周家蓬，裴智勇,2，陈禹保，陈润生

1. 北京市计算中心,北京 100094；

2. 中国科学院北京基因组研究所，北京 100101；

3. 中国科学院生物物理研究所，北京 100101

全基因组关联研究(Genome-wide association study,GWAS)是对多个个体在全基因组范围的遗传变异多态性进行检测，获得基因型，进而将基因型与可观测的性状，即表型，进行群体水平的统计学分析，根据统计量或P值筛选出最有可能影响该性状的遗传变异。GWAS的主要方法学依据是归纳法中的共变法，是探究复杂因果关系的最主要的思想和方法。因此，GWAS特别适用于遗传机理不明的复杂疾病或性状。得到高密度、高可信的遗传变异是GWAS的基础，这些遗传变异包括单核苷酸多态性(Single nucleotide polymorphisms,SNP)、插入缺失(Insertion and deletion,InDel)及拷贝数变异(Copy number variation,CNV)等，其中最主要的是SNP，占标记总量的90%以上。目前获得高密度SNP的方法主要是SNP芯片(Array)，因其高效、易用、廉价等特点在近些年被广泛使用。而第二代测序技术(Next-generation sequencing,NGS)的快速发展，为GWAS的相关研究工作提供了新的技术和思路。NGS可以获得大量低频甚至稀有的遗传变异，一些无法由芯片的高频或常规变异(即 MAF ≥ 5%的变异)检测到的表型关联，有望通过基于NGS的GWAS方法得到有意义的结果。本文对基于高通量测序的GWAS的原理、策略及相关研究进展进行了阐述，并对其如何应用于个体化医疗(Personalized medicine,PM)进行了展望。

1 GWAS概述

GWAS的研究思路最早于1996 年由Risch等[1]提出，目的是将人类复杂疾病的研究从候选基因转向全基因组水平，以期用更大规模的检测得到与疾病相关的每一个基因。近年来，已有多篇报道对GWAS研究进展进行了综述[2～4]。多年来，在人类疾病相关研究中，关于年龄相关性黄斑变性[5]、冠心病[6,7]、皮肤病[8,9]、2 型糖尿病[10～13]、癌症[14,15]、精神分裂症[16～18]、阿尔茨海默氏症[19]等 GWAS成果相继被报道。据NHGRI GWA Catalog网站(www.genome.gov/GWAStudies)的统计，截至 2013年底，在人类疾病或重要性状研究方面，共有1778篇高质量的GWAS研究工作被收录，累计发现12123个SNP位点，影响癌症、心血管系统、免疫系统、神经系统等17大类800多种重大疾病或其相关性状。GWAS不仅在人类医学研究中被广泛应用，在动植物遗传选育等方面也在逐渐兴起。在动物育种方面，利用GWAS方法研究了奶牛[20～24]、猪[25,26]、肉鸡[27,28]等经济动物的数量性状。GWAS在植物上也有较多报道，如玉米的开花时间[29]、叶片结构[30]、抗枯萎病[31,32]，还有水稻的十几种农艺性状的研究[33]等。以上研究主要采用基于芯片的GWAS方法。

