应用重组自交系群体检测水稻茎秆和剑叶硅含量QTL

[目的]硅在水稻生长发育过程中具有重要作用,本研究的主要目的在于通过QTL分析研究水稻茎秆和剑叶硅含量的遗传控制.[方法]以由244个株系组成的珍汕97B/密阳46重组自交系群体为材料,在2003和2004年应用两重复试验测定亲本和各株系的茎秆和剑叶硅含量,应用由240个标记组成的连锁图谱检测数量性状座位（QTL）.[结果]所分析的两个性状表现为典型的数量性状,并呈现出以基因型控制为主、基因型×环境互作和环境影响为辅的特点.经Windows QTL Cartographer 2.0分析,检测到3个茎秆硅含量QTL、6个剑叶硅含量QTL和2对控制茎秆硅含量的QTL间互作,它们分布于1、3、5、6、7和12等6条染色体的8个区间,单个QTL对群体性状表型变异的贡献率为3.4%～11.8%,联合贡献率为16.8%～29.2%.进一步分析发现,研究群体中实际上可能存在更多的控制茎秆和剑叶硅含量遗传变异的QTL,但其效应大小可因环境条件和性状测定参数的不同而发生变化,在既定条件下、对于既定性状,部分QTL因达不到给定的显著水平而得不到检测.[结论]控制水稻茎秆和剑叶硅含量性状遗传变异的QTL数目较大、单个QTL的效应一般较小;同一基因组区间常常表现为同时控制不同性状,其作用方向稳定而显著性、效应值和贡献率则变异较大.     

玉米光合性状的相关性及 QTL 分析

为了探讨玉米光合性状的相关性及遗传机制,利用2个不同遗传背景的F2群体对叶绿素a质量分数、叶绿素b质量分数、叶绿素总质量分数、净光合速率、气孔导度、胞间CO2浓度和蒸腾速率等10个光合性状进行了相关性及QTL分析.在相关性分析中,对于Y群体,叶绿素质量分数a、叶绿素质量分数b和叶绿素总质量分数3个性状在不同时期的相关性不显著;而对于R群体,这3个性状在不同时期的相关性极显著,达到高度相关.对于其余性状间的相关,两群体表现一致性高.同一时期的叶绿素a质量分数、叶绿素b质量分数和叶绿素总质量分数间为高度相关,净光合速率与气孔导度的相关、净光合速率与蒸腾速率的相关、气孔导度与蒸腾速率的相关为高度相关,气孔导度与胞间CO2浓度的相关为中度相关,叶绿素质量分数与净光合速率、气孔导度、胞间CO2浓度、蒸腾速率间均为弱度相关.在QTL分析中,对于Y群体,五叶期检测到控制叶绿素a质量分数、叶绿素b质量分数、叶绿素总质量分数的QTL各1个,位于第4染色体的umc2391-mmc0371之间,单个QTL可解释表型变异的8.65%~9.87%;乳熟期检测到控制叶绿素a质量分数、叶绿素b质量分数、叶绿素总质量分数的QTL各1个,位于第10染色体的mmc0501-bnlg1451之间,单个QTL可解释表型变异的6.77%~6.93%;散粉期检测到1个净光合速率QTL,1个气孔导度QTL,2个胞间CO2浓度QTL,2个蒸腾速率QTL,单个QTL可解释表型变异的5.64%~7.73%.对于R群体,五叶期检测到3个叶绿素a质量分数QTL、2个叶绿素b质量分数QTL、3个叶绿素总质量分数QTL,其中1个控制叶绿素总质量分数的QTLqRFCt-1-2贡献率超过10%,为主效QTL;乳熟期检测到2个叶绿素a质量分数QTL、3个叶绿素b质量分数QTL、2个叶绿素总质量分数QTL;散粉期检测到2个净光合速率QTL,1个气孔导度QTL,1个胞间CO2浓度QTL,1个蒸腾速率QTL,单个QTL可解释表型变异的5.79%~9.24%.2个群体没有检测到"一致性"QTL,而且单个QTL的贡献率较小,说明光合性状是受微效多基因控制的数量性状,遗传机理复杂,需做进一步深入研究.     

