基因拷贝数变异与人类疾病

拷贝数变异(copy number variation,CNV)是人类遗传多样性的一类重要形式。在前期的研究中,人们通过寡核苷酸分型、比较基因组杂交以及测序等技术手段,在人类基因组中鉴定出了大量拷贝数变异位点。这些变异可能是由于基因组重组或复制过程中的差错而产生。CNV在人群中的覆盖率远远高于寡核苷酸多态性(single nucleotide polymorphism,SNP),它们可以通过多种机制改变基因的表达水平,如基因剂量效应、基因断裂-融合效应,以及远距调控效应,进而引起多种人类复杂疾病。认识基因组中的拷贝数变异对于我们更好地认识基因与疾病的关系、遗传-环境因素的相互作用,以及基因组变异与物种进化的关系具有重要的意义。     

拷贝数变异的全基因组关联分析

基因组拷贝数变异(copy number variations,CNVs)是指与基因组参考序列相比,基因组中≥1 kb的DNA片段插入、缺失和/或扩增,及其互相组合衍生出的复杂变异．由于其具有分布范围广、可遗传、相对稳定和高度异质性等特点,目前认为,CNVs是一种新的可以作为疾病易感标志的基因组DNA多态性,其变异引起的基因剂量改变可以导致表型改变．最近,一种基于CNVs的新的疾病易感基因鉴定策略——CNV全基因组关联分析开始出现,这一策略和传统的基于单核苷酸多态性的关联分析具有互补性,通过认识基因组结构变异可以认识复杂疾病的分子机制和遗传基础．     

基因拷贝数变异-导致精神分裂症和自闭症的潜在风险

<正>基因重排可以导致基因拷贝数变异(CNV),即1 kb以上的基因组大片段的拷贝数增加或减少。对于小部分精神分裂症或自闭症患者而言,由等位基因CNV所造成的认知障碍或成为致病的主要因素。近日,冰岛人类遗传学研究与分析公司deCODE Genetics的科学家对一组冰岛的CNV携带者展开了相关研究。他们发现CNV携带者存在类似精神分裂症患者的大脑认知异常现象,且具有患精神分裂症或自闭症的风险。认知测试的结果显示,CNV携带者的IQ与正常人无明显区别,但CNV     

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。     

CNV及其与癌症间的关系

拷贝数变异(copy number variation,CNV)是指基因组发生1 Kb 以上的DNA片段的增添、缺失或重排。癌症的早期诊断与治疗一直是本世纪亟待解决的难题。CNV的相关研究为人类健康和疾病的治疗提供了宝贵的见解。目前,CNV的研究引发了人们对疾病的新探索,尤其体现在与遗传物质息息相关的疾病(例如,癌症)的病因研究、临床诊断、新药研发和治疗。该文主要综述了CNV的研究方法、形成机制以及其与癌症间的联系,以期推动癌症相关研究的发展。     

猪13号染色体上拷贝数变异区内基因信息发掘及遗传规律分析

拷贝数变异(Copy number variation, CNV)是染色体上发生的一种微结构变异, 已引起越来越多研究者的关注。本课题组前期已获得猪13号染色体上的32个CNV区域(CNV region, CNVR), 为了发掘CNVR内的基因信息, 文章在线检索了上述CNVR内的基因并进行基因本体(Gene Ontology)分析。结果共发现236个基因, 其中有注释基因169个, 主要参与蛋白质水解、细胞粘附、大分子降解等生物过程。为了探索这些基因拷贝数变异的遗传规律, 文章选择RCAN1(Regulators of calcineurin 1)基因为候选基因, 利用QPCR方法在莱芜猪群中检测了该基因的拷贝数, 并分析了CNV在莱芜猪3个家系中的遗传规律。结果表明, RCAN1基因在莱芜猪群体中存在拷贝数的缺失、重复现象, 其拷贝数变异的遗传规律符合孟德尔遗传方式。     

基因组拷贝数变异研究进展

基因组结构变异分为两个层次:显微水平(microscopic)和亚显微水平(submicroscopic)。显微水平的基因组结构变异主要是指显微镜下可见的染色体畸变,包括整倍体或非整倍体、缺失、插入、倒位、易位、脆性位点等结构变异。亚显微水平的基因组结构变异是指DNA片段长度在1Kb-3Mb的基因组结构变异,包括缺失、插入、重复、重排、倒位、DNA拷贝数目变化(copy numbervariation,CNV),这些统称为CNV或者CNP(copy number polymorphisms,CNP)。对CNV的研究能够帮助研究者建立遗传检测假说,进而发现疾病易感基因,同时加深对表型变异的理解,为今后研究人类生物功能、进化、疾病奠定基础。本文主要从CNV的研究历史、分子机制、研究方法、研究意义等四个方面进行综述.。     

