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。     

基于高通量测序的全基因组关联研究策略

全基因组关联研究（Genome-wide association study, GWAS）是人类复杂疾病研究的重要组成部分之一,在群体水平检测全基因组范围的遗传变异与可观测性状间的遗传关联。传统的GWAS是以芯片（Array）技术获得高密度的遗传变异,尽管硕果累累,但也存在不少问题。如：所谓的“缺失的遗传力”,即利用关联分析检测达到全基因组水平显著的遗传变异位点只能解释小部分遗传力;在某些性状上不同研究的结果一致性较弱;显著关联的遗传变异位点的功能较难解释等。高通量测序技术,也称第二代测序（Next-generation sequencing, NGS）技术,可以快速、准确地产出高通量的变异位点数据,为解决以上问题提供了可行的方案。基于NGS技术的GWAS方法（NGS-GWAS）,可在一定程度上弥补传统GWAS的不足。文章对NGS-GWAS策略和方法进行了系统性调研,提出了目前较为可行的NGS-GWAS的实施策略和方法,并对NGS-GWAS如何应用于个体化医疗（Personalized medicine, PM）进行了展望。     

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。     

原发性高血压全基因组关联研究进展

原发性高血压是一种由遗传与环境因素共同导致的复杂疾病,具有高度的遗传异质性。自2007年首个高血压全基因组关联研究(Genome-wide association studies,GWAS)报道以来,许多GWAS相继开展。文章首先对2007年1月至2011年9月期间报道的24篇血压/高血压易感基因的GWAS按人种与染色体位置对其结果进行汇总,经统计位点rs17249754、rs1378942和rs11191548报道频数最多。其次介绍了GWAS方法学的研究进展,包括选择高质量的数量表型和选择多阶段研究设计来增加研究发现阳性关联的机会。统计分析方面,除强调了已经报道过的多重比较和重复(验证)研究等问题外,文章还介绍了通过Meta分析对GWAS数据进行深度发掘,并应用基因型填补法对缺失数据进行填补可以提高全基因组遗传标记的覆盖率的方法。尽管GWAS发现了许多我们未知的基因与疾病表型的关联,为了解高血压的发病机制提供了更多线索,但是目前GWAS发现的血压/高血压相关变异多为对人群血压的影响极其微弱的常见变异。因此今后的研究中可加强深度功能学研究对易感基因精细定位和外显子组测序技术的应用,结合GWAS的成果进行生物信息学通路分析和表观遗传学机制研究等,逐步揭示高血压的遗传机制。     

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。     

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.     

干旱胁迫下小麦苗期根系性状的全基因组关联分析

研究小麦根系在干旱逆境下的形态特征和遗传机制是提升小麦抗旱能力并获得稳产的基础。本研究以300份国内外小麦品种（系）为材料,苗期采用PEG-6000模拟干旱胁迫对小麦根系的最长根长、根总长、根表面积、根体积、根平均直径、根尖数、根鲜重和根干重等8个性状进行表型鉴定,结合90K SNP芯片对8个性状的抗旱系数进行全基因组关联分析,并对稳定遗传的显著关联位点进行候选基因的挖掘。研究结果表明,干旱胁迫下小麦品种（系）的根系性状表现出丰富的表型变异,变异系数为0.17～0.58,全基因组多态信息量变异范围为0.01～0.38,LD衰减距离为7 Mb。群体结构分析表明,供试材料分为3个亚群。GWAS分析显示,共检测到与8个根系性状显著关联的41个SNP位点,单个遗传位点可解释3.91%～8.04%的表型变异。同时在两个及两个以上的性状中发现显著关联位点13个,其中Tdurum＿contig71499＿211(5A)、GENE-1743＿858(3B)、Tdurum＿contig28552＿211(5B)3个位点与4～5个性状显著关联,分别能解释遗传变异的4.12%～5.37%、5.77%～6.7...     

