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。     

限量灌溉对河套灌区间作小麦干物质转移与灌浆特征的影响

针对内蒙古河套灌区水资源短缺现状,为探究限量灌溉条件下间作春小麦的干物质转移与灌浆特征,掌握制约春小麦籽粒灌浆的因素,进而提高间作模式下春小麦的水分生产率,研究了限量灌溉(298、328、358和388 mm 4个水平)对间作模式(小麦/玉米间作、小麦/向日葵间作)下春小麦籽粒灌浆特征的影响。结果表明:间作向日葵模式下的小麦茎、叶干物质转移量是相同灌水处理下间作玉米模式下的小麦茎、叶干物质转移量的1.08~1.86倍与1.12~2.17倍,而颖轴干物质转移量则间作玉米模式是间作向日葵模式的1.00~1.19倍,且不同水分处理与种植模式下的小麦籽粒灌浆过程符合"S"型生长曲线,可用Logistic方程模拟;间作玉米模式下的小麦灌浆速率的峰值出现在花后25~30 d,较对照提前1.26~2.85 d,间作向日葵模式下的小麦灌浆速率的峰值出现在花后25~30 d,较对照提前1.80~2.44 d,各处理最大灌浆速率与平均灌浆速率均较对照提高,且减少了达到最大灌浆速率所需时间;小麦的活跃灌浆期则表现为灌水总量较少的处理(298~328 mm)有利于延长间作玉米模式下小麦的活跃灌浆期,而较高灌水量的处理(358~388 mm)则会延长间作向日葵种植模式下小麦的活跃灌浆期。     

不同生长环境下水稻穗伸出度的QTL分析

以十和田/昆明小白谷225个F14家系为作图群体,在云南省弥勒县(正常生长环境)、嵩明县(自然低温胁迫环境)、丽江市(自然低温胁迫环境)等3个试点不同年份共5种不同生长环境下进行了水稻主穗和分蘖穗穗伸出度的异地鉴定,并利用SSR标记对水稻穗伸出度进行了QTL分析。检测结果表明,在5种不同的生长环境下共检测到12个与水稻穗伸出度相关的QTL,分别分布于第1(2个QTLs)、2、4、6(3个QTLs)、7(3个QTLs)、9(2个QTLs)号染色体,对表型的贡献率为3.72%~22.17%。其中与主穗穗伸出度相关的QTL共11个,与分蘖穗穗伸出度相关的QTL共7个,其中6个在主穗和分蘖穗上均检测到。在与主穗穗伸出度相关的11个QTL中,q PE-7-1在4种环境下均被检测到,解释的表型变异为9.49%~22.17%;q PE-1-1、q PE-1-2、q PE-6-1和q PE-9-2 4个QTL在2种环境下均被检测到。在与分蘖穗穗伸出度相关的7个QTL中,q PE-1-2、q PE-7-1和q PE-6-1 3个QTL在2种环境中均被检测到,解释的表型变异率分别为4.35%~12.64%、13.22%~20.89%和11.49%~15.73%。     

控制水稻重要农艺性状的QTL在盐胁迫与非胁迫条件下的对比研究

基因型与环境的互作(G×E)对数量性状的影响常常掩盖了遗传因子引起的性状变化. 在盐胁迫环境与非胁迫环境下分别调查了水稻(Oriza sativa L.) 5个重要的农艺性状, 总共检测到24个QTL, 分布在除第9, 11号染色体外的各染色体上. 盐胁迫环境中检出了9个QTL: 千粒重1个; 抽穗期2个; 株高1个; 每穗粒数2个; 有效分蘖3个, 占总数的37.5%; 非胁迫环境中则检出了17个QTL: 千粒重5个; 抽穗期6个; 株高3个; 每穗粒数2个; 有效分蘖1个, 占总数的70.8%; 有两个QTL在两种环境中都检测到, 占总数的8.3%, 它们分别是位于第4染色体上控制抽穗期的QTL和位于第6染色体上控制每穗粒数的QTL. 此外, 还检测出3个包含多个QTL的区间, 它们分别位于第1, 4和8染色体上, 其中第1染色体上RG612分子标记附近检出两个QTL, 在盐胁迫环境与非胁迫环境中分别控制有效分蘖和抽穗期这两个重要的农艺性状, 其加性效应均由来源于JX17的等位基因提供; 第4染色体上的C975-RG449区间检测到2个QTL, qrHD-4c在非协迫环境中控制抽穗期, qrGPP-4s则在胁迫环境中控制每穗粒数; 第8染色体上的RG885-GA408区间检测到3个QTL, 在非胁迫环境下分别控制抽穗期、千粒重、株高3个性状, 在胁迫环境下则未能检测到. 通过对水稻在盐胁迫环境与非胁迫环境下的QTL对比研究, 发现水稻第8染色体上几个控制水稻重要农艺性状的QTL明显受盐胁迫的影响.     

