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。     

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。     

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。     

玉米叶夹角形成的遗传基础与分子机制解析*

玉米产量取决于植株捕获光能和固定CO₂合成有机化合物的效率。叶夹角是株型重要性状之一,较小叶夹角有利于提高玉米植株光合作用效率和种植密度,因而有利于提高玉米产量。研究表明玉米叶夹角为多基因控制的复杂数量性状,其遗传力较高,主要受基因的加性效应调控。目前,利用数量性状位点(quantitative trait loci, QTL)定位和全基因组关联分析(genome-wide association study, GWAS)等方法已鉴定数百个玉米叶夹角相关QTL;结合突变体分析等方法,已克隆数十个调控叶夹角关键基因,这为了解玉米叶夹角遗传机制提供了重要参考。由于前人研究所采用群体、分析方法及参考基因组版本不同,各研究之间所鉴定QTL差异较大,因此无法客观揭示叶夹角性状的遗传规律。为此,通过总结前人所定位叶夹角相关QTL和单核苷酸多态性(single nucleotide polymorphism,SNP)位点并构建一致性图谱,鉴定出叶夹角性状定位热点区间,并对调控叶夹角的已知基因进行功能分类。这不仅为了解玉米叶夹角的遗传结构、推动叶夹角相关重要基因克隆提供数据支撑,也对进一步开发叶夹角相关分子标记,指导玉米分子育种和提高玉米产量提供有益指导。     

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。     

玉米株高和穗位高的遗传基础与分子机制*

株高和穗位高是玉米重要育种性状,直接影响植株的养分利用效率及抗倒伏性,进而影响玉米产量。玉米株高和穗位高属于典型数量性状,目前通过数量性状位点(quantitative trait loci mapping,QTL)定位和全基因组关联分析(genome-wide association study, GWAS)等方法已挖掘到较多相关遗传位点,通过QTL精细定位及利用突变体克隆了一些调控株高和穗位高关键基因。但是由于各研究组所利用的群体类型和大小、标记类型和密度以及统计方法不同,所鉴定QTL差异较大,单个研究难以揭示玉米株高和穗位高遗传结构。早期QTL定位的结果多以遗传距离来展示,不同时期GWAS研究所使用参考基因组版本不同,这进一步增加了借鉴和利用前人研究结果的难度。首次将目前已鉴定株高和穗位高遗传定位信息统一锚定至玉米自交系B73参考基因组V4版本,构建了株高和穗位高性状定位的一致性图谱,并鉴定出可被多个独立研究定位的热点区间。进一步对已克隆玉米株高和穗位高调控基因进行总结与分类,揭示株高和穗位高性状调控机制,对深度解析株高和穗位高遗传结构、指导基因克隆和利用分子标记辅助选择优化玉米株高和穗位高性状均具有重要意义。     

茄子果实相关农艺性状全基因组关联分析研究进展与展望

茄子是重要的园艺作物,也是茄科植物中种植最广泛的蔬菜之一。茄子果实相关农艺性状是一种复杂的数量性状,传统育种选育效率低、周期长。高通量测序技术与生物信息学技术的快速发展,使得全基因组关联分析(genome-wide association study, GWAS)在解析茄子果实相关复杂农艺性状的遗传规律方面展现出巨大的应用前景。本文对全基因组关联分析在茄子的果形、果色等果实相关农艺性状中的研究进展进行了综述;针对茄子数量性状遗传研究中普遍存在的“丢失遗传力”(missing heritability)问题,从4个GWAS策略在茄子果实相关农艺性状研究中的应用热点出发,提出了未来茄子GWAS的发展对策;并结合当前茄子遗传改良的实践需求,展望了GWAS策略在茄子分子育种领域的广阔应用前景。本文为今后利用GWAS解析各种茄子果实相关性状的遗传基础以及选育符合消费者需求的果实材料提供了理论依据和参考。     

