QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

玉米叶部性状的QTL定位与候选基因分析

叶片在玉米生长过程中发挥着重要作用,它能够有效地进行光合作用,为玉米提供营养物质,通过影响耐密性等影响产量提升。本研究选用QR273和T32为亲本,构建150份F2、F2∶3家系材料,结合基因型和不同环境中叶部性状的表型评价数据,利用完备区间作图法进行数量性状座位(QTL)定位。结果发现,2个环境下共检测到85个叶部性状相关QTL,其中有12个全株叶片数相关QTL、14个穗上叶片数相关QTL、22个叶长相关QTL、17个叶宽相关QTL、20个叶夹角相关QTL。结合公共数据库和生物信息学分析方法共筛选出7个候选基因。其中Zm00001d013612编码微管蛋白,参与调控细胞骨架结构组成;Zm00001d053543参与油菜素甾醇介导的信号通路;Zm00001d031291编码的蛋白质具有组蛋白乙酰化功能;Zm00001d031292参与富含羟脯氨酸糖蛋白家族基因表达的调控;Zm00001d031296调控钾离子跨膜转运蛋白活性;Zm00001d031300、Zm00001d031303参与碳水化合物代谢过程。蛋白质功能分析结果表明,这7个候选基因均参与细胞分化,与植物的生长发育息息相关。本研究结果将为深度揭示玉米叶部性状变异的遗传基础提供更丰富的理论支持。

糯玉米茎秆穿刺强度QTL分析与基因组选择

茎秆穿刺强度是衡量玉米茎秆机械强度和抗倒伏能力的重要指标之一,本研究以衍生于糯玉米自交系衡白522和通系5的198个重组自交系为试验材料,对茎秆穿刺强度进行数量性状位点(QTL)分析和基因组选择研究。单个环境QTL分析共检测到4个控制糯玉米茎秆穿刺强度的QTL,每个QTL的表型变异贡献率均小于10.00%,且仅在单个环境中被检测到;多个环境QTL分析共检测到8个QTL与环境互作,其加性效应总共可解释24.64%的表型变异,加性效应与环境互作贡献率为17.51%;上位性QTL分析共检测到4对QTL与QTL互作,可解释8.25%的表型变异。基因组选择中,当训练群体占群体总数的80%,随机选择500个标记即可获得较高的预测准确性;但是根据单个环境QTL分析结果,选择机率常用对数值排名前200的标记,即可大幅度提高基因组选择预测准确性。

Genetic Analysis and QTL Mapping of Large Flag Leaf Angle Trait in Japonica Rice

Genetic segregation analysis for flag leaf angle was conducted using six generations of P1, P2, F1, B1, B2 and F2 derived from a cross of 863B （a maintainer line of japonica rice） and A7444 （a germplasm with large flag leaf angle）. Genotypes and phenotypes of flag leaf angle were investigated in 863B （P1）, A7444 （P2） and 141 plants in BC^F~ （863BIA744411863B） population. An SSR genetic linkage map was constructed and QTLs for flag leaf angle were detected. The genetic map containing 79 information loci was constructed, which covers a total distance of 441.6 cM, averaging 5.6 cM between two neighboring loci. Results showed that the trait was controlled by two major genes plus polygene and the major genes were more important. Fifteen markers showed highly significant correlations with flag leaf angle based on single marker regression analysis. Two QTLs （qFLA2 and qFLA8） for flag leaf angle were detected by both composite interval method in software WinQTLCart 2.5 and composite interval method based on mixed linear model in QTL Network 2.0. The qFLA2 explained 10.50% and 13.28% of phenotypic variation, respectively, and was located at the interval of RM300 and RM145 on the short arm of chromosome 2. The qFLA8 explained 9.59% and 7.64% of phenotypic variation, respectively, and was located at the interval flanking RM6215 and RM8265 on the long arm of chromosome 8. The positive alleles at the two QTLs were both contributed from A7444.

