小麦籽粒干物质积累的动态QTL定位

以波兰小麦品系‘XN555’与普通小麦品系‘中13’杂交产生的99个F10重组自交系(RILs)为材料,构建了包含241个SSR分子标记的A、B染色体组14个连锁群的遗传图谱,并采用Logistic方程拟合籽粒灌浆过程,对粒重增长的缓慢增长期、快速增长期和平稳期进行千粒重条件QTL和非条件QTL定位分析。结果显示:(1)在小麦A、B染色体组上共检测到5个非条件QTL和5个条件QTL。(2)在小麦粒重缓慢增长期和快速增长期各有2个非条件QTL,平稳期有1个非条件QTL,它们分别位于2B、3A、3B和7A染色体上,单个QTL可解释表型变异的9.66%～15.18%。(3)在小麦粒重快速增长期检测到1个条件QTL,平稳期检测到4个条件QTL,涉及1A、2B、5B和7B染色体,单个QTL可解释表型变异的13.01%～29.27%。(4)于2B染色体Xbarc361～Xwmc422标记区间距Xbarc361标记0.05cM处,在粒重快速增长期同时检测到一个条件QTL和非条件QTL,且在平稳期检测到一个非条件QTL。研究表明,小麦不同灌浆时期粒重增长相关QTL的数量和遗传效应各不同,同一QTL在不同灌浆时期的遗传效应也不同,即QTL的表达具有时序选择性。     

3B染色体短臂小麦赤霉病抗性主效QTL的分析

采用区间作图和复合区间作图方法对重组自交系群体宁894037／Alondram、望水白／Alondra和苏麦3号／A1ondra进行了抗赤霉病QTL分析,结果表明,用在田间和温室的赤霉病抗性鉴定资料,在3个赤霉病抗源宁894037、望水白和苏麦3号的3B染色体短臂上均检测到主效QTL的存在。宁894037主效QTL位于标记BARCl33与Xgwm493之间的5．0cM的区间内,最高可解释42．8％的赤霉病抗性;望水白的主效QTL位于标记BARCl47与Xgwm493之间11．5cM的区间内,最高可解释15．1％的赤霉病抗性;苏麦3号的主效QTL位于Xgwm533a与Xgwm493之间13．0cM的区间内,最高可解释10．6％的赤霉病抗性。与赤霉病抗性主效QTL紧密连锁的标记均为SSR标记,可直接用于分子辅助育种。     

小麦早衰及其相关生理性状的QTL分析

利用RIL群体及其分子标记遗传图谱,对小麦早衰指标和与早衰相关的6个生理性状进行了QTL定位分析。小麦早衰指标中,检测到2个籽柆饱满度的加性QTL,分别位于3A和3B染色体,可解释表型变异的9.62%和18.30%。生理性状中,共检测到可溶性蛋白含量、SOD活性和POD活性3个性状的5个加性QTL,涉及2A、2B、2D、4A和6B等5条染色体,可解释表型变异的8.1%~49.56%。这些QTL间不存在连锁关系。     

甘蓝型油菜千粒重性状的QTL定位分析

该研究利用油菜双单倍体株系(348份)群体和已构建的遗传连锁图谱,采用复合区间作图法,对2009~2013年连续5年的千粒重性状表型数据进行QTL初步定位和分析,结果共获得46个显著性千粒重QTL,主要分布在A7、C1和C6等11条染色体上;其中qTSW-09 DL11-1的表型变异最高(19.63%),qTSW-11 DL9的表型变异最小(2.73%)。通过元分析方法将所获得的46个QTL进行整合,结果显示:cqTSW-C1-2的表型变异最大(10.64%),并发现多个整合后的一致性QTL能够在连续多年试验中被检测到,其中cqTSW-C1-3连续5年被检测到,表明控制千粒重的QTL在种植环境中能够稳定表达;同时,新发现位于C1染色体上的千粒重主效QTL cqTSW-C1-2,解释表型变异达到10.64%。油菜千粒重性状的QTL分析和主效QTL的获得,为进一步实现油菜大籽粒的分子育种和高产新品种的培育提供了重要的理论指导。     

