人类基因组非翻译区“HARE5”参与大脑皮层发育

尽管人类与猿类的基因存在高相似性,但却只有人类能拥有高等智能,这一直是科学界的重大问题。最近,美国杜克大学学者Debra L.Silver等发表在《Current Biology》的研究成果,似乎在某种程度上切近了这一奥秘。目前已知,在人类基因组中,存在数量巨大的非翻译区,而这些非翻译区的功能,并未被充分揭示。该论文作者首先利用生物信息学技术,在人类基因组非翻译区,筛选出一个与大脑新皮层发育密切相关的特殊区域——HARE5。研究发现,人类HARE5虽与黑猩猩同源,但两者基因之间却有16处不同编码。随后,研究人员大胆地利用小鼠作为研究模型,将"人类-     

MutPrimerDesign:用于人类基因编码区域突变位点的引物设计程序

位于基因编码区的DNA突变与基因的功能密切相关。在已知人类基因编码区的突变位点时,如何在基因组上设计引物验证该突变是一个重要的问题。本文利用Python语言开发了引物设计程序MutPrimerDesign。MutPrimerDesign通过解析人类基因组序列数据库以及基因注释信息,转换基因编码区坐标为基因组坐标,并调用Primer3的python程序包接口,可批量自动化完成基因突变位点的引物及探针序列设计。MutPrimerDesign使用简便,可识别多种数据库的基因名称,并能够修改引物常规参数,实现引物的快速调整。     

X连锁非特异性精神发育迟滞相关基因PAK3研究进展

PAK3基因突变会导致非特异性精神发育迟滞, 因而与人类一般和特殊认知能力密切相关。研究该基因的生物学功能和认知功能将为临床诊断和防治由此引起的精神发育迟滞患者提供参考。文章综述了对PAK3基因产物、基因的生物学与认知功能的研究现状, 并对今后的进一步研究工作进行了展望。     

SMARCA1基因组结构鉴定及其在Smith-Fineman-Myers综合征家系中的突变分析

为探讨SMARCA1基因在中国山东SFMS家系患者发生中的作用,采用计算机杂交结合DNA序列分析方法,首先确定了SMARCA1基因的基因组结构,发现该基因的基因组DNA全长超过71．7kb,含有24个外显子和23个内含子,所有外显子和内含子接头皆遵循GT-AG法则,基因组结构的阐明,为进行基因突变检测和分析其生物学功能奠定了基础。在以上分析的基础上,通过PCR扩增结合测序分析,对在山东省发现的1个SFMS家系患者的SMARCA1基因的全部外显子和外显子内含子接头序列进行了基因突变检测,未检测到导致疾病的突变,提示中国山东SFMS家系患者不是由于SMARCA1基因编码区域内基因突变所致。     

蛋白质组技术在鼠胚胎早期发育研究中的应用

人类基因组大规模测序,揭示基因组精细结构的同时,还显示出基因数量的有限性和结构的相对稳定。随分析仪器和生物技术的飞速发展,创立了与基因组相对应的蛋白质组学,将精力集中于从生命功能的执行体---蛋白质水平研究基因的表达及功能。生殖技术的研究已取得了惊人的进展,但人们对生殖尤其是人类生殖的分子机制了解仍很贫乏。鼠胚胎发育过程蛋白质组的研究,为了解人类生殖健康和疾病发生的机制提供了有意义的资料 。     

五味子叶绿体基因组结构解析与比较分析

五味子是一种重要的药用植物.本研究通过Illumina HiSeq测序平台对五味子全基因组进行测序,完成了五味子叶绿体基因组的组装与结构解析,并与其他基部被子植物叶绿体基因组进行比较分析.五味子叶绿体基因组全长146730bp,GC含量为39.7%,由典型的4个区域组成,包括一个长单拷贝区、一个短单拷贝区和一对反向重复区(IR),其长度分别为95538、18270和16461 bp.共注释出113个基因,包括79个蛋白质编码基因、30个tRNA基因和4个rRNA基因.有4个蛋白质编码基因、5个tRNA基因和4个rRNA基因位于IR区.在五味子叶绿体基因组中共检测出47个简单重复序列和40个长重复序列.与一般被子植物叶绿体基因组相比,五味子叶绿体基因组IR区长度大约收缩10kb.IR区发生收缩的部分GC含量为37.4%,远小于IR区平均GC含量(45.7%).利用20个物种的53个共有蛋白质编码序列对五味子在被子植物中的系统发育位置进行了分析,结果表明五味子属与八角属为姊妹分支.对五味子叶绿体基因组序列进行分析,为五味子科物种鉴定、遗传多样性以及系统发育研究提供了数据基础.     

