植物的单核苷酸多态性及其在作物遗传育种中的应用

单核苷酸多态性(single nucleotide polymorphism, SNP)是基因组中最常见的遗传多态性,在遗传学研究的许多方面具有重要的作用。综述了单核苷酸多态性的发现、特点及其应用等方面对植物SNP的研究进展,并展望其在作物遗传育种中的应用前景。     

植物的单核苷酸多态性及其在作物遗传育种中的应用

单核苷酸多态性(single nucleotide polymorphism,SNP)是基因组中最常见的遗传多态性,在遗传学研究的许多方面具有重要的作用.综述了单核苷酸多态性的发现、特点及其应用等方面对植物SNP的研究进展,并展望其在作物遗传育种中的应用前景.     

单核苷酸多态性（SNPs）原理及其在植物功能功能基因组学中的应用前景

近缘种群或同种个体之间的单核苷酸多态性(SNPs)是存在于生物基因组上由单个核苷酸的变异所引起的一种DNA序列多态性。它具有高丰度、高信息量和良好稳定性的特点,而且比较适合自动化操作。SNPs将会在植物功能基因组学研究中得到广泛应用,并加强功能基因学与种群遗传学的联系。本文就SNPs及其在植物功能基因组学中的应用前景作一下探讨。     

单核苷酸多态性(SNPs)原理及其在植物功能基因组学中的应用前景

近缘种群或同种个体之间的单核苷酸多态性(SNPs)是存在于生物基因组上由单个核苷酸的变异所引起的一种DNA序列多态性.它具有高丰度、高信息量和良好稳定性的特点,而且比较适合自动化操作.SNPs将会在植物功能基因组学研究中得到广泛应用,并加强功能基因学与种群遗传学的联系.本文就SNPs及其在植物功能基因组学中的应用前景作一下探讨.     

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,本对方法的两种策略及其各自所存在的问题作一介绍。     

应用FP-TDI技术进行高通量单核苷酸多态分型

FP-TDI (fluorescence polarization template-directed dye-terminator incorporation）是一种操作简单、实验投入少、适于高通量反应的单核苷酸多态等位基因分型技术.使用两种评价分型图像质量的数值指标,可以有效地对分型结果进行评价,使该技术得到了改进.在此基础上优化了实验条件,并应用该技术,对人类基因组3号染色体上随机选取的337个单核苷酸多态性位点进行了高通量分型,反应的一次成功率达到59.94%.     

人类基因组SNPs的研究现状及应用前景

基因组DNA是生物体各种生理、病理性状的物质基础,人类DNA序列变异约90%表现为单核苷酸多态性(singlenucleotidepolymorphisms,SNPs),这是一种常见的遗传变异类型,在人类基因组中广泛存在,被认为是人类疾病易感性和药物反应的决定性因素。本文主要介绍了SNPs的分类及特点、人类基因组SNPs的研究现状、SNPs在实践中的应用,以及SNPs在遗传作图、医药、遗传易感性、个体化医疗等方面的研究前景,并探讨了当前SNPs研究中存在的问题。     

SARS病毒基因组的多态性

对SARS病人粪便样本直接测序,得到SRAS—CoV BJ202全基因组序列（AY864806）。应用比较基因组研究方法对GenBank中公布的115株SARS—CoV基因组序列以及BJ202进行分析。以GZ02序列为参照,发现2个以上基因组中同时存在单核苷酸多态（SNP）位点共278个。多态位点在SARS—CoV基因组中呈偏态分布,大约一半突变位点（50．4％,140／278）发生在基因组3’末端1／3区域。编码Orf10-11、Orf3／4、E蛋白、M蛋白和S蛋白区域突变率较高。克隆并测序含有BJ202基因组12个多态位点的11个cDNA以及4个不含已知多态位点的cDNA片段（15个片段总长度为6．0kb）,结果显示：BJ202特有的3个多态位点（13804、1503l和20792）以及另外3个多态位点（26428、26477和27243）均检出两种不同核苷酸;位点18379虽在已公布的115株SARS—CoV基因组中未发现突变,实际上也是多态位点。14个克隆中有8个克隆该位点为A,6个克隆为G。全部116个SARS—CoV基因组中共有18种缺失类型和2种插入类型。大部分缺失发生在编码ORF9和ORF10-11区域（基因组序列27700—28000bp处）。以邻位连接法（Neighbor-Joining）构建了116株SARS—CoV系统发育树,BJ202与BJ01和LLJ-2004等SARS—CoV的亲缘关系较接近。     