利用SNP进行遗传病致病基因搜寻的策略

SNP是一类基于单碱基变异引起的DNA多态性,被遗传学界称为第三代遗传标记。由于SNP的诸多优点,如位点丰富和与DNA芯片等技术上的结合,它将对人类致病基因的搜寻工作起到革命性的作用。本文综合了目前SNP领域的一些进展,对这一新的标记系统在人类遗传病研究中的应用策略进行了初步概括。     

全基因组测序及其在遗传性疾病研究及诊断中的应用

最近,随着测序成本的不断降低,数据分析策略的不断提升,全基因组测序（whole-genome sequencing,WGS）已经在癌症、孟德尔遗传病、复杂疾病的致病基因检测中得到了一定运用,并逐步走向了临床诊断。全基因组测序不但可以检测编码区和非编码区的点突变（SNVs）和插入缺失（InDels）,还可以在全基因组范围内检测拷贝数变异（copy number variation,CNV）以及结构变异（structure variation,SV）。本文详细地介绍了全基因组测序的标准生物信息分析流程与方法,及其在疾病研究、临床诊断中的应用,并对全基因组测序在医学遗传学中的应用与研究进展,以及数据分析方面面临的挑战进行了概述。     

家养动物基因组拷贝数变异研究进展及其育种应用展望

家养动物参考基因组组装的不断完善和群体重测序数据的持续增加促进了基因组中大量变异的发现。基因组上的变异主要包括单核苷酸变异(SNP)和拷贝数变异(CNV)两种类型。相对于数量众多,已经被广泛研究和用作分子育种标记SNP,目前已经被发现和经过实验验证其功能的CNV数量较少,鲜有被直接用作分子标记进行育种的报道。CNV片段长度大、在基因组中普遍存在且比SNP变异覆盖的基因组范围更广,所以可能对农艺性状造成很大影响,其在畜禽基因组研究和育种应用中具有广阔前景。重点讨论了家养动物CNV的研究进展,并对其在家养动物育种中的应用进行了分析展望。     

Prediction of a deletion copy number variant by a dense SNP panel

Background

A newly recognized type of genetic variation, Copy Number Variation (CNV), is detected in mammalian genomes, e.g. the cattle genome. This form of variation can potentially cause phenotypic variation. Our objective was to determine whether dense SNP (single nucleotide polymorphisms) panels can capture the genetic variation due to a simple bi-allelic CNV, with the prospect of including the effect of such structural variations into genomic predictions.

Methods

A deletion type CNV on bovine chromosome 6 was predicted from its neighboring SNP with a multiple regression model. Our dataset consisted of CNV genotypes of 1,682 cows, along with 100 surrounding SNP genotypes. A prediction model was fitted considering 10 to 100 surrounding SNP and the accuracy obtained directly from the model was confirmed by cross-validation.

Results and conclusions

The accuracy of prediction increased with an increasing number of SNP in the model and the predicted accuracies were similar to those obtained by cross-validation. A substantial increase in accuracy was observed when the number of SNP increased from 10 to 50 but thereafter the increase was smaller, reaching the highest accuracy (0.94) with 100 surrounding SNP. Thus, we conclude that the genotype of a deletion type CNV and its putative QTL effect can be predicted with a maximum accuracy of 0.94 from surrounding SNP. This high prediction accuracy suggests that genetic variation due to simple deletion CNV is well captured by dense SNP panels. Since genomic selection relies on the availability of a dense marker panel with markers in close linkage disequilibrium to the QTL in order to predict their genetic values, we also discuss opportunities for genomic selection to predict the effects of CNV by dense SNP panels, when CNV cause variation in quantitative traits.     

