Identification and validation of a yield-enhancing QTL cluster in rice (Oryza sativa L.)

Improvement of rice grain yield (YD) is an important goal in rice breeding. YD is determined by its related traits such as spikelet fertility (SF), 1,000-grain weight (TGW), and the number of spikelets per panicle (SPP). We previously mapped quantitative trait loci (QTLs) for SPP and TGW using the recombinant inbred lines (RILs) derived from the crosses between Minghui 63 and Teqing. In this study, four QTLs for SF and four QTLs for YD were detected in the RILs. Comparison of the locations of QTLs for these three yield-related traits identified one QTL cluster in the interval between RM3400 and RM3646 on chromosome 3. The QTL cluster contained three QTLs, SPP3a, SF3 and TGW3a, but no YD QTL was located there. To validate the QTL cluster, a BC₄F₂ population was obtained, in which SPP3a, SF3 and TGW3a were simultaneously mapped to the same region. SPP3a, SF3 and TGW3a explained 36.3, 29.5 and 59.0 % of phenotype variance with additive effect of 16.4 spikelets, 6 % SF and 1.8 g grain weight, respectively. In the BC₄F₂ population, though the region has opposite effects on TGW and SPP/SF, a YD QTL YD3 identified in this cluster region can increase 4.6 g grains per plant, which suggests this QTL cluster is a yield-enhancing QTL cluster and can be targeted to improve rice yield by marker aided selection.     

Novel quantitative trait loci for yield and yield related traits identified in Basmati rice (Oryza sativa)

Increasing crop productivity is one of the prime goals of crop breeding research. Rice grain yield is a complex quantitative trait governed by polygenes. Although several QTLs governing grain yield traits have been reported and limited attempts have been made to map QTLs for grain yield parameters in Basmati rice. A population from the cross Sonasal and Pusa Basmati 1121 comprising 352 RILs was generated through the single seed descent method. A total of 12 QTLs governing yield and yield-related traits were mapped on six chromosomes, namely, 1, 2, 3, 7, 8 and 9, of which five QTLs were novel. We identified a novel and robust epistatic QTL (qPH1.1 and qPL1.1) governing plant height and panicle length, flanked by the markers RM5336-RM1 on chromosome 1. The gene encoding brassinosteroid insensitive 1-associated receptor kinase 1 precursor is the putative candidate gene underlying this epistatic QTL. Another novel QTL, qNT3.1, governing tiller number was bracketed to a region of .77 Mb between the markers RM15247 and RM15281 on chromosome 3. Of the 57 annotated gene models, Os03g0437600 encoding alpha/beta-fold hydrolase, a homologous to AtKai2 is a putative candidate gene underlying the novel QTL qNT3.1. The other QTLs such as qDFF1.1 governing days to 50% flowering co-localizes with the gene Ghd7, QTL for plant height qPH1.2 co-localizes with the gene sd1, the QTLs for panicle length co-localizes with FUWA and DEP2, the QTL for tiller number co-localizes with OsRLCK57 and QTLs for thousand-grain weight co-localize with the major gene GS3. The QTLs identified in the current study can be effectively used in marker-assisted selection for developing Basmati rice varieties with a higher yield.     

Validating a segment on the short arm of chromosome 6 responsible for genetic variation in the hull silicon content and yield traits of rice

Rice is a typical silicon-accumulating plant and the beneficial effect of silicon on rice has long been recognized. In a previous study using 244 recombinant inbred lines (RILs) of an indica rice cross, Zhenshan 97B/Milyang 46 grown in 2003, four QTLs were detected for hull silicon content. QTL qHUS-6 had the largest effect among these, and the same interval also had significant effects on yield traits in the same population. The primary objective of this study was to validate the QTL effect in this region on HUS and yield traits. The same RIL population and another RIL population of lower heterogeneity were grown in 2004. QTL qHUS-6 was found to have significant additive effects on hull silicon content with a consistent direction in the two populations. From a residual heterozygous line selected from RILs of the same cross, 15 F_2:3 lines that differed only in a 2.15-Mb segment extending from RM587 to RM6119 on the short arm of chromosome 6 were derived. In these lines, qHUS-6 displayed a major effect, so did QTLs for yield traits previously detected in the same region. Two more QTLs for HUS detected in 2003, qHUS-1-1 and qHUS-1-2, also had consistent effects in the Zhenshan 97B/Milyang 46 RIL population in 2004. Thus this study verified three candidate regions for fine mapping HUS QTLs and determining the genetic relationship between silicon content and yield traits in rice.     