小麦旗叶长、宽及面积的QTL分析

以小麦品种‘小偃81’和‘西农1376’构建的含236个家系的自交重组系(RIL)群体(F2:7、F2:8代)为研究材料,采用完全随机区组设计,连续2年在陕西杨陵、河南驻马店和山东济南于灌浆期(花后20d)随机取每个株系10株测量旗叶长、宽,并利用172个SSR标记构建了遗传连锁图谱,通过基于完备区间作图法的QTL IciMapping V3.2软件,对控制小麦旗叶长、宽和面积的数量性状位点(QTL)进行了加性效应分析。结果发现:(1)9个旗叶长QTLs位于1A、4A、3B、5D和7D染色体上,单个QTL可解释5.10%~16.44%的表型变异;10个旗叶宽QTLs位于1A、3A、5A、7A、3B和5D染色体上,单个QTL可解释4.63%~14.24%的表型变异;12个旗叶面积QTLs位于1A、4A、3B、2D和5D染色体上,单个QTL可解释4.25%~22.67%的表型变异。(2)控制小麦旗叶长、宽和面积的QTLs存在差异,同一QTL在不同性状中的遗传贡献率也不同。(3)同一性状在同一年份,不同地点和在不同年份,相同地点下检测到的QTLs有的相同,但有的差异明显。(4)有些控制不同性状的QTLs在染色体的同一标记区间,表现一因多效。研究表明:位于1A和5D染色体上的2个加性QTLs都同时控制旗叶长、宽和面积,且前者为主效基因,后者遗传贡献率也较大,可用于标记辅助育种和分子聚合育种。     

玉米抗南方锈病基因的QTL定位

为发掘新的抗南方锈病基因资源,本研究以感病自交系黄早四为母本、抗病自交系W456为父本,构建F2群体并开展抗病基因定位研究。采用人工接种鉴定的方法对两个亲本、F1、F2群体及对照材料进行表型鉴定和遗传分析。利用均匀覆盖10条染色体的200个SSR标记,分析240个F2单株的基因型并构建含有200个SSR位点的遗传连锁图,连锁图总长度3331 cM,标记间平均距离16.6 cM。使用QTL IciMapping V4.1软件中的完备区间作图法对抗病QTL进行分析,共检测到6个控制南方锈病的QTL:qSCR3、qSCR7、qSCR8-1、qSCR8-2、qSCR9和qSCR10,邻近标记分别为umc2105和umc1729、umc1066和bnlg2271、umc1904和umc1984、umc1984和bnlg1651、umc1957和bnlg1401、umc2034和umc1291,分别位于3、7、8、9和10号染色体上,其中8号染色体上有两个位点,标记区间长度在5～19 cM之间。单个QTL的表型贡献率在2.61%～24.19%之间,可以解释表型总变异的62.3%,其中3个QTL贡献率大于10%,位于10号染色体上的qSCR10贡献率最大,可解释表型变异的24.19%。通过对目标区间标记加密,将该位点的定位区间进一步缩小到2.51 cM内,与两侧标记的距离分别是2.15 cM和0.36 cM。初步定位得到10号染色体上存在抗南方锈病的主效QTL,可为抗病品种的培育提供参考。     

绿豆种子休眠性和百粒重的QTLs和互作分析

利用绿豆Berken/ACC41重组自交系在北京种植得到的121个F10家系和79个RFLP分子标记,采用改进复合区间作图法对绿豆种子休眠性和百粒重进行数量性状基因定位及上位性互作分析.检测到与发芽势有关的QTL 3个,与发芽率有关的QTL 4个,分别位于第1、11连锁群,解释表型变异的8.17%～12.14%和4.34%～12.69%.检测到与百粒重有关的QTL 5个,分别位于第2、8、9、11连锁群,解释表型变异的4.58%～10.36%.增加发芽率和百粒重的基因效应均来自母本Berken.分别检测到发芽势、发芽率和百粒重的加性×加性上位性互作8、9、9对,对这3个性状的总表型贡献率分别达到66.58%、47.91%、39.90%.本文初步分析了休眠性和百粒重的关系,并与前人的研究结果作了比较,旨在通过分子标记辅助选择,培育适度休眠的优良绿豆品种,进而解决绿豆收获前的荚上子粒发芽问题.     

