小麦主胚根生长及主要性状全基因组关联分析

为解析小麦初生根系建成的遗传机制,本研究以黄淮麦区的198份小麦自然群体为材料,对在室内人工气候箱内水培21 d的小麦主胚根的一级分枝根数、分枝密度、长度、表面积、体积和平均直径6个性状进行调查分析,结合660K基因芯片用Q+K混合线性模型对主胚根性状进行全基因组关联分析,并对显著且稳定的关联位点进行功能注释和候选基因挖掘。结果表明,主胚根不同性状呈正态或近似正态分布,变异系数为5.56%～22.10%。通过全基因组关联分析,共检测到136个显著关联位点,这些位点分布在除7B以外的染色体上,可解释5.10%～13.60%的表型变异,同时检测到13个显著的多效位点,挖掘到TraesCS4A01G023100、TraesCS1B01G294400、TraesCS4A01G006200等16个可能与主胚根生长相关的候选基因,这些基因可能通过调控DNA拓扑结构异构酶、泛素结合酶E2、磷酸肌醇磷酸酶家族蛋白等参与小麦主胚根系的建成。本研究结果为小麦根系调控网络构建,以及优化根系构型和发挥根系功能提供了参考。     

干旱胁迫下小麦苗期根系性状的全基因组关联分析

研究小麦根系在干旱逆境下的形态特征和遗传机制是提升小麦抗旱能力并获得稳产的基础。本研究以300份国内外小麦品种（系）为材料,苗期采用PEG-6000模拟干旱胁迫对小麦根系的最长根长、根总长、根表面积、根体积、根平均直径、根尖数、根鲜重和根干重等8个性状进行表型鉴定,结合90K SNP芯片对8个性状的抗旱系数进行全基因组关联分析,并对稳定遗传的显著关联位点进行候选基因的挖掘。研究结果表明,干旱胁迫下小麦品种（系）的根系性状表现出丰富的表型变异,变异系数为0.17～0.58,全基因组多态信息量变异范围为0.01～0.38,LD衰减距离为7 Mb。群体结构分析表明,供试材料分为3个亚群。GWAS分析显示,共检测到与8个根系性状显著关联的41个SNP位点,单个遗传位点可解释3.91%～8.04%的表型变异。同时在两个及两个以上的性状中发现显著关联位点13个,其中Tdurum＿contig71499＿211(5A)、GENE-1743＿858(3B)、Tdurum＿contig28552＿211(5B)3个位点与4～5个性状显著关联,分别能解释遗传变异的4.12%～5.37%、5.77%～6.7...     

甘蓝型油菜籽粒着生密度及其相关性状全基因组关联分析

油菜角果内籽粒着生密度影响每果粒数,对油菜产量有直接或间接影响。本研究以不同遗传背景和地理来源的213份甘蓝型油菜品种(系)构成的自然群体为研究对象,利用芸薹属60 K Illumina Infinium SNP芯片进行群体结构、亲缘关系以及连锁不平衡分析;然后基于最优模型对籽粒着生密度及其相关性状进行全基因组关联(GWAS)分析。通过GWAS分析,共检测到10个SNP位点与籽粒着生密度及其相关性状关联。其中与籽粒着生密度关联的标记有2个,表型贡献率分别为9.49%和11.17%;与角果有效长关联的标记有6个,单个位点可解释9.81%~12.17%的表型变异;与每果粒数相关联的标记有2个,分别解释10.44%和10.87%的表型变异。通过分析关联SNP位点的LD区间的基因信息,筛选出16个与籽粒着生密度及其相关性状有关的候选基因,其中KMD4和UGT76C2基因与细胞分裂素的调控有关;AGL104和ADC2基因参与种子的形成过程;MCCB、NGA2和MATE等基因参与侧生器官的生长发育过程,它们的异常表达会导致一些侧生器官的变异。ADC2和UGT76C2两个候选基因可能对籽粒着生密度和每果粒数一因多效性。     