2 基于NGS的GWAS

2.1 “合成关联”假设

传统GWAS是基于“Common disease-common variation”(CD-CV)的假设，该假设比较符合诸如“身高”等数量性状的遗传模式。Allen等[34]收集了约18万例样本进行分析，发现了180个与身高显著关联的基因座(Loci)在生物学通路上高度富集，并且这些基因均与骨骼生长缺陷有关，累计可解释约10%～16%的表型变异。另一种观点认为，一个常见变异的“致病”效应可能是从一个稀有变异的致病效应“稀释”而来，即“Common disease-rare variation”(CD-RV)假设。Dickson等[35]首次提出“合成关联”(Synthetic association)的概念来描述这一现象，利用模拟数据证实合成关联普遍存在于常见变异与稀有变异之间。越来越多的研究证实“合成关联”模型的真实性[36～38]。Yang等[39]对近4000个无关个体的295 K芯片基因型数据和身高表型数据进行关联分析，发现全部SNP可解释高达45%的表型变异；然而，尚有35%的表型变异无法通过芯片上已有的SNP解释，这主要由常见变异和稀有变异(Rare variation)间的连锁不平衡所导致。千人基因组计划(1000 Genomics Project,http://www.1000genomes.org/)提供了大量可供研究的人群遗传变异数据，统计研究发现，人类基因区域的遗传变异一般在进化上是近期发生的，且具有稀有性和人群特异性的特点[40～42]。目前认为，常见变异和稀有变异都在致病效应上有所贡献[34,39,43](图1)，效应的大小可能与频率成反比[44,45]，符合进化和选择的观点。随着研究的不断深入，稀有变异所占的份量可能会越来越重，如Fu等[42]对6500例非、欧裔美国人外显子组进行测序发现，在多达1.1 M的外显子区域变异中，73%在进化上是近期发生，且频率较低；而在可能致病的变异中，这个比例高达86%。以上研究表明，NGS技术可以为“缺失的遗传力”[44]问题提供新的解决方案，而随着NGS技术的不断成熟与实验成本的降低，NGS- GWAS的研究和应用可能会逐渐兴起。

2.2 基于NGS的GWAS新策略和方法

围绕CD-CV假设而设计的GWAS芯片主要面向高频SNP，国际人类基因组单体型图计划(International HapMap Project，http://www.hapmap.org)的数据库主要基于芯片技术构建，因而也以常见变异为主，目前 HapMap III期共收录约 10 M 个 SNP。传统GWAS对 MAF<0.05的 SNP很少研究。随着 NGS技术的兴起，特别是千人基因组计划的实施及其第Ⅰ阶段工作的完成，获得了37 M SNP，此外还检测出1.4 M插入缺失(InDel)和14 K结构变异(SV)；2014年发布的第Ⅲ阶段结果，遗传变异位点总量已高达79 M。此外，外显子组测序和转录组测序也累积了大量的遗传变异。

基于NGS的GWAS可以验证CD-RV假设。该假设认为，复杂疾病是由低频或稀有变异引起的，且这些携带较大遗传效应的变异往往不与常规 SNP紧密连锁，这可能是造成芯片GWAS的所谓“缺失的遗传力”问题[44,45]的主要原因。NGS技术采用高通量的平行测序方式，可以快速地获取高密度的SNP。随着该技术的完善和成熟，以及实验成本的降低，研究者开始尝试进行基于NGS技术的GWAS工作，一些新的策略和方法也应运而生。

图1 基于等位基因频率的复杂疾病遗传假设

2.2.1 外显子组测序

根据对孟德尔遗传病的研究发现，外显子突变是其主要病因，而复杂疾病很可能是由与其功能相关孟德尔遗传病的致病变异所影响。因此，外显子组测序相当于对基因组水平的致病变异进行了浓缩，只考虑外显子组，这样较易于解释生物学功能，且易于取得医学上的应用，是一种合理的优化策略。一些稀有变异与复杂性状的关联关系，可以通过外显子组测序的方法进行研究[46]。