水稻穗下第1、2节节间角度的QTL定位

水稻茎秆弯折易引起倒伏,而茎秆弯折处多发生在穗下第1、2节之间。以穗下第1、2节节间角度差异明显的JX103和明恢63为亲本,构建了包括217个单株的F2群体,测量各单株穗下第1~2节节间角度,结果呈连续性多峰非正态分布,表明该性状是受多个基因控制的数量性状。利用420对SSR引物和WinQTLcart 2.5软件对水稻穗下第1、2节节间角度QTL进行分析,在第3、4、6、7、12条染色体上各检测到1个QTL,对表型变异的贡献率范围为3.37%～19.15%。其中第4染色体上位于RM307和RM3471区间的qIA-4为主效QTL,对表型变异的贡献率为19.15%,与RM307相距6 cM,对分子标记辅助育种有较大价值。     

基于两个相关群体的玉米花期相关性状QTL定位

【目的】利用具有共同亲本黄早四的2个F2﹕3群体,定位控制玉米的抽雄期(DTT)、散粉期(DTP)、吐丝期(DTS)以及散粉-吐丝间隔期(ASI)的QTL,为玉米分子育种与相关基础研究提供参考和依据。【方法】以自交系齐319和掖478分别与黄早四杂交构建的230个和235个F2﹕3家系为定位群体(分别写作Q/H和Y/H),利用完备区间作图方法,对在不同生态环境下(2007-北京、2008-北京、2007-河南、2008-河南、2007-新疆以及2008-新疆)玉米花期相关性状进行QTL定位。同时利用基于混合线性模型的QTL Network-2.0软件进行基因×环境互作及上位性的分析。【结果】尽管4个花期相关性状的表现在2个群体中存在明显差异,但它们之间均呈现高度的相关性。在6个环境下对2个群体的4个性状进行了QTL检测,Q/H群体共定位到了85个QTLs,分布在玉米的10条连锁群上;Y/H群体共检测到了30个QTLs,呈现成簇分布。在Q/H群体中检测到2个重要的与多个性状相关且在不同环境条件下同时表达的QTL区域,分别位于第8染色体的umc1562—bnlg1651和第10染色体的phi062—umc1115区段;在Y/H群体中也检测到了1个与多性状相关且在多环境表达的QTL区域,位于第3染色体的nc030—umc2166区域。进一步分析发现,贡献率较大的QTL同时控制着多个性状。对比2个群体的定位结果,共检测到4个在不同遗传背景下的"一致性"QTLs。【结论】玉米花期相关性状的遗传机制较为复杂,而在不同环境及不同遗传背景下能够稳定存在的QTL可为这类性状的生产应用以及精细定位与图位克隆提供有价值的参考。     

玉米重组自交系苗期耐盐相关性状QTL的初步定位

[目的]对玉米重组自交系苗期耐盐相关性状进行QTL定位。[方法]以玉米黄早四与Mo17杂交的RIL-F7代171份材料为作图群体,构建其玉米的分子标记遗传图谱,并在此基础上开展对玉米耐盐相差性状的QTL分析。[结果]构建了一张包含81个SSR位点,覆盖了玉米基因组10条染色体,共1 428.3 cM,标记间平均间距为17.63 cM的分子遗传连锁图谱;共检测到6个QTL,分别位于第1、5、6号染色体上。[结论]该研究对深入认识玉米耐盐遗传机理,了解控制玉米耐盐性的基因数目及其在染色体上的位置以及对耐盐性的遗传效应,进行玉米耐盐性育种的分子标记辅助选择和基因克隆均具有极其重要意义。     

阈性状的标记QTL连锁分析

采用最大似然区间定位法对阈性状的标记QTL进行连锁分析,并对影响阈性状QTL检测效率的主要因素(性状遗传力、QTL的方差贡献率、标记间重组率、阈值)进行了模拟研究。实验设计的群体为利用标记位点和QTL位点上都为杂合子的公畜与来自随机平衡群体的母畜群交配产生的半同胞个体组成,模拟产生的资源群体大小为500头。研究结果表明:当性状的遗传力较高,QTL方差贡献较大时,检验统计量LR值较大,检验效果较好;遗传力较低,QTL方差贡献较小时,LR值较小,QTL检测效果较差。另外,标记间重组率和阈值对QTL定位的准确度也有直接的影响,随着标记间重组率的增大,QTL定位的效果越差,当阈值在0.2~5之间时,QTL检测效果最好。     

