利用动态转录组学挖掘大豆百粒重候选基因

百粒重是影响大豆产量的重要农艺性状,揭示其分子基础发掘关键候选基因对大豆改良具有重要意义。本研究通过对12个大豆品种籽粒发育3个时期共36个样本的转录组数据进行加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA),得到20个基因共表达模块,与百粒重及4个粒形性状关联后发现green模块与表型最为相关,之后根据Gene Significance (GS)值和Eigengene Connectivity(kME)值筛选出13个green模块内的核心基因(hub gene);然后对2组百粒重存在极显著差异的大豆品种的籽粒发育3个时期分别进行基因差异表达分析发现大豆在籽粒发育前中期可能通过MAPK信号通路调节百粒重大小;之后对其进行SNPs/InDels挖掘并根据GeneOntology(GO)注释发现green模块内的Glyma.14G043900和Glyma.15G217400由于SNP变异造成同义以及非同义突变,且存在调控基因表达相关的GO Terms以及锌指结构域,表明它们可能通过调控hub基因和差异表达基因调...     

大豆百粒重相关基因的全基因组发掘分析

百粒重是大豆重要的产量性状之一,利用正向和反向遗传学方法鉴定与籽粒大小/粒重相关的基因具有重要的理论和实践意义。利用拟南芥和水稻等模式植物中已明确功能的调控籽粒大小/粒重的基因,基于序列相似和结构域相同的原则,在大豆全基因组内筛选到175个同源基因,通过基因表达谱分析发现有22个基因在大豆种子中特异性表达。利用56份大豆种质资源重测序数据查找这些基因内的SNP位点,共得到2769个SNP位点,从中筛选得到在野生大豆和栽培大豆中分化明显的SNP位点121个。通过扩增测序对其中的16个导致非同义变异的SNP位点进行验证,发现有5个SNP位点在野生大豆中为一种变异,而在栽培大豆中为另一种变异。利用2368份大豆种质资源的重测序数据获得了其中的4个SNP位点的变异数据,结合其中1695份材料的百粒重表型分析,发现每个SNP位点对应的野生和突变基因型材料的百粒重表型间都存在极显著差异,并且每个SNP位点中野生基因型材料的百粒重大部分≥12g,突变基因型材料的百粒重大部分＜12g,因此上述4个SNP位点所在的基因（Glyma.05G019800、Glyma.07G022800、Glyma.13G259700和Glyma.13G261700）可能与大豆籽粒大小/粒重的调控有关。获得了与大豆百粒重相关的4个候选基因,为大豆百粒重QTL的精细定位和功能标记的开发以及调控大豆籽粒大小/粒重的基因的功能研究提供了参考。     

大豆蛋白及百粒重 QTL 研究进展

大豆在我国有悠久的种植历史,是重要的粮油作物。传统的大豆育种方法耗时长,随着分子遗传学的不断发展以及分子标记技术的不断改进,分子标记辅助育种为加快育种进程提供新的思路。综述了 QTL 在农作物研究中的应用、不同大豆QTL 定位方法的优点与不足、大豆 QTL 作图群体的不断探索与应用以及不同作图群体的优缺点、大豆重要的品质蛋白和产量性状百粒重 QTL 的研究进展以及应用现状,并且对 QTL 定位技术以及研究存在的不完善之处进行讨论与展望,为未来更加深入的研究提供参考。     

大豆百粒重QTL定位

大豆百粒重是产量构成的重要因素之一,与产量呈正相关。本研究以溧水中子黄豆和南农493-1的504个F2正反交单株及其亲本间具有多态性的150个SSR标记信息构建连锁图谱,2008年分别在江苏南京和山东临沂两地种植其衍生的正反交F2:4家系,鉴定其百粒重,应用Win QTL CartographerV2.5复合区间作图法和两地正反交联合分析进行QTL定位。结果表明,复合区间作图法检测到16个主效QTL,联合分析检测到24个主效QTL、环境效应与细胞质效应、1个环境×QTL互作和12个细胞质×QTL互作。其中,两方法共同检测到10个主效QTL,正反交群体在两地中共同检测到3个主效QTL;Meta分析发现与其他研究一致的4个QTL。这些结果为大豆产量遗传与标记辅助育种实践提供理论基础。     

