遗传变异的又一来源：拷贝数变异

人们很早就发现DNA拷贝数变异与特定染色体重组和基因组异常相关这一现象,但最近才知道它与疾病的相关联系。我们对拷贝数变异的原理、最新研究方法,及其与复杂疾病的相关性研究等进展进行了综述;总结了拷贝数变异研究所存在的问题;对拷贝数变异未来的研究重点和需要解决的问题进行了展望。     

基因结构变异检测方法综述

在人类基因组中结构变异(SVs),拷贝数变化(CNVs),单核苷酸多态性(SNP)是非常普遍的,而且和人类健康与疾病密切相关,因此检测这些结构变异对于人类生命健康非常重要。基于第二代基因测序平台,目前已经有很多结构变异检测算法,这些算法主要分为五大类:微阵列方法、读对方法、读深方法、分裂读取方法、序列组装方法。本文系统地阐述了这五类方法的基本原理、优缺点以及使用范围,并简要介绍了每一种方法的经典检测算法及应用范围、检测性能等,并对未来检测算法的研究提出了展望。     

构建基于荧光多重cPCR的小鼠基因拷贝数检测方法

目的建立基于竞争性聚合酶链式反应(competitive polymerase chain reaction,cPCR)小鼠基因拷贝数变异(copy number variations,CNVs)的检测方法,用于检测野生小家鼠来源一号染色体替换系群体(population of specific chromosome 1 substitution strains,PCSSs)的CNVs。方法选取小鼠一号染色体上11个CNVs位点,及7、9和X染色体上各1个内对照位点,分别构建克隆质粒为竞争性粒模板,应用cPCR技术,建立荧光通用引物多重cPCR检测方法。结果多重cPCR方案适用于小鼠一号染色体上11个CNV位点的拷贝数检测,且能准确检测X染色体的拷贝数。结论实现小鼠快速、高通量的CNVs检测,可准确检测小鼠1号染色体中11个CNV位点的拷贝数变异。     

家畜全基因组分析中稀有变异上位效应检测方法

下一代测序技术的出现和检测费用不断下降清除了稀有变异检测的技术障碍,可以检测出包括常见变异和稀有变异在内的数以千万的遗传变异,其中绝大部分的变异都是稀有变异。这种情况对统计分析方法和结果解释提出了新的挑战。当前最为流行的全基因组分析方法主要针对常见变异间上位效应的检测问题,家畜育种中较少涉及稀有变异间、稀有变异-常见变异上位效应的研究。本综述探讨使用贝叶斯多元回归方法将上位效应检测单位由成对SNP扩展到基因组窗口间的检测,整合基因组信息进一步缩减数据集维度,并使用基因组窗口后验关联概率控制假阳性比例。这种新的研究策略无疑具有以下两个优良特性:1)这种方法将基因组窗口中所有SNP作为一个整体,可以利用该区域内的所有信息检测上位效应;2)该方法可以大幅度减少多重检测数量。其次,中国畜牧企业表型数据丰富,缺乏基因组测序数据,本研究借鉴单步基因组预测原理,设计检测包括上位效应在内的"穷"GWAS方法。     

高通量测序数据的基因组拷贝数变异检测方法综述

拷贝数变异是指基因组中发生大片段的DNA序列的拷贝数增加或者减少。根据现有的研究可知,拷贝数变异是多种人类疾病的成因,与其发生与发展机制密切相关。高通量测序技术的出现为拷贝数变异检测提供了技术支持,在人类疾病研究、临床诊疗等领域,高通量测序技术已经成为主流的拷贝数变异检测技术。虽然不断有新的基于高通量测序技术的算法和软件被人们开发出来,但是准确率仍然不理想。本文全面地综述基于高通量测序数据的拷贝数变异检测方法,包括基于reads深度的方法、基于双末端映射的方法、基于拆分read的方法、基于从头拼接的方法以及基于上述4种方法的组合方法,深入探讨了每类不同方法的原理,代表性的软件工具以及每类方法适用的数据以及优缺点等,并展望未来的发展方向。     