Limosilactobacillus fermentum F-6的遗传背景和功能基因组

【目的】Limosilactobacillus fermentum具有增强免疫力、产胞外多糖(exopolysaccharide,EPS)等多种功能特性,广泛应用于食品领域,具有较高经济价值。本文从群体遗传学角度,解析L. fermentum F-6的遗传背景和功能基因特征,为其开发利用提供遗传学基础。【方法】本研究对NCBI已公开的23株L. fermentum全基因组序列和1株模式菌株ATCC 14931T的基因组序列进行比较基因组学分析。利用Roary软件识别核心基因集与泛基因集;采用rapid annotation using subsystem technology(RAST)网站对基因组进行功能注释,以探究F-6基因组特征。【结果】以识别到的997个核心基因构建系统发育树,发现聚类趋势与分离源无关,但F-6与3株食品分离株聚在同一分支。功能注释分析发现,24株L. fermentum中仅F-6含有参与支链氨基酸合成途径的基因(ilvD、leuA等),可为机体提供必需氨基酸。F-6含有大量编码糖基转移酶和UDP-葡萄糖4-表异构酶的基因,且含有1个完整的eps基因簇。与其他L...     

外显子组测序技术在人类疾病研究中的应用

外显子组测序是针对基因组中的蛋白质编码区,靶向富集外显子区域测序,以发现疾病相关遗传变异的技术。该技术近年越来越多地应用于发现人类基因组低频变异、鉴定单基因遗传病致病基因和肿瘤等复杂疾病易感基因研究,成为人类疾病相关变异研究的重要工具。综述了外显子组测序技术的基本原理及其在人类疾病相关基因研究中的应用。     

非编码RNA与哺乳动物基因组印记的起源

基因组印记是由亲本来源不同而导致等位基因表达差异的一种遗传现象,主要发生在胎盘哺乳动物（真哺乳类）和显花植物中.大部分印记基因都分布在印记基因簇内,其中包含大量的非编码RNA基因.印记基因的表达受印记控制区（ICRs）的顺式调控.基因组印记产生的原因及过程是现代遗传学研究的一个热点问题,分析印记同源区从非印记物种到印记物种的过渡,为解决这一问题提供了重要启示.最近,原始哺乳动物（有袋类和单孔类）模式物种全基因组测序的完成,极大地促进了印记同源区的比较分析研究.本文对这些研究进行了回顾和分析,发现非编码RNA与哺乳动物基因组印记获得关系密切.主要依据为：（1）伴随着基因组印记的获得,印记区有大量的非编码RNA新基因出现;（2）与基因组印记相关的一些保守非编码RNA的表达发生了显著变化.此外,对15种脊椎动物中印记snoRNA基因系统分析的结果表明：印记snoRNA起源于真哺乳类与有袋类动物分化之后,并且在真哺乳类辐射进化之前发生了迅速的扩张,主要的基因家族在这一时期已经形成.这些结果进一步证明了非编码RNA与基因组印记获得的密切联系.非编码RNA可能主要通过调控印记表达和诱导染色体表观遗传修饰两种机制,参与哺乳动物基因组印记的获得.     

人类珠蛋白基因表达的遗传控制

人类珠蛋白基因表达的遗传控制曾溢滔,任兆瑞,黄淑帧（上海市儿童医院上海医学遗传研究所上海２０００４０）一、血红蛋白的遗传人类珠蛋白基因可以说是基因组中最具有代表性的基因,不仅由于它本身的遗传复杂性是研究人类基因组结构及其表达的理想材料,而且珠蛋白基因突变的多样性导致的血红蛋白合成异常（如各种类型的地中海贫血综合征和异常血红蛋白病）也是研究基因的结构和功能关系的典型模型。     

A Global View of 54,001 Single Nucleotide Polymorphisms (SNPs) on the Illumina BovineSNP50 BeadChip and Their Transferability to Water Buffalo