利用三倍体胚乳遗传模型定位玉米籽粒淀粉含量QTL

在两种环境条件下种植以普通玉米自交系丹232和爆裂玉米自交系N04为亲本构建的259个F2:3家系群体, 采用SSR标记构建了包含183个标记的玉米遗传连锁图谱, 覆盖玉米基因组1 762.2 cM, 标记间平均距离为9.6 cM。利用三倍体胚乳遗传模型和区间作图方法对籽粒淀粉含量进行了QTL定位和遗传效应分析, 春、夏播条件下共检测到10个QTL, 春播条件下检测到的QTL在夏播均被检测到, 分别位于第1、3、4、5、7染色体上,可解释淀粉的表型总变异分别为36.84%和72.65%, 单个QTL解释表型变异介于4.74%~11.26%。在检测到的 QTL中, 有2个QTL的遗传作用方式在春播均表现为超显性, 而夏播分别为加性和部分显性; 其他2个为加性, 1个为部分显性, 5个为超显性。3个QTL的增效基因来自丹232, 其余QTL的增效基因均来自N04。     

基于掖478导入系的玉米百粒重QTL鉴定

玉米百粒重是产量性状的主要组成因子,对其控制位点进行QTL鉴定或基因克隆,将有益于其遗传控制的研究和分子育种的实施。本研究以导入系SL19-41为材料,该导入系是以我国玉米育种中广泛应用的骨干自交系掖478(Ye478)为遗传背景导入QB80染色体片段的纯合系。使用该导入系与Ye478杂交构建分离群体(F2、F2:3家系和BC1F1),通过3个环境下的田间试验,利用Ici Mapping的逐步回归区间作图法进行百粒重QTL定位,以及进行QTL位点连锁标记的表型效应分析。结果表明:鉴定了2个百粒重QTL位点,其中位于第4染色体bnlg1784~umc1194区间QTL位点q KW4-1在3个环境下均被检测到,可解释的表型变异为6.74%~17.81%,阐明了导入系SL19-41百粒重性状的遗传机制,同时也获得了改良版的Ye478(Ye478QB80),为玉米百粒重的遗传改良提供有益的分子标记,也为克隆百粒重基因提供材料来源。     

控制大豆油分含量和百粒重的QTL定位

油分含量和百粒重是大豆中两个重要的性状。本研究利用东农46和L-100衍生的重组自交系(RIL)群体,经过两年3个地点种植,通过分子标记技术定位与大豆油分含量和百粒重相关的QTL(quantitative trait locus)。结果表明,检测到6个与油分含量相关的QTL,分别位于E、H、G和I连锁群上,可解释的表型贡献率范围为2.12%~2.77%;检测到5个与百粒重相关的QTL,分别位于K、H、B2和G连锁群上,可解释的表型贡献率范围为2.30%~7.59%,在H连锁群上有2个QTL两年均被检测到,标记区间分别为Satt279-Sat_122和Satt192-Satt568。在H连锁群上Satt192-Satt568标记区间内同时检测到与油分含量和百粒重相关的QTL。研究结果为大豆油分含量和百粒重等性状的分子辅助育种提供了理论依据。     

Quantitative trait loci responsible for Fusarium head blight resistance in Chinese landrace Baishanyuehuang

