林木全基因组关联分析研究进展与展望

随着新一代基因组测序技术的快速发展,全基因组关联分析(genome wide association study, GWAS)在揭示林木复杂性状的数量遗传变异规律、解析关键基因的遗传调控机制及推动林木分子辅助育种等方面展示出前所未有的应用前景.本文首先综述了GWAS的核心理论、研究方法及其在木材性状和适应性遗传基础研究中的研究进展.随后,针对林木数量性状遗传研究中普遍存在的"丢失遗传力"(missing heritability)问题,本文从高通量表型组学平台的组建,多种遗传标记的联合利用,多组学数据的系统剖析以及加性、显性与上位性关联模型的开发等方面提出了未来GWAS的发展对策.最后,结合当前林木遗传改良的实践需求,展望了GWAS策略在林木分子育种领域的广阔应用前景.     

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。     

普通小麦根系建成相关性状的全基因组关联分析

根系建成(RSA,Root system architecture)决定根系系统的构成,在作物生长发育过程中起着不可替代的作用。解析小麦根系建成遗传机制、选育具有较好根系建成的品种对于小麦高产、抗逆育种具有十分重要的意义。全基因组关联分析(GWAS)是解析小麦复杂数量遗传性状遗传机制的有效方法。本研究基于全基因组关联分析方法,发掘根系建成相关性状关联位点,以期为小麦根系建成分子育种提供参考。对160份来自于河南和山东等地的小麦品种根系建成相关性状(总根长、总根表面积、总根体积、平均根直径和根尖数)进行统计评价,并结合660K SNP芯片数据进行全基因组关联分析。检测到23个关联位点,分布于1A、2A、2B、3B、4A、5A、5B、5D、6A、6B和7B染色体上,解释7.2%～12.8%的表型变异。其中,11个位点与已报道的位点一致,其他12个位点为新的位点。本研究对于解析根系建成遗传机制,选育高产、抗逆小麦品种具有重要意义。     

全基因组关联分析实现水稻粒型自然变异的分子解析

全基因组关联分析(GWAS)近年来被广泛应用于解析生物自然变异的遗传基础。但限于其遗传定位精度, 在水稻(Oryza sativa)遗传学研究中, 该方法尚无法取代传统的图位克隆法在克隆复杂性状调控基因中的作用。近期, 中国科学家在应用GWAS等大数据来克隆控制水稻粒长和粒重等复杂性状的QTL方面取得了新突破。     

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。     

普通小麦抗倒伏相关性状的全基因组关联分析

解析小麦抗倒伏遗传机制,发掘抗倒伏位点,选育抗倒伏品种,对于保障我国粮食生产安全具有重要意义.周8425B是我国黄淮麦区重要的小麦骨干亲本,农艺性状优良,其衍生品种具有较好的抗倒伏特性.本研究对周8425B及其衍生品种等62份材料的8个抗倒伏相关性状进行评价,并结合50K SNP芯片数据进行全基因组关联分析(GWAS,...     

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。     

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。     

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。     

后GWAS时代结直肠癌致病SNP功能机制的研究进展

结直肠癌(colorectal cancer,CRC)是受遗传与环境因素共同影响的复杂疾病,其中遗传因素发挥重要作用。至今,全基因组关联研究(genome-wide association studies,GWAS)已经发现了大量与结直肠癌风险相关的遗传变异。随之而来的后GWAS时代,越来越多的研究侧重于利用多组学数据和功能实验对潜在的致病位点进行解析。分析表明绝大多数风险单核苷酸多态性(single nucleotide polymorphism,SNP)位于非编码区,可能通过影响转录因子结合、表观遗传修饰、染色质可及性、基因组高级结构等,调控靶基因表达。本文对后GWAS时代结直肠癌致病位点的机制研究进行综述,阐述了后GWAS对于理解结直肠癌分子机制的重要意义,并探讨了结直肠癌GWAS的应用和前景,为实现GWAS成果转化提供参考。     

银川番茄斑萎病毒的分子鉴定

为明确银川番茄(Lycopersicon esculentum)是否遭受了番茄斑萎病毒(TSWV)的危害, 采用国家标准TSWV RT- PCR检测技术对银川番茄上采集的14份疑似感染TSWV病叶样本进行分子鉴定, 对克隆得到的核衣壳蛋白基因N (Nucleocapsid)序列进行多序列比对和系统进化树分析, 随后对PCR阳性样本进行蛋白检测。结果表明, 14份病叶样本中有8份扩增出长度为394 bp的TSWV N基因序列, 且8条序列完全一致; 获得的银川番茄TSWV分离物与云南番茄、中国莴苣(Lactuca sativa)、中国鸢尾(Iris tectorum)和重庆辣椒(Capsicum annuum) TSWV分离物相对近缘, 与山东、黑龙江和北京等地及国外TSWV分离物相对远缘; 利用TSWV的抗体通过Western blot对8个PCR阳性样本进一步检测, 结果也证实8个阳性样本中存在TSWV感染。该研究首次通过分子鉴定及蛋白检测证明银川番茄上存在TSWV感染, 需要加快抗TSWV番茄品种的选育工作。     