The Illumina BovineSNP50 BeadChip features 54,001 informative single nucleotide polymorphisms (SNPs) that uniformly span the entire bovine genome. Among them, 52,255 SNPs have locations assigned in the current genome assembly (Btau_4.0), including 19,294 (37%) intragenic SNPs (i.e., located within genes) and 32,961 (63%) intergenic SNPs (i.e., located between genes). While the SNPs represented on the Illumina Bovine50K BeadChip are evenly distributed along each bovine chromosome, there are over 14,000 genes that have no SNPs placed on the current BeadChip. Kernel density estimation, a non-parametric method, was used in the present study to identify SNP-poor and SNP-rich regions on each bovine chromosome. With bandwidth = 0.05 Mb, we observed that most regions have SNP densities within 2 standard deviations of the chromosome SNP density mean. The SNP density on chromosome X was the most dynamic, with more than 30 SNP-rich regions and at least 20 regions with no SNPs. Genotyping ten water buffalo using the Illumina BovineSNP50 BeadChip revealed that 41,870 of the 54,001 SNPs are fully scored on all ten water buffalo, but 6,771 SNPs are partially scored on one to nine animals. Both fully scored and partially/no scored SNPs are clearly clustered with various sizes on each chromosome. However, among 43,687 bovine SNPs that were successfully genotyped on nine and ten water buffalo, only 1,159 were polymorphic in the species. These results indicate that the SNPs sites, but not the polymorphisms, are conserved between two species. Overall, our present study provides a solid foundation to further characterize the SNP evolutionary process, thus improving understanding of within- and between-species biodiversity, phylogenetics and adaption to environmental changes.     

A global view of 54,001 single nucleotide polymorphisms (SNPs) on the Illumina BovineSNP50 BeadChip and their transferability to water buffalo

The Illumina BovineSNP50 BeadChip features 54,001 informative single nucleotide polymorphisms (SNPs) that uniformly span the entire bovine genome. Among them, 52,255 SNPs have locations assigned in the current genome assembly (Btau_4.0), including 19,294 (37%) intragenic SNPs (i.e., located within genes) and 32,961 (63%) intergenic SNPs (i.e., located between genes). While the SNPs represented on the Illumina Bovine50K BeadChip are evenly distributed along each bovine chromosome, there are over 14,000 genes that have no SNPs placed on the current BeadChip. Kernel density estimation, a non-parametric method, was used in the present study to identify SNP-poor and SNP-rich regions on each bovine chromosome. With bandwidth = 0.05 Mb, we observed that most regions have SNP densities within 2 standard deviations of the chromosome SNP density mean. The SNP density on chromosome X was the most dynamic, with more than 30 SNP-rich regions and at least 20 regions with no SNPs. Genotyping ten water buffalo using the Illumina BovineSNP50 BeadChip revealed that 41,870 of the 54,001 SNPs are fully scored on all ten water buffalo, but 6,771 SNPs are partially scored on one to nine animals. Both fully scored and partially/no scored SNPs are clearly clustered with various sizes on each chromosome. However, among 43,687 bovine SNPs that were successfully genotyped on nine and ten water buffalo, only 1,159 were polymorphic in the species. These results indicate that the SNPs sites, but not the polymorphisms, are conserved between two species. Overall, our present study provides a solid foundation to further characterize the SNP evolutionary process, thus improving understanding of within- and between-species biodiversity, phylogenetics and adaption to environmental changes.     