水稻株型相关性状的QTL分析

以南方籼型杂交稻恢复系泸恢99和北方粳型超级稻沈农265杂交衍生的重组自交系群体(recombinant inbred lines, RILs)为试验材料, 对株型性状(株高、穗长、分蘖和叶片性状)进行不同环境下的数量性状基因位点(quantitative trait locus, QTL)分析。共检测到39个相关QTL, 分布在水稻第1、第2、第3、第6、第7、第8和第9染色体上, LOD值介于2.50~16.90之间, 有11个QTL能在两年中被检测到。株型相关的QTL在染色体上成簇分布, 主要分布于第1、第6和第9染色体上, 这可能与株型性状间显著或极显著相关有关。其中, 在第9染色体上RM3700B–RM7424区间存在1个QTL簇, 含4个QTL, 即qPH9、qPL9、qFLL9和qSLL9, 这4个QTL在两年中均被检测到。此外, 进一步鉴定出5个能稳定表达的QTL, 其中, qPH8、qFLW6和qSLW6效应较大。这些信息综合反映了株型相关性状遗传的复杂性, 有助于我们更全面地了解和掌握株型性状的遗传基础。     

Quantitative trait locus analysis of nitrogen use efficiency in barley (Hordeum vulgare L.)

Quantitative trait locus (QTL) analysis of nitrogen use efficiency (NUE) of barley (Hordeum vulgare L.) was conducted on data generated from two pot experiments carried out in 2005 (using four nitrogen rates) and 2008 (with three rates) with AFLP markers and 94 recombinant inbred lines (RILs) of the Prisma × Apex mapping population. In total 41 QTLs were detected on 6 chromosomes and for 18 traits in both trials. About 95 % of the detected QTLs were with major additive effects. The percentage of variance accounted for by individual QTLs in the multiple QTL mapping model ranged from 8.4 to 54.4 % across all mapped traits in both years. Fifteen QTLs were related to NUE and its components; most of these QTLs were detected at lower nitrogen rates and none at the highest rate in both trials. These QTLs were found on Chromosomes 3(3H) and 7(5H) in 2005 and Chromosome 2(2H) in 2008. Except for the QTLs of plant height and NUE based on grain yield, none of the QTLs which were detected for a given trait in 2005, expressed themselves in 2008 irrespective of the nitrogen levels. QTLs controlling some traits were co-located in each year, and QTLs for many traits were detected on the same chromosome and close to the denso locus. Further research is needed to investigate the possibility to reduce nitrogen fertilizer requirements through breeding while maintaining high yield of barley.     

Further QTL mapping for yield component traits using introgression lines in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) under drought field environments