水稻RIL群体高密度遗传图谱的构建及苗期耐热性QTL定位

【目的】 随着全球气候变暖,高温严重威胁粮食安全,发掘耐热基因资源是培育耐高温新品种和消除高温危害最直接的绿色生态途径,也是阐明耐热生理生化和分子遗传机理的基础。【方法】 构建苗期耐热性鉴定评价方法,以热敏感品种周南稻和强耐热品种赣早籼58号杂交衍生的重组自交系（recombinant inbred lines,RIL）群体为研究材料,利用高通量测序技术对亲本和RIL群体进行全基因组测序;依据171个家系的基因型数据,利用滑动窗口法将SNP信息转换成Bin基因型,预测染色体上的重组断点,构建RIL群体高密度BinMap遗传图谱,结合耐热表型数据,运用QTL IciMapping软件完备复合区间ICIM的作图方法,进行高温胁迫下幼苗存活率和耐热等级QTL分析。【结果】 构建了一张包含3 321个Bin标记高密度遗传图谱,各染色体Bin标记数为159—400个,标记间平均物理距离为106 kb;利用逐步高温胁迫方式鉴定亲本和RIL家系幼苗耐热表型,高温胁迫下,幼苗存活率和耐热等级存在极显著负相关性,且幼苗存活率与籼型基因频率存在显著正相关性,籼型基因频率越高,耐热性越好,RIL群体表型性状呈现双峰连续分布,苗期耐热性可能受少数几个主效QTL调控;共检测到12个苗期耐热性相关的QTL,其中,调控幼苗存活率和耐热等级的QTL分别有8和4个,幼苗存活率和耐热等级相关QTL存在遗传重叠现象,形成调控耐热性的主效QTL簇qHTS2、qHTS7和qHTS8,三者在调控苗期高温抗逆中具有重要作用,其中,qHTS7为新发现主效QTL,对增强苗期耐热性具有较强的功效。【结论】 构建了一张包含3 321个Bin标记的高密度分子遗传图谱,解析了耐热品种赣早籼58号苗期耐热基因,鉴定出3个苗期耐热调控关键QTL簇,发掘了一个新主效QTL簇qHTS7,基于高密度遗传图谱高效获取目标区段及候选基因,筛选出8个苗期耐热性调控的关键目标基因。     

玉米茎秆第三和第五茎节穿刺强度遗传模型选择

以B73和Mo17玉米自交系构成的6世代群体为材料,利用玉米茎秆穿刺仪,对玉米地上第3茎节和第5茎节中部椭圆形短轴垂直于茎秆进行穿刺,测定玉米茎秆穿刺强度。通过P_1、P_2、F_1、F_2、BC_1和BC_2 6个世代联合分析法,以玉米茎秆穿刺阻力为性状,研究控制玉米茎秆倒伏性的基因遗传分离规律。结果表明,玉米茎秆第3茎节穿刺强度的最适遗传模型为B-1(2对加性-显性-上位性主基因模型);玉米茎秆第5茎节穿刺强度的最适遗传模型为E-0(2对加性-显性-上位性主基因+加性-显性-上位性多基因模型)。这一研究结果为玉米抗倒伏性状的有效选择提供方法和理论依据。     

