大豆苗期耐淹性的遗传与QTL分析

涝害是世界上许多国家的重大自然灾害。耐涝性可分为耐湿(渍)性和耐淹性。以科丰1号(高度耐淹)×南农1138-2(不耐淹)衍生的RIL群体(NJRIKY)为材料, 以盆栽全淹条件下的存活率为耐淹性指标, 采用主基因+多基因混合遗传模型分离分析法进行遗传分析, 并利用WinQTL Cartographer Version 2.5程序的复合区间作图法(CIM)及多区间作图法(MIM)进行QTL定位。结果表明, 两次试验的耐淹性均存在超亲变异, 试验间、家系间以及试验与家系互作间的差异均极显著; NJRIKY大豆群体的耐淹性为3对等加性主基因遗传模型, 主基因遗传率为42.40%; 在QTL分析中, 用CIM和MIM共同检测到3个耐淹QTL, 分别位于A1、D1a和G连锁群上的Satt648~K418_2V、Satt531~A941V、Satt038~Satt275 (B53B~Satt038)区间, 表型贡献率为4.4%~7.6%。分离分析与QTL定位的结果相对一致, 可相互印证。     

大豆主茎节数数量性状座定位及遗传效应分析

为了定位控制主茎节数的QTL并明确其遗传效应,利用100对SSR引物,并采用Mapmaker Exp 3.0和复合区间法,研究构建了一张包括3个连锁群的连锁图谱。以‘黑农37’（栽培大豆）×ZYD581（野生大豆）组合的亲本、F₂、F₃为试材,分别在chr1连锁群上定位了一个影响大豆主茎节数的QTL,2007年QTL位于Satt238—Satt242这个区间内,与Satt238的遗传距离是0.01 cM,与Satt242的遗传距离是24.69 cM,其遗传贡献率为17.22%,加性效应为-3.2608;2008年QTL位于Satt238—Satt240之间,与Satt238的遗传距离为0.59 cM,与Satt240的遗传距离为6.01 cM,其遗传贡献率为6.68%,加性效应为-1.4965。2年大豆主茎节数QTL分析表明,在chr1连锁群上Satt238附近确定了1个控制大豆主茎节数QTL位点。     

棉花抗黄萎病基因的QTL定位

以高感黄萎病的陆地棉品种"邯郸208"与高抗黄萎病海岛棉品种"Pima90"的136个F2单株为作图群体,构建了一个包括17个连锁群、标记间平均间距18.61cM、全长1842.8cM的陆海种间分子标记遗传连锁图,该图约覆盖棉花基因组的36.8%。单因子方差分析和复合区间作图检测到与黄萎病抗性相关的3个QTL,分别位于第四连锁群和第七连锁群上,分别解释表型变异方差的15.39%、54.11%和57.18%。初步认为海岛棉"Pima90"对陆地棉"邯郸208"的黄萎病抗性由两个主效QTL和一个微效QTL共同控制。     

棉花重组自交系铃重性状的QTL定位

【目的】铃重是构成棉花产量的基本因子之一。本研究旨在定位铃重QTL(Quantitative trait loci),为分析铃重遗传组成提供参考。【方法】以中棉所36和海陆渐渗系G2005组配的137个RILs (Recombinant inbred lines)家系为作图群体,利用RAD-seq(Restriction-site associated DNA sequencing)技术及SSR(Simple sequence repeat)标记,构建遗传连锁图谱,并对5个环境下的铃重进行QTL分析。【结果】构建了包含26个连锁群、6 434个标记、总长为4 071.98 cM、标记间平均距离为0.63 cM的遗传图谱。采用WinQTLCart 2.5软件的复合区间作图法进行QTL定位,共得到32个铃重QTL,分布于15条染色体,单个位点解释的表型变异率为4.46%~15.84%;qBW-A4-1、qBW-A4-2、qBW-A5-2、qBW-D9-1和qBW-D9-2能够在2个环境中检测到,解释5.07%~15.84%的表型变异率。【结论】本研究定位的主效QTL可用于分析铃重遗传机理。     