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。     

甘蓝型油菜每角果粒数全基因组关联分析

为挖掘甘蓝型油菜每角果粒数显著关联单核苷酸多态性（SNP）位点及相关候选基因。本研究以300份甘蓝型油菜自交系为试验材料,对甘蓝油菜每角果粒数进行一年两地表型考察,并结合该群体前期开发的201 817个SNPs标记,采用一般线性模型（GLM）和混合线性模型（MLM）进行全基因组关联分析（GWAS）,此外,对性状显著关联SNP位点两侧100 kb区域内相关候选基因进行功能预测。300份甘蓝型油菜每角果粒数在两地均表现出广泛的表型变异,筛选出2份每角果粒数较多的油菜种质资源。基于GLM模型检测到39个与油菜每角果粒数显著关联SNPs,采用MLM分析发现,两地共检测到的3个每角果粒数显著关联SNPs位点均在GLM检测到。8个位点附近找到CIK,ERF022和EDE1等19个拟南芥已报道角果籽粒发育相关的同源基因。研究结果有助于解析甘蓝型油菜每角果粒数的遗传基础,为研究每角果粒数的调控机制、指导每角果粒数的遗传改良奠定基础。     

小麦根部耐盐性状全基因组关联分析

为了解小麦耐盐相关性状的遗传机理,挖掘与小麦耐盐性显著相关的SNP位点及候选基因,本研究利用浓度200 mmol/L的NaCl溶液和正常营养液对全国300份小麦品种(系)进行耐盐性试验,并利用小麦90 K芯片对分布于小麦全基因组的16650个SNP,采用Q+K关联混合模型对小麦最长根长、根干重、根鲜重、根平均直径、根尖...     

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。     

普通小麦小穗粒数性状全基因组关联分析

为发掘小麦小穗粒数相关基因位点,以384个重要小麦品种（系）组成的自然群体为材料,利用3个环境获得的表型和55K SNP芯片分型数据进行全基因组关联分析。结果发现,142个SNP和小穗粒数显著关联,解释的表型变异范围为3.27%～6.09%。有8个SNP在2或3个环境下与小穗粒数显著关联,其中AX-109986855、AX-109875224和AX-109843323位于2D染色体523.12～526.25 Mb区段,AX-111054388和AX-110671159在2B染色体上物理距离仅0.62 Mb。这8个SNP位点中,每个SNP的2个等位变异在3个环境的小穗粒数均达到显著水平（P<0.01）,例如,2D染色体上AX-109843323位点G/G等位变异在3个环境的平均小穗粒数分别比C/C等位变异增加0.32、0.37和0.39粒。8个SNP位点的优异等位变异在供试材料的分布比例为5.20%～76.80%,其中7个优异等位变异的分布频率低于45.00%。进一步分析小穗粒数优异等位变异对穗粒数的影响,发现8个SNP位点具有优异等位变异的材料穗粒数（48.45～53.61粒）明...     

玉米穗部性状及其一般配合力的关联分析

穗部性状是影响玉米产量的重要性状,一般配合力是评价玉米自交系利用价值的重要指标。为解析穗部性状及其一般配合力的遗传基础,本研究对248份玉米自交系组成的自然群体和以其中100份自交系为母本按照NCⅡ遗传交配设计与4个测验种(Mo17、昌7-2、E28和郑58)组配的400份F1杂交组合的穗部性状进行研究,并利用分布于全基因组的83057个SNP标记进行穗部性状及其一般配合力的关联分析。结果表明,穗长、穗粗2个穗部性状基因型间、环境间差异达极显著水平,其广义遗传率分别为81.22%和87.70%。母本间、父本间及不同杂交组合间穗长、穗粗差异均达极显著水平,在基因型方差中特殊配合力贡献率较大。利用2年2点4个环境下的数据分别进行关联分析,检测到34个性状SNP关联,利用BLUP值检测到7个性状SNP关联。这些性状SNP关联可解释的表型变异为0.01%～19.42%,其中有5个性状SNP关联的表型贡献率大于10%,未检测到穗部性状本身与一般配合力性状的相同SNP位点。基于该群体的LD衰减距离在显著关联SNP位点上下游各120 kb范围内进行候选基因搜索,共发现158个候选基因,推测可能的候选基因涉及泛素代谢相关基因(GRMZM2G360374、GRMZM2G049568、GRMZM2G178120),β半乳糖苷酶(GRMZM2G178106),丝氨酸苏氨酸蛋白激酶(GRMZM2G127050),赖氨酸和组氨酸特异性转运体(GRMZM2G116004)。研究结果为解析玉米穗长和穗粗及其一般配合力的遗传基础和分子辅助选择育种提供了参考。     

