复杂疾病的遗传易感基因区域的精细定位

以单核苷酸多态性(Single-nucleotide polymorphism, SNP)为遗传标记, 采用全基因组关联研究(Genome-wide association studies, GWAS)的策略, 已经在660多种疾病(或性状)中发现了3800多个遗传易感基因区域。但是, 其中最显著关联的遗传变异或致病性的遗传变异位点及其生物学功能并不完全清楚。这些位点的鉴定有助于阐明复杂疾病的生物学机制, 以及发现新的疾病标记物。后GWAS时代的主要任务之一就是通过精细定位研究找到复杂疾病易感基因区域内最显著关联的易感位点或致病性的易感位点并阐明其生物学功能。针对常见变异, 可通过推断或重测序增加SNP密度, 寻找最显著关联的SNP位点, 并通过功能元件分析、表达数量性状位点(Expression quantitative trait locus, eQTL)分析和单体型分析等方法寻找功能性的SNP位点和易感基因。针对罕见变异, 则可采用重测序、罕见单体型分析、家系分析和负荷检验等方法进行精细定位。文章对这些策略和所面临的问题进行了综述。     

全基因组关联研究的深度分析策略

2005年至今,全基因组关联研究(Genome-wide association study,GWAS)发现了大量复杂疾病/性状相关变异。近来,科学家们关注的焦点又集中在了如何利用GWAS数据进行深入分析,期待发现更多复杂疾病/性状的易感基因。一些新的策略和方法已经被尝试应用到复杂疾病/性状GWAS的后续研究中,例如深入分析GWAS数据;鉴定新的复杂疾病/性状易感基因/位点;国际合作和Meta分析;易感区域精细定位及测序;多种疾病共同易感基因研究;以及基因型填补,基于通路的关联分析,基因-基因、基因-环境交互作用和上位研究等。这些策略和方法的应用弥补了经典GWAS的一些不足之处,进一步推动了人类对复杂疾病/性状遗传机制的认识。文章对上述研究的策略、方法以及所面临的问题和挑战进行了综述,为读者描绘了GWAS后期工作的一个简要框架。     

冠心病全基因组关联研究进展

近年来全基因组关联研究在世界范围内发展迅猛,研究者应用全基因组关联研究策略发现了一系列疾病的相关基因或变异,将疾病的基因组研究推向一个新的阶段。冠心病是一种由环境因素和遗传因素共同作用导致的复杂疾病,且是世界范围内死亡和致残的首要原因之一,世界各地的研究者应用此策略发现了候选基因关联研究未曾发现的多个冠心病相关易感区域。文章对近年来世界范围内针对冠心病的全基因组关联研究取得的重要进展进行简要总结,然后就现阶段全基因组关联研究所面临的挑战以及对未来研究的发展趋势进行分析阐述,为进一步探究冠心病的遗传机制提供指导。     

基因拷贝数异常的研究进展

基因拷贝数异常(copy number variations,CNVs)是广泛存在于人体基因组的一种结构变异现象,主要包括拷贝数的缺失、插入、重组以及多位点的复杂变异等。最初是在病人的基因组中发现,后来的研究表明在正常人体中也普遍存在。有关CNVs的研究将随机个体之间的基因组差异估计值大大提高,极大的改变了人们的认识。目前,关于CNVs的研究多处在初步探索阶段,CNVs如何导致疾病,以及如何引起基因等的改变而诱发疾病的机理也需更进一步的研究加以验证和证实。该文主要就近年来关于CNVs的研究进展作一综述。     

基于单核苷酸多态性的基因互作分析方法学进展

基于单核苷酸多态性的关联分析已成为当前解析人类常见复杂疾病遗传机制的重要手段之一, 然而, 目前普遍使用的单位点分析策略仅能发现部分单独效应显著的易感SNP位点, 因此遗漏了重要的遗传力组分——基因上位效应或联合效应。识别全基因组多基因间复杂的互作关系已成为全面解析复杂疾病致病分子机制必不可少的一项任务。已有很多方法被应用于全基因组交互作用分析, 加深了人类对复杂疾病遗传机制的进一步认识。基于各类方法的理论基础及算法的异同, 文章对目前应用较为广泛的基于遗传互作模型的方法、不基于互作模型的方法和数据挖掘类算法3类方法进行了系统地评述, 着重介绍了这些方法的主要思想、实现过程及应用中的注意事项等, 并指出开展大规模全基因组范围互作检测面临的问题, 以期能为相关领域的研究者提供方法学参考。     