抑郁症易感基因和抗抑郁药物靶点相关[]神经细胞类型及相互作用网络分析

基于抑郁症的全基因组关联分析研究(GWAS),对于获得的单核苷酸多态性位点(SNP)使用Haploreg软件进行基因注释,得到SNP注释的102个易感基因.。使用MAGMA软件对GWAS的汇总统计数据做基因水平的分析,获得了270个校正之后显著的基因,两者合并共得到320个抑郁症易感基因。通过药物数据库Drugbank获取133个抗抑郁药物靶点基因。使用EWCE包对抑郁症易感基因和抗抑郁药物靶点在三套脑组织单细胞测序数据中,分别进行神经细胞类型富集分析。结果发现大脑皮质的GABA神经元(抑制性神经元)和谷氨酸能神经元(兴奋性神经元)是抑郁症易感基因和抗抑郁药物靶点共同的神经元。这两种类型的神经细胞可能是抗抑郁药物与抑郁症易感基因相互作用的神经细胞,另外少突胶质前体细胞可能是抑郁症特有的易感神经细胞。使用Network Calculator软件构建网络并进行进行网络拓扑学参数分析。结果表明抑郁症易感基因与抗抑郁药物靶点组成了一个具有显著的相互连接的网络。本研究从单细胞层面揭示抑郁症的遗传机制,在网络层面为寻找新的抗抑郁药物靶点提供了一定的启示。     

全基因组关联分析的进展与反思

全基因组关联分析(genomewide association study,GWAS)是应用人类基因组中数以百万计的单核苷酸多态性(single nucleotide polymorphism,SNP)为标记进行病例-对照关联分析,以期发现影响复杂性疾病发生的遗传特征的一种新策略。近年来,随着人类基因组计划和基因组单倍体图谱计划的实施,人们已通过GWAS方法发现并鉴定了大量与人类性状或复杂性疾病关联的遗传变异,为进一步了解控制人类复杂性疾病发生的遗传特征提供了重要的线索。然而,由于造成复杂性疾病/性状的因素较多,而且GWAS研究系统较为复杂,因此目前GWAS本身亦存在诸多的问题。本文将从研究方式、研究对象、遗传标记,以及统计分析等方面,探讨GWAS的研究现状以及存在的潜在问题,并展望GWAS今后的发展方向。     

小麦耐低磷相关性状的全基因组关联分析

通过对小麦耐低磷相关性状进行全基因组关联分析(GWAS,genome-wide association study),挖掘与小麦耐低磷性显著相关的单核苷酸多态性标记(SNP,single nucleotide polymorphism)位点及候选基因,为小麦耐低磷性状的遗传基础和分子机制研究提供理论参考。本试验以198份黄淮麦区小麦品种(系)为试验材料,设置低磷和正常磷营养液水培试验,利用小麦35K芯片对分布于小麦全基因组的11896个SNP,采用Q+K关联模型对小麦耐低磷性相关性状进行关联分析。结果表明,小麦耐低磷性状表现出广泛的表型变异,变异系数为15.65%~26.59%,多态性信息含量(PIC,polymorphic information content)为0.095~0.500。群体结构分析表明,试验所用自然群体可分为2个亚群,GWAS共检测到67个与小麦耐低磷相关性状显著关联的SNP位点(P≤0.001),这些位点分布在除3A、3B和3D以外的18条染色体上,单个SNP位点可解释5.826%~9.552%的表型变异。在这些显著位点中有4个SNP位点同时关联到了2个不同的耐低磷性状。对67个SNP位点进行发掘,筛选到7个可能与小麦耐低磷性有关的候选基因。TraesCS6A02G001000和TraesCS6A02G001100在锌指合成中有重要作用;TraesCS6A02G118100可能为低磷胁迫诱导基因;TraesCS5D02G536400、TraesCS1B02G154200和TraesCS5D02G536500与低磷胁迫相关酶类基因家族有关;TraesCS1D02G231200与植物DUF 538结构域蛋白有关,是植物胁迫相关调控蛋白候选基因。     