基于简化基因组测序的大黄鱼耐高温性状全基因组关联分析

利用Illumina HiSeqTM 2500测序平台, 对通过高温胁迫实验筛选得到的20尾耐高温和20尾不耐高温的大黄鱼(Larimichthys crocea)进行了简化基因组测序(SLAF-seq), 每个样本的平均测序深度达到10.26×, 共获得419211个高质量的群体单核苷酸多态性(SNP)位点 。利用TASSEL软件的混合线性模型(MLM)进行全基因组关联分析(GWAS), 共筛选到38个与大黄鱼耐高温性状显著相关的SNP位点(P<2.39E–08)。利用BLAST程序定位每个SNP位点在大黄鱼基因组中的位置, 并分析其周围的功能基因。结果在38个SNPs附近共找到26个已知的功能基因, 这些基因主要与细胞转录、代谢、免疫等功能相关。研究结果可为下一步大黄鱼耐高温分子机制解析及耐高温品种的选育提供参考。     

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。     

京海黄鸡体重性状全基因组关联分析

体重性状是肉鸡重要的经济性状。为了寻找可用于京海黄鸡体重性状遗传改良的分子标记及候选基因,本文以400只京海黄鸡核心群母鸡为基础,测定了0~14周龄体重,利用简化基因组测序技术(Specific-locus amplified fragment sequencing, SLAF-seq)对京海黄鸡体重性状进行全基因组关联研究(Genome-wide association stndy, GWAS),筛选与京海黄鸡体重性状相关的SNPs位点。结果共检测到100个与京海黄鸡体重相关的SNPs位点,其中15个位点效应达到全基因组显著水平(P<1.87E-06),85个位点效应达到全基因组潜在显著水平(P<3.73E-05)。通过筛选每个显著SNP周围1 Mb区域内的基因,共找到9个可能的候选基因,其中FAM124A(Family with sequence similarity 124A)、QDPR(Quinoid dihydropteridine reductase)、WDR1(WD repeat domain 1)和SLC2A9(Solute carrier family 2 (facilitated glucose transporter), member 9) 4个基因可能是影响体重性状的重要候选基因。同时还发现,4号染色体75.6~80.7 Mb区域集中了大部分与京海黄鸡中后期体重性状显著相关的SNPs位点,该区域可能是影响京海黄鸡中后期生长体重的重要候选区域。     

Animal Board Invited Review: Meta-analysis of genetic parameters for resilience and efficiency traits in goats and sheep

Genetic selection focused purely on production traits has proven very successful in improving the productive performance of livestock. However, heightened environmental and infectious disease challenges have raised the need to also improve the resilience of animals to such external stressors, as well as their efficiency in utilising available resources. A better understanding of the relationship between efficiency and production and health traits is needed to properly account for it in breeding programmes and to produce animals that can maintain high production performance in a range of environmental conditions with minimal environmental footprint. The aim of this study was to perform a meta-analysis of genetic parameters for production, efficiency and health traits in sheep and goats. The dataset comprised 963 estimates of heritability and 572 genetic correlations collated from 162 published studies. A threelevel meta-analysis model was fitted. Pooled heritability estimates for milk production traits ranged between 0.27 ± 0.03 and 0.48 ± 0.13 in dairy goats and between 0.21 ± 0.06 and 0.33 ± 0.07 in dairy sheep. In meat sheep, the heritability of efficiency traits ranged from 0.09 ± 0.02 (prolificacy) up to 0.32 ± 0.14 (residual feed intake). For health traits, pooled heritability was 0.07 ± 0.01 (faecal egg count) and 0.21 ± 0.01 (somatic cell score) in dairy goats and 0.14 ± 0.04 (faecal egg count) and 0.13 ± 0.02 (somatic cell score) in dairy sheep. In meat sheep, the heritability of disease resistance and survival traits ranged between 0.07 ± 0.02 (mastitis) and 0.50 ± 0.10 (breech strike). Pooled estimates of genetic correlations between resilience and efficiency traits in dairy goats were not significantly different from zero with the exception of somatic cell score and fat content (?0.19 ± 0.01). In dairy sheep, only the unfavourable genetic correlation between somatic cell score and protein content (0.12 ± 0.03) was statistically significant. In meat sheep only, the correlations between growth and faecal egg count (?0.28 ± 0.11) as well as between growth and dagginess (?0.33 ± 0.13) were statistically significant and favourable. Results of this meta-analysis provide evidence of genetic antagonism between production and health in dairy sheep and goats. This was not observed in meat sheep where most of the pooled estimates had high standard errors and were non-significant. Based on the obtained results, it seems feasible to simultaneously improve efficiency and health in addition to production by including the different types of traits in the breeding goal. However, a better understanding of potential trade-offs between these traits would be beneficial. Particularly, more studies focused on reproduction and resilience traits linked to the animal’s multi-trait response to challenges are required.     