稻米粒形的QTL定位及上位性和QE互作分析

利用'广陆矮4号'×'佳辐占'水稻重组自交系构建了SSR标记的遗传图谱.联合2007年和2008年获得的两组稻米粒长(GL)、粒宽(GW)、长宽比(L/W)数据应用混合线性模型方法进行QTL定位,并作加性效应、加性×加性上位互作效应以及加性QTL、上位性QTL与环境的互作效应分析.结果显示;(1)在加性效应分析中两个群体共检测到4个控制粒长的QTL,4个控制粒宽的QTL,5个控制长宽比的QTL,贡献率分别为13.81%、15.36%和 16.29%.(2)在上位互作效应分析中两个群体共检测到2对控制粒长的互作QTL,1对控制粒宽的互作QTL,3对控制长宽比的互作QTL,贡献率分别为5.77%、2.59%和7.42%.(3)环境互作检测中,发现共有13个加性QTL和4对QTL的加性×加性上位性与环境产生了互作效应.结果表明,上位性效应和加性效应都影响稻米粒形遗传,QE互作效应也对粒形有着显著的影响.     

玉米雄穗分枝数与主轴长的QTL鉴定

在包含103个SSR标记的连锁图谱基础上, 运用复合区间作图法检测玉米组合(N87-1×9526 )F3家系在正常与干旱胁迫环境下的雄穗分枝数与主轴长性状QTL。雄穗分枝数在正常环境下被检测到2个QTL座位, 分别位于第5和7连锁群上; 在胁迫环境下被检测到4个QTL座位分别位于 2、5、7和10连锁群上, 其中位于第5和7连锁群上的QTL不仅具有一致性而且与本作图群体中曾检测到的耐旱相关性状QTL存在连锁。雄穗主轴长在正常环境下被检测到2个QTL位于第2和第6连锁群上, 在干旱胁迫环境下被检测到了3个QTL分别于第2、4和10连锁群上, 其中位于第2染色体上的QTL是两种环境下所共同检测到的QTL。分析QTL的遗传作用方式表明, 雄穗分枝数以部分加性效应为主, 而雄主轴长全部表现为显性和超显性。     

玉米优异早熟种质单330开花相关性状的QTL分析

玉米开花相关性状与玉米的成熟期和产量有密切的联系。通过对玉米CN165×单330(早熟种质)群体的130个F2:3家系开花相关性状在3个环境下进行分子鉴定和数量性状位点(QTL)分析,结果表明,在3个环境中检测到控制抽雄天数的10个QTL,分别位于第2、3、4、5、7、8染色体上,在第8染色体上同一区域在3种环境下都检测到了QTL;检测到控制散粉天数的10个QTL,分别位于第1、2、3、5、7、8染色体上,在第8染色体上同一区域在2种环境下都检测到了QTL;检测到控制吐丝天数的4个QTL,分别位于第4、5、8染色体上,在第8染色体不同环境下都检测到了2个QTL;仅仅在一个环境中检测到控制ASI的2个QTL,分别位于第6、9染色体上。这些QTL的基因效应以部分显性和超显性为主。研究表明,第8染色体上ph i060-um c2401区域(8.03~8.04)是一个研究开花相关性状的重要基因组区段,涉及到的标记可以作为分子标记辅助选择的重要候选标记。     