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。     

染色体粉碎——基因组灾难性事件产物

"染色体粉碎"是最初在肿瘤细胞中发现的一种复杂的基因组重排现象.在该事件中,细胞的一条或几条染色体在短时间内发生大量的DNA双链断裂,形成小的DNA碎片,之后这些碎片被细胞的DNA修复机制随机地拼接起来,形成新的染色体.染色体粉碎事件会造成大量的基因组重排,引起DNA拷贝数的变异和基因融合,从而导致正常细胞向肿瘤细胞的快速转化.这与传统的癌症发生理论不同,传统理论认为肿瘤的发生是由基因突变逐步积累而导致的,因此染色体粉碎现象可能揭示了一种肿瘤发生的新机制.目前,该现象的内在机制还不完全清楚,其判别标准也存在争议.本文综述了近年来关于染色体粉碎现象的判别标准和产生机制,探讨了该现象与肿瘤发生发展的关系,为进一步研究染色体粉碎事件提供参考.     

植物离体培养中染色体的变异

随着植物细胞和组织培养的迅速发展,不断发现植物离体培养细胞中的染色体数目和组型与供体植物不同。这些变化包括染色体数目变异、结构变异和有丝分裂异常,与长期培养的动物细胞、动植物肿瘤细胞中染色体的各种变异类似。这意味着植物细胞和组织     

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。     

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。     

Quantification of Normal Cell Fraction and Copy Number Neutral LOH in Clinical Lung Cancer Samples Using SNP Array Data

Background

Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Since tumor tissue invariably contains genetically normal stromal cells, this may lead to a failure to detect aberrations in the tumor cells.

Principal Finding

Using SNP array data from 44 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples. The proportion of normal cells in combination with SNP array data can be used to detect and quantify copy number neutral loss-of-heterozygosity (CNNLOH) in the tumor cells both in crude tumor tissue and in samples enriched for tumor cells by laser capture microdissection.

Conclusion

Genome-wide quantitative analysis of CNNLOH using the CNNLOH Quantifier method can help to identify recurrent aberrations contributing to tumor development in clinical tumor samples. In addition, SNP-array based analysis of CNNLOH may become important for detection of aberrations that can be used for diagnostic and prognostic purposes.     

Single-nucleotide polymorphism array coupled with multiple displacement amplification: accuracy and spatial resolution for analysis of chromosome copy numbers in few cells

When coupled with multiple displacement amplification (MDA), microarray-based comparative genomic intensity allows detection of chromosome copy number aberrations even in single or few cells, but the actual performance of the system and their influencing factors have not been well defined. Here, using single-nucleotide polymorphism (SNP) array, we analyzed copy number profiles from DNA amplified by MDA in 1-10 cells and estimated the accuracy and spatial resolution of the analysis. Based on the concordance of SNP copy numbers for DNA with and without MDA, the accuracy of the system can be significantly enhanced by using MDA-amplified DNA as reference and also by increasing the cell numbers. Analyses under different smoothing treatments revealed a practical resolution of 2?Mb for 10 cells and 10?Mb for a single cell. When both cells with known chromosomal duplication and deletion were analyzed, this platform detected a copy number "loss" more accurately than a "gain" (P < 0.01), particularly in single-cell MDA products. Together, we demonstrated that SNP array coupled with MDA is reliable and efficient for detection of copy number aberrations in a small number of cells, and its accuracy and resolution can both be significantly enhanced with increasing the number of cells as MDA template.     