玉米穗上节间数的QTL分析

以来自于玉米杂交种农大108(许178×黄C)的一套包含166个家系的重组自交系群体为基础,组配包含243个杂交组合的IF_2群体。2013年和2014年分别在郑州和安阳进行田间试验,通过两年两点的田间试验,利用复合区间作图法对玉米穗上节间数(Internodes above the upmost ear,IAEs)进行QTL分析,结果表明,IF_2群体的穗上节间数显著高于RIL群体,这表明该性状存在杂种优势。对2个群体的穗上节间数进行单环境和环境间联合QTL分析,共定位到14个QTL,其中,RIL群体共定位到6个QTL,IF_2定位到8个QTL,所有定位到的QTL分布在第1,3,5,6和8染色体上。RIL群体穗上节间数QTL中,qIAEs1a和qIAEs1c分别在多个环境和环境联合中被检测到,贡献率均大于12%。IF_2群体中,qIAEs6b和qIAEs3在多个环境间和环境联合QTL分析中被检测到,但qIAEs3的贡献率较小。在2个群体间并没有定位到相同的QTL,这表明玉米自交系和杂交种调控植株性状所涉及的基因是不同的,在IF_2群体中所定位到的QTL表现出的效应包含有杂种优势。所定位到的主效QTL可能为相应群体中调控玉米穗上节间数的主效QTL。     

基于掖478导入系的玉米产量性状QTL鉴定

 目的】鉴定玉米产量相关性状基因位点及包含有利等位基因的导入系,为了解产量性状形成的遗传基础及针对玉米自交系产量性状的分子设计提供参考和依据。【方法】以QB80和Qi319为供体亲本,掖478为轮回亲本,采用回交结合定向选择,分别构建含有61和72个家系的基础导入系群体。通过2年4点田间试验,利用完备复合区间作图进行产量及其相关性状的QTL（quantitative trait locus,QTL）分析。【结果】4个环境下,在QB80为供体的导入系群体中,共检测到9个性状的49个QTL;在Qi319为供体的导入系群体中,检测到9个性状的42个QTL。在2个及以上环境中均检测到的QTL有16个。同一性状在不同环境下所检测的QTL定位在相同的染色体区域,不同性状的QTL也定位在相同或临近的染色体区域,形成多个QTL富集区。2个群体所检测的QTL位点具有较少的一致性,说明2个供体材料中含有不同的有利基因位点。同时,导入片段中含有利基因的导入系,其相关性状明显得以改良,这些导入系可用于QTL聚合以改良掖478的产量相关性状。【结论】QB80较Qi319与掖478间的遗传差异更大,能检测更多的产量性状QTL;2个导入系群体中含有优良等位基因的导入系可用于QTL聚合改良掖478;QTL富集区是为产量性状基因的克隆提供可供参考的重要染色体区域。     

Comparative mapping of QTLs for H+ secretion of root in maize (Zea mays L.) and cross phosphorus levels on two growth stages

H⁺ is a root secretion that affects P acquisition and P-use efficiency (PUE) under deficient phosphorus in maize. The secretion of H⁺, difference value of H⁺ between deficient and normal phosphorus (DH), and relative H⁺ (RH) as well as the quantitative trait loci (QTLs) associated with these traits were determined for a F_2:3 population derived from the cross of two contrasting maize (Zea mays L.) genotypes, 082 and Ye107. By using composite interval mapping (CIM), a total of 14, 8, and 9 distinct QTLs were identified for H⁺, DH, and RH, respectively. Most loci of QTLs for traits H⁺, DH, and RH had different cross environments. It showed that H⁺ secretion possessed an environment-sensitive and multi-gene nature. The gene × environment interaction was actually reflected by H⁺ secretion. One region for QTL of trait H⁺ was detected at the interval of bnlg2228-bnlg100 (bin 1.08) on chromosome 1. Coincident QTLs in the important genomic region reflected the cross phosphorus levels, different cross growth stages, and two different cross environments. The QTL explained 10% to 14% total phenotypic variance of H⁺. Therefore, the above segment (bnlg2228-bnlg100) (bin 1.08) identified on chromosome 1 may be used in the future for MAS to improve the phosphorus efficiency in maize.     