全基因组外显子测序及其应用

近年来,众多研究小组开展了大量的全基因组关联研究(Genome-wide association studies,GWAS),发现并鉴定了许多与复杂疾病/性状相关联的遗传变异,为复杂疾病发病机制的研究提供了重要线索。由于GWAS的结果存在假阳性、假阴性、检测到的单核苷酸多态性很少位于功能区以及对稀有变异和结构变异不敏感等问题,导致了其应用的局限性。而新一代测序技术的进步,促进了全基因组测序和全基因组外显子测序的快速发展,为解决上述问题提供了契机。全基因组外显子测序是利用序列捕获技术将全基因组外显子区域DNA捕捉并富集后进行高通量测序的基因组分析方法。由于其具有对常见和罕见变异高灵敏度,能发现外显子区绝大部分疾病相关变异以及仅需要对约1%的基因组进行测序等优点,促使全基因组外显子测序成为鉴定孟德尔疾病的致病基因最有效的策略,也被运用于复杂疾病易感基因的研究和临床诊断中。     

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。     

中国人群肝癌的易感基因研究

以单核苷酸多态性（single nucleotide polymorphism,SNP）为遗传标记的遗传关联研究是近年来鉴定复杂疾病易感基因的主要策略之一.尤其是新近发展成熟的全基因组关联研究（genome-wide association study,GWAS）,已被公认是行之有效的系统搜寻重大疾病易感基因的研究方法.军事医学科学院与国内同行合作开展的HBV相关肝癌GWAS结果表明,1p36.22的UBE4B-KIF1B-PGD区域是一个全新的肝癌易感基因区域,证明了遗传易感性在肝癌发生发展中的病因学意义.肝癌易感基因的发现,不仅为深入阐明肝癌的发生机制开辟了新的研究方向,而且为肝癌的风险预测和早期预警研究提供了理论依据;同时,也为后续开发新型的治疗药物奠定了基础.     

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…     

全基因组关联研究现状

在过去的5年中, 全基因组关联研究(Genome-wide association study, GWAS)方法已被证明是研究复杂疾病和性状遗传易感变异的一种有效手段。目前, 各国科学家在多种复杂疾病和性状中开展了大量的GWAS, 对肿瘤、糖尿病、心脏病、神经精神疾病、自身免疫及免疫相关疾病等复杂疾病以及一些常见性状(如身高、体重、血脂、色素等)的遗传易感基因研究取得了重大成果。截止到2010年9月11日, 运用GWAS开展了对近200种复杂疾病/性状的研究, 发现了3 000多个疾病相关的遗传变异。文章就GWAS的发展及其在复杂疾病/性状中的应用做一综述。     

Identification of copy number variation hotspots in human populations

Copy number variants (CNVs) in the human genome contribute to both Mendelian and complex traits as well as to genomic plasticity in evolution. The investigation of mutational rates of CNVs is critical to understanding genomic instability and the etiology of the copy number variation (CNV)-related traits. However, the evaluation of the CNV mutation rate at the genome level poses an insurmountable practical challenge that requires large samples and accurate typing. In this study, we show that an approximate estimation of the CNV mutation rate could be achieved by using the phylogeny information of flanking SNPs. This allows a genome-wide comparison of mutation rates between CNVs with the use of vast, readily available data of SNP genotyping. A total of 4187 CNV regions (CNVRs) previously identified in HapMap populations were investigated in this study. We showed that the mutation rates for the majority of these CNVRs are at the order of 10⁻⁵ per generation, consistent with experimental observations at individual loci. Notably, the mutation rates of 104 (2.5%) CNVRs were estimated at the order of 10⁻³ per generation; therefore, they were identified as potential hotspots. Additional analyses revealed that genome architecture at CNV loci has a potential role in inciting mutational hotspots in the human genome. Interestingly, 49 (47%) CNV hotspots include human genes, some of which are known to be functional CNV loci (e.g., CNVs of C4 and β-defensin causing autoimmune diseases and CNVs of HYDIN with implication in control of cerebral cortex size), implicating the important role of CNV in human health and evolution, especially in common and complex diseases.     