甘蓝型油菜胚色素成分的QTL定位

以甘蓝型黄籽油菜GH06和甘蓝型黑籽油菜中油821为亲本杂交,后代通过“一粒传法”连续自交7代构建重组自交系, 2007年分别在重庆市北碚区和万州区两个试验基地种植重组自交系群体, 利用本实验室已构建的遗传连锁图谱和复合区间作图法(CIM), 分析种胚色素的4种主要成分的QTL。结果共检测到31个QTL, 分别位于14个不同的连锁群, 其中5个花色素含量有QTL, 分别位于第1、5、10、16和20连锁群,单个QTL解释表型变异的6.08%~11.67%;10个类黄酮含量有QTL, 分别位于第1、3、6、7、12、20和25连锁群,单个QTL解释表型变异的4.48%~11.10%;8个总酚含量有QTL, 分别位于第1、2、12、16和20连锁群,单个QTL解释表型变异的5.24%~10.37%;8个黑色素含量检测到QTL, 分别位于第5、8、10、12、14和22连锁群,单个QTL解释表型变异的5.44%~11.32%。解释表型变异大于10%的5个QTL, 包括2个类黄酮含量QTL, 花色素含量、总酚含量和黑色素含量QTL各1个,它们分别解释11.10%、10.20%、11.67%、10.37%和11.32%的表型变异。研究结果表明胚色素表现为多基因控制的数量性状, 基因表达受环境影响较大, 胚与种皮色素的QTL吻合度不高, 推测种皮和胚色素合成可能受不同遗传体系控制, 与这些QTL紧密相关的分子标记可以用于胚主要色素的分子标记辅助选择。     

两种环境下甘蓝型油菜含油量差异的QTL分析

利用本实验室构建的遗传连锁图谱和复合区间作图法检测重组自交系GH06×P174(SWU-1)和GH06×中油821(SWU-2)群体在2个环境中含油量差值的QTL。以SWU-1群体在2个环境中检测到2个含油量差值QTL,分别位于2个不同的连锁群,单个QTL可解释表型变异的10.31%~12.45%;以SWU-2群体在2个环境中检测到3个含油量差值QTL,分别位于2个不同的连锁群,单个QTL可解释表型变异的6.60%~10.58%。分析结果表明,含油量受环境影响较大,差值的变异幅度达到0~18.66个百分点,变异系数达到58.24%,说明在油菜的油脂合成中,存在对环境敏感和钝感的基因。含油量差值QTL与2个环境中分别检测到的含油量QTL没有明显的连锁关系,初步分析说明对环境敏感或钝感的基因与油脂合成基因不是同一个系统。     

甘蓝型油菜含油量的遗传与QTL定位

利用主基因+多基因遗传模型对甘蓝型油菜品系APL01（低含油量亲本）与M083（高含油量亲本）杂交所获得的6个基本世代（P₁, P₂, F₁, B₁, B₂, F₂）的含油量进行遗传分析，并以（APL01/M083）BC₁F₁为作图群体，利用251个分子标记，构建了由19个连锁群组成的分子标记遗传图谱，经WinQTLCart 2.0对种子含油量进行QTL扫描。结果表明，该杂交组合种子含油量由1对加性-显性主基因＋加性-显性-上位性多基因控制，主基因遗传率为38.37%~47.16%，多基因遗传率为24.29%~38.28%。共获得qOC1、qOC8、qOC10、qOC13-1和qOC13-2等5个与含油量相关的QTL，其中qOC1位于N1连锁群的m19e21c~A0214Ra142区间，可解释含油量表型变异的5.21%；qOC8位于N8连锁群的A0216Gb206~m5e42区间，可解释含油量表型变异的6.34%；qOC10位于N10连锁群的m15e48~A0228Bb437区间，可解释含油量表型变异的9.45%；qOC13-1位于N13连锁群的A0224Rb157~A0301Gb399区间，可解释含油量表型变异的18.12%；qOC13-2位于N13连锁群的A0226Ba377~A0226Ba367区间，可解释含油量表型变异的10.17%。5个QTL中qOC10和qOC13-2位点APL01对含油量的贡献为正值，qOC1、qOC8和qOC13-1位点M083的贡献为正值。qOC13-1效应值较大，属主效基因位点，其余4个QTL效应相对较小，可作为多基因位点。     

