单核苷酸多态性及其在水稻中的应用(综述)

单核苷酸多态性(single nucleotide polymorphism,SNP)主要是指基因组的DNA由于单个核苷酸的变异所引起的DNA序列多态性。本文介绍SNP的检测方法及其在水稻中的研究进展和应用。     

SNPs在水生动物遗传多样性分析的应用

Lander于1996年提出的单核苷酸多态性(single nucleotide polymorphisms,SNPs)被认为是第三代理想的遗传标记.SNPs是基因组水平上由单个碱基变异引起的DNA序列多态性,广泛应用于生物的遗传多样性研究.本文就SNPs定义、特性,及其在水生动物遗传多样性分析的应用进行综述.     

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。     

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。     

SNPs分子标记技术在昆虫学研究中的应用

单核苷酸多态性(single nucleotide polymorphisms,SNPs)主要是指在染色体基因组水平上由于单个核苷酸的变异而引起的DNA序列多态性,包括单碱基的转换或颠换引起的点突变,其中最少出现1种等位基因频率不小于1%,常以双等位基因的形式出现,稳定而可靠。在目前的昆虫基因组研究中,SNPs标记的研究主要集中在果蝇、蚊媒、家蚕等一些模式生物。本文对SNPs标记在昆虫的种类鉴定、遗传图谱构建、种群遗传学、抗药性分子机理等方面进行了综述,最后展望了SNPs在种群遗传、标记辅助选择和生物进化等研究领域中的应用前景。     

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.     

微测序技术分析人类单核苷酸多态性

单核苷酸多态性 (singlenucleotidepolymorphism)是指染色体基因组水平上单个核苷酸变异引起的DNA序列多态性 ,即一种二等位基因标记。目前推断在基因组中至少有 30万个SNP[1 ] 。随着分子遗传学的发展 ,疾病研究从对单基因疾病的研究转向探讨多基因疾病 (如心血管疾病、神经系统疾病、各种肿瘤等 )的相关因素。因此 ,需要在人类基因组中找到一种数目众多、分布广泛且相对稳定的遗传标记 ,单核苷酸多态性正代表了这样一种标记。因此SNPs已成为继第一代限制性片段多态性标记 ,第二代微卫星多态性标记后具有重要研究价值的第三代基因遗传…     

SNPs及其在水产动物遗传学与育种学研究中的应用

1 SNP简介1.1 SNP的概念单核苷酸多态性(Single nucleotide polymorphism,SNP)指基因组DNA序列中某个特定位点的单个核苷酸发生变异而引起的序列多态性,包括单碱基的转换、颠换、插入及缺失等形式,其中一种等位基因在群体中的频     

单核苷酸多态性检测技术的研究进展

单核苷酸多态性是指在基因组水平上单个核苷酸变异引起的一种DNA序列多态性。因其具有密度高,遗传稳定,易于进行自动化、规模化分析等优势它已成为第三代分子标记,因此其检测技术也在近几年得到了快速的发展。将对未知单核苷酸突变位点的检测方法和已知单核苷酸突变位点的检测方法这两部分的研究进展作一综述。     

新一代分子标记--SNPs及其应用

单核苷酸多态性(SNPs)是广泛存在于基因组中的一类DNA序列变异,其频率为1%或更高。它是由单个碱基的转换或颠换引起的点突变,稳定而可靠,并通常以二等位基因的形式出现。采用生物芯片和DNA微阵列技术来检测SNP,便于对基因组进行大幅度和高通量分析。因此,作为新一代分子标记,SNP在生物学诸多领域具有广阔应用前景。本文简要叙述SNPs技术的发展历史、研究动态以及相关的理论,介绍了与SNPs相关的基本术语、概念及其特点,列举了发现与检测SNPs主要技术的原理和方法,同时还根据一些具体实例介绍了SNPs在模式动、植物遗传图谱构建、品种鉴定、物种起源与亲缘关系、连锁不平衡与关联分析及其在群体遗传结构及其变化机制研究中的应用。最后展望了SNPs在群体遗传、分子育种和生物进化等研究领域中的应用前景。     

Accurate prediction of deleterious protein kinase polymorphisms

MOTIVATION: Contemporary, high-throughput sequencing efforts have identified a rich source of naturally occurring single nucleotide polymorphisms (SNPs), a subset of which occur in the coding region of genes and result in a change in the encoded amino acid sequence (non-synonymous coding SNPs or 'nsSNPs'). It is hypothesized that a subset of these nsSNPs may underlie common human disease. Testing all these polymorphisms for disease association would be time consuming and expensive. Thus, computational methods have been developed to both prioritize candidate nsSNPs and make sense of their likely molecular physiologic impact. RESULTS: We have developed a method to prioritize nsSNPs and have applied it to the human protein kinase gene family. The results of our analyses provide high quality predictions and outperform available whole genome prediction methods (74% versus 83% prediction accuracy). Our analyses and methods consider both DNA sequence conservation, which most traditional methods are based on, as well unique structural and functional features of kinases. We provide a ranked list of common kinase nsSNPs that have a higher probability of impacting human disease based on our analyses.     

Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information

MOTIVATION: Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Alzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence. RESULTS: In this paper, we develop a method based on support vector machines (SVMs) that starting from the protein sequence information can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans. Using a dataset of 21 185 single point mutations, 61% of which are disease-related, out of 3587 proteins, we show that our predictor can reach more than 74% accuracy in the specific task of predicting whether a single point mutation can be disease related or not. Our method, although based on less information, outperforms other web-available predictors implementing different approaches. AVAILABILITY: A beta version of the web tool is available at http://gpcr.biocomp.unibo.it/cgi/predictors/PhD-SNP/PhD-SNP.cgi     

Applications of computational algorithm tools to identify functional SNPs in cytokine genes

Understanding the functions of single nucleotide polymorphisms (SNPs) can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. However, how to identify functional SNPs from a pool containing both functional and neutral SNPs is challenging. In this study, we analyzed the genetic variations that can alter the expression and function of a group of cytokine proteins using computational tools. As a result, we extracted 4552 SNPs from 45 cytokine proteins from SNPper database. Of particular interest, 828 SNPs were in the 5'UTR region, 961 SNPs were in the 3' UTR region, and 85 SNPs were non-synonymous SNPs (nsSNPs), which cause amino acid change. Evolutionary conservation analysis using the SIFT tool suggested that 8 nsSNPs may disrupt the protein function. Protein structure analysis using the PolyPhen tool suggested that 5 nsSNPs might alter protein structure. Binding motif analysis using the UTResource tool suggested that 27 SNPs in 5' or 3'UTR might change protein expression levels. Our study demonstrates the presence of naturally occurring genetic variations in the cytokine proteins that may affect their expressions and functions with possible roles in complex human disease, such as immune diseases.     

Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines

Background  

Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs) that lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases. One of the key problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. As such, the development of computational tools that can identify such nsSNPs would enhance our understanding of genetic diseases and help predict the disease.     

PicSNP: a browsable catalog of nonsynonymous single nucleotide polymorphisms in the human genome

Recent progress in identification and mapping of single nucleotide polymorphisms (SNPs) in the human genome generates an unprecedented opportunity to explore cause-effect relationships between genetic variations and susceptibility to common diseases. For this purpose, one promising strategy would be to select a set of SNPs that potentially alter the function of proteins involved in the pathogenesis of the diseases and compare their frequencies in the affected individuals and the healthy population. In this respect, SNPs that change amino acid sequences (nonsynonymous SNPs; nsSNPs) are of particular interest, since they are more likely to affect protein functions. In this study, we have constructed a catalog of nsSNPs (PicSNP), whose unique features are (i) nsSNPs are classified according to the functions of the affected genes and are searchable under the guidance of hierarchical lists of protein functions and (ii) nsSNPs that lead to amino acid changes in the known functional sites and domains of proteins are highlighted. Out of 1,190,295 SNPs extracted from public database, we identified 3793 nsSNPs and classified them in 1247 categories of protein functions. 495 sites and domains annotated in the Swiss-Prot database were found to include nsSNPs, including 2 nsSNPs in disulfide-binding sites and 38 nsSNPs in transmembrane regions. PicSNP is available via the World Wide Web (http://picsnp.org) and would support research questing for SNPs involved in common diseases.     

Exploration of methods to identify polymorphisms associated with variation in DNA repair capacity phenotypes

Elucidating the relationship between polymorphic sequences and risk of common disease is a challenge. For example, although it is clear that variation in DNA repair genes is associated with familial cancer, aging and neurological disease, progress toward identifying polymorphisms associated with elevated risk of sporadic disease has been slow. This is partly due to the complexity of the genetic variation, the existence of large numbers of mostly low frequency variants and the contribution of many genes to variation in susceptibility. There has been limited development of methods to find associations between genotypes having many polymorphisms and pathway function or health outcome. We have explored several statistical methods for identifying polymorphisms associated with variation in DNA repair phenotypes. The model system used was 80 cell lines that had been resequenced to identify variation; 191 single nucleotide substitution polymorphisms (SNPs) are included, of which 172 are in 31 base excision repair pathway genes, 19 in 5 anti-oxidation genes, and DNA repair phenotypes based on single strand breaks measured by the alkaline Comet assay. Univariate analyses were of limited value in identifying SNPs associated with phenotype variation. Of the multivariable model selection methods tested: the easiest that provided reduced error of prediction of phenotype was simple counting of the variant alleles predicted to encode proteins with reduced activity, which led to a genotype including 52 SNPs; the best and most parsimonious model was achieved using a two-step analysis without regard to potential functional relevance: first SNPs were ranked by importance determined by random forests regression (RFR), followed by cross-validation in a second round of RFR modeling that included ever more SNPs in declining order of importance. With this approach six SNPs were found to minimize prediction error. The results should encourage research into utilization of multivariate analytical methods for epidemiological studies of the association of genetic variation in complex genotypes with risk of common diseases.     

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources

MOTIVATION: The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. RESULTS: We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28,043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. AVAILABILITY: http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org SUPPLEMENTARY INFORMATION: http://salilab.org/LS-SNP/supp-info.pdf.     

Computational detection of deleterious SNPs and their effect on sequence and structural level of the <Emphasis Type="Italic">VHL</Emphasis> gene

In this work we have analyzed the genetic variation that can alter the expression and the function of the VHL gene using computational methods. Of 110 single nucleotide polymorphisms (SNPs), 33 were found to be nonsynonymous (nsSNPs) and 23 SNPs were found in untranslated regions. Of the 33 nsSNPs investigated, 36.3% were found to be deleterious by both SIFT and PolyPhen servers. An untranslated region (UTR) resource tool suggested that two SNPs in the 5' UTR region and six SNPs in the 3' UTR region might change the protein expression levels. It was found by both SIFT and PolyPhen servers that a mutation from histidine to arginine at position 115 of the native protein of the VHL gene was most deleterious. A structural analysis of this mutated protein and the native protein was performed and had a root mean square deviation (RMSD) of 2.78 A. Based on this work, we propose that the nsSNP with a SNPid of rs5030812 is an important candidate for the cause of von Hippel-Lindau syndrome via the VHL gene.