利用Bayes分层广义线性模型剖析大豆籽粒性状的遗传基础

以溧水中子黄豆(P₁)和南农493-1(P₂)组合的504个正反交F_2:3~F_2:7家系群体为材料, 调查大豆粒长、粒宽、粒厚、长宽比、长厚比、宽厚比和百粒重性状在2007—2011年的表型观测值, 扫描F₂群体SSR分子标记信息, 用Bayes分层广义线性模型方法检测了上述性状的主效QTL、QTL´环境(QE)互作、QTL´细胞质(QC)互作和QTL´QTL(QQ)互作。共检测到89个主效QTL、33对QE、20对QC和35对QQ互作。上述7个性状的主效QTL分别有7、10、10、19、19、17和7个; QQ互作分别有1、10、6、0、6、9和3对, 没有检测到显性´显性互作; QE互作分别有5、7、6、3、6、2和4对; QC互作分别有2、1、3、8、4、2和0对。主效、QQ互作、QC互作和QE互作QTL的总贡献率分别为12.42%~61.79%、0~23.21%、0.35%~1.51%和0~14.16%, 表明主效QTL贡献最大, QQ互作次之, QE互作最小。各类QTL都有一因多效现象, 同一基因座可通过不同方式影响性状表达。这些结果揭示了大豆粒形性状的遗传基础, 为标记辅助育种提供了参考信息。     

粳稻杂种优势遗传基础剖析

为了解控制粳稻产量相关性状及其中亲优势的基因作用类型, 利用秀堡RIL群体及其2个回交(BCF₁)群体对株高、生育期、单株有效穗数、穗长、每穗颖花数、结实率、一次枝梗数和二次枝梗数8个性状及其中亲杂种优势进行QTL定位。共检测到58个显著的主效QTL (M-QTL), 单个M-QTL的贡献率变幅为3.3%~41.9%。77.6%的M-QTL表现为加性效应, 15.5%的M-QTL表现为部分或完全显性效应, 6.9%的M-QTL表现为超显性效应。共检测到90对显著的双基因上位性QTL(E-QTL)。在RIL群体中检测到44对E-QTL, 单对E-QTL的贡献率变幅为1.7%~8.0%, 平均3.7%。在XSBCF₁群体中检测到27对E-QTL, 其中利用BCF₁表型值检测到16对E-QTL, 单对E-QTL的贡献率变幅为12.7%~78.5%, 平均29.2%; 利用中亲优势值检测到11对E-QTL, 单对E-QTL的贡献率变幅为15.0%~71.8%, 平均40.1%。在CBBCF₁群体中检测到19对E-QTL, 其中利用BCF₁表型值检测到12对E-QTL, 单对E-QTL的贡献率变幅为2.7%~64.4%, 平均30.1%; 利用中亲优势值检测到9对E-QTL, 单对E-QTL的贡献率变幅为21.7%~64.1%, 平均40.0%。在CBBCF₁群体中, 利用BCF₁表型值和中亲优势值都检测到的E-QTL有2对。上述结果表明上位性效应是粳稻秀堡组合杂种优势的主要遗传基础。     

越冬栽培稻产量性状相关QTL定位

越冬栽培稻是一类能越过自然冷冬季节并在第2年春季萌芽、正常开花结实、收获稻谷的水稻品种。本文通过对越冬栽培稻产量性状QTL分析,明确产量相关性状的遗传规律,旨在进一步解析越冬栽培稻产量性状的遗传机制,为育种创新利用提供理论依据。以3份越冬栽培稻构建的3个半同胞F2群体为材料。各考察15个产量相关性状,利用Excel 2003、GraphPad Prism 5.0和QTL IciMapping 4.10软件分析数据、绘制遗传图谱、定位QTL和联合分析。结果表明,产量性状表型值在3群体中呈连续正态分布,表现为数量性状遗传。共检测到37个QTL和26对上位性QTL,贡献率分别介于2.32%～36.31%和1.04%～2.05%;检测到9个同时影响2个及以上产量性状(一因多效)QTL标记区间;以联合分析检测到13个产量性状相关QTL,其中4个QTL区间与单群体检测QTL区间重叠;越冬栽培稻产量相关性状QTL以加–显性效应遗传为主、上位性遗传效应为辅。本研究将为越冬栽培稻产量相关基因挖掘及育种创新利用奠定基础。     