Biodiversity of 20 chicken breeds assessed by SNPs located in gene regions

Twenty-five single nucleotide polymorphisms (SNPs) were analyzed in 20 distinct chicken breeds. The SNPs, each located in a different gene and mostly on different chromosomes, were chosen to examine the use of SNPs in or close to genes (g-SNPs), for biodiversity studies. Phylogenetic trees were constructed from these data. When bootstrap values were used as a criterion for the tree repeatability, doubling the number of SNPs from 12 to 25 improved tree repeatability more than doubling the number of individuals per population, from five to ten. Clustering results of these 20 populations, based on the software STRUCTURE, are in agreement with those previously obtained from the analysis of microsatellites. When the number of clusters was similar to the number of populations, affiliation of birds to their original populations was correct (>95%) only when at least the 22 most polymorphic SNP loci (out of 25) were included. When ten populations were clustered into five groups based on STRUCTURE, we used membership coefficient (Q) of the major cluster at each population as an indicator for clustering success level. This value was used to compare between three marker types; microsatellites, SNPs in or close to genes (g-SNPs) and SNPs in random fragments (r-SNPs). In this comparison, the same individuals were used (five to ten birds per population) and the same number of loci (14) used for each of the marker types. The average membership coefficients (Q) of the major cluster for microsatellites, g-SNPs and r-SNPs were 0.85, 0.7, and 0.64, respectively. Analysis based on microsatellites resulted in significantly higher clustering success due to their multi-allelic nature. Nevertheless, SNPs have obvious advantages, and are an efficient and cost-effective genetic tool, providing broader genome coverage and reliable estimates of genetic relatedness.     

Sequence context analysis of 8.2 million single nucleotide polymorphisms in the human genome

We analyzed n-mers (n=3-8) in the local environment of 8,249,446 human SNPs and compared their distribution with that in the genome reference sequences. The results revealed that the short sequences, which contained at least one CpG dinucleotide, occurred more frequently in the local SNP sequences than in the genome sequences. To exclude the hypermutability effect of the methylated CpG dinucleotides on the sequence context of SNPs, we examined the distribution patterns for each of the six categories of substitution. We observed the similar pattern (i.e., CpG-containing n-mers vs. non-CpG-containing n-mers) in SNP categories A/G, C/T and C/G but the opposite pattern in category A/T. We next identified 34,928 putative CpG islands in the human genome and located 133,591 SNPs within these islands. In the CpG islands, CpG SNPs were 3.92-fold less prevalent relative to the presence of CpG dinucleotides. Conversely, in the human genome, the frequency of CpG dinucleotides at the polymorphic sites was 6.09 times that in the genome reference sequences. These results support the previous views of mutational suppression at the CpG sites in the CpG islands and hypermutability of the methylated CpG dinucleotides that are prevalent in the non-CpG island sequences in the human genome. Our study represents a comprehensive investigation of the sequence context of SNPs in the human genome and in human CpG islands.     

A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications

Over the years, ad-hoc procedures were used for designing SNP arrays, but the procedures and strategies varied considerably case by case. Recently, a multiple-objective, local optimization (MOLO) algorithm was proposed to select SNPs for SNP arrays, which maximizes the adjusted SNP information (E score) under multiple constraints, e.g. on MAF, uniformness of SNP locations (U score), the inclusion of obligatory SNPs and the number and size of gaps. In the MOLO, each chromosome is split into equally spaced segments and local optima are selected as the SNPs having the highest adjusted E score within each segment, conditional on the presence of obligatory SNPs. The computation of the adjusted E score, however, is empirical, and it does not scale well between the uniformness of SNP locations and SNP informativeness. In addition, the MOLO objective function does not accommodate the selection of uniformly distributed SNPs. In the present study, we proposed a unified local function for optimally selecting SNPs, as an amendment to the MOLO algorithm. This new local function takes scalable weights between the uniformness and informativeness of SNPs, which allows the selection of SNPs under varied scenarios. The results showed that the weighting between the U and the E scores led to a higher imputation concordance rate than the U score or E score alone. The results from the evaluation of six commercial bovine SNP chips further confirmed this conclusion.     

Shrunken methodology to genome-wide SNPs selection and construction of SNPs networks

Background

Recent development of high-resolution single nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. There is increasing recognition of the importance of SNPs for medicine and developmental biology. However, SNP data set typically has a large number of SNPs (e.g., 400 thousand SNPs in genome-wide Parkinson disease data set) and a few hundred of samples. Conventional classification methods may not be effective when applied to such genome-wide SNP data.

Results

In this paper, we use shrunken dissimilarity measure to analyze and select relevant SNPs for classification problems. Examples of HapMap data and Parkinson disease (PD) data are given to demonstrate the effectiveness of the proposed method, and illustrate it has a potential to become a useful analysis tool for SNP data sets. We use Parkinson disease data as an example, and perform a whole genome analysis. For the 367440 SNPs with less than 1% missing percentage from all 22 chromosomes, we can select 357 SNPs from this data set. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and SNPs with significance of P < 0.01 are chosen to identify SNPs networks based on their P values. Here SNPs networks are constructed based on Gene Ontology knowledge, and therefore each SNP network plays a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to Parkinson disease.