Quantitative trait loci detection of E dwardsiella tarda resistance in Japanese flounder Paralichthys olivaceus using bulked segregant analysis

In recent years, Edwardsiella tarda has become one of the most deadly pathogens of Japanese fl ounder( Paralichthys olivaceus), causing serious annual losses in commercial production. In contrast to the rapid advances in the aquaculture of P. o livaceus, the study of E. tarda resistance-related markers has lagged behind, hindering the development of a disease-resistant strain. Thus, a marker-trait association analysis was initiated, combining bulked segregant analysis(BSA) and quantitative trait loci(QTL) mapping. Based on 180 microsatellite loci across all chromosomes, 106 individuals from the F1333(♀: F0768 ×♂: F0915)(Nomenclature rule: F+year+family number) were used to detect simple sequence repeats(SSRs) and QTLs associated with E. tarda resistance. After a genomic scan, three markers(Scaffold 404-21589, Scaffold 404-21594 and Scaffold 270-13812) from the same linkage group(LG)-1 exhibited a signifi cant difference between DNA, pooled/bulked from the resistant and susceptible groups( P <0.001). Therefore, 106 individuals were genotyped using all the SSR markers in LG1 by single marker analysis. Two different analytical models were then employed to detect SSR markers with different levels of signifi cance in LG1, where 17 and 18 SSR markers were identifi ed, respectively. Each model found three resistance-related QTLs by composite interval mapping(CIM). These six QTLs, designated q E1–6, explained 16.0%–89.5% of the phenotypic variance. Two of the QTLs, q E-2 and q E-4, were located at the 66.7 c M region, which was considered a major candidate region for E. tarda resistance. This study will provide valuable data for further investigations of E. tarda resistance genes and facilitate the selective breeding of disease-resistant Japanese fl ounder in the future.

深水稻全生育期耐盐性状的QTL定位

盐胁迫是许多沿海地区水稻生产的主要制约因素,尤其是沿海地区的咸淡水交汇区域。耐盐性是一种复杂的性状,可以通过QTL定位来帮助耐盐育种,以培育更高耐盐性的水稻品种。本研究供体亲本为沿海深水稻品种赤禾,受体亲本为美国水稻品种Lemont,杂交获得174份F9代的重组自交系,在芽期、苗期和生殖生长期分别利用浓度为15 g L^(-1)、5 g L^(-1)和5~6 g L^(-1)的NaCl进行胁迫,通过芽期相对发芽率、苗期耐盐性评级和生殖生长期的7个表型性状为基础数据,利用142个SSR分子标记绘制连锁遗传图并进行QTL分析。鉴定结果发现,赤禾在芽期表现敏盐,在苗期和生殖生长期表现耐盐;Lemont相反。3个生长时期分别有70.11%、50.57%和60.34%的品系表现为弱耐盐性,而且耐盐性为弱的负相关。本研究共鉴定出33个LOD值为2.52~10.32的QTL,解释0.06%~13.68%的表型遗传变异,解释最大遗传变异的QTL均由耐盐亲本贡献,其中芽期4个、苗期6个和生殖生长期23个位点,并在生殖生长期发现4个QTL重叠区域。这些QTL可以进一步研究,不仅为提高水稻育种的耐盐性提供了新的遗传资源,还有助于在水稻耐盐育种中,提高水稻品种的耐盐性。

利用双向导入系定位再生稻外观品质的QTL

再生稻具有较好的外观品质。为解析再生稻外观品质的遗传基础,本研究利用籼稻明恢63和粳稻02428构建的双向导入系为材料,连续两年在湖北荆州考察了双向导入系头季和再生季的外观品质。结果表明,明恢63头季和再生季的外观品质均显著优于02428,双向导入系的所有性状均表现为连续分布。同一性状在头季和再生季间表现为显著正相关,同一季节内不同性状间也表现出显著的相关性,其中粒宽对外观品质的影响最大。结合双向导入系已有的4568个Bin的高密度基因型数据,共定位到57个影响再生稻外观品质的数量性状点位(QTL),位于全部12条染色体上。其中25个QTL在两年间稳定表达,17个QTL在两个遗传背景下被重复鉴定到。此外,第3号染色体16.28~17.33 Mb、第5号染色体3.35~4.28 Mb、第7号染色体24.68~25.46 Mb和第11号染色体6.19~6.97 Mb这4个区间同时影响4个以上性状,来自明恢63的等位基因在这4个区间均可提高外观品质。最后,利用分离群体验证了第11号染色体6.19~6.97 Mb具有真实性。本研究结果为分子育种改良再生稻的外观品质提供了遗传基础。