Multiple‐line cross QTL mapping for grain yield and thousand kernel weight in triticale

Grain yield and its component trait thousand kernel weight are important traits in triticale breeding programmes. Here, we used a large mapping population of 647 doubled haploid lines derived from four families to dissect the genetic architecture underlying grain yield and thousand kernel weight by multiple‐line cross QTL mapping. We identified 3 QTL for grain yield and 13 for thousand kernel weight which cross‐validated explained 5.2% and 48.2% of the genotypic variance, respectively. Both traits showed a positive phenotypic correlation, and we found two QTL overlapping between them. Full two‐dimensional epistasis scans revealed epistatic QTL for both traits, suggesting that epistatic interactions contribute to their genetic architecture. Based on QTL identified in our results, we conclude that the potential for marker‐assisted selection is limited for grain yield but more promising for thousand kernel weight.     

Quantitative trait loci mapping and epistatic analysis for grain yield and yield components using molecular markers with an elite maize hybrid

Summary The aim of this investigation was to map quantitative trait loci (QTL) associated with grain yield and yield components in maize and to analyze the role of epistasis in controlling these traits. An F_2:3 population from an elite hybrid (Zong3 × 87-1) was used to evaluate grain yield and yield components in two locations (Wuhan and Xiangfan, China) using a randomized complete-block design. The mapping population included 266 F_2:3 family lines. A genetic linkage map containing 150 simple sequence repeats and 24 restriction fragment length polymorphism markers was constructed, spanning a total of 2531.6 cM with an average interval of 14.5 cM. A logarithm-of-odds threshold of 2.8 was used as the criterion to confirm the presence of one QTL after 1000 permutations. Twenty-nine QTL were detected for four yield traits, with 11 of them detected simultaneously in both locations. Single QTL contribution to phenotypic variations ranged from 3.7% to 16.8%. Additive, partial dominance, dominance, and overdominance effects were all identified for investigated traits. A greater proportion of overdominance effects was always observed for traits that exhibited higher levels of heterosis. At the P ≤ 0.005 level with 1000 random permutations, 175 and 315 significant digenic interactions were detected in two locations for four yield traits using all possible locus pairs of molecular markers. Twenty-four significant digenic interactions were simultaneously detected for four yield traits at both locations. All three possible digenic interaction types were observed for investigated traits. Each of the interactions accounted for only a small proportion of the phenotypic variation, with an average of 4.0% for single interaction. Most interactions (74.9%) occurred among marker loci, in which significant effects were not detected by single-locus analysis. Some QTL (52.2%) detected by single-locus analysis were involved in epistatic interactions. These results demonstrate that digenic interactions at the two-locus level might play an important role in the genetic basis of maize heterosis.     

