基于全基因组关联的中国常见肿瘤遗传病因学研究

肿瘤是基因-环境交互作用引起的复杂性疾病.在同样的环境暴露下,不同遗传背景的个体发生肿瘤的风险有很大差异.研究肿瘤相关遗传因素对理解肿瘤发生发展乃至诊断治疗都有重要意义.近年来发展的全基因组关联研究(genome-wide association study,GWAS)可在全基因组范围内发现与复杂疾病或表型关联的遗传因素,为复杂疾病遗传学研究提供了强有力的手段.欧美研究者运用全基因组关联研究的方法,对各种常见肿瘤进行了研究,获得了重要成果.2010年以来,中国科学家在国际核心期刊发表了一系列高水平的肿瘤全基因组关联研究成果,在中国常见肿瘤的遗传病因学研究方面取得了重要进展.     

心源性卒中的遗传学研究进展

心源性卒中是缺血性脑卒中的重要病因之一,表现出病情重、预后差和复发率高的特点。在遗传学研究中已经有相当多与心源性卒中相关的基因被鉴定,这些易感基因在疾病风险预测及危险因素评估的潜力也陆续被发掘。本文从全基因组关联研究、拷贝数变异研究、全基因组测序研究等方面综述了心源性卒中遗传学研究的相关进展,并介绍了其遗传数据集在多基因风险评分、孟德尔随机化的应用,旨在为将来深入研究心源性卒中的遗传发生机制提供借鉴和参考。     

糖尿病肾病遗传学研究进展

糖尿病肾病是糖尿病最严重的慢性并发症之一, 不同种族的发病率分析和家族聚集性研究显示遗传因素是糖尿病肾病发生、发展的重要因素。文章从3个方面对糖尿病肾病的遗传学研究进展进行综述：“候选基因”的关联研究、连锁分析和全基因组关联研究。关联研究及荟萃分析显示一些候选基因与糖尿病肾病显著相关, 包括ACE、AGT和PPARG等基因; 连锁分析及全基因组连锁分析发现多个糖尿病肾病的易感染色体位点; 随着高通量测序技术和芯片技术的发展, 全基因组关联研究已成为糖尿病肾病遗传学研究的重要途径。虽然遗传因素在糖尿病肾病发病中占据重要的位置, 但还不能完全解释糖尿病肾病的发病原因, 因为糖尿病肾病的发生还受环境因素的影响, 然而糖尿病肾病的遗传学研究可为糖尿病肾病发病机制研究以及药物治疗靶点研究提供一定的理论依据。     

玉米籽粒淀粉含量遗传基础与调控机制*

玉米是世界上种植面积最大、总产量最高的粮食作物,其籽粒重量的70%来自于淀粉。淀粉不仅是人类及其他动物的主要能量来源,同时也是化工等行业的重要原料。利用拟南芥、水稻等模式植物,淀粉合成相关基因克隆与功能研究已取得较多进展。近年来,随着玉米淀粉含量相关遗传学研究的深入开展,通过数量性状位点(quantitative trait locus mapping,QTL)定位、全基因组关联分析(genome-wide association study, GWAS)及各种组学分析方法,发现了较多新的与淀粉含量相关的遗传位点及候选基因,但是尚缺乏归纳总结。综述了玉米籽粒淀粉合成与调控研究进展,对玉米籽粒淀粉含量相关的QTL和基因进行汇总和分析,通过构建一致性物理图谱,提炼玉米籽粒淀粉含量遗传定位热点区间,这为进一步解析玉米籽粒淀粉合成与代谢相关基因的功能提供参考,并为分子标记辅助育种提供遗传资源。     

基因水平的关联分析方法

全基因组关联研究(Genome wide association study, GWAS)已经在国内外的医学遗传学研究中得到广泛应用, 但是GWAS数据中所蕴含的与多基因复杂性状疾病机制相关的丰富信息尚未得到深度挖掘。近年来, 研究者采用生物网络分析和生物通路分析等生物信息学和生物统计学手段分析GWAS数据, 并探索潜在的疾病机制。生物网络分析和生物通路分析主要是以基因为单位进行的, 因此必须在分析前将基因上全部或者部分单个单核苷酸多态性(Single nucleotide polymorphism, SNP)的遗传关联结果综合起来, 即基因水平的关联分析。基因水平的关联分析需要考虑单个SNP的遗传关联、基因上SNP数量和SNP之间的连锁不平衡结构等多种因素, 因此不仅在遗传学的概念上也在统计方法方面具有一定的复杂性和挑战性。文章对基因水平的关联分析的研究进展、原理和应用进行了综述。     

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。     

我国科学家发现导致肥胖症新基因

正中国科学院遗传与发育生物学研究所李巍课题组领衔的研究发现,位于人类6号染色体长臂D6S1009位点旁侧的SLC35D3基因是人类肥胖症和代谢综合征的致病基因。相关研究于2013年2月13日发表于美国《科学公共图书馆—遗传学》。肥胖症的发生与遗传和环境两种因素有关,其中,遗传因素的贡献约占2/3。不过,研究人员虽已发现了一些单基因肥胖症的致病基因,如瘦素基因(LEP)等,还通过全基因组关联或连锁分析发现了150多个与肥胖症相关的基因位点,     