基于高密度遗传图谱的芝麻籽粒品质相关性状QTL定位

芝麻是我国重要的优质油料作物,对芝麻籽粒品质性状进行数量性状位点(QTL)定位对定向培育高品质芝麻品种具有重要意义。以豫芝4号为母本、孟加拉小籽为父本,分别构建F2、F2:3、BC1和BC1F2群体,结合特异位点扩增片段(SLAF)标记和简单重复序列(SSR)标记构建F2和BC1遗传图谱,以F2:3、BC1和BC1F23个群体的表型数据为基础,进行脂肪、蛋白质、芝麻素、芝麻林素含量等4个品质性状的QTL作图分析。结果表明,在F2:3群体中共检测到16个QTL,解释表型变异的5.08%~27.12%,其中仅有1个主效QTL qOC_10-1在2个环境中被重复检测到,分别解释表型变异的9.62%和27.12%。在BC1和BC1F2群体中共检测到35个QTL,其中3个主效QTL qOC_4-1、qOC_10-2和qSmin_7-2在3个环境中被重复检测到,分别解释表型变异的8.08%~12.42%、11.95%~12.60%和4.24%~10.56%;3个主效QTL qSmin_8、qSmol_5-2和qSmol_7-2在2个环境被重复检测到,分别解释表型变异的13.36%~26.75%、11.44%~14.33%和5.77%~12.38%。经过对2个图谱进行整合和比对分析,共发现10个QTL簇,其中QTL簇loci4、loci7、loci8和loci10均与多个性状相关联,并且均至少包含1个主效QTL,其对应的最大表型变异解释率分别为12.42%、12.38%、26.75%和27.12%。

作物杂种优势相关基因挖掘及QTL定位研究进展

杂种优势利用是提升作物产量、抗逆性和品质的重要手段之一,目前杂种优势已被广泛应用于杂交育种研究中。随着分子生物学、基因工程、高通量测序技术等的高速发展,研究人员在不同层面不断探索作物杂种优势的遗传基础,杂种优势数量性状基因座(Quantitative trait locus,QTL)定位与相关基因挖掘研究是其中的重要方面,对解析杂种优势分子机理具有重要的理论意义。本文对玉米、水稻、大豆等主要农作物中已定位的株型、粒质量及产量等杂种优势相关QTL或克隆基因的类型、功能及分子机理进行阐述和总结,以期通过结合现代分子生物学技术与高通量组学数据分析技术,深度解析作物杂种优势遗传基础,为推动杂种优势高效利用提供参考。

水稻和陆稻分化相关性状的QTL分析及水旱不同栽培条件下连续选择的影响

本研究以来自云南的地方品种Ch5-10和Ch6-11为亲本,后代用SSD方法而得到的RIL9群体为试验材料,对水、陆稻差异性状(始穗期、剑叶宽、株高等)进行了考察,并利用SSR分子标记构建遗传连锁图谱,进行了各性状的QTL定位;在自然和人工选择的过程中,栽培稻无论是形态上还是基因组上,都发生多种多样的变化。利用F 2群体(父、母本和前述的SSD群体相同)采用Bulk混合选择的方法,在F 4代开始进行水、旱田两种条件下种植,以模拟水陆稻的分化条件,经过5代的种植后繁殖成株系,分别得到CA、CB两个群体。对这两个群体的水陆稻差异性状(始穗期、最高分蘖数、有效分蘖数、剑叶宽、株高等)进行了考察。结果表明,利用SSD方法而得到的RIL9群体对始穗期、剑叶宽、株高等3个性状进行QTL分析,两年共检测到19个QTL,分别位于1,2,3,7,8,9,11号染色体上。其中株高qPH-1效应最大,贡献率为22%(2009年安徽),16%(2009年北京),16%(2010年北京),表现为来自亲本Ch5-10的等位基因增加株高;利用Bulk方法而得到的CA群体(经过水田筛选5代、289个株系)和CB群体(经过旱田筛选5代、332个株系),考察其最高分蘖数、有效分蘖数、始穗期、株高、剑叶宽等5个性状。2009年安徽和北京两地试验发现:两群体间除安徽始穗期和北京剑叶宽差异不显著外,其他性状差异均达到极显著水平。根据氯酸钾抗性和低温发芽力的QTL定位结果,选择qSLtr-2、qSLratio-2、qRL28-2-1和qRL28-4附近的标记RM166、RM5472、RI05751和RM5979,及未定位到QTL的标记RM259对CA群体和CB群体进行基因型分析,两群体间t测验结果表明,RM5472、RI05751和RM5979差异均达到极显著水平,RM259和RM166差异不显著。说明在人工模拟水、陆稻选择的条件下这2个群体有了明显的分化。