Fusarium head blight (FHB), mainly caused by Fusarium graminearum, is a destructive disease that can significantly reduce grain yield and quality. Deployment of quantitative trait loci (QTLs) for FHB resistance in commercial cultivars has been the most effective approach for minimizing the disease losses. 'Baishanyuehuang' is a highly FHB-resistant landrace from China. Recombinant inbred lines (RILs) developed from a cross of 'Baishanyuehuang' and 'Jagger' were evaluated for FHB resistance in three greenhouse experiments in 2010 and 2011 by single-floret inoculation. Percentage of symptomatic spikelets in an inoculated spike was recorded 18 days post-inoculation. The RIL population was screened with 251 polymorphic simple sequence repeats. Four QTLs were associated with FHB resistance and mapped on three chromosomes. Two QTLs were located on the short arm of chromosome 3B (3BS) with one in distal of 3BS and another near centromere (3BSc), designated as Qfhb.hwwg-3BSc. The QTL in the distal of 3BS is flanked by Xgwm533 and Xgwm493, thus corresponds to Fhb1. This QTL explained up to 15.7 % of phenotypic variation. Qfhb.hwwg-3BSc flanked by Xwmc307 and Xgwwm566 showed a smaller effect than Fhb1 and explained up to 8.5 % of phenotypic variation. The other two QTLs were located on 3A, designated as Qfhb.hwwg-3A, and 5A, designated as Qfhb.hwwg-5A. Qfhb.hwwg-3A was flanked by Xwmc651 and Xbarc356 and explained 4.8-7.5 % phenotypic variation, and Qfhb.hwwg-5A was flanked by markers Xgwm186 and Xbarc141, detected in only one experiment, and explained 4.5 % phenotypic variation for FHB resistance. 'Baishanyuehuang' carried all resistance alleles of the four QTL. Qfhb.hwwg-3BSc and Qfhb.hwwg-3A were new QTLs in 'Baishanyuehuang'. 'Baishanyuehuang' carries a combination of QTLs from different sources and can be a new source of parent to pyramid FHB-resistant QTLs for improving FHB resistance in wheat.     

Quantitative genetic analysis of flowering time in tomato.

Artificial selection of cultivated tomato (Solanum lycopersicum L.) has resulted in the generation of early-flowering, day-length-insensitive cultivars, despite its close relationship to other Solanum species that need more time and specific photoperiods to flower. To investigate the genetic mechanisms controlling flowering time in tomato and related species, we performed a quantitative trait locus (QTL) analysis for flowering time in an F2 mapping population derived from S. lycopersicum and its late-flowering wild relative S. chmielewskii. Flowering time was scored as the number of days from sowing to the opening of the first flower (days to flowering), and as the number of leaves under the first inflorescence (leaf number). QTL analyses detected 2 QTLs affecting days to flowering, which explained 55.3% of the total phenotypic variance, and 6 QTLs for leaf number, accounting for 66.7% of the corresponding phenotypic variance. Four of the leaf number QTLs had not previously been detected for this trait in tomato. Colocation of some QTLs with flowering-time genes included in the genetic map suggests PHYB2, FALSIFLORA, and a tomato FLC-like sequence as candidate genes that might have been targets of selection during the domestication of tomato.     

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.     

Mapping QTL associated with resistance to Fusarium head blight in the Nanda2419 × Wangshuibai population. II: Type I resistance

Fusarium head blight (FHB) is a serious disease in wheat and barley affecting both yield and quality. To identify genes for resistance to infection, the RIL population derived from ‘Nanda2419’ × ‘Wangshuibai’ and the parents were evaluated for percentage of infected spikes (PIS) in four different environments. Using a 2,960 cM marker framework map constructed for this population, ten chromosome regions were detected for their association with type I resistance through interval mapping with Mapmaker/QTL, among which QTLs mapped in the intervals of Xwmc349~Xgwm149 on chromosome 4B, of Xwmc96~Xgwm304 on chromosome 5A and of Xgwm408~Xbarc140 on chromosome 5B were revealed in at least three environments and have Wangshuibai as the source of resistance alleles. Qfhi.nau-4B and Qfhi.nau-5A had larger effects and explained up to 17.5 and 27.0% of the phenotypic variance, respectively. To detect epistasis QTLs, two-locus interactions were examined by whole genome scan. Interactions of five locus pairs were found to have significant effects on type I resistance with the LOD score ranging 3.8–6.5 and four of them conferred resistance in parental phase. The one with the most significant effect was Xcfd42~Xgwm469 (6D)/Xwmc390-2~Xbd04 (2A) pair. No QTL × E interaction was detected for PIS. It was found that flowering time did not have significant effects on PIS in this population. Our studies indicated that Wangshuibai is useful for breeding for both type I and type II scab resistance and the markers associated with the QTLs could be used in marker-assisted selection and isolation of scab-resistance QTLs. F. Lin and S.L. Xue equally contributed to this article     

QTLs for days to silking in a recombinant inbred line maize population subjected to high and low nitrogen regimes