甘蓝型油菜籽粒含油量、蛋白质、纤维素及半纤维素含量QTL分析

以甘蓝型黄籽油菜GH06和甘蓝型黑籽油菜P174为亲本,通过单粒法连续自交8代构建重组自交系群体,应用SSR标记绘制31个连锁群(LGs)的遗传连锁图谱,图谱总长1437.1 cM,相邻标记间的平均距离为3.89 cM。对4个不同环境下RIL8群体中每个株系籽粒含油量、蛋白质、纤维素和半纤维素含量进行了近红外分析,性状相关性表明含油量与其他3个性状均表现负相关,蛋白质含量与纤维素和半纤维素分别表现负相关和正相关。结合构建的遗传图谱采用复合区间作图法分析4个性状QTL,共检测到26个QTL,分布在N2、N3、N8、N9、N11、N13、N16和N17连锁群上,其中8个含油量QTL可解释表型变异的4.96%~21.83%;6个蛋白含量QTL,可解释表型变异的3.12%~14.28%;4个纤维素含量QTL,可解释表型变异的4.60%~17.29%;8个半纤维素含量QTL,可解释表型变异率的6.66%~16.68%。在N8上,发现有含油量QTL与半纤维素含量QTL重叠的区段。在N9上,发现有纤维素含量QTL与半纤维素含量QTL重叠的区段,上述2个区段重叠QTL加性效应方向相反。本研究认为油菜种子含油量、蛋白质、纤维素和半纤维素属于典型的数量性状,受环境影响较大,与这些QTL紧密相关的分子标记可为下一步分子标记辅助育种提供一定技术支撑。     

大豆脂肪及脂肪酸组分含量的QTL定位

脂肪及脂肪酸组分的改良是大豆油脂品质育种的主要方面。本研究旨在构建遗传图谱,定位大豆脂肪及脂肪酸组分的QTL,为大豆油脂品质育种提供参考。以Essex×ZDD2315的114个BC₁F₁单株为作图群体,构建了250个SSR标记和1个形态标记,具有25个连锁群的遗传图谱,覆盖大豆基因组2 963.5 cM,平均每个连锁群上10.0个标记,标记平均间距11.8 cM。用BC₁F₃家系3个重复的表型平均值代表相对应的BC₁F₁单株表型值,采用Win QTL Cartographer 2.5复合区间作图法（CIM）检测到18个控制脂肪及脂肪酸组分含量的QTL,位于9个不同的连锁群上,表型贡献率为9.6%~34.5%;多区间作图法（MIM）检测到与CIM区间相同的7个QTL（fat-1, pal-1, st-1, ole-1, lin-1, lin-4和lio-2）,区间相近的2个QTL（ole-4和lin-5）,位于6个不同的连锁群上,表型贡献率为8.2%~39.3%。CIM法检测到的其他9个QTL有待进一步验证。大豆脂肪及脂肪酸组分含量的主效QTL数量不多,效应大的不多,可能还受许多未能检测出来的微效基因控制,育种中既要注意主效QTL的利用,又要考虑微效多基因的积聚。     