复杂疾病的遗传易感基因区域的精细定位

以单核苷酸多态性(Single-nucleotide polymorphism, SNP)为遗传标记, 采用全基因组关联研究(Genome-wide association studies, GWAS)的策略, 已经在660多种疾病(或性状)中发现了3800多个遗传易感基因区域。但是, 其中最显著关联的遗传变异或致病性的遗传变异位点及其生物学功能并不完全清楚。这些位点的鉴定有助于阐明复杂疾病的生物学机制, 以及发现新的疾病标记物。后GWAS时代的主要任务之一就是通过精细定位研究找到复杂疾病易感基因区域内最显著关联的易感位点或致病性的易感位点并阐明其生物学功能。针对常见变异, 可通过推断或重测序增加SNP密度, 寻找最显著关联的SNP位点, 并通过功能元件分析、表达数量性状位点(Expression quantitative trait locus, eQTL)分析和单体型分析等方法寻找功能性的SNP位点和易感基因。针对罕见变异, 则可采用重测序、罕见单体型分析、家系分析和负荷检验等方法进行精细定位。文章对这些策略和所面临的问题进行了综述。     

Genome‐wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China

Bread wheat is a leading cereal crop worldwide. Limited amount of superior allele loci restricted the progress of molecular improvement in wheat breeding. Here, we revealed new allelic variation distribution for 13 yield‐related traits in series of genome‐wide association studies (GWAS) using the wheat 90K genotyping assay, characterized in 163 bread wheat cultivars. Agronomic traits were investigated in 14 environments at three locations over 3 years. After filtering SNP data sets, GWAS using 20 689 high‐quality SNPs associated 1769 significant loci that explained, on average, ~20% of the phenotypic variation, both detected already reported loci and new promising genomic regions. Of these, repetitive and pleiotropic SNPs on chromosomes 6AS, 6AL, 6BS, 5BL and 7AS were significantly linked to thousand kernel weight, for example BS00021705_51 on 6BS and wsnp_Ex_c32624_41252144 on 6AS, with phenotypic variation explained (PVE) of ~24%, consistently identified in 12 and 13 of the 14 environments, respectively. Kernel length‐related SNPs were mainly identified on chromosomes 7BS, 6AS, 5AL and 5BL. Plant height‐related SNPs on chromosomes 4DS, 6DL, 2DS and 1BL were, respectively, identified in more than 11 environments, with averaged PVE of ~55%. Four SNPs were confirmed to be important genetic loci in two RIL populations. Based on repetivity and PVE, a total of 41 SNP loci possibly played the key role in modulating yield‐related traits of the cultivars surveyed. Distribution of superior alleles at the 41 SNP loci indicated that superior alleles were getting popular with time and modern cultivars had integrated many superior alleles, especially for peduncle length‐ and plant height‐related superior alleles. However, there were still 19 SNP loci showing less than percentages of 50% in modern cultivars, suggesting they should be paid more attention to improve yield‐related traits of cultivars in the Yellow and Huai wheat region. This study could provide useful information for dissection of yield‐related traits and valuable genetic loci for marker‐assisted selection in Chinese wheat breeding programme.     

Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)

Salinity tolerance in rice is highly desirable to sustain production in areas rendered saline due to various reasons. It is a complex quantitative trait having different components, which can be dissected effectively by genome-wide association study (GWAS). Here, we implemented GWAS to identify loci controlling salinity tolerance in rice. A custom-designed array based on 6,000 single nucleotide polymorphisms (SNPs) in as many stress-responsive genes, distributed at an average physical interval of <100 kb on 12 rice chromosomes, was used to genotype 220 rice accessions using Infinium high-throughput assay. Genetic association was analysed with 12 different traits recorded on these accessions under field conditions at reproductive stage. We identified 20 SNPs (loci) significantly associated with Na⁺/K⁺ ratio, and 44 SNPs with other traits observed under stress condition. The loci identified for various salinity indices through GWAS explained 5–18% of the phenotypic variance. The region harbouring Saltol, a major quantitative trait loci (QTLs) on chromosome 1 in rice, which is known to control salinity tolerance at seedling stage, was detected as a major association with Na⁺/K⁺ ratio measured at reproductive stage in our study. In addition to Saltol, we also found GWAS peaks representing new QTLs on chromosomes 4, 6 and 7. The current association mapping panel contained mostly indica accessions that can serve as source of novel salt tolerance genes and alleles. The gene-based SNP array used in this study was found cost-effective and efficient in unveiling genomic regions/candidate genes regulating salinity stress tolerance in rice.     

A genome-wide association study uncovers novel genomic regions and candidate genes of yield-related traits in upland cotton

Key message

A total of 62 SNPs associated with yield-related traits were identified by a GWAS. Based on significant SNPs, two candidate genes pleiotropically increase lint yield.

Abstract

Improved fibre yield is considered a constant goal of upland cotton (Gossypium hirsutum) breeding worldwide, but the understanding of the genetic basis controlling yield-related traits remains limited. To better decipher the molecular mechanism underlying these traits, we conducted a genome-wide association study to determine candidate loci associated with six yield-related traits in a population of 719 upland cotton germplasm accessions; to accomplish this, we used 10,511 single-nucleotide polymorphisms (SNPs) genotyped by an Illumina CottonSNP63K array. Six traits, including the boll number, boll weight, lint percentage, fruit branch number, seed index and lint index, were assessed in multiple environments; large variation in all phenotypes was detected across accessions. We identified 62 SNP loci that were significantly associated with different traits on chromosomes A07, D03, D05, D09, D10 and D12. A total of 689 candidate genes were screened, and 27 of them contained at least one significant SNP. Furthermore, two genes (Gh_D03G1064 and Gh_D12G2354) that pleiotropically increase lint yield were identified. These identified SNPs and candidate genes provide important insights into the genetic control underlying high yields in G. hirsutum, ultimately facilitating breeding programmes of high-yielding cotton.

     

A large‐scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments

The incorporation of resistance genes into wheat commercial varieties is the ideal strategy to combat stripe or yellow rust (YR). In a search for novel resistance genes, we performed a large‐scale genomic association analysis with high‐density 660K single nucleotide polymorphism (SNP) arrays to determine the genetic components of YR resistance in 411 spring wheat lines. Following quality control, 371 972 SNPs were screened, covering over 50% of the high‐confidence annotated gene space. Nineteen stable genomic regions harbouring 292 significant SNPs were associated with adult‐plant YR resistance across nine environments. Of these, 14 SNPs were localized in the proximity of known loci widely used in breeding. Obvious candidate SNP variants were identified in certain confidence intervals, such as the cloned gene Yr18 and the major locus on chromosome 2BL, despite a large extent of linkage disequilibrium. The number of causal SNP variants was refined using an independent validation panel and consideration of the estimated functional importance of each nucleotide polymorphism. Interestingly, four natural polymorphisms causing amino acid changes in the gene TraesCS2B01G513100 that encodes a serine/threonine protein kinase (STPK) were significantly involved in YR responses. Gene expression and mutation analysis confirmed that STPK played an important role in YR resistance. PCR markers were developed to identify the favourable TraesCS2B01G513100 haplotype for marker‐assisted breeding. These results demonstrate that high‐resolution SNP‐based GWAS enables the rapid identification of putative resistance genes and can be used to improve the efficiency of marker‐assisted selection in wheat disease resistance breeding.     