Clinical significance of germline copy number variation in susceptibility of human diseases

Germline copy number variation (CNV) is considered to be an important form of human genetic polymorphisms. Previous studies have identified amounts of CNVs in human genome by advanced technologies, such as comparative genomic hybridization, single nucleotide genotyping, and high-throughput sequencing. CNV is speculated to be derived from multiple mechanisms, such as nonallelic homologous recombination (NAHR) and nonhomologous end-joining (NHEJ). CNVs cover a much larger genome scale than single nucleotide polymorphisms (SNPs), and may alter gene expression levels by means of gene dosage, gene fusion, gene disruption, and long-range regulation effects, thus affecting individual phenotypes and playing crucial roles in human pathogenesis. The number of studies linking CNVs with common complex diseases has increased dramatically in recent years. Here, we provide a comprehensive review of the current understanding of germline CNVs, and summarize the association of germline CNVs with the susceptibility to a wide variety of human diseases that were identified in recent years. We also propose potential issues that should be addressed in future studies.     

Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability

A considerable and unanticipated plasticity of the human genome, manifested as inter-individual copy number variation, has been discovered. These structural changes constitute a major source of inter-individual genetic variation that could explain variable penetrance of inherited (Mendelian and polygenic) diseases and variation in the phenotypic expression of aneuploidies and sporadic traits, and might represent a major factor in the aetiology of complex, multifactorial traits. For these reasons, an effort should be made to discover all common and rare copy number variants (CNVs) in the human population. This will also enable systematic exploration of both SNPs and CNVs in association studies to identify the genomic contributors to the common disorders and complex traits.     

Copy number variants and common disorders: filling the gaps and exploring complexity in genome-wide association studies

Genome-wide association scans (GWASs) using single nucleotide polymorphisms (SNPs) have been completed successfully for several common disorders and have detected over 30 new associations. Considering the large sample sizes and genome-wide SNP coverage of the scans, one might have expected many of the common variants underpinning the genetic component of various disorders to have been identified by now. However, these studies have not evaluated the contribution of other forms of genetic variation, such as structural variation, mainly in the form of copy number variants (CNVs). Known CNVs account for over 15% of the assembled human genome sequence. Since CNVs are not easily tagged by SNPs, might have a wide range of copy number variability, and often fall in genomic regions not well covered by whole-genome arrays or not genotyped by the HapMap project, current GWASs have largely missed the contribution of CNVs to complex disorders. In fact, some CNVs have already been reported to show association with several complex disorders using candidate gene/region approaches, underpinning the importance of regions not investigated in current GWASs. This reveals the need for new generation arrays (some already in the market) and the use of tailored approaches to explore the full dimension of genome variability beyond the single nucleotide scale.     

Identifying Human Genome-Wide CNV,LOH and UPD by Targeted Sequencing of Selected Regions

Copy-number variations (CNV), loss of heterozygosity (LOH), and uniparental disomy (UPD) are large genomic aberrations leading to many common inherited diseases, cancers, and other complex diseases. An integrated tool to identify these aberrations is essential in understanding diseases and in designing clinical interventions. Previous discovery methods based on whole-genome sequencing (WGS) require very high depth of coverage on the whole genome scale, and are cost-wise inefficient. Another approach, whole exome genome sequencing (WEGS), is limited to discovering variations within exons. Thus, we are lacking efficient methods to detect genomic aberrations on the whole genome scale using next-generation sequencing technology. Here we present a method to identify genome-wide CNV, LOH and UPD for the human genome via selectively sequencing a small portion of genome termed Selected Target Regions (SeTRs). In our experiments, the SeTRs are covered by 99.73%~99.95% with sufficient depth. Our developed bioinformatics pipeline calls genome-wide CNVs with high confidence, revealing 8 credible events of LOH and 3 UPD events larger than 5M from 15 individual samples. We demonstrate that genome-wide CNV, LOH and UPD can be detected using a cost-effective SeTRs sequencing approach, and that LOH and UPD can be identified using just a sample grouping technique, without using a matched sample or familial information.     