基于四交群体的玉米叶夹角和叶向值QTL定位分析

选育耐密紧凑株型是增加玉米单位面积产量的重要途径之一,而叶夹角和叶向值是衡量株型的重要参数。本研究选用叶夹角和叶向值存在差异的玉米自交系郑58、PH6WC、87-1和自330构建1个四交(郑58/豫87-1//PH6WC/自330)组合,以228个四交F1单株为作图群体,构建了1张含225个SSR位点,全长1 387.2cM的玉米分子标记遗传连锁图谱,标记间平均间距为6.19cM。基于四交群体应用区间作图法检测4个环境下的QTL,共检测到13个叶夹角相关QTL,分别位于第1、2、3、4、5、7和10染色体上,单个QTL可解释5.1%~20.0%的表型变异;检测到15个叶向值相关QTL,分别位于第1、2、4、5、7、8和9染色体上,单个QTL可解释5.4%~20.1%的表型变异。其中qLA-E2-2和qLA-E4-2落在同一标记区间umc1692-umc2297(bin 5.03),分别解释16.6%和13.2%的表型变异;qLO-E1-1、qLO-E3-2和qLA-E4-1落在同一标记区间umc1568-bnlg1953(bin1.02),分别解释10.1%、19.9%和12.3%的表型变异;qLO-E2-1和qLO-E3-1落在同一标记区间phi056-phi427913(bin 1.01),分别解释13.8%和10.0%的表型变异。这些多个环境共同检测到的QTL将为玉米耐密理想株型育种中叶夹角叶向值的分子标记辅助选择提供有益信息,加速耐密株型玉米品种的选育。     

基于SSR对火炬松×加勒比松杂种形质性状的QLT定位分析

对火炬松×加勒比松杂种部分形质性状的QTL进行定位分析,为杂种育种和株型培育提供借鉴。以火炬松1.5代种子园优良单株T1为母本,加勒比松第一代种子园优良单株C1为父本,杂交产生F₁代113个单株。使用JoinMap? 3.0作图软件构建火炬松母本和加勒比松父本的SSR 标记遗传图谱,并用MapQTL4.0^?作图软件对形质性状的QTL进行定位。火炬松母本图谱总长度293.9 cM,标记间距范围为3.7~ 47.9 cM,标记平均间距15.5 cM。加勒比松父本图谱总长度245.5 cM,标记间距范围为6.8~ 49.8 cM,标记平均间距13.6 cM。检测到4个与形质性状有关的QTL位点,其中有2个QTL定位于火炬松染色体LG4上。4个形质性状的QTLs解释方差为8.6%～13.6%,可以利用这些QTLs进行特定株型的培育,如分枝角度的控制培育。     

鲜食甜玉米南方锈病抗性QTL定位分析

为挖掘新的抗南方锈病基因资源,本研究以甜玉米组合M5×M114的216个F2单株为遗传作图群体,应用BSA方法从500对SSR引物中筛选出2对在F2代抗病和感病DNA池间具有多态性的引物,分别位于4和9号染色体上;在4和9号染色体上重新设计100对SSR引物,构建了包含33个标记位点总长为241.2cM的连锁遗传图,各个标记间的平均距离为7.53cM。结合F2单株对南方锈病的抗性表现,用复合区间作图法在4和9号染色体上共检测到7个显著的南方锈病抗性QTLs,其中:4个QTLs位于4号染色体上,可解释12.1%、7.8%、18.2%和14.9%表型变异;3个位于9号染色体上,分别解释17.0%、13.3%与19.2%的表型变异。研究结果可为抗南方锈病的精细定位、主效基因克隆和抗南方锈病鲜食甜玉米品种选育提供理论依据。     

Construction of a genetic linkage map and QTL analysis for some leaf traits in pear (Pyrus L.)

The major incompatibility barriers to specific inbred lines and the long generation duration in Pyrus L. may hinder the Pyrus breeding process. A genetic linkage map provides the foundation for quantitative trait loci (QTL) mapping and molecular marker-assisted breeding. In this study, we constructed a genetic map with 145 F₁ populations from a cross of two cultivars, Yali and Jingbaili, using AFLP and SSR markers. The map consisted of 18 linkage groups which included 402 genetic markers and covered 1395.9 cM, with an average genetic distance of 3.8 cM. The interval mapping was used to identify quantitative trait loci associated with four leaf agronomic traits in the F₁ population. The results indicated that four QTLs were associated with leaf length, two QTLs with leaf width, two with leaf length/leaf width, and three with petiole length. The eleven QTLs were associated with 9.9%–48.5% of the phenotypic variation in different traits. It is considered that the map covers almost the whole genome, and molecular markers will be greatly helpful to the related breeding.     