菜用大豆百粒重与主要品质性状的灰色关联度分析

为探明菜用大豆主要品质性状与百粒重的关系,采用灰色关联度分析法,对全国不同地区的 51 个菜用大豆品种的主要品质性状及百粒重进行关联分析。结果表明,51 个菜用大豆品种中亚油酸含量的变异幅度最大,其次是油酸含量,不同氨基酸含量的变异幅度均比较小;依据 12 个性状进行聚类分析,可将 51 份菜用大豆品种分成 4 个类群,类群Ⅰ为综合性状比较均衡的 23 个品种,类群Ⅱ为粗脂肪含量和亚油酸含量高的 15 个品种,类群Ⅲ为油酸含量高的 12 个品种,类群Ⅳ只有 1 个品种,具有粗蛋白含量和氨基酸含量高的特点;主要品质性状与百粒重的关联度依次为：天冬氨酸含量 > 棕榈酸含量 > 谷氨酸含量> 赖氨酸含量 > 油酸含量 > 亮氨酸含量 > 粗蛋白含量 > 精氨酸含量 > 粗纤维含量 > 粗脂肪含量 > 亚油酸含量。因此,在菜用大豆大粒品种的育种过程中,可以优先对天冬氨酸含量、棕榈酸含量、谷氨酸含量等性状进行选择。     

蓖麻种子百粒重与发芽率的关系

试验表明，蓖麻种子百粒重与种子发芽率之间呈极显著正相关。百粒重从13.2g到24.5g每增加1g，种子发芽率增加4．31个百分点。     

野生大豆染色体片段代换系群体中与百粒重关联的野生片段及其候选基因

一年生野生大豆是栽培大豆的祖先,在长期驯化改良过程中,百粒重逐渐增大,阐明该变化的遗传基础,对大豆的进化研究与品种改良具有重要意义。为了解析大豆百粒重驯化的遗传基础,本研究以177份全基因组重测序的野生大豆染色体片段代换系(SojaCSSLP5)为材料,通过3个不同环境的表型评价,检测到13个与大豆百粒重相关的野生染色体片段,均具有减小大豆百粒重的加性效应,变幅为-0.49～-1.19 g,这与野生大豆具有较小百粒重相符。检测到的这些野生染色体片段分布在大豆11条染色体上,可以解释76.70%的表型变异,单个片段表型贡献率变幅为2.45%～15.14%。其中片段Gm03＿LDB＿15和Gm12＿LDB＿46的贡献率超过10%,为大豆百粒重由野生向栽培进化的大效应片段。结合双亲栽培大豆南农1138-2和野生大豆N24852的转录组数据和基因组数据,在这些区段内共预测到13个百粒重候选基因,涉及以下调控植物种子大小的途径:泛素蛋白激酶调控途径、G蛋白信号途径、裂原活化蛋白激酶途径、植物激素途径、转录调控因子途径和HAIKU途径。与前人利用栽培大豆的研究结果相比,本研究检测到13个大豆百粒重...     

群体构成方式对大豆百粒重全基因组选择预测准确度的影响

百粒重是大豆产量的重要构成因子,在一定条件下与产量呈显著正相关。百粒重是一个复杂的数量性状,用传统的育种方法其遗传增益不明显。本研究对280份大豆品种多年多点田间鉴定,通过混合线性模型预测获得品种百粒重的最佳线性无偏预测值。同时利用分布在大豆全基因组的5361个SNP标记鉴定参试品种基因型,结合随机回归最佳线性无偏预测模型和交互验证方法,探讨了群体构成方式对大豆百粒重的全基因组选择预测准确度的影响。结果表明,大豆百粒重的全基因组选择预测准确度变化范围为–0.15~ +0.75;群体构成方式对百粒重的预测准确度影响明显;亚群内的预测准确度(0.24~0.75)高于亚群间(?0.15~ +0.29);当群体间遗传距离由0.1566增加到0.2201时,预测准确度下降27.87%;相比随机构建的训练群体,基于群体遗传结构构建的训练群体能将百粒重的预测准确度提高2.34%。本研究明确了大豆百粒重的全基因组选择预测准确度,阐明了群体结构对大豆百粒重的全基因组选择预测准确度的影响,为大豆分子育种提供了新的思路和方法。     