Characterization and mapping of QTLs on chromosome 2D for grain size and yield traits using a mutant line induced by EMS in wheat

Production of mutants with altered phenotypes is a powerful approach for determining the biological functions of genes in an organism. In this study, a high-grain-weight mutant line M8008 was identified from a library of mutants of the common wheat cultivar YN15 treated with ethylmethane sulfonate(EMS). F2 and F2:3generations produced from crosses of M8008 × YN15(MY) and M8008 × SJZ54(MS) were used for genetic analysis. There were significant differences between M8008 and YN15 in plant height(PH), spike length(SL),fertile spikelet number per spike(FSS), grain width(GW), grain length(GL), GL/GW ratio(GLW), and thousand-grain weight(TGW). Most simple correlation coefficients were significant for the investigated traits, suggesting that the correlative mutations occurred in M8008. Approximately 21% of simple sequence repeat(SSR) markers showed polymorphisms between M8008 and YN15, indicating that EMS can induce a large number of mutated loci. Twelve quantitative trait loci(QTLs) forming QTL clusters(one in MY and two in MS) were detected. The QTL clusters coinciding with(MY population) or near(MS population) the marker wmc41 were associated mainly with grain-size traits, among which the M8008 locus led to decreases in GW, factor form density(FFD), and TGW and to increases in GLW. The cluster in the wmc25–barc168 interval in the MS population was associated with yield traits, for which the M8008 locus led to decreased PH, spike number per plant(SN), and SL.

Identification of quantitative trait loci for the dead leaf rate and the seedling dead rate under alkaline stress in rice

The quantitative trait loci （QTLs） for the dead leaf rate （DLR） and the dead seedling rate （DSR） at the different rice growing periods after transplanting under alkaline stress were identified using an F2：3 population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F3 lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent ＂Gaochanl06＂. The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.

葡萄遗传图谱构建与抗病QTL定位研究进展

葡萄在生长发育过程中易遭受多种病原菌的侵染,影响果实品质和产量,制约葡萄产业的发展。生产中多采用杀菌剂对病原菌进行防治,增加了投资成本,且会对环境造成污染,因此培育高品质抗病品种对葡萄生产具有重要意义。葡萄抗病性为多基因控制的数量性状,QTL定位是研究数量性状的一种有效方法,遗传图谱的构建是检测QTLs和克隆基因的基础。多年来,研究人员通过构建遗传图谱鉴定了一系列霜霉病、白粉病、皮尔斯病等抗性遗传位点,同时,根据抗性位点的基因组区域,开发了多个连锁标记,并应用到葡萄抗病性遗传研究中,加速了葡萄的育种进程。对葡萄遗传连锁图谱的构建和抗病相关QTL定位研究进展进行了综述,分析了目前研究中存在的问题并提出建议,为今后葡萄抗病基因定位和分子标记辅助选择育种提供借鉴。

基于高密度遗传图谱对高粱穗部性状的QTL定位

利用高密度遗传图谱进行高粱穗部相关性状的QTL分析,为酒用高粱标记辅助育种和重要基因遗传机制解析提供理论依据。以美国籽粒高粱品种BTx623和酱香型白酒用高粱品种红缨子构建的包含205个家系的RIL群体为试验材料,2020-2021年在贵州贵阳、安顺和海南乐东的5个环境下,开展了5个穗型性状(穗长、穗柄长、穗轴节数、一级枝梗数和最长一级枝梗长)的调查。利用高密度连锁图谱,采用完备区间作图法(ICIM)开展QTL定位。在高粱的基因组上一共检测到61个QTL涉及45个不同QTL遗传位点,其中与穗长、穗柄长、穗轴节数、一级枝梗数和最长一级枝梗长相关QTL分别为14、10、8、11和18个。在多个性状或多个环境中被重复检测到的19个重要QTL位点分别位于1(3个)、3(4个)、4(2个)、5(1个)、6(4个)、7(1个)、8(3个)和9(1个)号染色体上,并在其中12个QTL位点置信区间内或附近确定了13个候选基因,它们与控制水稻穗型的基因同源(DEP1、RGN1、OsPID、OsSPL7和WTG1等),为进一步克隆和功能验证这些候选基因提供依据。