Days to silking (DTS) is one of the most important traits in maize (Zea mays). To investigate its genetic basis, a recombinant inbred line population was subjected to high and low nitrogen (N) regimes to detect quantitative trait loci (QTLs) associated with DTS. Three QTLs were identified under the high N regime; these explained 25.4% of the phenotypic variance. Due to additive effects, the QTL on chromosome 6 increased DTS up to 0.66 days; while the other two QTLs mapped on chromosome 9 (one linked with Phi061 and the other linked with Nc134) decreased DTS 0.89 and 0.91 days, respectively. Under low N regime, two QTLs were mapped on chromosomes 6 and 9, which accounted for 25.9% of the phenotypic variance. Owing to additive effects, the QTL on chromosome 6 increased DTS 0.67 days, while the other QTL on chromosome 9 decreased it 1.48 days. The QTL on chromosome 6, flanked by microsatellite markers Bnlg1600 and Phi077, was detected under both N regimes. In conclusion, we identified four QTLs, one on chromosome 6 and three on chromosome 9. These results contribute to our understanding of the genetic basis of DTS and will be useful for developing marker-assisted selection in maize breeding programs.     

Fine mapping of the region on wheat chromosome 7D controlling grain weight

We report the fine mapping of the previously described quantitative trait loci (QTL) for grain weight QTgw.ipk-7D associated with microsatellite marker Xgwm1002-7D by using introgression lines (ILs) carrying introgressions of the synthetic wheat W-7984 in the genetic background of the German winter wheat variety ‘Prinz’. The BC₄F₃ ILs had a 10% increased thousand grain weight compared to the control group and the recurrent parent ‘Prinz’, and 84.7% of the phenotypic variance could be explained by the segregation of marker Xgwm1002-7D, suggesting the presence of a gene modulating grain weight, which was preliminarily designated gw1. It was possible to delimit the QTL QTgw.ipk-7D to the interval Xgwm295–Xgwm1002, which is located in the most telomeric bin 7DS4-0.61-1.00 in the physical map of wheat chromosome arm 7DS. Furthermore, our data suggest the presence of a novel plant height-reducing locus Rht on chromosome arm 7DS of ‘Prinz’. Larger grain and increased plant height may reflect the pleiotropic action of one gene or may be caused by two linked genes. In general, our data support the concept of using nearly isogenic ILs for validating and dissecting QTLs into single Mendelian genes and open the gateway for map-based cloning of a grain-weight QTL in wheat.     

Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in <Emphasis Type="Italic">Brassica oleracea</Emphasis>

Studying quantitative traits is complicated due to genotype by environment interactions. One strategy to overcome these difficulties is to combine quantitative trait loci (QTL) and ecophysiological models, e.g. by identifying QTLs for the response curves of adaptive traits to influential environmental factors. A B. oleracea DH-population segregating for time to flowering was cultivated at different temperature regimes. Composite interval mapping was carried out on the three parameters of a model describing time to flowering as a function of temperature, i.e. on the intercept and slope of the response of time to floral induction to temperature and on the duration from transition to flowering. The additive effects of QTLs detected for the parameters have been used to estimate time to floral induction and flowering in the B. oleracea DH-population. The combined QTL and crop model explained 66% of the phenotypic variation for time to floral induction and 56% of the phenotypic variation for time to flowering. Estimation of time to floral induction and flowering based on environment specific QTLs explained 61 and 41% of the phenotypic variation. Results suggest that flowering time can be predicted effectively by coupling QTL and crop models and that using crop modelling tools for QTL analysis increases the power of QTL detection.     

Identification of quantitative trait loci associated with small brown planthopper (Laodelphax striatellus Fallén) resistance in rice (Oryza sativa L.)

F(2) and BC(1) populations derived from the cross between 02428 / Rathu Heenati were used to investigate small brown planthopper (SBPH) resistance. Using the F(2) population, three QTLs for antixenosis against SBPH were located on chromosomes 2, 5 and 6, and accounted for 30.75% of the phenotypic variance; three QTLs for antibiosis against SBPH were detected on chromosomes 8, 9 and 12. qSBPH5-c explaining 7.21% of phenotypic variance for antibiosis was identified on chromosome 5 using the BC(1) population. A major QTL, qSBPH12-a1, explained about 40% of the phenotypic variance, and a minor QTL, qSBPH4-a, was detected by the SSST method in both the F(2) and BC(1) populations. The QTLs indentified in the present study will be useful for marker assisted selection of SBPH resistance in rice.     