To better understand the underlying mechanisms of agronomic traits related to drought resistance and discover candidate genes or chromosome segments for drought-tolerant rice breeding, a fundamental introgression population, BC₃, derived from the backcross of local upland rice cv. Haogelao (donor parent) and super yield lowland rice cv. Shennong265 (recurrent parent) had been constructed before 2006. Previous quantitative trait locus (QTL) mapping results using 180 and 94 BC₃F_6,7 rice introgression lines (ILs) with 187 and 130 simple sequence repeat (SSR) markers for agronomy and physiology traits under drought in the field have been reported in 2009 and 2012, respectively. In this report, we conducted further QTL mapping for grain yield component traits under water-stressed (WS) and well-watered (WW) field conditions during 3 years (2012, 2013 and 2014). We used 62 SSR markers, 41 of which were newly screened, and 492 BC₄F_2,4 core lines derived from the fourth backcross between D123, an elite drought-tolerant IL (BC₃F₇), and Shennong265. Under WS conditions, a total of 19 QTLs were detected, all of which were associated with the new SSRs. Each QTL was only identified in 1 year and one site except for qPL-12-1 and qPL-5, which additively increased panicle length under drought stress. qPL-12-1 was detected in 2013 between new marker RM1337 and old marker RM3455 (34.39 cM) and was a major QTL with high reliability and 15.36% phenotypic variance. qPL-5 was a minor QTL detected in 2013 and 2014 between new marker RM5693 and old marker RM3476. Two QTLs for plant height (qPHL-3-1 and qPHP-12) were detected under both WS and WW conditions in 1 year and one site. qPHL-3-1, a major QTL from Shennong265 for decreasing plant height of leaf located on chromosome 3 between two new markers, explained 22.57% of phenotypic variation with high reliability under WS conditions. On the contrary, qPHP-12 was a minor QTL for increasing plant height of panicle from Haogelao on chromosome 12. Except for these two QTLs, all other 17 QTLs mapped under WS conditions were not mapped under WW conditions; thus, they were all related to drought tolerance. Thirteen QTLs mapped from Haogelao under WS conditions showed improved drought tolerance. However, a major QTL for delayed heading date from Shennong265, qDHD-12, enhanced drought tolerance, was located on chromosome 12 between new marker RM1337 and old marker RM3455 (11.11 cM), explained 21.84% of phenotypic variance and showed a negative additive effect (shortening delay days under WS compared with WW). Importantly, chromosome 12 was enriched with seven QTLs, five of which, including major qDHD-12, congregated near new marker RM1337. In addition, four of the seven QTLs improved drought resistance and were located between RM1337 and RM3455, including three minor QTLs from Haogelao for thousand kernel weight, tiller number and panicle length, respectively, and the major QTL qDHD-12 from Shennong265. These results strongly suggested that the newly screened RM1337 marker may be used for marker-assisted selection (MAS) in drought-tolerant rice breeding and that there is a pleiotropic gene or cluster of genes linked to drought tolerance. Another major QTL (qTKW-1-2) for increasing thousand kernel weight from Haogelao was also identified under WW conditions. These results are helpful for MAS in rice breeding and drought-resistant gene cloning.     

水稻抗纹枯病QTL表达的遗传背景及环境效应

利用水稻纹枯病菌强致病菌系RH-9人工接种Lemont导入到特青背景的213个近等基因导入系(TQ-ILs)群体和特青导入到Lemont背景的195个近等基因导入系(LT-ILs)群体,定位和分析了水稻抗纹枯病数量性状座位(quantitative trait loci, QTL)及其表达的环境与遗传背景效应。亲本Lemont对RH-9表现为高度感病,特青表现为中等抗病。人工接种后TQ-ILs群体的相对病斑高度(病斑高度与株高比)呈连续正态分布,LT-IL群体则明显偏向感病亲本Lemont。在不同年份和遗传背景下检测到影响纹枯病相对病斑高度的主效QTL 10个和互作QTL 13个,其中2006年在TQ-IL群体定位到的6个主效QTL在2007年均得到验证,表明这些QTL具有较好年度间的重复性。QSh4是唯一在双向导入系背景下表达的QTL,该位点特青等位基因降低相对病斑高度,提高抗性水平。在TQ-ILs群体中定位到位于第10染色体RM216~RM311区间的QSb10a与在LT-IL群体中定位到的位于相邻区间RM222~RM216的QSb10b的基因作用方向不同,推断这两个QTL存在紧密连锁关系。绝大多数在TQ-IL群体中表达的主效及互作QTL在LT-ILs群体中不表达,表明水稻抗纹枯病QTL具有明显的遗传背景效应。通过比较作图,本研究定位到的其中8个QTL在以往不同群体中同样被检测到,这些主效QTL对通过分子标记辅助选择(marker-assisted selection, MAS)培育水稻抗纹枯病育种可能具有应用价值。研究指出,标记辅助选择在不同遗传背景中能稳定表达的QTL或通过聚合不同抗病QTL是进一步提高水稻纹枯病抗性水平的一个有效途径。     