玉米自交系杂交当代母本籽粒百粒重变化的研究

通过2003～2004年两年的试验，初步进行了玉米自交系杂交当代母本籽粒百粒重变化的分析研究。结果表明，多数组合杂交当代的母本籽粒的百粒重高于自交。     

爆裂玉米百粒重性状的主基因+多基因混合遗传分析

应用植物数量性状主基因+多基因混合遗传模型多世代联合分析方法,研究了爆裂玉米吉爆902(吉812×吉704)的P1、P2、F1、B1:2、B2:2和F2:3 6个世代百粒重的遗传.结果表明:百粒重性状的遗传适合D-2模型,即1对加性主基因+加性-显性多基因模型.爆裂玉米的百粒重是由1对独立主基因控制的加性遗传,主基因的...     

Identification of quantitative trait loci underlying soybean (Glycine max) 100-seed weight under different levels of phosphorus fertilizer application

The “100-seed weight (100-SW)” trait is an important component of soybean quality and yield. Phosphorus (P) deficiency induces a wide array of metabolic effects that limits plant growth, especially in soybean. In this study, the 100-SW values of a recombinant inbred line (RIL) population constructed by a cross between “Zhongdou27” and “Jiunong20” were evaluated in three tested environments under regular P and low P level conditions. We detected 12 additive quantitative trait loci (QTL) on nine linkage groups, explaining 8.11%–17.21% of the total phenotype variations. Two of these identified QTLs, qSW2-3 and qSW17-2, were identified in multi-environments both under regular and low P level conditions, and explained 10.10%–14.11% and 10.12%–12.06% of the observed variations, respectively. One QTL, qSW17-2, was novel which has been reported for the first time. Additionally, three QTLs (qSW10-1, qSW13-1 and qSW17-1) were detected under low P conditions and the other QTLs were detected specifically under regular P levels. These particular QTLs improve our understanding of the genetic basis of P efficiency in soybean.     

Identification and validation of quantitative trait loci associated with seed yield in soybean

In soybean [Glycine max (L.) Merrill], the genetic analysis of seed yield is important to aid in the breeding of high-yielding cultivars. Seed yield is a complex trait, and the number of quantitative trait loci (QTL) involved in seed yield is high. The aims of this study were to identify QTL associated with seed yield and validate their effects on seed yield using near-isogenic lines. The QTL analysis was conducted using a recombinant inbred line population derived from a cross between Japanese cultivars ‘Toyoharuka’ and ‘Toyomusume’, and eight seed yield-associated QTL were identified. There were significant positive correlations between seed yield and the number of favorable alleles at QTL associated with seed yield in the recombinant inbred lines for three years. The effects of qSY8-1, a QTL promoting greater seed yield, was validated in the Toyoharuka background. In a two-year yield trial, the 100-seed weight and seed yield of Toyoharuka-NIL, the near-isogenic line having the Toyomusume allele at qSY8-1, were significantly greater than those of Toyoharuka (106% and 107%, respectively) without any change for days to flowering and maturity. Our results suggest that qSY8-1 was not associated with maturity genes, and contributed to the 100-seed weight.     