Assessment of differences in morphological and physiological leaf lodging characteristics between two cultivars of Hippeastrum rutilum

Kernel weight and morphology are important traits affecting cereal yields and quality. Dissecting the genetic basis of thousand kernel weight (TKW) and its related traits is an effective method to improve wheat yield. In this study, we performed quantitative trait loci (QTL) analysis using recombinant inbred lines derived from the cross ‘PuBing3228 × Gao8901’ (PG-RIL) to dissect the genetic basis of kernel traits. A total of 17 stable QTLs related to kernel traits were identified, notably, two stable QTLs QTkw.cas-1A.2 and QTkw.cas-4A explained the largest portion of the phenotypic variance for TKW and kernel length (KL), and the other two stable QTLs QTkw.cas-6A.1 and QTkw.cas-7D.2 contributed more effects on kernel width (KW). Conditional QTL analysis revealed that the stable QTLs for TKW were mainly affected by KW. The QTLs QTkw.cas-7D.2 and QKw.cas-7D.1 associated with TKW and KW were delimited to the physical interval of approximately 3.82 Mb harboring 47 candidate genes. Among them, the candidate gene TaFT-D1 had a 1 bp insertions/deletion (InDel) within the third exon, which might be the reason for diversity in TKW and KW between the two parents. A Kompetitive Allele-Specific PCR (KASP) marker of TaFT-D1 allele was developed and verified by PG-RIL and a natural population consisted of 141 cultivar/lines. It was found that the favorable TaFT-D1 (G)-allele has been positively selected during Chinese wheat breeding. Thus, these results can be used for further positional cloning and marker-assisted selection in wheat breeding programs. Seventeen stable QTLs related to kernel traits were identified. The stable QTLs for thousand kernel weight were mainly affected by kernel width. TaFT-D1 could be the candidate gene for QTLs QTkw.cas-7D.2 and QKw.cas-7D.1.     

Genetic and physiological analysis of germination efficiency in maize in relation to nitrogen metabolism reveals the importance of cytosolic glutamine synthetase

We have developed an approach combining physiology and quantitative genetics to enhance our understanding of nitrogen (N) metabolism during kernel germination. The physiological study highlighted the central role of glutamine (Gln) synthetase (GS) and Gln synthesis during this developmental process because a concomitant increase of both the enzyme activity and the amino acid content was observed. This result suggests that Gln is acting either as a sink for ammonium released during both storage protein degradation and amino acid deamination or as a source for amino acid de novo synthesis by transamination. In the two parental lines used for the quantitative genetics approach, we found that the increase in Gln occurred earlier in Io compared with F(2), a result consistent with its faster germinating capacity. The genetic study was carried out on 140 F6 recombinant inbred lines derived from the cross between F(2) and Io. Quantitative trait locus mapping identified three quantitative trait loci (QTLs) related to germination trait (T50, time at which 50% of the kernels germinated) that explain 18.2% of the phenotypic variance; three QTLs related to a trait linked to germination performance, kernel size/weight (thousand kernels weight), that explain 17% of the phenotypic variance; two QTLs related to GS activity at early stages of germination that explain 17.7% of the phenotypic variance; and one QTL related to GS activity at late stages of germination that explains 7.3% of the phenotypic variance. Coincidences of QTL for germination efficiency and its components with genes encoding cytosolic GS (GS1) and the corresponding enzyme activity were detected, confirming the important role of the enzyme during the germination process. A triple colocalization on chromosome 4 between gln3 (a structural gene encoding GS1) and a QTL for GS activity and T50 was found; whereas on chromosome 5, a QTL for GS activity and thousand kernels weight colocalized with gln4, another structural gene encoding GS1. This observation suggests that for each gene, the corresponding enzyme activity is of major importance for germination efficiency either through the size of the grain or through its faster germinating capacity. Consistent with the possible nonoverlapping function of the two GS1 genes, we found that in the parental line Io, the expression of Gln3 was transiently enhanced during the first hours of germination, whereas that of gln4 was constitutive.