利用SNP进行遗传病致病基因搜寻的策略

SNP是一类基于单碱基变异引起的DNA多态性,被遗传学界称为第三代遗传标记。由于SNP的诸多优点,如位点丰富和与DNA芯片等技术上的结合,它将对人类致病基因的搜寻工作起到革命性的作用。本文综合了目前SNP领域的一些进展,对这一新的标记系统在人类遗传病研究中的应用策略进行了初步概括。     

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。     

生物信息学分析方法I: 全基因组关联分析概述

全基因组关联分析(GWAS)是动植物复杂性状相关基因定位的常用手段。高通量基因分型技术的应用极大地推动了GWAS的发展。在植物中, 利用GWAS不仅能够以较高的分辨率在全基因组水平鉴定出各种自然群体特定性状相关的基因或区间, 而且可揭示表型变异的遗传架构全景图。目前, 人们利用GWAS分析方法已在拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)、小麦(Triticum aestivum)、玉米(Zea mays)和大豆(Glycine max)等模式植物和重要农作物品系中发掘出与各种性状显著相关的数量性状座位(QTL)及其候选基因位点, 阐明了这些性状的遗传基础, 并为揭示这些性状背后的分子机理提供候选基因, 也为作物高产优质品种的选育提供了理论依据。该文对GWAS的方法、影响因素及数据分析流程进行了详细描述, 以期为相关研究提供参考。     

A practical guide to environmental association analysis in landscape genomics

Landscape genomics is an emerging research field that aims to identify the environmental factors that shape adaptive genetic variation and the gene variants that drive local adaptation. Its development has been facilitated by next‐generation sequencing, which allows for screening thousands to millions of single nucleotide polymorphisms in many individuals and populations at reasonable costs. In parallel, data sets describing environmental factors have greatly improved and increasingly become publicly accessible. Accordingly, numerous analytical methods for environmental association studies have been developed. Environmental association analysis identifies genetic variants associated with particular environmental factors and has the potential to uncover adaptive patterns that are not discovered by traditional tests for the detection of outlier loci based on population genetic differentiation. We review methods for conducting environmental association analysis including categorical tests, logistic regressions, matrix correlations, general linear models and mixed effects models. We discuss the advantages and disadvantages of different approaches, provide a list of dedicated software packages and their specific properties, and stress the importance of incorporating neutral genetic structure in the analysis. We also touch on additional important aspects such as sampling design, environmental data preparation, pooled and reduced‐representation sequencing, candidate‐gene approaches, linearity of allele–environment associations and the combination of environmental association analyses with traditional outlier detection tests. We conclude by summarizing expected future directions in the field, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies.     

Recent progress in the study of the genetics of height

Adult height is a classic polygenic trait of high narrow-sense heritability (h ² = 0.8). In the late nineteenth to early twentieth century, variation in adult height was used as a model to set the foundation of the fields of statistics and quantitative genetics. More recently, with our increasing knowledge concerning the extent of genetic variation in the human genome, human geneticists have used genome-wide association studies to identify hundreds of loci robustly associated with adult height, providing new insights into human growth and development, and into the architecture of complex human traits. In this review, I highlight the progress made in the last 2 years in understanding how genetic variation controls height variation in humans, including non-Caucasian populations and children.     

Current opportunities and challenges: genome-wide association studies on pigmentation and skin cancer

Genome-wide association studies (GWAS) have become a widely used approach for genetic association studies of various human traits. A few GWAS have been conducted with the goal of identifying novel loci for pigmentation traits, melanoma, and non-melanoma skin cancer. Nevertheless, the phenotype variation explained by the genetic markers identified so far is limited. In this review, we discuss the GWAS study design and its application in pigmentation and skin cancer research. Furthermore, we summarize recent developments in post-GWAS activities such as meta-analysis, pathway analysis, and risk prediction.     

人类白细胞抗原与鼻咽癌的相关性

鼻咽癌是一种多因素影响的复杂性疾病, 其发病具有显著的地理分布差异。Epstein-Barr(EB)病毒感染与鼻咽癌发病密切相关已得到公认, 但环境因素及遗传因素在鼻咽癌的发病中也具有重要作用。在鼻咽癌的遗传相关因素中, 位于6号染色体上具有高度多态性的人类白细胞抗原(Human leukocyte antigen, HLA)与鼻咽癌发病风险相关在多个研究组中被报道。随着DNA测序技术的发展, 高分辨基因分型技术的应用, HLA新等位基因数目呈指数级的上升, 更多的HLA全基因序列被研究者所报道。近年来, 等位基因关联性分析、微卫星连锁不平衡分析及全基因组关联性分析的研究结果均证实了6号染色体HLA区域与鼻咽癌具有显著关联。为了进一步探讨遗传相关性因子HLA在鼻咽癌发生发展中的作用, 文章着重综述了HLA与鼻咽癌相关性研究的最新进展, 为鼻咽癌HLA相关性研究提供新的思路。     

