中国人群参考基因组及基因组变异图谱资源库

随着人类基因组计划和国际千人基因组计划的实施,已公开数百个中国人个体的全基因组数据。建立高精度的中国人群参考基因组序列,发现并解析中国人群特有的序列变异,是我国未来精准医学研究的基础。为满足未来精准医学研究中国人基因组数据持续增长的科学管理和深入研究的需求,中国科学院北京基因组研究所发展并建立了基于中国人群全基因组测序数据的虚拟中国人基因组数据库(Virtual Chinese Genome Database, VCGDB)和中国人群基因组变异数据库(Genome Variation Map, GVM),面向国内外用户提供数据检索、共享、下载和在线分析服务。本文重点介绍了这两个数据库的特点和功能,以及未来发展与应用前景,以期为中国人群参考基因组及基因组变异图谱资源库的推广使用、发展完善提供有益信息。     

拷贝数变异: 基因组多样性的新形式

基因拷贝数变异是指DNA片段大小范围从kb到Mb的亚微观突变, 是一可能具有致病性、良性或未知临床意义的基因组改变。Fosmid末端配对序列比较策略、比较基因组杂交芯片是当前较多使用的检测手段。染色体非等位的同源重排、非同源突变和非b DNA结构是造成基因组拷贝数变异的重要原因。拷贝数变异可导致不同程度的基因表达差异, 对正常表型的构成及疾病的发生发展具有一定作用。文章在总结基因拷贝数变异的认识过程和研究策略的基础上, 分析了拷贝数变异的形成和作用机制, 介绍了第一代人类基因组拷贝数变异图谱, 阐述了拷贝数变异研究的临床意义, 提示在探索疾病相关的遗传变异时不能错失拷贝数变异这一基因组多样性的新形式。     

人类基因组结构变异检测方法

本研究介绍了基因组结构变异检测的生物信息学基本方法和前沿技术。对基于第二代测序技术的四种检测方法(读对方法,读深方法,分裂片段方法和序列拼接方法)的原理和特点进行了详细解读,分析了第二代测序技术应用在检测结构变异上的特点与发展趋势。最后介绍了三代测序、Linked-reads和光学物理图谱等新技术在基因组结构变异检测中的应用,论述了融合新技术的结构变异检测方法的特点与优势。     

人类变异组计划及其进展

基因组学构建了人类的基因组图谱,后基因组时代的主要任务是解释基因组如何影响生命活动,由此产生了各种新类型的组学:结构基因组学,功能基因组学,蛋白质组学,代谢组学等。人类基因组突变学会于2006年6月在澳大利亚的墨尔本会议上正式启动了人类变异组计划。该计划旨在全球范围内广泛收集所有基因和蛋白质序列变异和多态性的数据,采用全基因组级别的基因型与表型关联等方法,系统地搜索并确定与人类疾病相关的变异,以指导临床应用。鉴于该计划对人类健康领域将产生的潜在影响,文章较为全面地介绍了该计划的起源和主要内容,并对其意义和前景进行了讨论。     

基于Hi-C技术识别基因组结构变异及其在肿瘤研究中的应用

基因组结构变异是多种肿瘤发生的重要驱动因素.虽然目前有基于核型分析、PCR免疫荧光和芯片杂交以及高通量测序等技术可用于基因组结构变异的检测,但由于技术的局限性,现今仍缺乏被广泛认可的基因组结构变异检测方法和相应的分析工具.在肿瘤样本中检测基因组结构变异更是面临严峻的挑战.近20年来,染色体构象捕获技术及其衍生的高通量技术Hi-C等,已经为三维基因组结构的解析提供了大量的组学数据.基因组结构变异通常引起三维基因组空间图谱的异常,通过Hi-C图谱的异常来检测结构变异成为一个新的研究方向.基于Hi-C技术的检测方法有其独特的优势,如可以比较准确地检测位于基因组上重复序列区域的结构变异,但也存在一定的局限性,如不能检测小的结构变异等.本文系统回顾了基因组结构变异的主要研究方法、工具及相应的原理等,并重点讨论了运用Hi-C技术检测结构变异的基本原理、技术优势和局限性,最后介绍了该技术在肿瘤研究中的实际应用.     