小麦籽粒品质性状的QTL分析

利用小麦中国春(母本)和兰考大粒(父本)F2群体构建了169个标记的分子遗传图谱,将F2∶3家系分别种植于3个环境中,利用基于完备区间混合模型的单环境作图模型和多环境作图模型对小麦籽粒容重、硬度、蛋白含量和结合水含量性状进行了QTL分析。结果显示:(1)两种作图模型下,检测到容重的6个共同QTL(QTW-6B-6、QTW-7B-6、QTW-7B-9、QTW-5D-2、QTW-6D-1、QTW-6D-4),单环境模型下遗传贡献率为1.99%~6.57%,多环境模型下遗传贡献率为3.66%~20.07%,其中QM TW-7B-9、QM TW-6D-1和QM TW-6D-4在多环境模型中表现为主效QTL。(2)检测到硬度的3个共同QTL(QHD-4A-5、QHD-7A-1和QHD-7B-9),单环境模型下的遗传贡献率为6.00%~6.95%,多环境模型中遗传贡献率为5.43%~9.64%。(3)检测到蛋白含量1个共同QTL(QPR-6D-1),单环境模型下的遗传贡献率为5.39%,多环境模型中遗传贡献率为10.06%,表现为主效QTL。(4)检测到籽粒结合水含量1个共同QTL(QMO-1B-4),单环境模型下的遗传贡献率为39.20%,多环境模型下的遗传贡献率为75.01%,均表现为主效QTL。(5)1B染色体上存在同时控制籽粒容重、硬度、蛋白和结合水含量的QTL,说明1B染色体对小麦品质的影响可能很大。研究表明,小麦容重、硬度、蛋白含量、结合水含量的遗传主要受加性效应控制。该研究初步定位的一些重要QTL可为进一步精细定位、基因挖掘和育种早代品质性状分子标记辅助选择提供依据。     

小麦苗期水分利用效率及其相关性状的QTL分析

以小麦DH群体(旱选10号×鲁麦14)为研究材料,采用复合区间作图法,对小麦幼苗在水分胁迫及非胁迫条件下的水分利用效率(WUE)及其相关性状的QTL进行定位,并对比分析QTL的加性效应.两种水分条件下共检测到14个具显著加性效应的QTL,分布在2A、3A、4A、5A、6A、7A、1B、3B、3D染色体上,可解释表型变异的范围在6.36%～19.73%.其中,非胁迫(对照)条件下检测到10个QTL,包括2个单株WUE的QTL,5个地上部WUE的QTL,1个根系WUE的QTL及2个总耗水量的QTL;水分胁迫条件下上述性状各检测到1个QTL.对于同一性状没有检测到在两种水分条件下均位于同一标记区间的QTL,表明不同水分环境条件下同一性状的QTL表达模式是不同的.论文也讨论了可能用于标记辅助选择的QTL及其分子标记.     

小麦高密度遗传图谱构建及抗旱相关生理性状的遗传解析

生理调控是小麦应对干旱胁迫的主要途径,解析小麦抗旱相关生理性状的遗传基础,发掘利用分子标记将为小麦抗旱性的高效改良提供有力支撑。本研究以加倍单倍体(DH)群体(旱选10号×鲁麦14)的150个株系为材料,利用小麦660K SNP芯片及SSR标记构建高密度遗传图谱,解析不同水分环境下孕穗期及灌浆中期小麦冠层温度(CT)、叶绿素含量(SPAD value)和植被覆盖指数(NDVI)的遗传基础。遗传图谱覆盖小麦21条染色体,分为30个连锁群,总长度4082.44 c M,标记间平均距离为2.20 c M。共检测到抗旱相关生理性状QTL 86个,分布于除3D以外的20条染色体上。冠层温度、叶绿素含量和植被覆盖指数的QTL数目分别为30、40和34个;17个QTL具有一因多效性,其中4个QTL与冠层温度和植被覆盖指数相关,8个QTL与冠层温度和叶绿素含量相关,7个QTL与叶绿素含量和植被覆盖指数相关,位于4D染色体的QPT52与3种性状均相关。本研究为小麦抗旱基因挖掘及分子育种提供了参考信息和技术支撑。     

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.     

Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds

Wheat thousand kernel weight (TKW) is a complex trait, and is largely controlled by several kernel traits, including kernel length (KL) and kernel width (KW). In order to reveal the genetic relationship between TKW and these kernel traits (KW and KL) as accurate as possible, we applied both unconditional and conditional mapping analyses to three distinct genetic populations, one DH population and two RIL populations. This report describes the identifications of 36 unconditional and conditional additive QTLs and 30 pairs of unconditional and conditional epistatic QTLs, all of which are closely associated with TKW. While the conditional additive locus Qtkw1B, detected in the RIL2 population, exhibited the largest contribution, explaining 14.12 % of TKW variance, the unconditional epistatic QTLs Qtkw3A-2/Qtkw5B.1, detected in the DH population, accounted for 11.95 % of phenotypic variance. This study also showed that, compared with unconditional mapping, conditional mapping resulted in very different numbers and different extent of effects of additive and epistatic QTLs that were associated with TKW when TKW was conditioned on kernel traits (KW and KL). These data strongly suggest that KW and KL indeed play a significant role in determining TKW. Furthermore, we demonstrated that the effects of the 25 additive QTLs for TKW were either entirely or largely determined by KW, while the effects of the other 25 additive QTLs for TKW were either entirely or largely affected by KL. We conclude that the conditional mapping can be useful for a better understanding of the interrelationship between the yield contributing traits at the QTL level.     

Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level?

Kernel dimensions (KD) contribute greatly to thousand-kernel weight (TKW) in wheat. In the present study, quantitative trait loci (QTL) for TKW, kernel length (KL), kernel width (KW) and kernel diameter ratio (KDR) were detected by both conditional and unconditional QTL mapping methods. Two related F(8:9) recombinant inbred line (RIL) populations, comprising 485 and 229 lines, respectively, were used in this study, and the trait phenotypes were evaluated in four environments. Unconditional QTL mapping analysis detected 77 additive QTL for four traits in two populations. Of these, 24 QTL were verified in at least three trials, and five of them were major QTL, thus being of great value for marker assisted selection in breeding programmes. Conditional QTL mapping analysis, compared with unconditional QTL mapping analysis, resulted in reduction in the number of QTL for TKW due to the elimination of TKW variations caused by its conditional traits; based on which we first dissected genetic control system involved in the synthetic process between TKW and KD at an individual QTL level. Results indicated that, at the QTL level, KW had the strongest influence on TKW, followed by KL, and KDR had the lowest level contribution to TKW. In addition, the present study proved that it is not all-inclusive to determine genetic relationships of a pairwise QTL for two related/causal traits based on whether they were co-located. Thus, conditional QTL mapping method should be used to evaluate possible genetic relationships of two related/causal traits.     

Molecular mapping of quantitative trait loci for three kernel-related traits in maize using a double haploid population

Kernel size and kernel weight are important factors possibly involved in the determination of grain yield in maize, so identifying the genetic basis of kernel-related traits provides insights into the breeding of high-yield maize varieties. Kernel length (KL), kernel width (KW) and hundred kernel weight (HKW) were evaluated in three various planting conditions for the 240 field-grown double haploid (DH) lines derived from the single-cross hybrid Xianyu335. Variations in KL, KW and HKW were observed among DH lines, and all three traits showed a broad sense heritability of 76%. A total of 964 single nucleotide polymorphisms (SNPs) from the MaizeSNP3072 chip was utilised to create a high-density genetic map of 1546.4 cM and to identify quantitative trait loci (QTLs). Using composite interval mapping, a total of five, seven and five QTLs have been mapped for KL, KW and HKW, respectively. qkl1-2 and qkl4-1 explained 17.8% and 14.2% of the phenotypic variation in KL, respectively, and the other three QTLs contributed 3.2–4.0%. The phenotypic variation explained (PVE) of seven QTLs responsible for KW ranged from 3.3 to 9.5%. Three QTLs for HKW, qhkw1, qhkw5 and qhkw10 each explained more than 10% of the phenotypic variation, and qhkw4 and qhkw9 accounted for 3.0% and 6.0%, respectively. Due to their detection in multiple planting environments, the loci mapped here appear to be potential targets for the improvement of maize grain yield.     

Single nucleotide polymorphism tightly linked to a major QTL on chromosome 7A for both kernel length and kernel weight in wheat

Thousand-kernel weight (TKW) is one of the major components of grain yield in wheat (Triticum aestivum). Identifying major quantitative trait loci (QTLs) for TKW and developing effective markers are prerequisite for success in marker-assisted selection (MAS) to improve wheat yield through breeding. This study mapped a major QTL, designated as TaTKW-7AL, for increasing TKW on the long arm of chromosome 7A of ‘Clark’ to a 1.3-cM interval between single nucleotide polymorphism (SNP) markers IWB13913 and IWA5913. This QTL explained 19.7 % of the phenotypic variation for TKW. A QTL for increasing kernel length (KL), one of the major components of TKW, was mapped in the same interval as TaTKW-7AL, suggesting that increased TKW by the QTL in ‘Clark’ is most likely due to the increased KL. Association analysis on a diversity panel of 200 US winter wheat accessions also identified a haplotype of three SNP markers (IWB13913, IWB6693 and IWA5913) that were tightly associated with the both KL and TKW. The analysis of allele frequencies of the haplotype in the diversity panel suggested that the favorable allele of TaTKW-7AL has not been strongly selected for in practice and has potential to be used to improve grain yield in US hard winter wheat breeding. Two user-friendly flanking KASPar markers, IWB13913 and IWA5913, were developed for MAS of TaTKW-7AL.     