水稻品种魔王谷粒形、剑叶性状和株高QTL定位

以粳稻魔王谷和籼稻CO39配组衍生的280个重组自交系为材料, 2015年和2016年对其粒形、剑叶形态、株高性状进行了相关性分析和QTL检测。剑叶长分别与粒厚和株高存在极显著负相关和正相关, 剑叶宽与粒宽存在极显著正相关。检测到17个粒形QTL, 分布于第1、第2、第3、第4、第5、第6、第7、第9和第10染色体上, 贡献率为3.51%~48.65%; 其中, 第3染色体RM6080-RM6283区间对粒长和千粒重兼具显著作用, 第5染色体RM8211-RM3381区间同时影响粒宽和粒厚。检测到12个控制剑叶形态性状的QTL, 分布于第1、第3、第4、第6、第7和第9染色体上, 贡献率为4.26%~38.40%; 有5个多效QTL区间, 其中, 第4染色体RM252-SFP4_6区间同时控制剑叶长、剑叶宽、剑叶面积和粒长, 第9染色体RM257-RM3909区间同时影响剑叶面积和粒长。只检测到一个控制株高的QTL, 位于第1染色体的RM6333-RM5536区间, 是一个主效QTL, 贡献率为28.76%。这些结果为进一步开展粒形、剑叶形态、株高基因的精细定位、克隆和分子辅助育种奠定了基础。     

Trait identification and QTL validation for reproductive stage drought resistance in rice using selective genotyping of near flowering RILs

Drought resistance is becoming an indispensable character for rice improvement due to the dwindling global water resources. Genetic improvement for drought resistance is achieved through physiological dissection and genetic analysis of independent component traits associated with crop productivity under stress. A subset mapping population of 93 near flowering recombinant inbred lines with uniform phenology was constituted for genetic analysis of reproductive stage drought resistance. The population was phenotyped for 22 physio-morphological traits under two contrasting water regimes imposed at reproductive stage. Broad sense heritabilities of morphological traits were lower under stress than irrigated. Predominant association of plant height, panicle exsertion and harvest index with grain yield were observed under stress. The sustenance of panicle exsertion through maintaining growth during moisture stress was found as a significant trait associated with the grain yield through minimizing spikelet sterility. Selective genotyping was carried out with 23 polymorphic microsatellite markers of the established target genomic regions for drought resistance. The study validated the association of a QTL region on the long arm of chromosome 1 with plant height, panicle length, panicle exsertion, biological yield and stomatal conductance under stress. This region, flanked by markers RM246 and RM315, was known to possess the semi-dwarf gene, sd-1. Role of another major interval lying between RM256 and RM149 on chromosome 8 in defining the drought resistance could be established through identification of QTLs associated with leaf rolling, panicle exsertion, plant height, panicle length, senescence and biological yield under moisture stress condition. Few other QTLs were also identified.     

大豆倒伏性及其相关性状的QTL分析

利用来自中豆29×中豆32的165个重组自交系F₁₀进行2年田间试验, 以复合区间作图法检测与大豆倒伏及形态性状有关的QTL。结果表明, 2年分别检测到25个和19个与大豆倒伏及茎杆性状和根系性状有关的QTL, 分布于A2、C1、C2、D1a、F、G、I和L连锁群, 可解释4.4%~50.1%的表型变异。在F连锁群上, 2年均检测到倒伏主效QTL(qLD-15-1)和株高主效QTL(qPH-15-2);G连锁群和L连锁群上分别有1个主茎节数QTL和2个根重QTL在2个年份重复出现。在倒伏QTL的附近检测出株高、根重、茎叶重、茎粗、主茎节数和分枝数QTL, 表明植株地上部和地下部性状与抗倒性普遍关联;QTL定位结果与表型相关分析一致, 反映了这些形态性状表型相关的遗传特性。部分性状QTL存在共位性, 但是未在2个年份稳定表达。     

Identification of quantitative trait loci across interspecific F2, F2:3 and testcross populations for agronomic and fiber traits in tetraploid cotton

The most widely grown tetraploid Gossypium hirsutum and G. barbadense differ greatly in yield potential and fiber quality and numerous quantitative trait loci (QTLs) have been reported. However, correspondence of QTLs between experiments and populations is poor due to limited number of markers, small population size and inaccurate phenotyping. The purpose of the present study was to map QTLs for yield, yield components and fiber quality traits using testcross progenies between a large interspecific F₂ population and a commercial cotton cultivar as the tester. The results were compared to these from its F₂ and F_2:3 progenies. Of the 177 QTLs identified from the three populations, 65 fiber QTLs and 51 yield QTLs were unique with an average of 8–12 QTLs per traits. All the 26 chromosomes carried QTLs, but differed in the number of QTLs and the number of QTLs between fiber and yield QTLs. The congruence of QTLs identified across populations was higher (20–60 %) for traits with higher heritabilities including fiber quality, seed index and lint percentage, but lower (10–25 %) for lower heritability traits-seedcotton and lint yields. Major QTLs, QTL clusters for the same traits and QTL ‘hotspots’ for different traits were also identified. This research represents the first report using a testcross population in QTL mapping in interspecific cotton crosses and provides useful information for further comparative analysis and marker-assisted selection.     