利用微卫星标记评估大豆重组近交系ＮＪＲＩＫＹ

分离群体的构建及其合理性评估是遗传作图等基因组研究的基础,利用 ＳＳＲs标记以对科丰１号为母本、南农１１３８－２为父本构建的大豆ＲＩＬs群体ＮＪＲＩＫＹ进行了分析评估。随机选取覆盖大豆基因组的１３８对ＳＳＲs引物对两个亲本进行多态性筛选分析,８６对具有多态性,多态率高达约６２．２３％,共计９０个多态位点。进一步利用具多态性的ＳＳＲs位点对供试群体的分析表明不仅绝大多数位点符合１：１分离比,而且各家系也趋于纯合。该ＲＩＳs群体各家系的基因型组成近似正态分布,是一种适于遗传作图及其他基因组研究理想的ＲＩＬs群体     

牛ACAD8基因结构及其多态性与生产性状的关联分析

乙酰辅酶A脱氢酶(ACAD)是由核基因编码存在于线粒体中黄色酶的一个家族,它催化脂肪酸的α和β-氧化,该家族第八成员负责催化缬氨酸分解代谢。本研究中获得了牛的ACAD8基因的mRNA和基因组DNA序列并通过基因组DNA与mRNA的比对确定了其基因结构。其mRNA序列包含一个1,251 bp的开放阅读框,一个37 bp 的5′非翻译区和一个 444 bp 的 3′非翻译区;其基因组DNA全长13,814 bp,包含11个外显子和10个内含子。我们从样本中随机选取6个个体进行扩增测序,结果表明牛的ACAD8基因13,408位(GenBank登录号:DQ435445)一个A-G单核苷酸多态。运用PCR-RFLP对不同的基因型进行了区分。对该SNP与来自5个品种共178头无亲缘关系的牛的生产性状进行了关联分析,结果表明:该A-G单核苷酸多态对日增重和嫩度有显著影响(P < 0.05)。具有G等位基因的牛比具有A等位基因的牛生长较快,其肉质也较嫩。因此,牛ACAD8基因在该位点的单核苷酸多态可以作为提高生长速度和牛肉嫩度的标记。     

High throughput genotyping technologies.

A comprehensive genetic map containing several hundred microsatellite markers resulted from a large microsatellite mapping project. This was the first real study that introduced high throughput methods to the genetic community. This map and the concurrent technological advances, which will briefly be reviewed, led to further numerous mapping investigations of simple and complex diseases. The annotated draft sequence of approximately three billion base pairs (bp) of the human genome has been completed much sooner than many imagined, due to considerable technological advancements and the international enterprise that resulted. This was a major development for the genetics community, but is only the precursor to the next phase of studying and understanding the variation within the human genome. The awareness of the differences may help us understand the effects on the genetics of the variation between individuals and disease. It is these variations at the nucleotide level that determine the physiological differences, or phenotypes of each individual, including all biological functions at the cellular and body level. Single nucleotide polymorphisms (SNPs) will provide the next high density map, and be the genetic tool to study these genetic variations. There are many sources of SNPs and exhaustive numbers of methods of SNP detection to be considered. The focus in this paper will be on the merits of selected, varied SNP typing methodologies that are emerging to genotype many individuals with the required huge number of SNPs to make the study of complex diseases and pharmacogenomics a practical and economically viable option.     

Identification of novel SNPs in glioblastoma using targeted resequencing

High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other "omics" approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens.     

The essence of SNPs.

Single nucleotide polymorphisms (SNPs) are an abundant form of genome variation, distinguished from rare variations by a requirement for the least abundant allele to have a frequency of 1% or more. A wide range of genetics disciplines stand to benefit greatly from the study and use of SNPs. The recent surge of interest in SNPs stems from, and continues to depend upon, the merging and coincident maturation of several research areas, i.e. (i) large-scale genome analysis and related technologies, (ii) bio-informatics and computing, (iii) genetic analysis of simple and complex disease states, and (iv) global human population genetics. These fields will now be propelled forward, often into uncharted territories, by ongoing discovery efforts that promise to yield hundreds of thousands of human SNPs in the next few years. Major questions are now being asked, experimentally, theoretically and ethically, about the most effective ways to unlock the full potential of the upcoming SNP revolution.     

The use of single-nucleotide polymorphism maps in pharmacogenomics

Single-nucleotide polymorphisms (SNPs), common variations among the DNA of individuals, are being uncovered and assembled into large SNP databases that promise to enable the dissection of the genetic basis of disease and drug response (i.e., pharmacogenomics). Although great strides have been made in understanding the diversity of the human genome, such as the frequency, distribution, and type of genetic variation that exists, the feasibility of applying this information to uncover useful pharmacogenomic markers is uncertain. The health care industry is clamoring for access to SNP databases for use in research in the hope of revolutionizing the drug development process. As the reality of using SNPs to uncover drug response markers is rarely addressed, this review discusses practical issues, such as patient sample size, SNP density and genome coverage, and data interpretation, that will be important for determining the applicability of pharmacogenomic information to medical practice.     