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。     

Genome-wide association study of milk production traits in a crossbred dairy sheep population using three statistical models

Milk production is one of the most important characteristics of dairy sheep, and the identification of genes affecting milk production traits is critical to understanding the genetics and improve milk production in future generations. Three statistical techniques, namely GWAS, ridge-regression BLUP and BayesC , were used to identify SNPs in significant association with three milk production traits (milk yield, fat yield and protein yield) in a crossbred dairy sheep population. The results suggested that chromosomes 1, 3, 4, 5, 7 and 11 were likely to harbor genes important to milk production because these chromosomes had the greatest top-100-SNP variance contributions on the three milk production traits. The GWAS analysis identified between 74 and 288 genome-wide significant SNP (P < 0.05) whereas the BayesCπ model revealed between six and 63 SNPs, each with >95% posterior probability of inclusion as having a non-zero association effect on at least one of the three milk production traits. Positional candidate genes for milk production in sheep were searched, based on the sheep genomic assembly OAR version 3.1, such as those which map position coincided with or was located within 0.1 Mbp of a genome-wide suggestive or significant SNP. These identified SNPs and candidate genes supported some previous findings and also added new information about genetic markers for genetic improvement of lactation in dairy sheep, but keeping in mind that the majority of these positional candidate genes are not necessarily true causative loci for these traits and future validations are thus necessary.     

Optimising the identification of causal variants across varying genetic architectures in crops

Association studies use statistical links between genetic markers and the phenotype variation across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome‐wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures. In this study, we employ real‐world genotype datasets from four crop species with distinct minor allele frequency distributions, population structures and linkage disequilibrium patterns. We demonstrate that different GWAS statistical approaches provide favourable trade‐offs between power and accuracy for traits controlled by different types of genetic architectures. FarmCPU provides the most favourable outcomes for moderately complex traits while a Bayesian approach adopted from genomic prediction provides the most favourable outcomes for extremely complex traits. We assert that by estimating the complexity of genetic architectures for target traits and selecting an appropriate statistical approach for the degree of complexity detected, researchers can substantially improve the ability to dissect the genetic factors controlling complex traits such as flowering time, plant height and yield component.     

Genome-Wide Association Study for Wool Production Traits in a Chinese Merino Sheep Population

Genome-wide association studies (GWAS) provide a powerful approach for identifying quantitative trait loci without prior knowledge of location or function. To identify loci associated with wool production traits, we performed a genome-wide association study on a total of 765 Chinese Merino sheep (JunKen type) genotyped with 50 K single nucleotide polymorphisms (SNPs). In the present study, five wool production traits were examined: fiber diameter, fiber diameter coefficient of variation, fineness dispersion, staple length and crimp. We detected 28 genome-wide significant SNPs for fiber diameter, fiber diameter coefficient of variation, fineness dispersion, and crimp trait in the Chinese Merino sheep. About 43% of the significant SNP markers were located within known or predicted genes, including YWHAZ, KRTCAP3, TSPEAR, PIK3R4, KIF16B, PTPN3, GPRC5A, DDX47, TCF9, TPTE2, EPHA5 and NBEA genes. Our results not only confirm the results of previous reports, but also provide a suite of novel SNP markers and candidate genes associated with wool traits. Our findings will be useful for exploring the genetic control of wool traits in sheep.     