大豆叶片性状和叶绿素含量QTL间的上位性和环境互作效应

以丰产性好、抗旱力强的栽培大豆晋豆23为母本,山西农家品种半野生大豆灰布支黑豆为父本杂交衍生的447个RIL作为供试群体。将亲本及447个家系分别于2011、2012和2013年采用随机试验种植,按照标准测量叶长、叶宽和叶柄长3个性状,并于2012年8月1日和8月8日和2013年8月2日和8月9日各测量1次叶绿素含量。采用QTLNETwork 2.0混合线性模型分析方法和主基因+多基因混合遗传分离分析法,对大豆叶片性状和叶绿素含量进行遗传分析和QTL间的上位性和环境互作效应研究。结果表明,叶长受2对加性-加性×加性上位性混合主基因控制,叶宽受3对等效主基因控制,叶柄长受4对加性-加性×加性上位性主基因控制,叶绿素含量受4对加性主基因控制;检测到10个与叶长、叶宽、叶柄长和叶绿素含量相关的QTL,分别位于A1、A2、C2、H_1、L和O染色体。其中2个叶长QTL分别位于C2和L染色体,是2对加性×加性上位互作效应及环境互作效应QTL;3个叶宽加性与环境互作QTL分别位于A2、C2和O染色体;2个叶柄长QTL分别位于L和O染色体;3个叶绿素含量QTL分别位于A1、C2和H_1染色体。叶片性状和叶绿素含量的遗传机制较复杂,加性效应、加性×加性上位互作效应及环境互作效应是大豆叶片性状和叶绿素含量的重要遗传基础。建议大豆分子标记辅助育种中,一方面要考虑起主要作用的QTL,另一方面要注重上位性QTL的影响,这对于性状的遗传和稳定表达具有积极的意义。     

利用BC2F2高代回交群体定位水稻籽粒大小和形状QTL

以我国优良籼稻恢复系蜀恢527为轮回亲本, 以来自菲律宾的Milagrosa为供体亲本, 培育了样本容量为199株的BC₂F₂高代回交群体。选取85个均匀分布在12条染色体上的多态性SSR标记进行基因型分析, 同时对粒长、粒宽、长宽比和千粒重4种性状进行了表型鉴定。采用性状－标记间的单向和双向方差分析对上述性状进行了QTL定位。单向方差分析(P<0.01)共检测到了10个控制粒长、粒宽、长宽比和千粒重的QTL, 其中有3个具有多效性。由于粒长和长宽比的高度相关性, 控制长宽比的2个QTL均能在粒长QTL中检测到。位于第3染色体着丝粒区域的qgl3b是一个控制粒长、长宽比和千粒重的主效QTL, 它可以分别解释粒长、长宽比和千粒重表型变异的29.37%、26.15%和17.15%。该QTL对于粒长、长宽比和千粒重均表现较大的加性效应(来自蜀恢527的等位基因为增效)和负向超显性。位于第8染色体的qgw8位点是一个控制粒宽的主效QTL, 同时也是控制千粒重的微效QTL, 能解释粒宽表型变异的21.47%和千粒重表型变异的5.16%。该QTL对粒宽和千粒重均具有较大的加性效应(来自蜀恢527的等位基因为增效)和正向部分显性。双向方差分析(P<0.005)共检测到61对显著的上位性互作, 涉及54个QTL, 其中23个是能同时影响2~4个性状的多效位点, 且有8个位点与单向方差分析检测到的相同。控制长宽比的13对上位性互作位点中, 与控制粒长的上位性互作位点完全相同的有8对。以上结果为进一步开展水稻籽粒大小和形状有利基因的精细定位、克隆和分子设计育种奠定了基础。     

多环境下水稻DH群体剑叶长度的QTL分析

种植由籼稻品种和粳稻品种杂交衍生的DH群体,连续4年测定剑叶长度,运用基于混合模型的复合区间作图法,定位其QTL及上位性互作,估算遗传主效应和环境互作效应。结果表明,全部18个QTL都参与了上位性的形成,其中3个没有自身的遗传效应,但参与了3对上位性互作,这是传统方法不能发现的。另外,一个QTL可与多个QTL发生互作,这可能预示着存在更高阶互作。QTL与上位性互作可以具有不受环境影响而稳定表达的效应,以及与环境的互作效应。有些QTL与环境的互作效应可以在多环境下被检测到,但却不具有主效应,这种QTL可能易受环境因子的影响。QTL与环境的互作效应为随机效应,一个QTL或一对上位性与环境的互作效应总和理论上应等于零,否则会影响对遗传效应的估算,因此多环境下估算的遗传效应更可靠。     