利用SNP高密度遗传连锁图谱定位甘蓝型油菜种子硫苷含量的QTL

种子硫苷在甘蓝型油菜中有着重要的生物学作用和经济价值。本文旨在通过复合区间作图法利用高密度SNP遗传连锁图谱定位种子硫苷的QTL。用近红外扫描获得种子硫苷含量,每株系扫描3次,取平均值。所用的高密度SNP遗传图谱包含2795个SNP多态性标记位点,图谱总长1832.9 cM,相邻标记间平均距离为0.66 cM。定位了2年的种子硫苷含量QTL,其中有5个在2年内被重复检测到,分别分布在A03、A09和C02染色体上,LOD阈值在2.90~10.40之间。这些QTL在2011和2012年试验中分别解释了56.9%和55.1%的表型变异。另外有5个QTL仅在其中一年被检测到,这些QTL能够解释4.1%~7.9%的表型变异,QTL阈值在2.53~3.83之间。     

QTL identification of yield-related traits and their association with flowering and maturity in soybean

Two soybean recombinant inbred line populations, Jinpumkong 2 × SS2-2 (J × S) and Iksannamulkong × SS2-2 (I x S) showed population-specific quantitative trait loci (QTLs) for days to flowering (DF) and days to maturity (DM) and these were closely correlated within population. In the present study, we identified QTLs for six yield-related traits with simple sequence repeat markers, and biological correlations between flowering traits and yield-related traits. The yield-related traits included plant height (PH), node numbers of main stem (NNMS), pod numbers per plant (PNPP), seed numbers per pod (SNPP), 100-seed weight (SW), and seed yield per plant (SYPP). Eighteen QTLs for six yield-related traits were detected on nine chromosomes (Chrs), containing four QTLs for PH, two for NNMS, two for PNPP, three for SNPP, five for SW, and two for SYPP. Two highly significant QTLs for PH and NNMS were identified on Chr 6 (LG C2) in both populations where the major flowering gene, E1, and two DF and DM QTLs were located. One other PNPP QTL was also located on this region, explaining 12.9% of phenotypic variation. Other QTLs for yield-related traits showed population-specificity. Two significant SYPP QTLs potentially related with QTLs for SNPP and PNPP were found on the same loci of Chrs 8 (Satt390) and 10 (Sat_108). Also, highly significant positive phenotypic correlations (P < 0.01) were found between DF with PH, NNMS, PNPP, and SYPP in both populations, while flowering was negatively correlated with SNPP and SW in the J × S (P < 0.05) and I × S (P < 0.01) populations. Similar results were also shown between DM and yield-related traits, except for one SW. These QTLs identified may be useful for marker-assisted selection by soybean breeders.     

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.     

大豆不同环境下脂肪酸组分含量的QTL分析

大豆油的品质取决于脂肪酸各组分在大豆中的比例, 为发掘控制大豆5种脂肪酸含量的数量性状位点(QTL), 利用冀豆12和黑豆重组自交系群体构建遗传图谱, 采用Windows QTL Cartographer 2.5和QTL Network-2.0软件的CIM和MCIM法对大豆5种脂肪酸组分进行数量性状定位。结果表明,在石家庄和三亚各环境下共检测到16个QTL, 位于连锁群A2、B2、C2、F、G、I、L上。对2个环境联合分析, 检测到13个QTL, 其中9个用2种方法被检测到, 但这13个位点与环境互作的贡献率明显小于加性效应。其中在B2连锁群Satt168~Satt556控制硬脂酸的QTL Ste-1在河北石家庄和海南三亚均能被检测到, 贡献率均为12%, 在双尾群体和间隔挑选群体中也能检测到控制硬脂酸的QTL Ste-1, 说明这一QTL稳定存在于本组合群体中, 为今后大豆硬脂酸的QTL精细定位奠定了基础。     