羊慢病毒及其抗性基因研究进展

羊慢病毒也称小反刍动物慢病毒, 主要包括绵羊梅迪–维斯纳病毒和山羊关节炎–脑炎病毒, 二者主要感染绵羊和山羊, 目前该病在世界范围内流行并给养羊业带来很大的经济损失。研究表明, 不同绵羊品种对慢病毒易感性存在差异, 这种差异表明易感性不同的绵羊可能存在遗传多样性的差异。全基因组关联分析发现绵羊跨膜蛋白TMEM154 (Transmembrane protein 154)基因中的一个点突变E35K与抗病力高度相关, 可以作为绵羊抗病选育的分子标记。文章详述了绵羊TMEM154基因E35K突变对抗病力的影响和当前慢病毒抗病基因研究概况, 包括锌指家族、趋化因子受体CCR5、三重基序蛋白TRIM5α、载脂蛋白B mRNA剪辑酶催化多肽样蛋白3、多能发育相关基因2和4, 并简要介绍了羊慢病毒特征和我国羊慢病毒病的流行状况, 以期为我国绵羊养殖业和抗病选育提供参考。     

Heterogeneity of genetic architecture of body size traits in a free‐living population

Knowledge of the underlying genetic architecture of quantitative traits could aid in understanding how they evolve. In wild populations, it is still largely unknown whether complex traits are polygenic or influenced by few loci with major effect, due to often small sample sizes and low resolution of marker panels. Here, we examine the genetic architecture of five adult body size traits in a free‐living population of Soay sheep on St Kilda using 37 037 polymorphic SNPs. Two traits (jaw and weight) show classical signs of a polygenic trait: the proportion of variance explained by a chromosome was proportional to its length, multiple chromosomes and genomic regions explained significant amounts of phenotypic variance, but no SNPs were associated with trait variance when using GWAS. In comparison, genetic variance for leg length traits (foreleg, hindleg and metacarpal) was disproportionately explained by two SNPs on chromosomes 16 (s23172.1) and 19 (s74894.1), which each explained >10% of the additive genetic variance. After controlling for environmental differences, females heterozygous for s74894.1 produced more lambs and recruits during their lifetime than females homozygous for the common allele conferring long legs. We also demonstrate that alleles conferring shorter legs have likely entered the population through a historic admixture event with the Dunface sheep. In summary, we show that different proxies for body size can have very different genetic architecture and that dense SNP helps in understanding both the mode of selection and the evolutionary history at loci underlying quantitative traits in natural populations.     

QTL mapping for sexually dimorphic fitness-related traits in wild bighorn sheep

Dissecting the genetic architecture of fitness-related traits in wild populations is key to understanding evolution and the mechanisms maintaining adaptive genetic variation. We took advantage of a recently developed genetic linkage map and phenotypic information from wild pedigreed individuals from Ram Mountain, Alberta, Canada, to study the genetic architecture of ecologically important traits (horn volume, length, base circumference and body mass) in bighorn sheep. In addition to estimating sex-specific and cross-sex quantitative genetic parameters, we tested for the presence of quantitative trait loci (QTLs), colocalization of QTLs between bighorn sheep and domestic sheep, and sex × QTL interactions. All traits showed significant additive genetic variance and genetic correlations tended to be positive. Linkage analysis based on 241 microsatellite loci typed in 310 pedigreed animals resulted in no significant and five suggestive QTLs (four for horn dimension on chromosomes 1, 18 and 23, and one for body mass on chromosome 26) using genome-wide significance thresholds (Logarithm of odds (LOD) >3.31 and >1.88, respectively). We also confirmed the presence of a horn dimension QTL in bighorn sheep at the only position known to contain a similar QTL in domestic sheep (on chromosome 10 near the horns locus; nominal P<0.01) and highlighted a number of regions potentially containing weight-related QTLs in both species. As expected for sexually dimorphic traits involved in male-male combat, loci with sex-specific effects were detected. This study lays the foundation for future work on adaptive genetic variation and the evolutionary dynamics of sexually dimorphic traits in bighorn sheep.