作为基因诊断标记在预防医学中具有十分重要的作用。近年来,随着人类基因组计划的发展,数以百万计的SNP被陆续发现,并可在公共数据库中免费获得。SNP数量的快速增加和SNP检测方法的发展,为其在肿瘤易感性领城的应用提供了可能。在本综述中,我们介绍了几种高通量检测SNP的分析方法,总结了大规模SNP分析技术在肿瘤易感性中的应用,介绍了目前人们对于不同人群中的SNP分析、肿瘤易感基因、个体肿瘤易感性的理解,以及研究SNP标记与肿瘤易感性关系时存在的难点。     

Correcting for ascertainment biases when analyzing SNP data: applications to the estimation of linkage disequilibrium

As large-scale sequencing efforts turn from single genome sequencing to polymorphism discovery, single nucleotide polymorphisms (SNPs) are becoming an increasingly important class of population genetic data. But because of the ascertainment biases introduced by many methods of SNP discovery, most SNP data cannot be analyzed using classical population genetic methods. Statistical methods must instead be developed that can explicitly take into account each method of SNP discovery. Here we review some of the current methods for analyzing SNPs and derive sampling distributions for single SNPs and pairs of SNPs for some common SNP discovery schemes. We also show that the ascertainment scheme has a large effect on the estimation of linkage disequilibrium and recombination, and describe some methods of correcting for ascertainment biases when estimating recombination rates from SNP data.     

High-resolution melting analysis for SNP genotyping and mapping in tetraploid alfalfa (Medicago sativa L.)

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic polymorphism in plant genomes. SNP markers are valuable tools for genetic analysis of complex traits of agronomic importance, linkage and association mapping, genome-wide selection, map-based cloning, and marker-assisted selection. Current challenges for SNP genotyping in polyploid outcrossing species include multiple alleles per loci and lack of high-throughput methods suitable for variant detection. In this study, we report on a high-resolution melting (HRM) analysis system for SNP genotyping and mapping in outcrossing tetraploid genotypes. The sensitivity and utility of this technology is demonstrated by identification of the parental genotypes and segregating progeny in six alfalfa populations based on unique melting curve profiles due to differences in allelic composition at one or multiple loci. HRM using a 384-well format is a fast, consistent, and efficient approach for SNP discovery and genotyping, useful in polyploid species with uncharacterized genomes. Possible applications of this method include variation discovery, analysis of candidate genes, genotyping for comparative and association mapping, and integration of genome-wide selection in breeding programs.     

Genotyping by Genome Reducing and Sequencing for Outbred Animals

Next-generation sequencing (NGS) approaches are widely used in genome-wide genetic marker discovery and genotyping. However, current NGS approaches are not easy to apply to general outbred populations (human and some major farm animals) for SNP identification because of the high level of heterogeneity and phase ambiguity in the haplotype. Here, we reported a new method for SNP genotyping, called genotyping by genome reducing and sequencing (GGRS) to genotype outbred species. Through an improved procedure for library preparation and a marker discovery and genotyping pipeline, the GGRS approach can genotype outbred species cost-effectively and high-reproducibly. We also evaluated the efficiency and accuracy of our approach for high-density SNP discovery and genotyping in a large genome pig species (2.8 Gb), for which more than 70,000 single nucleotide polymorphisms (SNPs) can be identified for an expenditure of only $80 (USD)/sample.     

Development of single nucleotide polymorphism (SNP) markers for use in commercial maize (Zea mays L.) germplasm

The development of single nucleotide polymorphism (SNP) markers in maize offers the opportunity to utilize DNA markers in many new areas of population genetics, gene discovery, plant breeding and germplasm identification. However, the steps from sequencing and SNP discovery to SNP marker design and validation are lengthy and expensive. Access to a set of validated SNP markers is a significant advantage to maize researchers who wish to apply SNPs in scientific inquiry. We mined 1,088 loci sequenced across 60 public inbreds that have been used in maize breeding in North America and Europe. We then selected 640 SNPs using generalized marker design criteria that enable utilization with several SNP chemistries. While SNPs were found on average every 43 bases in 1,088 maize gene sequences, SNPs that were amenable to marker design were found on average every 623 bases; representing only 7% of the total SNPs discovered. We also describe the development of a 768 marker multiplex assay for use on the Illumina^® BeadArray? platform. SNP markers were mapped on the IBM2 intermated B73 × Mo17 high resolution genetic map using either the IBM2 segregating population, or segregation in multiple parent-progeny triplets. A high degree of colinearity was found with the genetic nested association map. For each SNP presented we give information on map location, polymorphism rates in different heterotic groups and performance on the Illumina^® platform.     