Comparison of QTLs mapped in RILs and their test-cross progenies of tropical maize for insect resistance and agronomic traits

Quantitative trait loci (QTL) affecting resistance to south-western corn borer Diatraea grandiosella (SWCB) and sugarcane borer Diatraea saccharalis (SCB) have been identified previously in F_2:3 lines and recombinant inbred lines (RILs) of tropical maize using restriction fragment length polymorphism (RFLP) analyses. Our objective was to determine whether QTLs identified in these generations are also expressed in test crosses (TC) of RILs. A population of 166 TC progenies was developed by crossing RILs from the cross CML131 (susceptible) × CML67 (resistant) with the unrelated, susceptible tester line CML216. Resistance to first-generation SWCB, measured as leaf-feeding damage (LFD) under artificial infestation, and other agronomic traits were evaluated in two environments for the TC progenies and three environments for 183 RILs. The correlation between line per se and TC performance was low for LFD and intermediate for most agronomic traits. Estimates of the genotypic variance and heritabilities were smaller in the TC progenies than in the RILs for all traits. Quantitative trait loci were identified using an RFLP linkage map with 136 loci. For LFD, four QTLs were detected in the TC progenies, of which two were in common with nine QTLs previously mapped in the RILs. Few QTLs for agronomic traits were common to the two types of progeny, because of the low consistency of QTL positions for all traits in RIL and TC progenies, the use of TC progenies should be considered in QTL mapping studies as the first step for marker-assisted selection in hybrid breeding.     

QTL analysis of quality traits in the spring wheat cross RL4452 × 'AC Domain'

Wheat grain quality is a complex group of traits of tremendous importance to wheat producers, end‐users and breeders. Quantitative trait locus (QTL) analysis studied the genetics of milling, mixograph, farinograph, baking, starch and noodle colour traits in the spring wheat population RL4452/‘AC Domain’. Forty‐seven traits were measured on the population and 99 QTLs were detected over 18 chromosomes for 41 quality traits. Forty‐four of these QTLs mapped to three major QTL clusters on chromosomes 1B, 4D, and 7D. Fourteen QTLs mapped near Glu‐B1, 20 QTLs mapped near a major plant height QTL on chromosome 4D, and 10 QTLs mapped near a major time to maturity QTL on chromosome 7D. Large QTLs were detected for grain and flour protein content, farinograph absorption, mixograph parameters, and dietary fibre on chromosome 2BS. QTLs for yellow alkaline noodle colour parameter L* mapped to chromosomes 5B and 5D, while the largest QTL for the b* parameter mapped to 7AL.     

大豆产量及主要农艺性状QTL的上位性互作和环境互作分析

以栽培大豆晋豆23为母本,半野生大豆灰布支黑豆ZDD2315为父本杂交衍生的F2:15和F2:16的447个RIL家系为遗传群体,绘制SSR遗传图谱,采用混合线性模型方法,对2年大豆小区产量及主要农艺性状进行加性QTL、加性×加性上位互作及环境互作分析。结果检测到9个与小区产量、茎粗、有效分枝、主茎节数、株高、结荚高度相关的QTL,分别位于J_2、I、M连锁群上,其中小区产量、茎粗、株高、有效分枝和主茎节数QTL的加性效应为正值,说明增加这些性状的等位基因来源于母本晋豆23。同时,检测到7对影响小区产量、茎粗、株高和结荚高度的加性×加性上位互作效应及环境互作效应的QTL,共发现14个与环境存在互作的QTL。上位效应和QE互作效应对大豆小区产量及主要农艺性状的遗传影响较大。大豆分子标记辅助育种中,既要考虑起主要作用的QTL,又要注重上位性QTL,才有利于性状的稳定表达和遗传。     