A study of CNVs as trait-associated polymorphisms and as expression quantitative trait loci

We conducted a comprehensive study of copy number variants (CNVs) well-tagged by SNPs (r(2)≥ 0.8) by analyzing their effect on gene expression and their association with disease susceptibility and other complex human traits. We tested whether these CNVs were more likely to be functional than frequency-matched SNPs as trait-associated loci or as expression quantitative trait loci (eQTLs) influencing phenotype by altering gene regulation. Our study found that CNV-tagging SNPs are significantly enriched for cis eQTLs; furthermore, we observed that trait associations from the NHGRI catalog show an overrepresentation of SNPs tagging CNVs relative to frequency-matched SNPs. We found that these SNPs tagging CNVs are more likely to affect multiple expression traits than frequency-matched variants. Given these findings on the functional relevance of CNVs, we created an online resource of expression-associated CNVs (eCNVs) using the most comprehensive population-based map of CNVs to inform future studies of complex traits. Although previous studies of common CNVs that can be typed on existing platforms and/or interrogated by SNPs in genome-wide association studies concluded that such CNVs appear unlikely to have a major role in the genetic basis of several complex diseases examined, our findings indicate that it would be premature to dismiss the possibility that even common CNVs may contribute to complex phenotypes and at least some common diseases.     

What a difference copy number variation makes

DNA copy number variation (CNV) represents a considerable source of human genetic diversity. Recently,1 a global map of copy number variation in the human genome has been drawn up which reveals not only the ubiquity but also the complexity of this type of variation. Thus, two human genomes may differ by more than 20 Mb and it is likely that the full extent of CNV still remains to be discovered. Nearly 3000 genes are associated with CNV. This high degree of variability with regard to gene copy number between two individuals challenges definitions of normality. Many CNVs are located in regions of complex genomic structure and this currently limits the extent to which these variants can be genotyped by using tagging SNPs. However, some CNVs are already amenable to genome-wide association studies so that their influence on human phenotypic diversity and disease susceptibility may soon be determined.     

Copy number variants (CNVs) in primate species using array-based comparative genomic hybridization

A substantial amount of genomic variation is now known to exist in humans and other primate species. Single nucleotide polymorphisms (SNPs) are thought to represent the vast majority of genomic differences among individuals in a given primate species and comprise about 0.1% of the genomes of two humans. However, recent studies have now shown that structural variation msay account for as much as 0.7% of the genomic differences in humans, of which copy number variants (CNVs) are the largest component. CNVs are segments of DNA that can range in size from hundreds of bases to millions of base pairs in length and have different number of copies between individuals. Recent technological advancements in array technologies led to genome-wide identification of CNVs and consequently revealed thousands of variable loci in humans, comprising as much as 12% of the human genome [A.J. Iafrate, L. Feuk, M.N. Rivera, M.L. Listewnik, P.K. Donahoe, Y. Qi, S.W. Scherer, C. Lee, Nat. Genet. 36 (2004) 949–951, [3]]. CNVs in humans have already been associated with susceptibility to certain complex diseases, dietary adaptation, and several neurological conditions. In addition, recent studies have shown that CNVs can be successfully implemented in population genetics research, providing important insights into human genetic variation. Nevertheless, the important role of CNVs in primate evolution and genetic diversity is still largely unknown. This article aims to outline the strengths and weaknesses of current comparative genomic hybridization array technologies that have been employed to detect CNV variation and the applications of these techniques to primate genetic research.     

Identification of Genome-Wide Copy Number Variations among Diverse Pig Breeds Using SNP Genotyping Arrays

Copy number variations (CNVs) are important forms of genetic variation complementary to SNPs, and can be considered as promising markers for some phenotypic and economically important traits or diseases susceptibility in domestic animals. In the present study, we performed a genome-wide CNV identification in 14 individuals selected from diverse populations, including six types of Chinese indigenous breeds, one Asian wild boar population, as well as three modern commercial foreign breeds. We identified 63 CNVRs in total, which covered 9.98 Mb of polymorphic sequence and corresponded to 0.36% of the genome sequence. The length of these CNVRs ranged from 3.20 to 827.21 kb, with an average of 158.37 kb and a median of 97.85 kb. Functional annotation revealed these identified CNVR have important molecular function, and may play an important role in exploring the genetic basis of phenotypic variability and disease susceptibility among pigs. Additionally, to confirm these potential CNVRs, we performed qPCR for 12 randomly selected CNVRs and 8 of them (66.67%) were confirmed successfully. CNVs detected in diverse populations herein are essential complementary to the CNV map in the pig genome, which provide an important resource for studies of genomic variation and the association between various economically important traits and CNVs.     

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.