Exome Sequencing of Only Seven Qataris Identifies Potentially Deleterious Variants in the Qatari Population

The Qatari population, located at the Arabian migration crossroads of African and Eurasia, is comprised of Bedouin, Persian and African genetic subgroups. By deep exome sequencing of only 7 Qataris, including individuals in each subgroup, we identified 2,750 nonsynonymous SNPs predicted to be deleterious, many of which are linked to human health, or are in genes linked to human health. Many of these SNPs were at significantly elevated deleterious allele frequency in Qataris compared to other populations worldwide. Despite the small sample size, SNP allele frequency was highly correlated with a larger Qatari sample. Together, the data demonstrate that exome sequencing of only a small number of individuals can reveal genetic variations with potential health consequences in understudied populations.     

Single-nucleotide polymorphisms in NAGNAG acceptors are highly predictive for variations of alternative splicing

Aberrant or modified splicing patterns of genes are causative for many human diseases. Therefore, the identification of genetic variations that cause changes in the splicing pattern of a gene is important. Elsewhere, we described the widespread occurrence of alternative splicing at NAGNAG acceptors. Here, we report a genomewide screen for single-nucleotide polymorphisms (SNPs) that affect such tandem acceptors. From 121 SNPs identified, we extracted 64 SNPs that most likely affect alternative NAGNAG splicing. We demonstrate that the NAGNAG motif is necessary and sufficient for this type of alternative splicing. The evolutionarily young NAGNAG alleles, as determined by the comparison with the chimpanzee genome, exhibit the same biases toward intron phase 1 and single-amino acid insertion/deletions that were already observed for all human NAGNAG acceptors. Since 28% of the NAGNAG SNPs occur in known disease genes, they represent preferable candidates for a more-detailed functional analysis, especially since the splice relevance for some of the coding SNPs is overlooked. Against the background of a general lack of methods for identifying splice-relevant SNPs, the presented approach is highly effective in the prediction of polymorphisms that are causal for variations in alternative splicing.     

Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis

Since the initial sequencing of the human genome, many projects are underway to understand the effects of genetic variation between individuals. Predicting and understanding the downstream effects of genetic variation using computational methods are becoming increasingly important for single nucleotide polymorphism (SNP) selection in genetics studies and understanding the molecular basis of disease. According to the NIH, there are now more than four million validated SNPs in the human genome. The volume of known genetic variations lends itself well to an informatics approach. Bioinformaticians have become very good at functional inference methods derived from functional and structural genomics. This review will present a broad overview of the tools and resources available to collect and understand functional variation from the perspective of structure, expression, evolution and phenotype. Additionally, public resources available for SNP identification and characterisation are summarised.     

Large-scale genotyping of complex DNA

Genetic studies aimed at understanding the molecular basis of complex human phenotypes require the genotyping of many thousands of single-nucleotide polymorphisms (SNPs) across large numbers of individuals. Public efforts have so far identified over two million common human SNPs; however, the scoring of these SNPs is labor-intensive and requires a substantial amount of automation. Here we describe a simple but effective approach, termed whole-genome sampling analysis (WGSA), for genotyping thousands of SNPs simultaneously in a complex DNA sample without locus-specific primers or automation. Our method amplifies highly reproducible fractions of the genome across multiple DNA samples and calls genotypes at >99% accuracy. We rapidly genotyped 14,548 SNPs in three different human populations and identified a subset of them with significant allele frequency differences between groups. We also determined the ancestral allele for 8,386 SNPs by genotyping chimpanzee and gorilla DNA. WGSA is highly scaleable and enables the creation of ultrahigh density SNP maps for use in genetic studies.     

Computational identification of candidate loci for recessively inherited mutation using high-throughput SNP arrays

MOTIVATION: Single nucleic polymorphisms (SNPs) are one of the most abundant genetic variations in the human genome. Recently, several platforms for high-throughput SNP analysis have become available, capable of measuring thousands of SNPs across the genome. Tools for analysing and visualizing these large genetic data sets in biologically relevant manner are rare. This hinders effective use of the SNP-array data in research on complex diseases, such as cancer. RESULTS: We describe a computational framework to analyse and visualize SNP-array data, and link the results in relevant databases. Our major objective is to develop methods for identifying DNA regions that likely harbour recessive mutations. Thus, the algorithms are designed to have high sensitivity and the identified regions are ranked using a scoring algorithm. We have also developed annotation tools that automatically query gene IDs, exon counts, microarray probe IDs, etc. In our case study, we apply the methods for identifying candidate regions for recessively inherited colorectal cancer predisposition and suggest directions for wet-lab experiments. AVAILABILITY: R-package implementation is available at http://www.ltdk.helsinki.fi/sysbio/csb/downloads/CohortComparator/