人类基因组结构变异检测研究进展

高通量、高分辨率基因组学技术的出现推动了人类基因组中长度在1kb～3Mb的亚显微水平结构变异检测方法的发展,这些结构变异主要包括基因拷贝数变异、倒置、插入、缺失、重复及其他基因重排．而传统的细胞遗传学技术达不到如此高的分辨率．本文介绍了目前主要的基因组结构变异的检测技术,包括基于芯片的比较基因组杂交技术和代表性寡核苷酸芯片分析技术,基于PCR的多重扩增探针杂交技术和依赖于连接反应的多重探针扩增技术,配对末端图谱技术等．还比较和分析了各种方法的优劣势并提出了目前结构变异数据库存在的问题．最后讨论了这些变异对于人类表型多态性、疾病易感性、药物反应程度及群体遗传学的影响．     

离子束介导玉米DNA导入水稻引起遗传变异的RAPD分析

本实验采用４０ 个随机引物对低能离子束介导紫玉米全ＤＮＡ 转化水稻早籼２１３ 获得的８ 个玉米稻株系基因组ＤＮＡ 进行ＲＡＰＤ检测。其中３６ 个随机引物得到扩增图谱。统计分析图谱中的各类扩增带,其中变异株系与早籼２１３ 的相似率为９１．２—９５．５％ 。差异带占总带数的８．９—１７．７％ ,说明外源ＤＮＡ 导入受体细胞引起后代基因组ＤＮＡ 的显著变异,这些变异很可能是表型及生理性状变异得以稳定遗传的物质基础。     

利用两个测序水稻品种构建微卫星连锁图谱

利用已完成基因组测序的两个水稻品种日本晴和931l的数据库成功开发出水稻微卫星新标记,并利用由90个单株组成的日本晴×9311 F2作图群体,构建了一张包含152个SSR标记位点、覆盖基因组总长度2 455.7 cM的连锁图谱,有46个SSR新标记为自主开发,该图谱标记间的平均遗传距离为16.16 cM;并将未能在Temnykh等人(2001)构建的图谱上定位的微卫星标记RM345和RM494定位在第6染色体上.通过与Temnykh等人(2001)和兰涛等人(2003)所构建的图谱从作图群体的类型和大小、标记的类型和数量、标记在染色体上的线性排列顺序等几个方面进行比较,所绘制的图谱其标记在染色体线性排列上与Temnykh等人绘制的图谱具有很高的一致性,达93.81%.     

新型冠状病毒(SARS-CoV-2)SNV分型检测技术

新型冠状病毒肺炎(COVID-19)的全球大流行对整个人类社会造成了重大影响,人类面临着财政刺激、金融压力、债务重整等挑战。在特效治疗药物与方法出现之前,大规模的人群筛查隔离成为现在疫情治理的最有效方法。然而,这一次的新冠病毒(SARS-CoV-2)展示出了极高的遗传变异性,截至2022年3月31日统计突变率超过了2.3‰,迄今为止高传染性的新病毒株不断出现,被世界卫生组织正式警告的变异株就达到了7个。因此,在接下来的病毒防控与研究中,不但需要检测SARS-CoV-2,更需要精准、实用的单核苷酸变异(single nucleotide variation, SNV)基因分型技术,特别针对大规模人群筛查中,不仅需要获得SRAS-CoV-2的信息,还需要精准快速区分具有更高传染性与毒性的变异株感染。对病毒的感染和突变机制进行了简要介绍,并着重对现有主要的SARS-CoV-2 SNV分型技术进行了分类综述,希望为新型检测技术的开发提供参考。     

荧光原位杂交技术在植物多倍体起源与进化研究中的应用

多倍化是植物物种形成与多样化的重要原动力。研究植物特别是一些重要经济作物和园艺植物多倍体的起源与进化,不仅对于揭示多倍体形成过程中性状变异的分子机制具有重要意义,而且可为植物遗传资源的保护与利用提供理论和技术支持。作为连接基因组序列片段到染色体组的桥梁,荧光原位杂交技术长期被广泛用来研究多倍体形成与进化过程中相关特异基因或序列的表达定位、外源染色体检测和鉴定、基因组结构变异等科学问题。因此,在简单介绍荧光原位杂交技术发展历史和植物多倍体主要类型的基础上,主要总结了荧光原位杂交技术在植物多倍体起源与进化相关研究上的应用。     

Statistical significance of optical map alignments

The Optical Mapping System constructs ordered restriction maps spanning entire genomes through the assembly and analysis of large datasets comprising individually analyzed genomic DNA molecules. Such restriction maps uniquely reveal mammalian genome structure and variation, but also raise computational and statistical questions beyond those that have been solved in the analysis of smaller, microbial genomes. We address the problem of how to filter maps that align poorly to a reference genome. We obtain map-specific thresholds that control errors and improve iterative assembly. We also show how an optimal self-alignment score provides an accurate approximation to the probability of alignment, which is useful in applications seeking to identify structural genomic abnormalities.     