甘蓝型油菜千粒重全基因组关联分析

千粒重是油菜产量构成的重要因素之一。本研究利用高通量SNP芯片对496份具有代表性的油菜种质资源进行基因型分析,考察群体在3个环境(14NJ、15TZ、16TZ)中的千粒重表型,利用混合线性模型(mixed linear model,MLM)和一般线性模型(general linear model,GLM)进行全基因组关联分析。结果表明,本群体在3个环境中千粒重的广义遗传力为63.12%。MLM模型检测到6个显著位点,解释28.92%的表型变异;GLM模型检测到61个显著位点,解释47.08%的表型变异。合并共同位点后得到62个显著位点,联合解释47.31%的表型变异。这些位点分布在基因组所有染色体上,在A07、A03和C06染色体上分别检测到数目最多的9、8和7个位点。其中效应最大的位点Bn-scaff_17526_1-p1066214位于C09染色体,在MLM和GLM模型中表型贡献值分别为5.55%和15.26%。21个位点与前人报道的QTL重叠,其中8个位点得到至少2个群体的验证。其余41个位点为新鉴定的位点,其中多个位点效应高且在多环境中被检测到,如位点Bn-A03-p560769、Bn-scaff_15743_1-p599416和Bn-scaff_15743_1-p590955等。在11个位点附近找到DGAT、EOD3、AGL61、WRI1、DA2、RAV1等拟南芥已报道千粒重基因的同源基因。本研究结果有助于解析甘蓝型油菜千粒重的遗传基础,为研究千粒重的调控机制、指导千粒重的遗传改良奠定基础。     

Identification of QTLs for growth period traits in soybean using association analysis and linkage mapping

The growth period traits of soybean (Glycine max L. Merr.) are quantitatively inherited and crucial for its adaptation to different environments. Association analysis and linkage mapping were used to identify the quantitative trait loci (QTLs) for days to flowering (DF), days from flowering to maturity (DFM) and days to maturity (DM). Considering the effect of sowing date, the phenotypes were evaluated in three or four sowing‐date‐experiments in each year. A total of 96 associations, involving 19 SSRs corresponding to DF, DFM and/or DM, were identified by association mapping. Six, eight and two QTLs were observed relating to DF, DFM and DM by linkage mapping, respectively, and some QTLs were shared by DF, DFM and DM. Four SSRs (Satt150, Satt489, Satt172 and Sat_312) were found to be related to the growth period traits using the two mapping methods. In summary, association analysis and linkage mapping can complement and verify results from both methods to identify QTLs in soybean, and these findings may be useful in facilitating the selection of growth period–related traits via marker‐assisted selection.     

Identification of quantitative trait loci (QTLs) for seed protein concentration in soybean and analysis for additive effects and epistatic effects of QTLs under multiple environments

Soybean protein concentration is a key trait driver of successful soybean quality. A recombination inbred lines derived from a cross between ‘Charleston’ and ‘Dongnong594’, were planted in three environments across four years in China. Then, the genetic effects were partitioned into additive main effects, epistatic main effects and their environment interaction effects by using composite interval mapping, multiple interval mapping and composite interval mapping in a mixed linear model. Forty‐three quantitative trait loci QTLs were identified on 17 of 20 soybean chromosomes excluding Ch 7, Ch 8 and Ch 17. Two QTLs showed a good stability across multiple environments, qPRO20‐1 was detected under four environments, which explained 4.4–9.95% phenotypic variances and the allele was from ‘Charleston’ among four environments. qPRO7‐5 was detected under three environments, which explained 7.2–14.5% phenotypic variances and the allele was from ‘Dongnong 594’, three pathway genes of protein biosynthesis were detected in the interval of qPRO7‐5. The additive main‐effect QTLs contributed more phenotypic variation than the epistasis and environmental interaction. This indicated that it is feasible by marker‐assisted selection to improve soybean protein concentration.     