在一些疾病研究中，如帕金森氏综合征，基于外显子测序的关联分析可在一定程度上得到更加丰富的数据[47]。Ng等[48]对4个Freeman-Sheldon综合征患者和 8个正常对照进行了外显子组测序，在 4个患者上发现的致病基因变异位点，不存在于任一对照个体中，也未在dbSNP数据库中发现，表明该策略在发掘致病变异研究中是可靠的。外显子组测序同样可用于检测病因未知的疾病，Ng等[49]通过对4个未知病因的Miller综合征患者和8个正常对照进行外显子组测序，通过对非同义突变、InDel、可变剪切变异等筛查，检测到DHODH基因在4个病例中存在上述变异而在对照个体中不存在。外显子组测序还可用于疾病辅助诊断和治疗。在皮肤病的相关研究中，Tang等[50]对781名银屑病患者以及676名健康对照个体的样本进行了外显子组测序，并在第二阶段扩大样本总量至 21309例。通过 GWAS分析，科研人员在IL23R和GJB2基因上检测到了2个低频错义突变，在LCE3D、ERAP1、CARD14 和ZNF816A基因上检测到了5个常见错义突变，都与银屑病的发生显着关联。Leslie等[51]通过对2005个个体进行外显子组测序及GWAS分析，发现PCSK9、LDLR和 APOB基因与人体内低密度脂蛋白胆固醇的含量相关。这两项研究在GWAS分析中均采用了BURDEN test的方法对稀有变异进行检测，该方法在近些年的相关研究中应用较为普遍。Choi等[52]通过检测致病突变位点，间接确认某患者的疾病症状。他们识别出一些纯合同义突变，这些突变从无脊椎动物到人类都高度保守。检测这些突变可能引发的疾病类型和症状后，发现其中一个突变位于导致先天性失氯性腹泻(Congenital chloridediarrhea,CCD)的基因上。外显子组测序也在复杂疾病研究中得以应用，如 Bowden等[53]对浆乙二腈水平无显著性差别的2个家系中的3个患者进行外显子组测序，发现ADIPOQ基因上的1个频率为1.1%的低频变异，能解释 17%的西班牙裔美国人的血浆乙二腈水平，63%的家族存在该突变。Bilguvar等[54]对1列脑皮质发育异常患者进行测序，发现WDR62基因与该疾病相关，结合功能分析可以解释一系列的严重皮质畸形，如小头畸形、胼胝体发育不全等；WDR62基因突变的某些患者还会发生脑裂畸形、小脑发育不全等。

基于外显子测序进行GWAS的计算和统计方法[55]包括：(1)沿用传统的单SNP位点模型，由于低频突变样本数少，可选用Fisher精确检验；(2)使用多因素模型，将单SNP位点的效应加和及校正，计算过程也需要借助一些降维的方法如Lasso[56]等，以减少运算量；(3)折叠法(Collapsing methods)，其原理是将同一个功能元件上的变异合并，根据功能元件的不同，分为 CAST[57]和 CMC[58]两种主要检测方法，前者考虑全部稀有变异，后者则关注非同义稀有突变，前文提到的BURDEN test即属于此方法，此外还有“变量阈值”法[59]和 RareCover[60]等方法；(4)聚合法(Aggregation methods)，相当于对折叠法中稀有变异以及常规GWAS中的常规变异进行加权，可分为“加权和”法[61]和 KBAC[62]两种。

由于外显子组测序只关注外显子及其剪切位点，因而对某些类型的致病变异无能为力，如线粒体基因中的突变、结构变异、内含子中的基因、调控序列、CNV、表观遗传学改变、“单亲二倍体”、基因之间的相互作用等；另外，有些外显子藏在染色体末端的重复区域内，因而无法被外显子测序所检测。

2.2.2 低覆盖度测序结合基因型填充

低覆盖度测序结合基因型填充的策略，是利用已有的公共基因组数据，如千人基因组数据，来填充覆盖度较低的测序数据，使之达到有效进行GWAS研究的数据量。该策略的有效性已有许多研究报道。该策略方案中应当主要注重两点：一是检测效力；二是计算速度。

检测效力的高低主要取决于数据量，因此，关键要找到SNP数量与样本量的均衡点。SNP数据量要足以涵盖致病突变；样本数量也应充足，以期检测到致病效应较小的变异，获得更多的缺失遗传力。Zheng等[63]对153例样本分别用3种芯片(317 K、610K和1 M)进行基因分型，分别用HapMap2和千人基因组预实验(1000G pilot)数据作为参考，进行缺失基因型填充。HapMap2填充的准确性大约 94%，1000G pilot约84%。对于MAF介于0.3%～5%的稀有SNP，三款芯片数据的填充准确性分别为49%，60%和69%。值得一提的是，尽管1000G pilot的准确性比HapMap2低，但其填充SNP的数据量(约8.5 M)要远高于后者(约2.5 M)。