Exploration of methods to identify polymorphisms associated with variation in DNA repair capacity phenotypes

Elucidating the relationship between polymorphic sequences and risk of common disease is a challenge. For example, although it is clear that variation in DNA repair genes is associated with familial cancer, aging and neurological disease, progress toward identifying polymorphisms associated with elevated risk of sporadic disease has been slow. This is partly due to the complexity of the genetic variation, the existence of large numbers of mostly low frequency variants and the contribution of many genes to variation in susceptibility. There has been limited development of methods to find associations between genotypes having many polymorphisms and pathway function or health outcome. We have explored several statistical methods for identifying polymorphisms associated with variation in DNA repair phenotypes. The model system used was 80 cell lines that had been resequenced to identify variation; 191 single nucleotide substitution polymorphisms (SNPs) are included, of which 172 are in 31 base excision repair pathway genes, 19 in 5 anti-oxidation genes, and DNA repair phenotypes based on single strand breaks measured by the alkaline Comet assay. Univariate analyses were of limited value in identifying SNPs associated with phenotype variation. Of the multivariable model selection methods tested: the easiest that provided reduced error of prediction of phenotype was simple counting of the variant alleles predicted to encode proteins with reduced activity, which led to a genotype including 52 SNPs; the best and most parsimonious model was achieved using a two-step analysis without regard to potential functional relevance: first SNPs were ranked by importance determined by random forests regression (RFR), followed by cross-validation in a second round of RFR modeling that included ever more SNPs in declining order of importance. With this approach six SNPs were found to minimize prediction error. The results should encourage research into utilization of multivariate analytical methods for epidemiological studies of the association of genetic variation in complex genotypes with risk of common diseases.     

Genes and human elite athletic performance

Physical fitness is a complex phenotype influenced by a myriad of environmental and genetic factors, and variation in human physical performance and athletic ability has long been recognised as having a strong heritable component. Recently, the development of technology for rapid DNA sequencing and genotyping has allowed the identification of some of the individual genetic variations that contribute to athletic performance. This review will examine the evidence that has accumulated over the last three decades for a strong genetic influence on human physical performance, with an emphasis on two sets of physical traits, viz. cardiorespiratory and skeletal muscle function, which are particularly important for performance in a variety of sports. We will then review recent studies that have identified individual genetic variants associated with variation in these traits and the polymorphisms that have been directly associated with elite athlete status. Finally, we explore the scientific implications of our rapidly growing understanding of the genetic basis of variation in performance.     

Quantitative Genetics in the Genomics Era

The genetic analysis of quantitative or complex traits has been based mainly on statistical quantities such as genetic variances and heritability. These analyses continue to be developed, for example in studies of natural populations. Genomic methods are having an impact on progress and prospects. Actual relationships of individuals can be estimated enabling novel quantitative analyses. Increasing precision of linkage mapping is feasible with dense marker panels and designed stocks allowing multiple generations of recombination, and large SNP panels enable the use of genome wide association analysis utilising historical recombination. Whilst such analyses are identifying many loci for disease genes and traits such as height, typically each individually contributes a small amount of the variation. Only by fitting all SNPs without regard to significance can a high proportion be accounted for, so a classical polygenic model with near infinitesimally small effects remains a useful one. Theory indicates that a high proportion of variants will have low minor allele frequency, making detection difficult. Genomic selection, based on simultaneously fitting very dense markers and incorporating these with phenotypic data in breeding value prediction is revolutionising breeding programmes in agriculture and has a major potential role in human disease prediction.     

植物分子群体遗传学研究动态

分子群体遗传学是当代进化生物学研究的支柱学科, 也是遗传育种和关于遗传关联作图和连锁分析的基础理论学科。分子群体遗传学是在经典群体遗传的基础上发展起来的, 它利用大分子主要是DNA序列的变异式样来研究群体的遗传结构及引起群体遗传变化的因素与群体遗传结构的关系, 从而使得遗传学家能够从数量上精确地推知群体的进化演变, 不仅克服了经典的群体遗传学通常只能研究群体遗传结构短期变化的局限性, 而且可检验以往关于长期进化或遗传系统稳定性推论的可靠程度。同时, 对群体中分子序列变异式样的研究也使人们开始重新审视达尔文的以“自然选择”为核心的进化学说。到目前为止, 分子群体遗传学已经取得长足的发展, 阐明了许多重要的科学问题, 如一些重要农作物的DNA多态性式样、连锁不平衡水平及其影响因素、种群的变迁历史、基因进化的遗传学动力等, 更为重要的是, 在分子群体遗传学基础上建立起来的新兴的学科如分子系统地理学等也得到了迅速的发展。文中综述了植物分子群体遗传研究的内容及最新成果。     

Pleiotropy informed adaptive association test of multiple traits using genome‐wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large‐scale genome‐wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease‐related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans‐Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the “single trait” association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well‐characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome‐phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual‐level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P‐value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta‐analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta‐analysis. All the proposed methods are implemented in a publicly available R package.