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。     

全基因组关联分析:基因组学研究的机遇与挑战

全基因组关联分析(genome wide association study,GWAS)是利用全基因组范围内筛选出高密度的分子标记对所研究的群体进行扫描,分析扫描得出的分子标记数据与表型性状之间关联关系的方法。GWAS的出现为全面系统地研究基因组学掀开了新的一页,目前主要应用于人类疾病复杂性状的分析,已鉴定出大量与人类复杂疾病或数量性状相关的遗传变异,成为研究人类基因组学的关键手段。在植物基因组中的研究应用虽刚刚起步,但也取得了良好的效果,应用GWAS发掘植物复杂数量性状基因、为植物分子育种提供依据已成为国际植物基因组学研究的热点。然而,GWAS的结果还存在一些问题,并非早期预测和想象的那样简单。现针对GWAS的特点,对其在人类基因组和植物基因组中的应用及其未来发展进行综述。     

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。     

用于GWAS结果后续研究的PBA方法简介

自提出全基因组关联研究(genome-wide association study,GWAS)设想以来,在人类复杂疾病和水稻农艺性状关联研究方面,GWAS已得到广泛运用。但作为一种典型的单标记研究方法,GWAS不能检测小效应的遗传变异,而稀有变异间的联合效应往往与表型密切相关,因此,需对GWAS结果进行深入的数据挖掘。基于通路的分析方法(pathway-based analysis,PBA)就是利用基因功能、生物代谢通路等相关信息建立的对GWAS结果进行二次挖掘的方法。该方法能从GWAS结果挖掘出与性状、疾病相关联的通路及具有相同功能的基因集等数据,从而获得更多的遗传信息。现对PBA的出现、计算方法和相关软件进行简要综述,以期为人们进行通路分析提供参考。     

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。     

细菌全基因组关联研究的方法与应用

随着测序技术的发展和全基因组序列的不断积累,全基因组关联研究(genome-wide association study, GWAS)在人类复杂疾病研究中取得了丰硕成果,10余年间发现了数以万计的疾病风险因子。同样,GWAS也为探索细菌表型的遗传机制提供了新的工具。自2013年第一项细菌GWAS(bacterial GWAS, BGWAS)工作发表以来,目前已有10多项相关研究报道,分别揭示了细菌宿主适应性、耐药性及毒力等表型的遗传机制,极大加深了人们对细菌遗传、进化及传播等方面的认识。本文对目前BGWAS的研究方法、应用成果及存在的问题进行了总结,并对BGWAS的研究前景进行了展望,旨在为微生物学领域开展BGWAS研究提供参考。     

外显子组测序在人类疾病中的应用

据估计,约85%的人类遗传变异集中在蛋白编码区,因此对全部的蛋白编码区（外显子组）进行重测序,可以快速、有效地鉴定人类疾病遗传变异。以往鉴定孟德尔遗传病的致病基因多采用连锁分析结合候选定位克隆的方法,不仅耗时长,而且成功率低。2009年,科学家第一次应用外显子组测序在4名弗里曼谢尔登综合征（常染色体显性遗传病）中发现了位于MYH3中的点突变,显示出外显子组测序在孟德尔遗传病致病基因鉴定中的强大功效。就复杂疾病而言,传统的关联研究,包括全基因组关联研究（GWAS）,虽然鉴定了大量的常见变异,但对低频变异和罕见变异的检测能力十分有限;深度测序的发展为解决上述问题提供了良好的契机。本文就外显子组测序在人类疾病中的应用作一简要综述。     

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。     

慢性阻塞性肺疾病全基因组关联研究进展

慢性阻塞性肺疾病(chronic obstructive pulmonary disease, COPD)是一种以不完全可逆的气流受限为主要特征的慢性气道炎症,是一种由遗传因素和环境因素共同作用的复杂疾病,也是世界主要致死疾病之一。近年来,随着全基因组关联研究(genome-wide association study, GWAS)的不断深入,研究者们发现了大量与肺功能或COPD相关的遗传变异或基因位点、药物靶点等。本文综述了2007年以来世界范围内针对肺功能或COPD的GWAS方面的研究工作及其进展综述,分析了可能存在的药物靶点,并探讨了COPD在全基因组关联研究中面临的挑战和困难,为深入研究COPD发病机制提供新思路。     

Improving genetic risk prediction by leveraging pleiotropy

An important task of human genetics studies is to predict accurately disease risks in individuals based on genetic markers, which allows for identifying individuals at high disease risks, and facilitating their disease treatment and prevention. Although hundreds of genome-wide association studies (GWAS) have been conducted on many complex human traits in recent years, there has been only limited success in translating these GWAS data into clinically useful risk prediction models. The predictive capability of GWAS data is largely bottlenecked by the available training sample size due to the presence of numerous variants carrying only small to modest effects. Recent studies have shown that different human traits may share common genetic bases. Therefore, an attractive strategy to increase the training sample size and hence improve the prediction accuracy is to integrate data from genetically correlated phenotypes. Yet, the utility of genetic correlation in risk prediction has not been explored in the literature. In this paper, we analyzed GWAS data for bipolar and related disorders and schizophrenia with a bivariate ridge regression method, and found that jointly predicting the two phenotypes could substantially increase prediction accuracy as measured by the area under the receiver operating characteristic curve. We also found similar prediction accuracy improvements when we jointly analyzed GWAS data for Crohn’s disease and ulcerative colitis. The empirical observations were substantiated through our comprehensive simulation studies, suggesting that a gain in prediction accuracy can be obtained by combining phenotypes with relatively high genetic correlations. Through both real data and simulation studies, we demonstrated pleiotropy can be leveraged as a valuable asset that opens up a new opportunity to improve genetic risk prediction in the future.     