对原始数据的产出量的选择问题，即如何控制测序覆盖度以达到SNP分型目的，千人基因组研究[64]给出了参考，即2～4×测序深度即可获得个人基因组约 85%的区域，数据产出和测序成本的比例最优。而这个最优解是针对个人基因组而非群体基因组测序而估计的，随着参考数据的不断累积，个体测序的覆盖度可以降得更低，如Pasaniuc等[65]采用极低覆盖度的策略(平均～0.24×)依然可以获得较好的填充效果。

由于NGS的数据量大，基因型填充的过程运算量大、耗时长，因而一些研究者开发出了加快运算的优化算法。Howie等[66]开发的Pre-phasing填充方法，通过对GWAS样本进行连锁相构建，进而利用参考库的单倍型进行缺失基因型填充。该方法可以在很大程度上缩短运算时间，在大样本中效果更加明显。Howie等使用MaCH、IMPUTE2软件，利用WTCCC2、GAIN、WHI以及 1000G数据，对该方法进行了测试，结果显示Pre-phasing方法的效率明显高于常规方法。

表1 高密度芯片与低覆盖度测序技术对比

因此，低覆盖度全基因组重测序结合缺失基因型填充的方法应当是一种可行的策略。高密度 SNP芯片与低覆盖度测序的技术参数比较见表1。

Rohland等[67]发明了一种廉价高效的建库方法，一个技术人员可以在一天内构建 192个测序库，使建库成本降至每样本15美元。这些库不仅可以用于低覆盖度测序，还可以在多达 100例加标签样本(Barcoded samples)混池的条件下进行有效测序。他们用极低覆盖度的外显子组测序数据(0.1～0.5×)结合千人基因组基因型数据做填充，证明了该方法的有效性。这使得在成本降低的情况下，捕获或填充的SNP的数目、分型的准确性都有所增加，检测效力也得到提高[65]。目前该策略的缺点是对稀有变异的基因型推断与填充效果不够理想。填充策略的准确性和有效性取决于实验样本和参考数据库的数据样本量的多少，随着测序技术不断提高、成本不断降低，以及公共数据库数据量的快速增加，低覆盖度测序的策略可能将会更多地被采用。此外，测序的准确性对于科学研究十分重要，测序错误来源有许多因素[68]，应当在研究中注意。

2.2.3 家系病例或极端性状个体重测序

由于全基因组测序的费用比较高，要求对样本进行选择性测序。在这一策略中，可挑选有多个发病个体的家系进行测序，也可以挑选表型比较极端的个体进行测序。Yang等[69]使用发病个体家系的设计，对发病家系的19个发病个体和27个正常对照进行选择性测序，最终识别出11个风险CNV位点。之后对其中4个CNV在大群体进行验证，发现其确实在发病人群中高度富集。Sobreira等[70]使用类似的设计发现了混合性软骨瘤的致病变异。Lander等[71]在 1989年就提出选择极端表型个体进行分析可以降低实验成本，同时保证检测效力不会过多丧失。在高密度芯片兴起之后，Manolio等[72]根据HapMap高密度SNP数据讨论了这种策略的可行性；Verlaan等[73]进一步对其进行了检测效力的研究。Cirulli等[74]首先筛选出一般人群中的极端表型个体，然后对这些个体进行高覆盖度测序，找出明显高于一般人群等位基因频率的SNP位点，再对这些位点区域进行目标区域测序或分型。

基于医院病例(Hospital-based)数据的实验设计属于该策略[50,51]，例如高血压患者就是由极端表型个体(160/100 mmHg)转化为病例的，其他种类疾病的诊断过程也可看作是极端表型的筛选，即以某指标为阈值进行病例筛选。该策略目前主要的问题是有可能会因为抽样偏差或忽略某些重要的协变量产生假阳性结果。如基于医院病例的数据，一般只重视对症状的诊断，而忽略患者的发病影响因素。