Highly accurate-single chromosomal complete genomes using IonTorrent and MinION sequencing of clinical pathogens

Oxford Nanopore MinION sequencing technology has been gaining immense importance in identification of pathogen and antimicrobial resistance, though with 10–15% error rate. Short read technologies generates high accurate genome but with multiple fragments of genome. This study proposes a novel workflow to reduce the indels resulted from MinION long read sequencing by overlaying short read sequences from IonTorrent in the clinical isolates. Best of both techniques were employed which generated highly accurate-single chromosomal microbial genomes with increase in completeness of genomes from 44.5%, 30% and 43% to 98.6%, 98.6% and 96.6% for P. aeruginosa, A. veronii and B. pertussis respectively. To the best of our knowledge, this is the first study to generate a hybrid of IonTorrent and MinION reads to obtain single chromosomal genomes. This would enable to precisely infer both structural arrangement of genes and SNP based analysis for phylogenetic information.     

Evidence That Inconsistent Gene Prediction Can Mislead Analysis of Dinoflagellate Genomes

Comparative algal genomics often relies on predicted genes from de novo assembled genomes. However, the artifacts introduced by different gene-prediction approaches, and their impact on comparative genomic analysis remain poorly understood. Here, using available genome data from six dinoflagellate species in the Symbiodiniaceae, we identified methodological biases in the published genes that were predicted using different approaches and putative contaminant sequences in the published genome assemblies. We developed and applied a comprehensive customized workflow to predict genes from these genomes. The observed variation among predicted genes resulting from our workflow agreed with current understanding of phylogenetic relationships among these taxa, whereas the variation among the previously published genes was largely biased by the distinct approaches used in each instance. Importantly, these biases affect the inference of homologous gene families and synteny among genomes, thus impacting biological interpretation of these data. Our results demonstrate that a consistent gene-prediction approach is critical for comparative analysis of dinoflagellate genomes.     

Repetitive DNA variation and pivotal-differential evolution of wild wheats.

Several polyploid species in the genus Triticum contain a U genome derived from the diploid T. umbellulatum. In these species, the U genome is considered to be unmodified from the diploid based on chromosome pairing analysis, and it is referred to as pivotal. The additional genome(s) are considered to be modified, and they are thus referred to as differential genomes. The M genome derived from the diploid T. comosum is found in many U genome polyploids. In this study, we cloned three repetitive DNA sequences found primarily in the U genome and two repetitive DNA sequences found primarily in the M genome. We used these to monitor variation for these sequences in a large set of species containing U and M genomes. Investigation of sympatric and allopatric accessions of polyploid species did not show repetitive DNA similarities among sympatric species. This result does not support the idea that the polyploid species are continually exchanging genetic information through introgression. However, it is also possible that repetitive DNA is not a suitable means of addressing the question of introgression. The U genomes of both diploid and polyploid U genome species were similar regarding hybridization patterns observed with U genome probes. Much more variation was found both among diploid T. comosum accessions and polyploids containing M genomes. The observed variation supports the cytogenetic evidence that the M genome is more variable than the U genome. It also raises the possibility that the differential nature of the M genome may be due to variation within the diploid T. comosum, as well as among polyploid M genome species and accessions.     

Phylogenetic analysis of C, M, N, and U genomes and their relationships with Triticum and other related genomes as revealed by LMW-GS genes at Glu-3 loci

Phylogenetic relationships between the C, U, N, and M genomes of Aegilops species and the genomes of common wheat and other related species were investigated by using three types of low-molecular-weight glutenin subunit (LMW-GS) genes at Glu-3 loci. A total of 20 LMW-GS genes from Aegilops and Triticum species were isolated, including 11 LMW-m type and 9 LMW-i type genes. Particularly, four LMW-m type and three LMW-i type subunits encoded by the genes on the C, N, and U genomes possessed an extra cysteine residue at conserved positions, which could provide useful information for understanding phylogenetic relationships among Aegilops and Triticum genomes. Phylogenetic trees constructed by using either LMW-i or the combination of LMW-m and LMW-s, as well as analysis of all the three types of LMW-GS genes together, demonstrated that the C and U genomes were closely related to the A genome, whereas the N and M genomes were closely related to the D genome. Our results support previous findings that the A genome was derived from Triticum uratu, the B genome was from Aegilops speltoides, and the D genome was from Aegilops tauschii. In addition, phylogenetic relationships among different genomes analysed in this study support the concept that Aegilops is not monophyletic.     

Intrapopulation genome size dynamics in Festuca pallens

Background and Aims

It is well known that genome size differs among species. However, information on the variation and dynamics of genome size in wild populations and on the early phase of genome size divergence between taxa is currently lacking. Genome size dynamics, heritability and phenotype effects are analysed here in a wild population of Festuca pallens (Poaceae).

Methods

Genome size was measured using flow cytometry with DAPI dye in 562 seedlings from 17 maternal plants varying in genome size. The repeatability of genome size measurements was verified at different seasons through the use of different standards and with propidium iodide dye; the range of variation observed was tested via analysis of double-peaks. Additionally, chromosome counts were made in selected seedlings.