Genome-wide searching of rare genetic variants in WTCCC data

Although they have demonstrated success in searching for common variants for complex diseases, genome-wide association (GWA) studies are less successful in detecting rare genetic variants because of the poor statistical power of most of current methods. We developed a two-stage method that can apply to GWA studies for detecting rare variants. Here we report the results of applying this two-stage method to the Wellcome Trust Case Control Consortium (WTCCC) dataset that include seven complex diseases: bipolar disorder, cardiovascular disease, hypertension (HT), rheumatoid arthritis, Crohn’s disease, type 1 diabetes and type 2 diabetes (T2D). We identified 24 genes or regions that reach genome wide significance. Eight of them are novel and were not reported in the WTCCC study. The cumulative risk (or protective) haplotype frequency for each of the 8 genes or regions is small, being at most 11%. For each of the novel genes, the risk (or protective) haplotype set cannot be tagged by the common SNPs available in chips (r ² < 0.32). The gene identified in HT was further replicated in the Framingham Heart Study, and is also significantly associated with T2D. Our analysis suggests that searching for rare genetic variants is feasible in current GWA studies and candidate gene studies, and the results can severe as guides to future resequencing studies to identify the underlying rare functional variants.     

Gene SNPs and mutations in clinical genetic testing: haplotype-based testing and analysis

Haplotype-based analysis using high-density single nucleotide polymorphism (SNP) markers have gained increasing attention in evaluating candidate genes in various clinical situations. For example, haplotype information is useful for predicting the severity and prognosis of certain genetic disorders. The intragenic cis-interactions between the common polymorphisms and the pathogenic mutations of prion protein (PRNP) and cystic fibrosis transmembrane conductance regulator (CFTR) genes greatly influence the phenotypes and the disease penetrance of hereditary Creutzfeldt-Jakob disease and cystic fibrosis. Merits of haplotype study are more evident in the fine mapping of complex diseases and in identifying genetic variations that influence individual's response to drugs. Knowledge-based approaches and/or linkage analyses using SNP tagged haplotypes are effective tools in detecting genetic associations. For example, haplotype studies in the inflammatory bowel disease susceptibility loci revealed diverse cis and trans gene-gene interactions, which can affect the clinical outcomes. Although currently, we have very limited knowledge on haplotype-phenotypic characterizations of most genes, these examples demonstrate that increased understanding of the clinically relevant haplotypes will provide better results in the diagnosis and possibly in the treatment of both monogenic and polygenic diseases.     

Association Studies of Sporadic Parkinson’s Disease in the Genomic Era

Parkinson’s disease is a common age-related progressive neurodegenerative disorder. Over the last 10 years, advances have been made in our understanding of the etiology of the disease with the greatest insights perhaps coming from genetic studies, including genome-wide association approaches. These large scale studies allow the identification of genomic regions harboring common variants associated to disease risk. Since the first genome-wide association study on sporadic Parkinson’s disease performed in 2005, improvements in study design, including the advent of meta-analyses, have allowed the identification of ~21 susceptibility loci. The first loci to be nominated were previously associated to familial PD (SNCA, MAPT, LRRK2) and these have been extensively replicated. For other more recently identified loci (SREBF1, SCARB2, RIT2) independent replication is still warranted. Cumulative risk estimates of associated variants suggest that more loci are still to be discovered. Additional association studies combined with deep re-sequencing of known genome-wide association study loci are necessary to identify the functional variants that drive disease risk. As each of these associated genes and variants are identified they will give insight into the biological pathways involved the etiology of Parkinson’s disease. This will ultimately lead to the identification of molecules that can be used as biomarkers for diagnosis and as targets for the development of better, personalized treatment.     

Genome-Wide Linkage Scan Identifies Two Novel Genetic Loci for Coronary Artery Disease: In GeneQuest Families

Coronary artery disease (CAD) is the leading cause of death worldwide. Recent genome-wide association studies (GWAS) identified >50 common variants associated with CAD or its complication myocardial infarction (MI), but collectively they account for <20% of heritability, generating a phenomena of “missing heritability”. Rare variants with large effects may account for a large portion of missing heritability. Genome-wide linkage studies of large families and follow-up fine mapping and deep sequencing are particularly effective in identifying rare variants with large effects. Here we show results from a genome-wide linkage scan for CAD in multiplex GeneQuest families with early onset CAD and MI. Whole genome genotyping was carried out with 408 markers that span the human genome by every 10 cM and linkage analyses were performed using the affected relative pair analysis implemented in GENEHUNTER. Affected only nonparametric linkage (NPL) analysis identified two novel CAD loci with highly significant evidence of linkage on chromosome 3p25.1 (peak NPL  = 5.49) and 3q29 (NPL  = 6.84). We also identified four loci with suggestive linkage on 9q22.33, 9q34.11, 17p12, and 21q22.3 (NPL  = 3.18–4.07). These results identify novel loci for CAD and provide a framework for fine mapping and deep sequencing to identify new susceptibility genes and novel variants associated with risk of CAD.     

Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability

Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ～10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.     

Potential association between <Emphasis Type="Italic">ITPKC</Emphasis> genetic variations and Hirschsprung disease

Hirschsprung disease (HSCR) is a congenital and complex disorder characterized by intestinal obstruction due to the absence of enteric neurons along variable lengths of the hindgut. Our recent genome-wide association study (GWAS) has revealed regional associations with HSCR at several loci of inositol-trisphosphate 3-kinase C (ITPKC). For fine mapping, we additionally selected and genotyped a total of 12 single nucleotide polymorphisms (SNPs) of ITPKC in 187 HSCR patients and 283 unaffected controls, and performed a further combined imputation analysis based on genotype data from this second stage of fine mapping and our previous GWAS stage, totaling 902 subjects (187 HSCR cases and 715 controls). As a result, several SNPs (minimum P?=?0.004) and a haplotype (P?=?0.02) were found to be significantly associated with HSCR. In further in silico analyses to ascertain the potential functions of the significant variants, the change from the common allele to the rare allele of the highly conserved nonsynonymous rs76785336 showed a difference in mRNA folding structure. In the case of intronic SNPs, rs2607420 with a high consensus value was predicted to be a new splice site. Although this study has limitations (such as lack of functional evaluations, small number of cases, and further need of replication in other cohorts), our findings suggest that genetic variants of ITPKC may have a potential association with HSCR susceptibility and/or developmental diseases related to enteric nervous system development.     

Mining the LIPG allelic spectrum reveals the contribution of rare and common regulatory variants to HDL cholesterol

Genome-wide association studies (GWAS) have successfully identified loci associated with quantitative traits, such as blood lipids. Deep resequencing studies are being utilized to catalogue the allelic spectrum at GWAS loci. The goal of these studies is to identify causative variants and missing heritability, including heritability due to low frequency and rare alleles with large phenotypic impact. Whereas rare variant efforts have primarily focused on nonsynonymous coding variants, we hypothesized that noncoding variants in these loci are also functionally important. Using the HDL-C gene LIPG as an example, we explored the effect of regulatory variants identified through resequencing of subjects at HDL-C extremes on gene expression, protein levels, and phenotype. Resequencing a portion of the LIPG promoter and 5' UTR in human subjects with extreme HDL-C, we identified several rare variants in individuals from both extremes. Luciferase reporter assays were used to measure the effect of these rare variants on LIPG expression. Variants conferring opposing effects on gene expression were enriched in opposite extremes of the phenotypic distribution. Minor alleles of a common regulatory haplotype and noncoding GWAS SNPs were associated with reduced plasma levels of the LIPG gene product endothelial lipase (EL), consistent with its role in HDL-C catabolism. Additionally, we found that a common nonfunctional coding variant associated with HDL-C (rs2000813) is in linkage disequilibrium with a 5' UTR variant (rs34474737) that decreases LIPG promoter activity. We attribute the gene regulatory role of rs34474737 to the observed association of the coding variant with plasma EL levels and HDL-C. Taken together, the findings show that both rare and common noncoding regulatory variants are important contributors to the allelic spectrum in complex trait loci.     

Quantifying the underestimation of relative risks from genome-wide association studies

Genome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will identify alleles with larger effects on genetic risk than those currently estimated from GWAS replication studies. We show that under plausible assumptions, whilst the majority of the per-allele relative risks (RR) estimated from GWAS data will be close to the true risk at the causal variant, some could be considerable underestimates. For example, for an estimated RR in the range 1.2-1.3, there is approximately a 38% chance that it exceeds 1.4 and a 10% chance that it is over 2. We show how these probabilities can vary depending on the true effects associated with low-frequency variants and on the minor allele frequency (MAF) of the most associated SNP. We investigate the consequences of the underestimation of effect sizes for predictions of an individual's disease risk and interpret our results for the design of fine mapping experiments. Although these effects mean that the amount of heritability explained by known GWAS loci is expected to be larger than current projections, this increase is likely to explain a relatively small amount of the so-called "missing" heritability.