The effect of SNP discovery method and sample size on estimation of population genetic data for Chinese and Indian rhesus macaques (<Emphasis Type="Italic">Macaca mulatta</Emphasis>)

This study was designed to address issues regarding sample size and marker location that have arisen from the discovery of SNPs in the genomes of poorly characterized primate species and the application of these markers to the study of primate population genetics. We predict the effect of discovery sample size on the probability of discovering both rare and common SNPs and then compare this prediction with the proportion of common and rare SNPs discovered when different numbers of individuals are sequenced. Second, we examine the effect of genomic region on estimates of common population genetic data, comparing markers from both coding and non-coding regions of the rhesus macaque genome and the population genetic data calculated from these markers, to measure the degree and direction of bias introduced by SNPs located in coding versus non-coding regions of the genome. We found that both discovery sample size and genomic region surveyed affect SNP marker attributes and population genetic estimates, even when these are calculated from an expanded data set containing more individuals than the original discovery data set. Although none of the SNP detection methods or genomic regions tested in this study was completely uninformative, these results show that each has a different kind of genetic variation that is suitable for different purposes, and each introduces specific types of bias. Given that each SNP marker has an individual evolutionary history, we calculated that the most complete and unbiased representation of the genetic diversity present in the individual can be obtained by incorporating at least 10 individuals into the discovery sample set, to ensure the discovery of both common and rare polymorphisms.     

Sequence-based genotyping for marker discovery and co-dominant scoring in germplasm and populations

Conventional marker-based genotyping platforms are widely available, but not without their limitations. In this context, we developed Sequence-Based Genotyping (SBG), a technology for simultaneous marker discovery and co-dominant scoring, using next-generation sequencing. SBG offers users several advantages including a generic sample preparation method, a highly robust genome complexity reduction strategy to facilitate de novo marker discovery across entire genomes, and a uniform bioinformatics workflow strategy to achieve genotyping goals tailored to individual species, regardless of the availability of a reference sequence. The most distinguishing features of this technology are the ability to genotype any population structure, regardless whether parental data is included, and the ability to co-dominantly score SNP markers segregating in populations. To demonstrate the capabilities of SBG, we performed marker discovery and genotyping in Arabidopsis thaliana and lettuce, two plant species of diverse genetic complexity and backgrounds. Initially we obtained 1,409 SNPs for arabidopsis, and 5,583 SNPs for lettuce. Further filtering of the SNP dataset produced over 1,000 high quality SNP markers for each species. We obtained a genotyping rate of 201.2 genotypes/SNP and 58.3 genotypes/SNP for arabidopsis (n?=?222 samples) and lettuce (n?=?87 samples), respectively. Linkage mapping using these SNPs resulted in stable map configurations. We have therefore shown that the SBG approach presented provides users with the utmost flexibility in garnering high quality markers that can be directly used for genotyping and downstream applications. Until advances and costs will allow for routine whole-genome sequencing of populations, we expect that sequence-based genotyping technologies such as SBG will be essential for genotyping of model and non-model genomes alike.     

Use of Ecotilling as an efficient SNP discovery tool to survey genetic variation in wild populations of Populus trichocarpa

Abstract Ecotilling was used as a simple nucleotide polymorphism (SNP) discovery tool to examine DNA variation in natural populations of the western black cottonwood, Populus trichocarpa, and was found to be more efficient than sequencing for large-scale studies of genetic variation in this tree. A publicly available, live reference collection of P. trichocarpa from the University of British Columbia Botanical Garden was used in this study to survey variation in nine different genes among individuals from 41 different populations. A large amount of genetic variation was detected, but the level of variation appears to be less than in the related species, Populus tremula, based on reported statistics for that tree. Genes examined varied considerably in their level of variation, from PoptrTB1 which had a single SNP, to PoptrLFY which had more than 23 in the 1000-bp region examined. Overall nucleotide diversity, measured as (Total), was relatively low at 0.00184. Linkage disequilibrium, on the other hand, was higher than reported for some woody plant species, with mean r2 equal to 0.34. This study reveals the potential of Ecotilling as a rapid genotype discovery method to explore and utilize the large pool of genetic variation in tree species.     

Simulating multiplexed SNP discovery rates using base-specific cleavage and mass spectrometry

MOTIVATION: Single Nucleotide Polymorphisms (SNPs) are believed to contribute strongly to the genetic variability in living beings, and SNP and mutation discovery are of great interest in today's Life Sciences. A comparatively new method to discover such polymorphisms is based on base-specific cleavage, where resulting cleavage products are analyzed by mass spectrometry (MS). One particular advantage of this method is the possibility of multiplexing the biochemical reactions, i.e. examining multiple genomic regions in parallel. Simulations can help estimating the performance of a method for polymorphism discovery, and allow us to evaluate the influence of method parameters on the discovery rate, and also to investigate whether the method is well suited for a certain genomic region. RESULTS: We show how to efficiently conduct such simulations for polymorphism discovery using base-specific cleavage and MS. Simulating multiplexed polymorphism discovery leads us to the problem of uniformly drawing a multiplex. Given a multiset of natural numbers we want to uniformly draw a subset of fixed cardinality so that the elements sum up to some fixed total length. We show how to enumerate multiplex layouts using dynamic programming, which allows us to uniformly draw a multiplex.