Key Results and Conclusions

Analysis of double-peaks showed that genome size varied up to 1·188-fold within all 562 seedlings, 1·119-fold within the progeny of a single maternal plant and 1·117-fold in seedlings from grains of a single inflorescence. Generally, genome sizes of seedlings and their mothers were highly correlated. However, in maternal plants with both larger and smaller genomes, genome sizes of seedlings were shifted towards the population median. This was probably due to the frequency of available paternal genomes (pollen grains) in the population. There was a stabilizing selection on genome size during the development of seedlings into adults, which may be important for stabilizing genome size within species. Furthermore, a positive correlation was found between genome size and the development rate of seedlings. A larger genome may therefore provide a competitive advantage, perhaps explaining the higher proportion of plants with larger genomes in the population studied. The reason for the observed variation may be the recent induction of genome size variation, e.g. by activity of retrotransposons, which may be preserved in the long term by the segregation of homeologous chromosomes of different sizes during gametogenesis.Key words: Nuclear DNA content, intraspecific variation, genome size evolution, heritability, stabilizing selection, grasses, flow cytometry     

Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era

Identification of the polymorphisms controlling quantitative traits remains a challenge for plant geneticists. Multiparent advanced generation intercross (MAGIC) populations offer an alternative to traditional linkage or association mapping populations by increasing the precision of quantitative trait loci (QTL) mapping. Here, we present the first tomato MAGIC population and highlight its potential for the valorization of intraspecific variation, QTL mapping and causal polymorphism identification. The population was developed by crossing eight founder lines, selected to include a wide range of genetic diversity, whose genomes have been previously resequenced. We selected 1536 SNPs among the 4 million available to enhance haplotype prediction and recombination detection in the population. The linkage map obtained showed an 87% increase in recombination frequencies compared to biparental populations. The prediction of the haplotype origin was possible for 89% of the MAGIC line genomes, allowing QTL detection at the haplotype level. We grew the population in two greenhouse trials and detected QTLs for fruit weight. We mapped three stable QTLs and six specific of a location. Finally, we showed the potential of the MAGIC population when coupled with whole genome sequencing of founder lines to detect candidate SNPs underlying the QTLs. For a previously cloned QTL on chromosome 3, we used the predicted allelic effect of each founder and their genome sequences to select putative causal polymorphisms in the supporting interval. The number of candidate polymorphisms was reduced from 12 284 (in 800 genes) to 96 (in 54 genes), including the actual causal polymorphism. This population represents a new permanent resource for the tomato genetics community.     

Towards a comprehensive structural variation map of an individual human genome

Background

Several genomes have now been sequenced, with millions of genetic variants annotated. While significant progress has been made in mapping single nucleotide polymorphisms (SNPs) and small (<10 bp) insertion/deletions (indels), the annotation of larger structural variants has been less comprehensive. It is still unclear to what extent a typical genome differs from the reference assembly, and the analysis of the genomes sequenced to date have shown varying results for copy number variation (CNV) and inversions.

Results

We have combined computational re-analysis of existing whole genome sequence data with novel microarray-based analysis, and detect 12,178 structural variants covering 40.6 Mb that were not reported in the initial sequencing of the first published personal genome. We estimate a total non-SNP variation content of 48.8 Mb in a single genome. Our results indicate that this genome differs from the consensus reference sequence by approximately 1.2% when considering indels/CNVs, 0.1% by SNPs and approximately 0.3% by inversions. The structural variants impact 4,867 genes, and >24% of structural variants would not be imputed by SNP-association.

Conclusions

Our results indicate that a large number of structural variants have been unreported in the individual genomes published to date. This significant extent and complexity of structural variants, as well as the growing recognition of their medical relevance, necessitate they be actively studied in health-related analyses of personal genomes. The new catalogue of structural variants generated for this genome provides a crucial resource for future comparison studies.     

Fish mitochondrial genomics: sequence, inheritance and functional variation

Mitochondrial genomic research currently primarily focuses on the analysis and understanding of how mitochondrial mutations produce detrimental phenotypes in humans. Reasons for this focus on negative impacts include the large number of human diseases that are known to result from specific mitochondrial genomes, and the long held belief that mitochondria change only through the accumulation of mutations due to its clonal, maternal inheritance. Recent studies are beginning to challenge these preconceptions and have shown that mitochondrial genomes can have significant positive impacts. Although the number of studies using fishes as models in mitochondrial research is limited, many fish model species provide excellent opportunity for furthering the understanding of mitochondrial genomes, their interactions with the nuclear genome, the potential for understanding the mechanisms of how functional variation effects organisms and how selection for positive functional variation effects population variation.     

Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions

ABSTRACT: BACKGROUND: Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome. RESULTS: Here we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents. CONCLUSIONS: The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.