Germ-line DNA copy number variation frequencies in a large North American population

Genomic copy number variation (CNV) is a recently identified form of global genetic variation in the human genome. The Affymetrix GeneChip 100 and 500 K SNP genotyping platforms were used to perform a large-scale population-based study of CNV frequency. We constructed a genomic map of 578 CNV regions, covering approximately 220 Mb (7.3%) of the human genome, identifying 183 previously unknown intervals. Copy number changes were observed to occur infrequently (<1%) in the majority (>93%) of these genomic regions, but encompass hundreds of genes and disease loci. This North American population-based map will be a useful resource for future genetic studies. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Genome-wide mapping of copy number variation in humans: comparative analysis of high resolution array platforms

Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.     

Copy number variations identified in the chicken using a 60K SNP BeadChip

Copy number variation (CNV) is considered an important genetic variation, contributing to many economically important traits in the chicken. Although CNVs can be detected using a comparative genomic hybridization array, the high‐density SNP array has provided an alternative way to identify CNVs in the chicken. In the current study, a chicken 60K SNP BeadChip was used to identify CNVs in two distinct chicken genetic lines (White Leghorn and dwarf) using the penncnv program. A total of 209 CNV regions were identified, distributing on chromosomes 1–22 and 24–28 and encompassing 13.55 Mb (1.42%) of chicken autosomal genome area. Three of seven selected CNVs (73.2% individuals) were completely validated by quantitative PCR. To our knowledge, this is the first report in the chicken identifying CNVs using a SNP array. Identification of 190 new identified CNVs illustrates the feasibility of the chicken 60K SNP BeadChip to detect CNVs in the chicken, which lays a solid foundation for future analyses of associations of CNVs with economically important phenotypes in chickens.     

Genome-Wide Identification of Copy Number Variations in Chinese Holstein

Recent studies of mammalian genomes have uncovered the vast extent of copy number variations (CNVs) that contribute to phenotypic diversity. Compared to SNP, a CNV can cover a wider chromosome region, which may potentially incur substantial sequence changes and induce more significant effects on phenotypes. CNV has been becoming an alternative promising genetic marker in the field of genetic analyses. Here we firstly report an account of CNV regions in the cattle genome in Chinese Holstein population. The Illumina Bovine SNP50K Beadchips were used for screening 2047 Holstein individuals. Three different programes (PennCNV, cnvPartition and GADA) were implemented to detect potential CNVs. After a strict CNV calling pipeline, a total of 99 CNV regions were identified in cattle genome. These CNV regions cover 23.24 Mb in total with an average size of 151.69 Kb. 52 out of these CNV regions have frequencies of above 1%. 51 out of these CNV regions completely or partially overlap with 138 cattle genes, which are significantly enriched for specific biological functions, such as signaling pathway, sensory perception response and cellular processes. The results provide valuable information for constructing a more comprehensive CNV map in the cattle genome and offer an important resource for investigation of genome structure and genomic variation underlying traits of interest in cattle.     

Identification of SNP markers for common CNV regions and association analysis of risk of subarachnoid aneurysmal hemorrhage in Japanese population

Copy number variation (CNV) is emerging as a new tool for understanding human genomic variation, but its relationship with human disease is not yet fully understood. The data for a total of 317,503 genotypes were collected for a genome-wide association study of subarachnoid aneurismal hemorrhage (SAH) in a Japanese population (cases and controls, n = 497) using Illumina HumanHap300 BeadChip^®. To identify multi-allelic CNV markers, we visually inspected all genotype clusters of 317,503 SNP markers covering the whole genome using Illumina’s BeadStudio 3.0^® software. As a result, we identified 597 multi-allelic CNV markers for common (copy loss frequency > 0.05) CNV regions in a Japanese population (n = 497). The identified CNV markers shared the following characteristics: enrichment of Hardy–Weinberg disequilibria, Mendelian inconsistency among families, and high missing genotype rate. All annotated information for those markers is summarized in our database (http://www.snp-genetics.com/user/srch.htm). In addition, we performed case-control association analyses of identified multi-allelic CNV markers with the risk of subarachnoid aneurysmal hemorrhage. One SNP marker (rs1242541) within a CNV region neighboring the Sel-1 suppressor of lin-12-like protein (SEL1L) was significantly associated with a risk of SAH (P = 0.0006). We also validated the CNV around rs1242541 using real-time quantitative polymerase chain reaction (PCR). Information and methods used in this study would be helpful for accurate genotyping of SNPs on CNV regions, which could be used for association analysis of SNP markers within CNV regions.     