Allele-specific copy number analysis of tumor samples with aneuploidy and tumor heterogeneity

We describe a bioinformatic tool, Tumor Aberration Prediction Suite (TAPS), for the identification of allele-specific copy numbers in tumor samples using data from Affymetrix SNP arrays. It includes detailed visualization of genomic segment characteristics and iterative pattern recognition for copy number identification, and does not require patient-matched normal samples. TAPS can be used to identify chromosomal aberrations with high sensitivity even when the proportion of tumor cells is as low as 30%. Analysis of cancer samples indicates that TAPS is well suited to investigate samples with aneuploidy and tumor heterogeneity, which is commonly found in many types of solid tumors.     

Allele-specific copy number analysis of tumor samples with aneuploidy and tumor heterogeneity

We describe a bioinformatic tool, Tumor Aberration Prediction Suite (TAPS), for the identification of allele-specific copy numbers in tumor samples using data from Affymetrix SNP arrays. It includes detailed visualization of genomic segment characteristics and iterative pattern recognition for copy number identification, and does not require patient-matched normal samples. TAPS can be used to identify chromosomal aberrations with high sensitivity even when the proportion of tumor cells is as low as 30%. Analysis of cancer samples indicates that TAPS is well suited to investigate samples with aneuploidy and tumor heterogeneity, which is commonly found in many types of solid tumors.     

Genome-Wide Identification of Somatic Aberrations from Paired Normal-Tumor Samples

Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.     

TAFFYS: An Integrated Tool for Comprehensive Analysis of Genomic Aberrations in Tumor Samples

Background

Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification.

Results

We propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate.

Conclusions

TAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data.     

Microarray analysis of copy number variation in single cells

We present a protocol for reliably detecting DNA copy number aberrations in a single human cell. Multiple displacement-amplified DNAs of a cell are hybridized to a 3,000-bacterial artificial chromosome (BAC) array and to an Affymetrix 250,000 (250K)-SNP array. Subsequent copy number calling is based on the integration of BAC probe-specific copy number probabilities that are estimated by comparing probe intensities with a single-cell whole-genome amplification (WGA) reference model for diploid chromosomes, as well as SNP copy number and loss-of-heterozygosity states estimated by hidden Markov models (HMM). All methods for detecting DNA copy number aberrations in single human cells have difficulty in confidently discriminating WGA artifacts from true genetic variants. Furthermore, some methods lack thorough validation for segmental DNA imbalance detection. Our protocol minimizes false-positive variant calling and enables uniparental isodisomy detection in single cells. Additionally, it provides quality assessment, allowing the exclusion of uninterpretable single-cell WGA samples. The protocol takes 5-7 d.     

High resolution array in the clinical approach to chromosomal phenotypes

Array genomic hybridization (AGH) has recently been implemented as a diagnostic tool for the detection of submicroscopic copy number variants (CNVs) in patients with developmental disorders. However, there is no consensus regarding the choice of the platform, the minimal resolution needed and systematic interpretation of CNVs. We report our experience in the clinical diagnostic use of high resolution AGH up to 100 kb on 131 patients with chromosomal phenotypes but previously normal karyotype. We evaluated the usefulness in our clinics and laboratories by the detection rate of causal CNVs and CNVs of unknown clinical significance and to what extent their interpretation would challenge the systematic use of high-resolution arrays in clinical application. Prioritizing phenotype-genotype correlation in our interpretation strategy to criteria previously described, we identified 33 (25.2%) potentially pathogenic aberrations. 16 aberrations were confirmed pathogenic (16.4% syndromic, 8.5% non-syndromic patients); 9 were new and individual aberrations, 3 of them were pathogenic although inherited and one is as small as approx 200 kb. 13 of 16 further CNVs of unknown significance were classified likely benign, for 3 the significance remained unclear. High resolution array allows the detection of up to 12.2% of pathogenic aberrations in a diagnostic clinical setting. Although the majority of aberrations are larger, the detection of small causal aberrations may be relevant for family counseling. The number of remaining unclear CNVs is limited. Careful phenotype-genotype correlations of the individual CNVs and clinical features are challenging but remain a hallmark for CNV interpretation.     

THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.