Genetic dissection of ear-related traits using immortalized F2 population in maize

Ear-related traits are often selection targets for maize improvement. This study used an immortalized F₂ (IF₂) population to elucidate the genetic basis of ear-related traits. Twelve ear-related traits (namely, row number (RN), kernel number per row (KNPR), ear length (EL), ear diameter (ED), ten-kernel thickness (TKT), ear weight (EW), cob diameter (CD), kernel length (KL), kernel width (KW), grain weight per ear (GW), 100-kernel weight (HKW), and grain yield per plot (GY)), were collected from the IF₂ population. The ear-related traits were comprised of 265 crosses derived from 516 individuals of the recombinant inbred lines (RILs) under two separated environments in 2017 and 2018, respectively. Quantitative trait loci (QTLs) analyses identified 165 ear traits related QTLs, which explained phenotypic variation ranging from 0.1 to 12.66%. Among the 165 QTLs, 19 underlying nine ear-related traits (CD, ED, GY, RN, TKT, HKW, KL, GW, and KNPR) were identified across multiple environments and recognized as reliable QTLs. Furthermore, 44.85% of the total QTLs showed an overdominance effect, and 12.72% showed a dominance effect. Additionally, we found 35 genomic regions exhibiting pleiotropic effects across the whole maize genome, and 17 heterotic loci (HLs) for RN, EL, ED and EW were identified. The results provide insights into genetic components of ear-related traits and enhance the understanding of the genetic basis of heterosis in maize.     

The candidate QTLs affecting phosphorus absorption efficiency and root weight in maize (Zea mays L.)

A maize F₂ population was first used to construct a genetic linkage map of Chromosome 6 covering 117.6 cM with an average interval of 3.68 cM between adjacent markers. Based on composite interval mapping (CIM), the quantitative trait loci (QTL) for phosphorus absorption efficiency (PAE) and root-related traits was detected in four environments, i.e., Kaixian County under deficient phosphorus (KXDP), Kaixian County under normal phosphorus (KXNP), SUDP1, and SUDP2. QTLs affecting root weight (RW) were detected simultaneously at the dupssr15 locus region (bin 6.06) on Chromosome 6 in the four environments, while QTL affecting taproot length and fiber number was only detected in one or two environments. The result suggested that taproot length and fiber number were more easily affected by the environment than PAE and RW. The alleles originating from 082 increased PAE and RW on Chromosome 6. The QTL on bin 6.06 explained 4%–10% and 4%–8% of the total phenotypic variance of PAE and RW, respectively, and the estimates of the genetic effects presented dominance and overdominance. The QTL for RW in the dupssr15 locus is the minor QTLs environment interactive effects, which should be particularly useful in MAS manipulation of breeding maize.     

QTL analysis of the developmental changes in cell wall components and forage digestibility in maize (Zea mays L.)

Cell wall architecture plays a key role in stalk strength and forage digestibility. Lignin, cellulose, and hemicellulose are the three main components of plant cell walls, and they can impact stalk quality by affecting the structure and strength of the cell wall. To explore cell wall development during secondary cell wall lignification in maize stalks, conventional and conditional genetic mapping were used to identify the dynamic quantitative trait loci (QTLs) of the cell wall components and digestibility traits during five growth stages after silking. Acid detergent lignin (ADL), cellulose (CEL), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro dry matter digestibility (IVDMD) were evaluated in a maize recombinant inbred line (RIL) population. ADL, CEL, ADF, and NDF gradually increased from 10 to 40 days after silking (DAS), and then they decreased. IVDMD initially decreased until 40 DAS, and then it increased slightly. Seventy-two QTLs were identified for the five traits, and each accounted for 3.48–24.04% of the phenotypic variation. Six QTL hotspots were found, and they were localized in the 1.08, 2.04, 2.07, 7.03, 8.05, and 9.03 bins of the maize genome. Within the interval of the pleiotropic QTL identified in bin 1.08 of the maize genome, six genes associated with cell wall component biosynthesis were identified as potential candidate genes for stalk strength as well as cell wall-related traits. In addition, 26 conditional QTLs were detected in the five stages for all of the investigated traits. Twenty-two of the 26 conditional QTLs were found at 30 DAS conditioned using the values of 20 DAS, and at 50 DAS conditioned using the values of 40 DAS. These results indicated that cell wall-related traits are regulated by many genes, which are specifically expressed at different stages after silking. Simultaneous improvements in both forage digestibility and lodging resistance could be achieved by pyramiding multiple beneficial QTL alleles identified in this study.     