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。     

Increased 90-kDa ribosomal S6 kinase (Rsk) activity is protective against mutant huntingtin toxicity

Uncovering the underlying genetic component of any disease is key to the understanding of its pathophysiology and may open new avenues for development of therapeutic strategies and biomarkers. In the past several years, there has been an explosion of genome-wide association studies (GWAS) resulting in the discovery of novel candidate genes conferring risk for complex diseases, including neurodegenerative diseases. Despite this success, there still remains a substantial genetic component for many complex traits and conditions that is unexplained by the GWAS findings. Additionally, in many cases, the mechanism of action of the newly discovered disease risk variants is not inherently obvious. Furthermore, a genetic region with multiple genes may be identified via GWAS, making it difficult to discern the true disease risk gene. Several alternative approaches are proposed to overcome these potential shortcomings of GWAS, including the use of quantitative, biologically relevant phenotypes. Gene expression levels represent an important class of endophenotypes. Genetic linkage and association studies that utilize gene expression levels as endophenotypes determined that the expression levels of many genes are under genetic influence. This led to the postulate that there may exist many genetic variants that confer disease risk via modifying gene expression levels. Results from the handful of genetic studies which assess gene expression level endophenotypes in conjunction with disease risk suggest that this combined phenotype approach may both increase the power for gene discovery and lead to an enhanced understanding of their mode of action. This review summarizes the evidence in support of gene expression levels as promising endophenotypes in the discovery and characterization of novel candidate genes for complex diseases, which may also represent a novel approach in the genetic studies of Alzheimer's and other neurodegenerative diseases.     

Will family studies return to prominence in human genetics and genomics? Rare variants and linkage analysis of complex traits

A major focus of modern human genetics has been the search for genetic variations that contribute to human disease. These studies originated in families and used linkage methods as a primary analytical tool. With continued technical improvements, these family-based linkage studies have been very powerful in identifying genes contributing to monogenic disorders. When these methods were applied to disorders with complex, non-Mendelian patterns of inheritance they largely failed. The development of effective capabilities for Genome Wide Association Studies (GWAS) relegated family-based studies to a peripheral role in human genetics research. Despite the remarkable record of GWAS discoveries, common variations identified in GWAS account for a limited (frequently less than 10%) proportion of the heritable risk of qualitative traits or variance of quantitative traits. Next generation sequencing is facilitating a re-examination of family-based methods with surprising and intriguing results. We propose that rare variants of large effect underlie many linkage peaks, including complex quantitative phenotypes, and review the issues underlying this proposed basis for complex traits.     

SCtool:识别复杂性状和疾病间遗传关联的工具

识别复杂性状和疾病间遗传关联可以提供有用的病因学见解,并有助于确定可能的因果关系的优先级。尽管已有很多工具可以实现复杂性状和疾病间遗传关联,但是某些工具代码可读性差、并且不同工具基于不同的计算机语言、工具间的串联性较差。因此,本研究基于全基因组关联研究(GWAS)数据,提出了SCtool,一个开源、跨平台和用户友好的软件工具。SCtool整合了ldsc, TwosampleMR和MR-BMA三种软件,其主要功能是基于GWAS汇总水平的数据,识别复杂性状和疾病、复杂性状和复杂性状以及疾病与疾病间的遗传相关性并探究其间潜在的因果关联。最后,使用SCtool揭示了全身性铁状态(铁蛋白,血清铁,转铁蛋白,转铁蛋白饱和度)与表观遗传时钟GrimAge之间的遗传关联。     

The impact of genome-wide association studies on biomedical research publications

The past decade has seen major investment in genome-wide association studies (GWAS). Among the many goals of GWAS, a major one is to identify and motivate research on novel genes involved in complex human disease. To assess whether this goal is being met, we quantified the effect of GWAS on the overall distribution of biomedical research publications and on the subsequent publication history of genes newly associated with complex disease. We found that the historical skew of publications toward genes involved in Mendelian disease has not changed since the advent of GWAS. Genes newly implicated by GWAS in complex disease do experience additional publications compared to control genes, and they are more likely to become exceptionally studied. But the magnitude of both effects has declined over the past decade. Our results suggest that reforms to encourage follow-up studies may be needed for GWAS to most successfully guide biomedical research toward the molecular mechanisms underlying complex human disease.     

Prediction of individual genetic risk of complex disease

Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's genetic risk for a disease? In this review we consider the lessons from GWAS and the implications for genetic risk prediction to common disease. We conclude that with larger GWAS sample sizes or by combining studies, accurate prediction of genetic risk will be possible, even if the causal mutations or the mechanisms by which they affect susceptibility are unknown.     

GWAS在2型糖尿病相关基因研究中的应用

2型糖尿病(type 2 diabetes,T2D)是一种常见的复杂疾病,其发病受到遗传和环境因素的共同作用.全基因组关联研究(genome-wide association study,GWAS)是一种可在全基因组范围筛查疾病相关的序列变异的新型群体关联研究方法.近年来,采用GWAS以及在此基础上展开的meta分析,已分别在TCF7L2、HHEX-IDE、SLC30A8、CDKAL1、CDKN2A-CDKN2B、IGF2BP2、NOTCH2、CDC123-CAMK1D、ADAMTS9、THADA、TSPAN8-LGR5、JAZF1等12个基因区域鉴定出多个T2D相关的多态位点.已有的研究提示,上述多个基因可能在胰岛β细胞发育和功能维持方面扮演着重要角色.本文集中介绍了GWAS的原理及其在T2D研究中的优势;回顾了GWAS在T2D研究中的主要发现;并对运用GWAS在T2D研究中尚需解决的问题进行了总结和展望.