2.2.4 其他策略

除了以上3种主要的策略，还有其他研究策略或方法，如目标区域捕获测序[75]、混池测序[76]等。目标区域捕获测序的原理和外显子组测序一样，而目标更加明确，通常将几个至几十个疾病风险基因的外显子、内含子、上下游序列进行测序，从而降低实验成本。混池测序则是将多个样本混合成一个样本进行测序，该策略适用于病例对照设计，而不太适合于连续型表型；若结合加标签(Barcoding)技术则可区分个体，可用于连续型表型。另外，随着Illumina Hiseq X Ten和Nextseq 500等新的测序平台的应用，实验成本将进一步降低。

3 结语与展望

NGS技术在实验成本和速度上优于传统的Sanger测序，在数据类型和通量等方面优于芯片技术。目前，在植物育种方面已经有多篇NGS-GWAS的文章发表，如采用低覆盖度测序结合基因型填充策略对水稻14个农艺性状的研究[77]，利用RNA-seq数据对玉米产油量性状的 eQTL研究[78]等。然而，对关联分析显著性结果的解释及功能发掘，需要进一步研究。例如，利用生物信息学的工具可以发掘出许多新的功能作用元件。此外，用变异位点解释复杂疾病的机理有一定难度。目前，传统GWAS主要使用单SNP位点模型，显得过于单薄，因此需要开发更加复杂和精密的模型，例如针对外显子组测序数据的 Lasso回归、折叠法、聚合法，及针对生物调控网络的互作模型等。随着大量新的遗传变异类型及其变异位点被发现，对变异的注释和使用方式将面临新的挑战。NGS将产生海量的新变异，不仅包括SNP、InDel、SV，还包括cSNP、表达量变异、可变剪切、甲基化变异等数据，使分析变得更加复杂。

关于人类基因组研究，目前基于NGS的GWAS策略多是围绕降低成本而设计，但不同策略中需要考虑的问题是相同的，即如何更全面系统地检测出致病变异并有效应用于医药。因此，不断积累并共享的数据必不可少，系统性的生物信息学挖掘也至关重要。由于NGS和GWAS两种技术成本高，因此人们需要将其策略和实验设计进行优化，在保证不过多丧失检测效力和准确性的情况下，极大提高研究实施的可行性。目前，随着公共数据库的不断累积和共享，外显子组测序、极低覆盖度重测序结合基因型填充策略，可能会在医学健康研究领域被广泛采用。随着基于NGS的RNA-seq、ChIP-seq等功能学方面数据不断积累，以及非编码RNA(ncRNA)等新型功能数据库的发展，有必要对前人的一些结果进行重新注释，并尝试在医学上加以实际应用。

运用GWAS方法对复杂疾病的研究、早期预警及个性化医疗方面已开始起步。以高血压为例，结合遗传变异信息的降压治疗方法已有多篇文献报道[79]。高血压的传统疗法是服用抗高血压类药物，如噻嗪类利尿剂、β-受体阻滞剂、ACE抑制剂、血管紧张素受体阻滞剂和钙通道阻滞剂等。全世界范围内约有30%的患者只服用一种药物，40%服用两种，30%服用三种或以上。但是这类药物对收缩压或舒张压的控制率不到35%[80]。其根本原因之一在于个体遗传变异对药物反应的特异性，因此开展药物基因组学研究具有重要意义。药物基因组学研究药物反应的遗传机制及药物反应的个体差异性，是功能基因组学和分子药物学的结合。早期的研究主要围绕单个候选基因与降压药物的作用关系，如ACE、ADD1、NEDD4L、ADRB1和KCNMB1基因。2008年，第1篇基于GWAS的药物基因组学研究被报道，发现人类12号染色体YEATS4基因附近区段影响噻嗪类利尿剂的治疗效果[81]。之后，越来越多的采用GWAS方法的药物基因组学工作被相继报道，如抗高血压药物氢氯噻嗪的治疗效果[82]等。未来的疾病防治工作，应该是治疗向预防前移，防大于治，并且应该结合遗传因素、环境因素、生活方式、药物反应等，对患者或潜在患者进行全方位、个体化的评估、预警、诊断和治疗。基于不断发展的 NGS新技术的GWAS策略将在人类医学研究领域发挥重要作用。

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