Genetic studies of seed longevity in hexaploid wheat using segregation and association mapping approaches

Xieyou9308 is the first commercial super hybrid rice released in 1996 in China. To clarify its genetic mechanism underlying high yield potential, a recombinant inbred line (RIL) population derived from the cross between the maintainer line XieqingzaoB (XQZB) and the restorer line Zhonghui9308 (ZH9308) and two derived backcross F₁ (BCF₁) populations were developed for the identification of quantitative trait loci (QTLs) related to ten important agronomic traits (tiller number (TN), heading date (HD), and grain yield per plant (GYPP), etc.). The BCF₁ performance was closely correlated with the performance of their parental RILs according to both the analysis of broad-sense heritability (h _B ²) and phenotypic correlation coefficient (PCC) in the two BCF₁ populations, but not proved by QTL analysis. A total of 21 additive-effect main QTLs (M-QTLs), 22 dominant-effect M-QTLs, and 19 dominant-effect M-QTLs were detected with the WinQTLCart 2.50 software for the ten traits in the RIL and two BCF₁ populations, respectively. Of theses, three QTLs (qHD7a, qPPP3a, and qPL10) of 21 were detected repeatedly in the RIL and one BCF₁ populations, ten QTLs underlying four traits were only detected repeatedly in two BCF₁ populations, and nine QTLs controlling more than two traits were detected repeatedly, the additive-effect QTLs and dominant-effect QTLs play an important role in the performance of agronomic traits and no epistatic QTL of additive by additive effect and dominant by dominant-effect was detected for all traits in three populations. This research is valuable for M-QTL related to important agronomic trait in future fine mapping and positional cloning.     

陆地棉重组自交系产量及产量构成因子性状的上位性QTL分析

棉花的产量及产量构成因子性状是以复杂的方式遗传,遗传力较低并易受环境条件影响。经典数量遗传学指出,上位性是复杂性状的遗传基础。本研究以湘杂棉2号F8和F9世代重组自交系为材料,调查了3个环境下的产量及产量构成因子性状,并构建了遗传连锁图。旨在定位产量及产量构成因子性状的上位性QTL并分析QTL与环境的互作效应。所有产量及产量构成因子性状均检测到上位性QTL,共检测到16对加性互作QTL（AA）,涉及的位点中仅4个有单位点效应,这反映了上位性的复杂性及其对产量和产量构成因子性状的重要贡献。共检测到17对QTL加性和环境互作（AE）,以及14对上位性QTL与环境的互作,表明环境因素对产量和产量构成因子性状起重要影响作用。研究结果还表明上位性效应作为湘杂棉2号的遗传基础起着重要作用。对各性状在不同环境的优良基因型进行了预测。综合优良家系（GSL）和特定环境下的优良家系（SL）的性状表现高于两亲本,表明湘杂棉2号重组自交系各性状都有提高的潜力。由于QTL加性和环境互作以及上位性QTL与环境互作的影响,预测的优良家系基因型会随着环境的改变而不同,表明应针对特定环境开展棉花育种。     

水稻千粒重和垩白粒率的QTL及其互作分析

产量因子千粒重和稻米品质指标垩白粒率密切相关。本研究以越光/Kasalath//越光BIL群体为材料,分析千粒重和垩白粒率的相关性、QTL、上位性互作及其环境的互作效应。相关分析表明,群体千粒重和垩白粒率在2005年和2006年均呈极显著正相关,相关系数分别为0.42和0.35 (P<0.001)。2年共检测到千粒重QTL 11个,其中5个在2年重复检测到,5个具有环境互作效应;千粒重上位性互作8对,7对与环境存在互作。垩白粒率QTL 6个,3个具有环境互作效应;上位性互作9对,其中4对具有上位性环境互作效应。比较分析发现3个主效QTL同时控制千粒重和垩白粒率的表现,千粒重和垩白粒率的增效等位基因来自同一亲本;1对上位性互作同时对千粒重和垩白粒率有相同的影响。一些与垩白粒率不相关的千粒重主效QTL,如qTGW-3c、qTGW-4a和qTGW-6b,可为育种所利用。对利用QTL定位结果进行千粒重和垩白粒率分子辅助选择育种进行了探讨。     