利用回交导入系定位干旱环境下水稻植株水分状况相关QTL

日益严重的水资源危机使水稻的耐旱育种尤为迫切, 干旱环境下较高的植株水分含量有助于提高或维持作物产量的稳定性, 挖掘与耐旱性密切相关的分子标记有助于提高耐旱品种的选育效率。从供体Lemont (粳稻)导入到特青(籼稻)背景的254个高代回交导入系中筛选出覆盖供体全基因组的55个回交导入系, 采用PVC管栽培, 分析了干旱(胁迫)条件下水稻植株水分状况相关性状与籽粒产量、生物量的相关性并定位了相关QTL。研究表明, 植株水分相关性状(相对含水量、叶片水势、渗透势、卷叶度)均与籽粒产量显著相关。检测到7个相对含水量QTL, 7个叶片水势QTL, 5个渗透势QTL及5个卷叶QTL; 另检测到5个产量QTL, 7个生物量QTL。分析发现, 不仅QLwp5、QLr5、QRwc5和QY5同时分布在RM509~RM163区域, 且该区域还分布有对水分环境表现稳定的产量QTL(QGy5), 效应方向一致, 从遗传学角度解释了籽粒产量与水分相关性状之间的显著相关性。另外, QLr5、QRwc5、QY5、QLr2、QLr7、QLr8、QLr9、QRwc3、QRwc4a、QRwc12及QY7 等11个QTL曾在不同遗传背景群体中被检测到, 它们控制相同目标性状。研究认为RM509~RM163区域及QLr2、QLr7、QLr8、QLr9、QRwc3、QRwc4a、QRwc12和QY7所分布的染色体区域对水分环境或者遗传背景相对稳定, 在水稻分子标记辅助选择(MAS)耐旱育种实践中有较重要利用价值。     

大豆产量和产量构成因子及倒伏性的QTL分析

随机选取中豆29×中豆32重组自交系群体中165个家系作为2年田间试验材料,分析大豆单株产量、产量构成因子及倒伏性等性状的相关性和遗传效应,并检测各性状QTL。结果表明,38个与产量、产量构成因子及倒伏性状等有关的QTL,主要集中在C2、F和I连锁群。表型相关分析结果与QTL定位结果一致。在F连锁群上,2年均检测到倒伏QTL qLD-15-1,解释的表型变异超过20%,与百粒重和分枝荚数QTL分别位于相同和相邻标记区间,表明产量相关性状与倒伏性存在一定的关联。在I连锁群上,每荚粒数QTL和二、三、四粒荚数QTL不仅于同一位置,解释的表型变异为32%~65%,并且2个年份均重复出现,每荚粒数和四粒荚数QTL与二、三粒荚数QTL的增效基因分别来自不同的亲本。这4个粒荚性状QTL的共位性与表型相关分析结果一致,证实每荚粒数和四粒荚数与二、三粒荚数分别由不同的机制调控,对于育种上探讨以改良大豆粒荚性状为途径提高大豆产量,提供了重要依据。     

Mapping quantitative trait loci for awnness and yield component traits in isogenic lines derived from an Oryza sativa / O. rufipogon cross

An advanced backcross line, HR9118, was produced from a single plant of BC₂F₃ families derived from a cross between Oryza rufipogon Griff. (IRGC 105491) as a donor parent and the O. sativa subsp. japonica cv. Hwaseongbyeo as a recurrent parent. Although HR9118 resembled Hwaseongbyeo, several traits were different from those of Hwasoengbyeo, including days to heading, plant height, and awn. These differences between Hwasongbyeo and HR9118 could be attributed to introgressed O. rufipogon chromosome segments into HR9118. Introgression analysis using 460 SSR markers revealed that three O. rufipogon-specific chromosome segments were detected in HR9118 genome. F_2:3 populations derived from the cross between Hwaseongbyo and HR9118, consisting of 340 F₂ plants and 137 F₃ lines, were used to map and characterize QTLs for 12 traits. QTL analysis identified a total of 17 QTLs in the F_2:3 populations. Of these, seven QTLs were shared by the F₂ and F₃ populations, whereas the other ten QTLs were identified only in the F₃ population. In seven (41.2%) QTLs identified in this study, the O. rufipogon-derived alleles contributed desirable agronomic effects despite the overall undesirable characteristics of the wild phenotype. Each of three O. rufipogon introgressed segments contained multiple QTLs, indicating linkage and/or pleotropic effects. A cluster of eight QTLs was detected on chromosome 8 including a major QTL for awn. Substitution mapping using F₂ population indicated that awn8 was located within an interval between two SSR makers RM23326 and RM23356 which are 590 kb apart. SSR markers tightly linked to QTLs for yield components detected in this study will facilitate cloning of the gene underlying this QTL as well as marker-assisted selection for variation in grain weight in an applied breeding program.     