Large-scale Genotyping and Genetic Mapping in Plasmodium Parasites

The completion of many malaria parasite genomes provides great opportunities for genomewide characterization of gene expression and high-throughput genotyping. Substantial progress in malaria genomics and genotyping has been made recently, particularly the development of various microarray platforms for large-scale characterization of the Plasmodium falciparum genome. Microarray has been used for gene expression analysis, detection of single nucleotide polymorphism (SNP) and copy number variation (CNV), characterization of chromatin modifications, and other applications. Here we discuss some recent advances in genetic mapping and genomic studies of malaria parasites, focusing on the use of high-throughput arrays for the detection of SNP and CNV in the P. falciparum genome. Strategies for genetic mapping of malaria traits are also discussed.     

Genomic and genetic variability of six chicken populations using single nucleotide polymorphism and copy number variants as markers

Genomic and genetic variation among six Italian chicken native breeds (Livornese, Mericanel della Brianza, Milanino, Bionda Piemontese, Bianca di Saluzzo and Siciliana) were studied using single nucleotide polymorphism (SNP) and copy number variants (CNV) as markers. A total of 94 DNA samples genotyped with Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix) were used in the analyses. The results showed the genetic and genomic variability occurring among the six Italian chicken breeds. The genetic relationship among animals was established with a principal component analysis. The genetic diversity within breeds was calculated using heterozygosity values (expected and observed) and with Wright’s F-statistics. The individual-based CNV calling, based on log R ratio and B-allele frequency values, was done by the Hidden–Markov Model (HMM) of PennCNV software on autosomes. A hierarchical agglomerative clustering was applied in each population according to the absence or presence of definite CNV regions (CNV were grouped by overlapping of at least 1 bp). The CNV map was built on a total of 1003 CNV found in individual samples, after grouping by overlaps, resulting in 564 unique CNV regions (344 gains, 213 losses and 7 complex), for a total of 9.43 Mb of sequence and 1.03% of the chicken assembly autosome. All the approaches using SNP data showed that the Siciliana breed clearly differentiate from other populations, the Livornese breed separates into two distinct groups according to the feather colour (i.e. white and black) and the Bionda Piemontese and Bianca di Saluzzo breeds are closely related. The genetic variability found using SNP is comparable with that found by other authors in the same breeds using microsatellite markers. The CNV markers analysis clearly confirmed the SNP results.     

Diversity of copy number variation in a worldwide population of sheep

Copy number variation (CNV) represents a major source of genomic variation. We investigated the diversity of CNV distribution using SNP array data collected from a comprehensive collection of geographically dispersed sheep breeds. We identified 24,558 putative CNVs, which can be merged into 619 CNV regions, spanning 197 Mb of total length and corresponding to ~ 6.9% of the sheep genome. Our results reveal a population differentiation in CNV between different geographical areas, including Africa, America, Asia, Southwestern Asia, Central Europe, Northern Europe and Southwestern Europe. We observed clear distinctions in CNV prevalence between diverse groups, possibly reflecting the population history of different sheep breeds. We sought to determine the gene content of CNV, and found several important CNV-overlapping genes (BTG3, PTGS1 and PSPH) which were involved in fetal muscle development, prostaglandin (PG) synthesis, and bone color. Our study generates a comprehensive CNV map, which may contribute to genome annotation in sheep.     

Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms

Whole-genome microarrays with large-insert clones designed to determine DNA copy number often show variation in hybridization intensity that is related to the genomic position of the clones. We found these ‘genomic waves’ to be present in Illumina and Affymetrix SNP genotyping arrays, confirming that they are not platform-specific. The causes of genomic waves are not well-understood, and they may prevent accurate inference of copy number variations (CNVs). By measuring DNA concentration for 1444 samples and by genotyping the same sample multiple times with varying DNA quantity, we demonstrated that DNA quantity correlates with the magnitude of waves. We further showed that wavy signal patterns correlate best with GC content, among multiple genomic features considered. To measure the magnitude of waves, we proposed a GC-wave factor (GCWF) measure, which is a reliable predictor of DNA quantity (correlation coefficient = 0.994 based on samples with serial dilution). Finally, we developed a computational approach by fitting regression models with GC content included as a predictor variable, and we show that this approach improves the accuracy of CNV detection. With the wide application of whole-genome SNP genotyping techniques, our wave adjustment method will be important for taking full advantage of genotyped samples for CNV analysis.