Genome‐wide detection of genetic loci and candidate genes for teat number and body conformation traits at birth in Chinese Sushan pigs

Body conformation at birth and teat number are economically important traits in the pig industry, as these traits are usually explored to evaluate the growth and reproductive potential of piglets. To detect genetic loci and candidate genes for these traits, we performed a GWAS on 269 pigs from a recently developed Chinese breed (Sushan) using 38  128 informative SNPs on the Affymetrix Porcine SNP 55K Array. In total, we detected one genome‐wide significant (P = 1.31e‐6) SNP for teat number on chromosome X and 15 chromosome‐wide significant SNPs for teat number, body weight, body length, chest circumference and cannon circumference at birth on chromosomes 1, 3, 4, 6, 7, 9, 10, 13, 14, 15, 17 and 18. The most significant SNP had an additive effect of 0.74 × total teat number, explaining 20% of phenotypic variance. Five significant SNPs resided in the previously reported quantitative trait loci for these traits and seven significant SNPs had a pleiotropic effect on multiple traits. Intriguingly, 12 of the genes nearest to the significant SNPs are functionally related to body conformation and teat number traits, including SPRED2, MKX, TMSB4X and ESR1. GO analysis revealed that candidate genes proximal to the significant SNPs were enriched in the G‐protein coupled receptor and steroid hormone‐mediated signaling pathway. Our findings shed light on the genetic basis of the measured traits and provide molecular markers especially for the genetic improvement of teat number in Sushan and related pigs.     

Mapping QTLs for chlorophyll content and chlorophyll fluorescence in wheat under heat stress

Heat stress, one of the major abiotic stresses in wheat, affects chlorophyll fluorescence and chlorophyll content and thereby photosynthesis. To identify quantitative trait loci (QTLs) associated with these traits under terminal heat stress, 251 recombinant inbred lines (RILs) derived from a cross HD 2808/HUW510 were phenotyped. Using composite interval mapping, 40 QTLs were identified; 17 were related to conditions after timely sowing and 23 to heat stress after late sowing. The various parameters of chlorophyll fluorescence were associated with 23 QTLs, which were located on chromosomes 1A, 2A, 3A, and 2D and explained 3.67 to 18.04 % of phenotypic variation, whereas chlorophyll content was associated with 17 QTLs on chromosomes 2A, 2B, 2D, 5B, and 7A explaining 3.49 to 31.36 % of phenotypic variation. Most of the identified QTLs were clustered on chromosome 2D followed by 2A and 1A. The QTL Qchc.iiwbr-2A for chlorophyll content linked with marker gwm372 was stable over conditions and explained 3.81 to 18.05 % of phenotypic variation. In addition, 7 epistatic QTL pairs were also detected which explained 1.67 to 11.0 % of phenotypic variance. These identified genomic regions can be used in marker assisted breeding after validation for heat tolerance in wheat.     

A genome-wide association study of early-maturation traits in upland cotton based on the CottonSNP8oK array

Genome-wide association studies(GWASs)efficiently identify genetic loci controlling traits at a relatively high resolution.In this study,variations in major early-maturation traits,including seedling period(SP),bud period(BP),flower and boll period(FBP),and growth period(GP),of 169 upland cotton accessions were investigated,and a GWAS of early maturation was performed based on a CottonSNP80K array.A total of49,650 high-quality single-nucleotide polymorphisms(SNPs)were screened,and 29 significant SNPs located on chromosomes A6,A7,A8,D1,D2,and D9,were repeatedly identified as associated with early-maturation traits,in at least two environments or two algorithms.Of these 29 significant SNPs,1,12,11,and 5 were related to SP,BP,FBP,and GP,respectively.Six peak SNPs,TM47967,TM13732,TM20937,TM28428,TM50283,and TM72552,exhibited phenotypic contributions of approximately 10%,which could allow them to be used for marker-assisted selection.One of these,TM72552,as well as four other SNPs,TM72554,TM72555,TM72558,and TM72559,corresponded to the quantitative trait loci previously reported.In total,274 candidate genes were identified from the genome sequences of upland cotton and were categorized based on their functional annotations.Finally,our studies identified Gh_D01G0340 and Gh_D01G0341 as potential candidate genes for improving cotton early maturity.