Impact of seasonal changes on spikelets per panicle,panicle length and plant height in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Temperature and photoperiod fluctuate rapidly in different seasons of the year, and analyzing their effects on rice yield components is crucial for adaptation of rice under various climatic conditions. To study the effects of seasonal changes on yield components, 168 recombinant inbred lines derived from a cross between two Oryza sativa L. indica varieties, Zhenshan 97 and Zhongzao 18 were grown for phenotype collection, in three different seasons, within a year. The results implied that temperatures across the three seasons played a crucial role in determining the trait effects. Spikelets per panicle (SPP), panicle length (PL) and plant height (PH) traits increased with high temperatures in middle season. Genetic analysis detected major quantitative trait loci (QTLs) qSPP10, qPL10 and qPH10 for SPP, PL and PH in the interval between markers RM1375 and RM3229 on chromosome 10, in all the three seasons. Two-way ANOVA showed that genotype by environment and QTL by environment interactions for these traits were highly significant (P < 0.0001). The region with a cluster of QTLs detected in all three seasons could be the preferred target to breeders in developing rice varieties that can be accustomed to different seasonal changes.     

Quantitative trait loci for grain-quality traits across a rice F2 population and backcross inbred lines

Improving grain-quality is an important goal in rice breeding programs. One vital step is to find major quantitative trait loci (QTLs) for quality related traits and then investigate the relationships among them. We crossed ‘N22’, an indica variety with good appearance but low grain weight, to a japonica variety, ‘Nanjing35’, with superior grain yield but poor appearance. This enabled us to construct an F₂ population and a set of backcross inbred lines (BILs) for QTL-mapping for the traits related grain appearance. In all, 37 QTLs were identified for grain length (GL), grain width (GW), grain thickness (GT), thousand-grain weight (TGW), and the percentage of grains with chalkiness (PGWC). Of these, 17 QTLs detected from 184 plants in the F₂ population explained 4.97–27.26 % of the phenotypic variance, another 20 QTLs were identified using BILs from 2009 to 2010. Quantitative trait loci for major effects were detected in different populations and across years. A new QTL hot spot (marker interval RM504–RM520) was found on Chromosome 3, which harbored QTLs for GL, GW, GT, and TGW. Among our five examined traits, grain shape was significantly correlated with TGW and PGWC. The PGWC values of two heavier grains BILs, L93, and L145 are much lower than Nanjiing35, the analysis of genotype showed that this greater weight may due to the locus for GL occurring within RM504–RM520 on Chromosome 3. Therefore, those two lines will allow us to develop a long-grain high-yield rice variety with less chalkiness.     

Soybean plant height QTL mapping and meta‐analysis for mining candidate genes

Plant height is closely related to seed yield of soybean. The goal of this study was to identify important loci affecting soybean plant height using meta‐analysis based on a reference physical map. Plant height related to QTLs was mapped across eight years with a RIL population by WinQTLCart v2.5. 182 QTLs related to plant height of soybean from database and our research were collected, and each QTL was projected onto the soybean physical map by software BioMercator v2.1. The confidence interval of meta‐QTL ranged from 0.09 to 5.07 Mb, and the mean phenotypic variance ranged from 4.9% to 73.0%. Furthermore, 4,259 candidate genes were located in these consensus QTLs, and 40 of them were involved in the plant growth and stem elongation and annotated as plant hormone signal transduction (pathway ID ko04075) in KEGG pathway. These results would lay a foundation for fine mapping of QTLs/genes related to plant height and marker‐assisted selection for breeding in soybean.