Genetic analysis of grain protein-content,grain yield and thousand-kernel weight in bread wheat

Grain yield and grain protein content are two very important traits in bread wheat. They are controlled by genetic factors, but environmental conditions considerably affect their expression. The aim of this study was to determine the genetic basis of these two traits by analysis of a segregating population of 194 F(7) recombinant inbred lines derived from a cross between two wheat varieties, grown at six locations in France in 1999. A genetic map of 254 loci was constructed, covering about 75% of the bread wheat genome. QTLs were detected for grain protein-content (GPC), yield and thousand-kernel weight (TKW). 'Stable' QTLs (i.e. detected in at least four of the six locations) were identified for grain protein-content on chromosomes 2A, 3A, 4D and 7D, each explaining about 10% of the phenotypic variation of GPC. For yield, only one important QTL was found on chromosome 7D, explaining up to 15.7% of the phenotypic variation. For TKW, three QTLs were detected on chromosomes 2B, 5B and 7A for all environments. No negative relationships between QTLs for yield and GPC were observed. Factorial Regression on GxE interaction allowed determination of some genetic regions involved in the differential reaction of genotypes to specific climatic factors, such as mean temperature and the number of days with a maximum temperature above 25 degrees C during grain filling.     

QTL mapping for yield and photosynthetic related traits under different water regimes in wheat

The improvement for drought tolerance requires understanding of the genetic control of wheat (Triticum aestivum L.) reaction to drought. In this study, a set of 131 recombinant inbred lines of wheat were investigated under well-watered (WW) and drought stress (DS) environments across 2 years to map quantitative trait loci (QTLs) for yield and physiological traits. A total of 225 QTLs were detected, including 32 non-environment-specific loci that were significant in both DS and WW, one drought-specific locus and two watering-specific loci. Three consistently-expressed QTLs (QTkw-3A.2, QTss-1A, and QScn-7A.1) were identified in at least three environments and the QTkw-1D.1 was significant in DS across the 2 years. By unconditional and conditional QTL analysis, spike number per plant and kernel number per spike were more important than thousand-kernel weight for grain yield (GY) at the given genetic background. Meta-analysis identified 67 meta-QTLs that contained QTLs for at least two traits. High frequency co-location of QTLs was found among either the spike-related traits or the six physiological traits. Four photosynthesis traits (CHL, LWUE, P _N, and C _i) were co-located with GY and/or yield components on various MQTLs. The results provided QTLs that warrant further study for drought tolerance breeding and are helpful for understanding the genetic basis of drought tolerance and the genetic contribution of yield components to GY at individual QTL level in wheat.     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

A genetic linkage map of the Durum ×<Emphasis Type="Italic"> Triticum dicoccoides</Emphasis> backcross population based on SSRs and AFLP markers,and QTL analysis for milling traits

Durum wheat (Triticum turgidum L. var durum) is mainly produced and consumed in the Mediterranean region; it is used to produce several specific end-products; such as local pasta, couscous and burghul. To study the genetics of grain-milling quality traits, chromosomal locations, and interaction with the environment, a genetic linkage map of durum was constructed and the quantitative trait loci QTLs for the milling-related traits, test weight (TW) and thousand-kernel weight (TKW), were identified. The population constituted 114 recombinant inbred lines derived from the cross: Omrabi 5/Triticum dicoccoides 600545// Omrabi 5. TW and TKW were analyzed over 18 environments (sites × years). Single-sequence-repeat markers (SSRs), Amplified-fragment-length-polymorphism markers (AFLPs), and seed storage proteins (SSPs) showed a high level of polymorphism (>60%). The map was constructed with 124 SSRs, 149 AFLPs and 6 SSPs; its length covered 2,288.8 cM (8.2 cM/marker). The map showed high synteny with previous wheat maps, and both SSRs and AFLPs mapped evenly across the genome, with more markers in the B genome. However, some rearrangements were observed. For TW, a high genotypic effect was detected and two QTLs with epistasic effect were identified on 7AS and 6BS, explaining 30% of the total variation. The TKW showed a significant transgressive inheritance and five QTLs were identified, explaining 32% of the total variation, out of which 25% was of a genetic nature, and showing QTL×E interaction. The major TKW-QTLs were around the centromere region of 6B. For both traits, Omrabi 5 alleles had a significant positive effect. This population will be used to determine other QTLs of interest, as its parents are likely to harbor different genes for diseases and drought tolerance.Communicated by P. Langridge     

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population

Key message

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement.

Abstract

Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419?×?Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.