Conclusions

Experimental results show that our approach is suitable to handle genetic variations, and provide useful knowledge in a genome-wide SNP study.

     

结合基因功能分类体系Gene Ontology筛选聚类特征基因

使用两套基因表达谱数据,按各基因的表达值方差,选择表达变异基因对样本聚类,发现一般使用方差较大的前10％的基因作为特征基因,就可以较好地对疾病样本聚类。对不同的疾病,包含聚类信息的特征基因有不同的分布特点。在此基础上,结合基因功能分类体系(Gene Ontology,GO),进一步筛选聚类的特征基因。通过检验在Gene Ontology中的每个功能类中的表达变异基因是否非随机地聚集,寻找疾病相关功能类,再根据相关功能类中的表达变异基因进行聚类分析。实验结果显示：结合基因功能体系进一步筛选表达变异基因作为聚类特征基因,可以保持或提高聚类准确性,并使得聚类结果具有明确的生物学意义。另外,发现了一些可能和淋巴瘤和白血病相关的基因。     

Genomic Selection Using Low-Density Marker Panels

Genomic selection (GS) using high-density single-nucleotide polymorphisms (SNPs) is promising to improve response to selection in populations that are under artificial selection. High-density SNP genotyping of all selection candidates each generation, however, may not be cost effective. Smaller panels with SNPs that show strong associations with phenotype can be used, but this may require separate SNPs for each trait and each population. As an alternative, we propose to use a panel of evenly spaced low-density SNPs across the genome to estimate genome-assisted breeding values of selection candidates in pedigreed populations. The principle of this approach is to utilize cosegregation information from low-density SNPs to track effects of high-density SNP alleles within families. Simulations were used to analyze the loss of accuracy of estimated breeding values from using evenly spaced and selected SNP panels compared to using all high-density SNPs in a Bayesian analysis. Forward stepwise selection and a Bayesian approach were used to select SNPs. Loss of accuracy was nearly independent of the number of simulated quantitative trait loci (QTL) with evenly spaced SNPs, but increased with number of QTL for the selected SNP panels. Loss of accuracy with evenly spaced SNPs increased steadily over generations but was constant when the smaller number individuals that are selected for breeding each generation were also genotyped using the high-density SNP panel. With equal numbers of low-density SNPs, panels with SNPs selected on the basis of the Bayesian approach had the smallest loss in accuracy for a single trait, but a panel with evenly spaced SNPs at 10 cM was only slightly worse, whereas a panel with SNPs selected by forward stepwise selection was inferior. Panels with evenly spaced SNPs can, however, be used across traits and populations and their performance is independent of the number of QTL affecting the trait and of the methods used to estimate effects in the training data and are, therefore, preferred for broad applications in pedigreed populations under artificial selection.     

‘Satellite DNA transcripts have diverse biological roles in Drosophila'

Recent years have seen considerable progress in applying single nucleotide polymorphisms (SNPs) to population genetics studies. However, relatively few have attempted to use them to study the genetic differentiation of wild bird populations and none have examined possible differences of exonic and intronic SNPs in these studies. Here, using 144 SNPs, we examined population genetic differentiation in the saker falcon (Falco cherrug) across Eurasia. The position of each SNP was verified using the recently sequenced saker genome with 108 SNPs positioned within the introns of 10 fragments and 36 SNPs in the exons of six genes, comprising MHC, MC1R and four others. In contrast to intronic SNPs, both Bayesian clustering and principal component analyses using exonic SNPs consistently revealed two genetic clusters, within which the least admixed individuals were found in Europe/central Asia and Qinghai (China), respectively. Pairwise D analysis for exonic SNPs showed that the two populations were significantly differentiated and between the two clusters the frequencies of five SNP markers were inferred to be influenced by selection. Central Eurasian populations clustered in as intermediate between the two main groups, consistent with their geographic position. But the westernmost populations of central Europe showed evidence of demographic isolation. Our work highlights the importance of functional exonic SNPs for studying population genetic pattern in a widespread avian species.