QTL consistency for agronomic traits across three generations and potential applications in popcorn

Favorable agronomic traits are important to improve productivity of popcorn. In this study, a recombinant inbred line（RIL） population consisting of 258 lines was evaluated to identify quantitative trait loci（QTLs） for nine agronomic traits（plant height, ear height, top height（plant height subtracted ear height）, top height/plant height, number of leaves above the top ear, leaf area, stalk diameter, number of tassel branches and the length of tassel） under three environments. Meta-analysis was conducted then to integrate QTLs identified across three generations（RIL, F2：3 and BC2F2） developed from the same crosses. In total, 179 QTLs and 36 meta-QTLs（m QTL） were identified. The percentage of phenotypic variation（R2） explained by any single QTL varied from 3.86 to 28.4%, and 24 QTLs with contributions over 15%. Nine common QTLs located in the same or similar chromosome regions were detected across three generations. Five meta-QTLs were identified including QTLs in three independent studies. Seven important m QTLs were composed of 11–26 QTLs for 4–7 traits, respectively. Only 11 m QTLs were commonly identified in the same or similar chromosome regions across agronomic traits, popping characteristics（popping fold, popping volume and popping rate） and grain yield components（ear weight per plant, grain weight per plant, 100-grain weight, ear length, kernel number per row, ear diameter, row number per ear and kernel ratio） by meta-QTL analysis. In conclusion, we identified a list of QTLs, some of which with much higher contributions to agronomic traits should be valuable for further study in improving both popping characteristics and grain yield components in popcorn.

Genetic architecture of maize yield traits dissected by QTL mapping and GWAS in maize

The study of yield traits can reveal the genetic architecture of grain yield for improving maize production.In this study, an association panel comprising 362 inbred lines and a recombinant inbred line population derived from X178 × 9782 were used to identify candidate genes for nine yield traits. High-priority overlap(HPO) genes, which are genes prioritized in a genome-wide association study(GWAS), were investigated using coexpression networks. The GWAS identified 51 environmentally stable SNPs in two environments and 36 pleiotropic SNPs, including three SNPs with both attributes. Seven hotspots containing 41 trait-associated SNPs were identified on six chromosomes by permutation. Pyramiding of superior alleles showed a highly positive effect on all traits, and the phenotypic values of ear diameter and ear weight consistently corresponded with the number of superior alleles in tropical and temperate germplasm. A total of 61 HPO genes were detected after trait-associated SNPs were combined with the coexpression networks. Linkage mapping identified 16 environmentally stable and 16 pleiotropic QTL.Seven SNPs that were located in QTL intervals were assigned as consensus SNPs for the yield traits.Among the candidate genes predicted by our study, some genes were confirmed to function in seed development. The gene Zm00001 d016656 encoding a serine/threonine protein kinase was associated with five different traits across multiple environments. Some genes were uniquely expressed in specific tissues and at certain stages of seed development. These findings will provide genetic information and resources for molecular breeding of maize grain yield.

Genetic background effects on QTL and QTL × environment interaction for yield and its component traits as revealed by reciprocal introgression lines in rice