中国栽培和野生大豆豆腐与豆乳得率的遗传变异

我国不同生态区大豆种质豆腐与豆乳得率的遗传变异是专用型品种选育的基础。以来自各生态区的564份地方品种、101份育成品种、193份野生大豆加上88份国外品种,合计946份大豆种质为材料,采用小样品定量分析技术,测定干豆腐与干豆乳得率,研究其遗传变异。结果表明,全国野生大豆和栽培大豆的干豆腐与干豆乳得率均存在很大变异,干豆腐得率变幅分别为25.32~69.59、25.52~85.89 g 100 g^-1,干豆乳得率变幅分别为40.75~82.86、39.05~91.86 g 100 g-1,栽培大豆两者的得率在野生豆基础上均有较大幅度改进;各生态区均存在与全国相同的变异情况,区内变异大于区间变异,但南方一些生态区栽培种豆腐(乳)得率变异程度相对较大,高得率材料相对较多,因本底（野生种）得率与地理纬度无关,推测与各地区栽培大豆利用方向的不同有关而形成了栽培种微弱的地理相关性;栽培材料中2.75%干豆腐得率超过75 g 100 g^-1,5.50%干豆乳得率超过85 g 100 g^-1,从中优选出来自Ⅱ、Ⅲ、Ⅳ、Ⅵ生态区的双高种质14份,可供各地区豆腐(乳)育种利用。     

大豆苗期耐淹性的遗传与QTL分析

洪涝灾害是大豆生产的主要逆境之一,培育耐涝品种是抗灾保收的重要措施。大豆耐涝性育种方案的设计必须以耐涝性遗传为前提。以苏88-M21(淹水不敏感)×新沂小黑豆(淹水敏感)衍生的175个重组自交系(NJRISX)为材料,在盆栽V2期土壤表层保持5~7 cm水层20 d的淹水条件下,研究大豆苗期耐淹性的遗传和QTL定位。通过对8个耐淹性有关性状的相关分析和主成份分析,确定以处理前后株高变化量、处理终叶龄和成熟期株高3个性状的平均耐淹指数为评价指标。NJRISX家系间耐淹性差异极显著,存在超亲分离。主基因+多基因分离分析表明该群体的耐淹性为2对连锁主基因+多基因遗传,主基因遗传率为62.83%,多基因的遗传率为8.90%。WinQTLCart2.5复合区间及多区间QTL定位分析均检测到2个QTL,位于连锁群L2上的satt229~satt527和satt527~satt286区间,对表型的解释率分别为11.76%~25.20%和10.10%~12.34%。大豆NJRISX群体苗期耐淹性遗传分离分析与QTL定位结果相对一致。     

基于元分析的大豆生育期QTL的整合

共搜集整理了12年来已经报道的与大豆生育期有关的98个QTL,通过BioMercator2.1和公共标记映射整合到大豆公共遗传连锁图谱soymap2上,并利用元分析技术推断QTL位置,计算提取真正有效的QTL。发掘出大豆两个重要生育时期,共9个“真实QTL”及其连锁标记,其中与开花期(R1)相关的有7个,与成熟期(R8)相关的有2个,建立了QTL的一致性图谱,其中L连锁群上的一个定位区间包含一个已发表的有关R1的基因。在5个连锁群上共发现10个控制多个生育时期的QTL。本研究结果为大豆生育期QTL精细定位和基因克隆奠定了基础。     

Identification of quantitative trait loci underlying soybean (Glycine max [L.] Merr.) seed weight including main,epistatic and QTL × environment effects in different regions of Northeast China