小豆百粒重性状遗传体系分析

小豆百粒重是具有重要经济价值的商品品质性状。研究百粒重的遗传体系对杂交亲本的选配、杂种分离世代的选择具有实践指导意义。本文利用子粒大小不同的5个小豆亲本组配了4个杂交组合,对其衍生后代家系群体百粒重性状的遗传体系应用主基因+多基因家系世代联合分离分析方法进行了分析。结果表明,B-1(大粒)×HB801(大粒)和HB8     

Identification of quantitative trait loci and candidate genes controlling the tocopherol synthesis pathway in soybean (Glycine max)

A recombinant inbred line (RIL) population was used to identify quantitative trait loci (QTLs) and their candidate genes controlling the tocopherol (Toc) synthesis pathway. The RIL population was cultivated in field conditions in 3 years. A genetic map constructed using 1624 DNA markers was used for QTL analysis. We identified 22 QTLs for seed tocopherol contents and their ratios, of which two QTL clusters on chromosomes (Chr) 9 and 14 exerted consistent large effects on tocopherol composition across the 3 years. The QTL cluster localized on Chr 9 might correspond to γ-TMT3, which controls the conversion of γ-Toc into α-Toc. The QTL cluster localized on Chr 14 was novel, which might regulate the conversion of MPBQ (a precursor of δ-Toc) into DMPBQ (the precursor of γ-Toc). The effect of the QTL cluster on Chr 14 was validated in a pair of near isogenic lines, and its candidate gene was mined. The identified QTLs and their candidate genes might be used in breeding programmes to improve α-Toc content in soybean seeds.     

大豆叶柄角的QTL定位分析

叶柄角是大豆株型的重要构成因素,影响大豆冠层结构、光合作用效率以及最终产量。解析大豆叶柄角的遗传基础对提升大豆产量具有重要意义。本研究以2个叶柄角具有显著差异的亲本BLA和SLA以及它们衍生的RIL群体为材料,构建高密度的遗传图谱,对大豆不同部位的叶柄角进行QTL分析,并利用近等基因系验证部分QTL。遗传分析结果显示,叶柄角呈连续正态分布,符合数量性状遗传特征。利用GBS技术构建了包含859个Bin标记的大豆高密度遗传图谱,总遗传长度为2326.9cM,标记间平均距离为2.763cM;共检测到14个调控叶柄角的QTL,LOD值在2.58~4.80之间,可解释遗传变异范围在6.9%~12.4%之间,其中5个QTL定位在第12染色体上且成簇存在;构建的qLA12和qLA18的近等基因系表型结果显示,叶柄角在同一对近等基因家系间差异显著,表明qLA12和qLA18是2个可信的QTL。本研究为进一步克隆调控叶柄角功能基因奠定了基础,为大豆理想株型育种提供了遗传材料。     

Identification of population-specific QTLs for flowering in soybean

Flowering is an important stage in plant development and crucial for adaptation of plant species to different environments. Two soybean mapping populations were used to identify quantitative trait loci (QTLs) for days to flowering (DF) and days to maturity (DM) by genotyping simple sequence repeat (SSR) markers. Single-factor analysis of variance detected association of phenotypic data with SSR markers in each population. DF QTLs were identified on four chromosomes (chrs.); two QTLs located on chrs. 2 and 13 with Satt041 and Satt206 in the Jinpumkong 2 × SS2-2 population and other two DF QTLs were detected on chrs. 6 and 19 with Satt100 and Satt373 in the Iksannamulkong × SS2-2 population. The major QTLs associated with Satt100 explained 30.3% of maximum phenotypic variation. Especially, all DF QTLs included QTLs for DM, except Satt206 on chr. 13. Moreover, two additional DM QTLs were mapped on chrs. 10 and 11 with Satt243 and Satt359, respectively. DF QTL on chr. 2 with Satt041 was the newly identified QTL only in the Jinpumkong 2 × SS2-2 population and explained 10.3% of the phenotypic variation. The single locus of Satt100 on chr. 6 and Satt373 on chr. 19 were located on soybean genomic regions of the known flowering gene loci E1 and E3, respectively. These population-specific QTLs (Satt100 and Satt373) are the major QTLs for flowering time, putatively, they may be related to maturity QTLs with large effect. Additionally, these QTLs are valuable for marker-assisted approaches and could be widely adopted by soybean breeders.