QTLs for quantitative traits are influenced by genetic background(GB) and environment.Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection(MAS). In this study, QTLs and QTL × environment interactions affecting grain yield per plant(GY) and its component traits, filled grain number per panicle(FGN), panicle number per plant(PN) and 1000-grain weight(TGW) across six environments were dissected using two sets of reciprocal introgression lines(ILs) derived from the cross Lemont × Teqing and SNP genotypic data. ANOVA indicated that the differences among genotypes and environments within each set of ILs were highly significant for all traits. A total of 72 distinct QTLs for GY and its component traits including 15 for GY, 25 for FGN, 18 for PN, and 29 for TGW were detected over the six environments. Most QTLs(87.4%) showed significant QTL × environment interactions(QEIs) and appeared to be more or less environment-specific. Among 72 QTLs, 15(20.8%) QTLs and 12(16.7%) QEIs were commonly identified in both backgrounds, indicating QTL especially QEI for yield and its component traits had strong GB effects. Four QTL regions affecting GY and its component traits, including S1269707–S4288071, S16661497–S17511092, and S35861863–S36341768 on chromosome 3, and S4134205–S7643153 on chromosome 5, were detected in both backgrounds and coincided with cloned genes for yield-related traits. These regions can be the targeted in rice breeding for high yield potential through MAS. Application of QTL main effects and their environmental interaction effects in MAS was discussed in detail.

Evaluation and Bulked Segregant Analysis of Major Yield QTL qtl12.1 Introgressed into Indigenous Elite Line for Low Water Availability under Water Stress

Near isogenic lines carrying large-effect QTL （qtl2.1）, which has a consistent influence on grain yield under upland drought stress conditions in a wide range of environments, were evaluated under water stress in the fields. The line which gave higher yield under drought was crossed with a local elite line, PMK3, and forwarded to F2：3 generation. Significant variation was found among the F2：3 lines for agronomic traits under water stress in the fields. Low to high broad sense heritability （H） for investigated traits was also found. Water stress indicators such as leaf rolling and leaf drying were negatively correlated with plant height, biomass and grain yield under stress. Bulked segregant analysis （BSA） was performed with the markers in the vicinity of qUl2.1, and RM27933 was found to be segregated perfectly well in individual components of drought resistant and drought susceptible bulks which were bulked based on yield under water stress among F2：3 lines. Hence, this simple and breeder friendly marker, RM27933, may be useful as a potentially valuable candidate marker for the transfer of the QTL qtl12.1 in the regional breeding program. Bioinformatic analysis of the DNA sequence of the qtl12.1 region was also done to identify and analyze positional candidate genes associated with this QTL and to ascertain the putative molecular basis of qUl2.1.

Association Mapping of Quantitative Trait Loci for Grain Size in Introgression Line Derived from Oryza rufipogon

Grain size is one of the critical agronomic traits governing grain yield and quality in rice.However,the underlying genetic mechanisms that control grain size in rice are poorly understood.We used an introgression line derived from Zhonghui 8015 and Oryza rufipogon Griff.This introgression line was evaluated under two different environmental conditions to dissect the quantitative trait loci controlling grain size.Genome-wide association study(GWAS)was performed using 28193 SNPs through a general linear model,and 56 significant SNPs on different loci associated with the 4 grain size traits were detected.Cloned genes including GS3 and q GL3 showed substantial effects on grain length and size.Seven new stable loci were identified with pleiotropic effects on grain size.Haplotype,gene expression analyses,combined gene-based associations,and functional annotations permitted the shortlisting of important dominant genes including GS3 and q GL3.

紫薇遗传图谱构建及株型性状的QTL定位

紫薇是中国重要的木本观花植物,株型是其重要的观赏性状,但是目前尚未见关于其株型数量性状基因座(QTL)定位的研究。以紫薇金幌、堇秀构建的181个F_(1)代分离群体为研究材料,构建1张由36个连锁群组成的包含429个SSR标记位点的遗传连锁图谱,总遗传距离为1998.81 cM,位点间平均距离为4.63 cM;LG1连锁群的遗传距离最大,为177.60 cM,LG27连锁群覆盖的遗传距离最小,为19.32 cM。基于区间作图法共检测到7个株型的QTL位点,其中控制株高的QTL位点有4个,解释4.32%~56.93%的表型变异;控制地径的QTL位点有2个,分别解释56.43%和59.02%的表型变异;控制分枝数的QTL位点有1个,解释54.60%的表型变异。本研究率先构建了基于功能分子标记的紫薇种内遗传连锁图谱,并定位了7个紫薇株型的QTL位点,可以解释4.32%~59.02%的表型变异。研究结果可为紫薇基因定位、基因克隆及分子标记辅助选择奠定基础。