Seed weight (SW) is the important soybean (Glycine max [L.] Merr.), yield component and also affected the quality of soybean‐derived foods. The aim of this study was to identify the quantitative trait loci (QTL) underlying SW through 112 recombinant inbred lines (RILs) derived from the cross between “Zhongdou27” (G. max, designated by its bigger seed size, 21.9 g/100 seeds) and “Jiunong 20” (G. max, smaller seed size, 17.5 g/100 seeds). Phenotypic data were collected from this RIL population after it was grown in the sixteen tested environments. A total of eight QTL (QSW1‐1, QSW2‐1, QSW2‐2, QSW5‐1, QSW15‐1, QSW17‐1, QSW19‐1 and QSW20‐1) were identified, and they could explain 4.23%–14.65% of the phenotypic variation. Among these eight QTL, three QTL (QSW1‐1 located on the interval of Sat_159‐Satt603 of chromosome (Chr) 1 (LGD1a), QSW19‐1 located on the interval of Sat_340‐Satt523 of Chr 19 (LGL) and QSW20‐1 located on Sat_418‐Sat_105 of Chr 20 (LGI)) were newly identified and could explain 4.235%–10.08%, 8.45%–13.49% and 8.08%–10.18% of the phenotypic variation, respectively. Six of the eight identified QTL including QSW2‐2, QSW5‐1, QSW15‐1, QSW17‐1, QSW19‐1 and QSW20‐1 exhibited a significant additive (a) effect, while two QTL (QSW2‐1 and QSW19‐1) only displayed significant additive‐by‐environment (ae) effects. A total of four epistatic pairwise QTL for SW were identified in the different environments. These eight QTL and their genetic information obtained here were valuable for molecular marker‐assisted selection and the realization of a reasonable SW breeding programme in soybean.     

栽培种花生荚果大小相关性状QTL定位

以远杂9102为母本,徐州68-4为父本杂交衍生的F5和F6共188个家系,构建了一张包含365个标记,总长度713.07 c M,标记间平均距离1.96 c M的栽培种花生遗传图谱。图谱包含22个连锁群,各连锁群平均长度12.37~81.39 c M,连锁群上标记数量3~46个。结合2013和2014年采集的荚果表型数据,采用Win QTLcart 2.5软件的复合区间作图法(composite interval mapping,CIM)进行QTL定位和效应估计。2个环境下共检测到41个QTL,其中与荚果长、宽、厚和百果重相关的QTL分别为13、7、13和8个,表型变异解释率为3.14%~18.27%。有6个QTL在2种环境下被重复检测到,其中百果重相关的2个(q HPWLG13.1、q HPWLG14.1),分布在LG13和LG14连锁群,遗传贡献率为6.95%~14.60%;与荚果长相关的3个(q LPLG2.2、q LPLG13.1、q LPLG14.1),分布在LG2、LG13和LG14连锁群,遗传贡献率为3.14%~18.27%;与荚果厚相关的1个(q TPLG3.4),分布在LG3连锁群,遗传贡献率为8.24%~9.24%。本研究涉及性状存在9个QTL热点区,每个热点区涉及2~3个性状,表型贡献率为3.57%~18.27%。     

Quantitative trait loci analysis of soluble sugar contents in soybean

The soluble sugar content in soybean seeds, mainly sucrose, stachyose, raffinose and trace amounts of glucose and fructose, is important for the increasing global market demand for various soyfoods including tofu, soymilk, natto, bean sprouts and edamame due to their nutritional value and health benefits. The objective of this study was to conduct quantitative trait loci (QTL) analysis and identify molecular markers for soluble sugar content in soybean seeds for marker‐assisted selection (MAS) in soybean breeding. The content of the five previously mentioned sugars were measured and associated QTLs were mapped based on a F₂ population that was derived from a cross between V97‐3000 and V99‐5089. Eleven QTLs were detected for the five sugar contents: one for glucose, three each for fructose and sucrose, and two each for raffinose and stachyose. However, only one QTL for sucrose, one QTL for raffinose, and two QTLs for stachyose were identified with LOD > 3.0 and R² > 10% from this research. The QTL on chromosome 11 [linkage group (LG) B1] was identified as associated with sucrose, raffinose and stachyose in the same region as previously reported for sucrose and stachyose. The SSR marker, Satt359, on the QTL B1 region had an significant association with sucrose (LOD = 5.192; R² = 0.134), raffinose (LOD = 3.95; R² = 0.104), and stachyose (LOD = 13.572; R² = 0.314); therefore it can be used to assist breeding selection for sucrose, raffinose and stachyose contents simultaneously.