Identification of genetic loci for grain yield-related traits in the wheat population Zhongmai 578/Jimai 22

The identification of stable quantitative trait locus (QTL) for yield-related traits and tightly linked molecular markers is important for improving wheat grain yield. In the present study, six yield-related traits in a recombinant inbred line (RIL) population derived from the Zhongmai 578/Jimai 22 cross were phenotyped in five environments. The parents and 262 RILs were genotyped using the wheat 50K single nucleotide polymorphism (SNP) array. A high-density genetic map was constructed with 1 501 non-redundant bin markers, spanning 2 384.95 cM. Fifty-three QTLs for six yield-related traits were mapped on chromosomes 1D (2), 2A (9), 2B (6), 2D, 3A (2), 3B (2), 4A (5), 4D, 5B (8), 5D (2), 7A (7), 7B (3) and 7D (5), which explained 2.7–25.5% of the phenotypic variances. Among the 53 QTLs, 23 were detected in at least three environments, including seven for thousand-kernel weight (TKW), four for kernel length (KL), four for kernel width (KW), three for average grain filling rate (GFR), one for kernel number per spike (KNS) and four for plant height (PH). The stable QTLs QKl.caas-2A.1, QKl.caas-7D, QKw.caas-7D, QGfr.caas-2B.1, QGfr.caas-4A, QGfr.caas-7A and QPh.caas-2A.1 are likely to be new loci. Six QTL-rich regions on 2A, 2B, 4A, 5B, 7A and 7D, showed pleiotropic effects on various yield traits. TaSus2-2B and WAPO-A1 are potential candidate genes for the pleiotropic regions on 2B and 7A, respectively. The pleiotropic QTL on 7D for TKW, KL, KW and PH was verified in a natural population. The results of this study enrich our knowledge of the genetic basis underlying yield-related traits and provide molecular markers for high-yield wheat breeding.     

Construction of high-density SNP genetic maps and QTL mapping for dwarf-related traits in Litchi chinensis Sonn

Litchi chinensis Sonn is widely cultivated in subtropical regions and has an important economic value. A high-density genetic map is a valuable tool for mapping quantitative trait loci (QTL) and marker-assisted breeding programs. In this study, a single nucleotide polymorphism (SNP)-based high-density linkage map was constructed by a genotyping-by-sequencing (GBS) protocol using an F₁ population of 178 progenies between two commercial litchi cultivars, ‘Ziniangxi’ (dwarf) and ‘Feizixiao’ (vigorous). The genetic map consisted of 3027 SNP markers with a total length of 1711.97 cM in 15 linkage groups (LGs) and an average marker distance of 0.57 cM. Based on this high-density linkage map and three years of phenotyping, a total of 37 QTLs were detected for eight dwarf-related traits, including length of new branch (LNB), diameter of new branch (DNB), length of common petiole (LCP), diameter of common petiole (DCP), length of internode (LI), length of single leaf (LSL), width of single leaf (WSL), and plant height (PH). These QTLs could explain 8.0 to 14.7% (mean=9.7%) of the phenotypic variation. Among them, several QTL clusters were observed, particularly on LG04 and LG11, which might show enrichment for genes regulating the dwarf-related traits in litchi. There were 126 candidate genes identified within the QTL regions, 55 of which are differentially expressed genes by RNA-seq analysis between ‘Ziniangxi’ and ‘Feizixiao’. These DEGs were found to participate in the regulation of cell development, material transportation, signal transduction, and plant morphogenesis, so they might play important roles in regulating plant dwarf-related traits. The high-density genetic map and QTLs identification related to dwarf traits can provide a valuable genetic resource and a basis for marker-assisted selection and genomic studies of litchi.