Identification of loci governing eight agronomic traits using a GBS‐GWAS approach and validation by QTL mapping in soya bean

Soya bean is a major source of edible oil and protein for human consumption as well as animal feed. Understanding the genetic basis of different traits in soya bean will provide important insights for improving breeding strategies for this crop. A genome‐wide association study (GWAS) was conducted to accelerate molecular breeding for the improvement of agronomic traits in soya bean. A genotyping‐by‐sequencing (GBS) approach was used to provide dense genome‐wide marker coverage (>47 000 SNPs) for a panel of 304 short‐season soya bean lines. A subset of 139 lines, representative of the diversity among these, was characterized phenotypically for eight traits under six environments (3 sites × 2 years). Marker coverage proved sufficient to ensure highly significant associations between the genes known to control simple traits (flower, hilum and pubescence colour) and flanking SNPs. Between one and eight genomic loci associated with more complex traits (maturity, plant height, seed weight, seed oil and protein) were also identified. Importantly, most of these GWAS loci were located within genomic regions identified by previously reported quantitative trait locus (QTL) for these traits. In some cases, the reported QTLs were also successfully validated by additional QTL mapping in a biparental population. This study demonstrates that integrating GBS and GWAS can be used as a powerful complementary approach to classical biparental mapping for dissecting complex traits in soya bean.     

Genome-wide association mapping of agronomic and morphologic traits in highly structured populations of barley cultivars

Genome-wide association study (GWAS) has become an obvious general approach for studying traits of agricultural importance in higher plants, especially crops. Here, we present a GWAS of 32 morphologic and 10 agronomic traits in a collection of 615 barley cultivars genotyped by genome-wide polymorphisms from a recently developed barley oligonucleotide pool assay. Strong population structure effect related to mixed sampling based on seasonal growth habit and ear row number is present in this barley collection. Comparison of seven statistical approaches in a genome-wide scan for significant associations with or without correction for confounding by population structure, revealed that in reducing false positive rates while maintaining statistical power, a mixed linear model solution outperforms genomic control, structured association, stepwise regression control and principal components adjustment. The present study reports significant associations for sixteen morphologic and nine agronomic traits and demonstrates the power and feasibility of applying GWAS to explore complex traits in highly structured plant samples.     

燕麦基因组学研究进展

燕麦具有较高的营养价值和保健功能,是一种可用于均衡营养、科学饮食的健康食品,正逐渐受到人们的青睐和认可。基因组学研究有助于燕麦重要农艺性状的定位和克隆,对开发利用燕麦优质种质资源具有重要意义。本文从以下几个方面对燕麦基因组学研究进展进行综述:(1)燕麦属基因组类型、大小及染色体倍性研究;(2)基于多种分子标记手段构建燕麦基因组遗传图谱进展;(3)二倍体、六倍体燕麦基因组测序进展;(4)基于数量性状基因座定位和全基因组关联性分析手段对燕麦基因组功能基因的注释研究;(5)燕麦群体基因组/泛基因组学研究。同时对燕麦基因组学研究方向进行了探讨,以期为今后燕麦遗传育种提供参考信息。     

Population genetics of genomics-based crop improvement methods

Many genome-wide association studies (GWAS) in humans are concluding that, even with very large sample sizes and high marker densities, most of the genetic basis of complex traits may remain unexplained. At the same time, recent research in plant GWAS is showing much greater success with fewer resources. Both GWAS and genomic selection (GS), a method for predicting phenotypes by the use of genome-wide marker data, are receiving considerable attention among plant breeders. In this review we explore how differences in population genetic histories, as well as past selection for traits of interest, have produced trait architectures and patterns of linkage disequilibrium (LD) that frequently differ dramatically between domesticated plants and humans, making detection of quantitative trait loci (QTL) effects in crops more rewarding and less costly than in humans.     

Genome-wide association study-based identification genes influencing agronomic traits in rice (Oryza sativa L.)

Rice is one of the most important cereal crops, providing the daily dietary intake for approximately 50% of the global human population. Here, we re-sequenced 259 rice accessions, generating 1371.65 Gb of raw data. Furthermore, we performed genome-wide association studies (GWAS) on 13 agronomic traits using 2.8 million single nucleotide polymorphisms (SNPs) characterized in 259 rice accessions. Phenotypic data and best linear unbiased prediction (BLUP) values of each of the 13 traits over two years of each trait were used for the GWAS. The results showed that 816 SNP signals were significantly associated with the 13 agronomic traits. Then we detected candidate genes related to target traits within 200 kb upstream and downstream of the associated SNP loci, based on linkage disequilibrium (LD) blocks in the whole rice genome. These candidate genes were further identified through haplotype block constructions. This comprehensive study provides a timely and important genomic resource for breeding high yielding rice cultivars.     

Genome-Wide Association Mapping for Yield and Other Agronomic Traits in an Elite Breeding Population of Tropical Rice (Oryza sativa)

Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.