QTL mapping and analysis of epistatic interactions for grain yield and yield-related traits in <Emphasis Type="Italic">Triticum turgidum</Emphasis> L. var. <Emphasis Type="Italic">durum</Emphasis>

A quantitative trait loci (QTL) analysis of grain yield and yield-related traits was performed on 93 durum wheat recombinant inbred lines derived from the cross UC1113 × Kofa. The mapping population and parental lines were analyzed considering 19 traits assessed in different Argentine environments, namely grain yield, heading date, flowering time, plant height, biomass per plant, and spikelet number per ear, among others. A total of 224 QTL with logarithm of odds ratio (LOD) ≥ 3 and 47 additional QTL with LOD > 2.0 were detected. These QTL were clustered in 35 regions with overlapping QTL, and 12 genomic regions were associated with only one phenotypic trait. The regions with the highest number of multi-trait and stable QTL were 3BS.1, 3BS.2, 2BS.1, 1BL.1, 3AL.1, 1AS, and 4AL.3. The effects of epistatic QTL and QTL × environment interactions were also analyzed. QTL putatively located at major gene loci (Rht, Vrn, Eps, and Ppd) as well as additional major/minor QTL involved in the complex genetic basis of yield-related traits expressed in Argentine environments were identified. Interestingly, the 3AL.1 region was found to increase yield without altering grain quality or crop phenology.     

多种环境下大豆单株粒重QTL的定位与互作分析

定位大豆单株粒重QTL、分析QTL间的上位效应及QTL与环境互作效应, 有利于大豆单株粒重遗传机理的深入研究。利用147个F_2:14~F_2:18 RIL群体, 5年2点多环境下以CIM和MIM方法同时定位大豆单株粒重QTL, 检测到17个控制单株粒重的QTL, 分别位于D1a、B1、B2、C2、F、G和A1连锁群上, 贡献率为6.0%~47.9%;用2种方法同时检测到3个QTL, 即qSWPP-DIa-3、qSWPP-F-1和qSWPP-D1a-5, 贡献率为6.3%~38.3%;2年以上同时检测到4个QTL, 即qSWPP-DIa-1、qSWPP-DIa-2、qSWPP-B1-1和qSWPP-G-1, 贡献率为8.1%~47.9%;利用QTLMapper分析QE互作效应和QTL间上位效应, 7种环境下的数据联合分析得到1个QE互作QTL和4对上位效应QTL, 贡献率和加性效应都较小。在分子标记辅助育种中应该同时考虑主效QTL及各微效QTL之间的互作。     

Genome mapping and molecular markers identification for yield,yield component and fibre quality traits in tetraploid cotton

The identification of quantitative trait loci (QTL) across different environments is a prerequisite for marker‐assisted selection (MAS) in crop improvement programmes. CottonSNP63k Illumina infinium array was used for genotyping 178 inter‐specific recombinant inbred lines and the parents, and identified 1,667 homozygous polymorphic markers between the parents. Of these, 1,430 markers were used for the construction of linkage map after removing 237 redundant markers. The genetic map spans a total genetic length of 3,149.8 cM with an average marker interval size of 2.2 cM. The phenotypic data from five environments were analysed separately using inclusive composite interval mapping which identified a total of 56 QTL explaining phenotypic variances (PVE) in the range of 8.18%–28.91%. There were 11 and 24 major QTL found for fibre quality and yield components, respectively. A total of 64 QTL were identified through Multi‐Environment Trials analysis, of which 34 recorded QTL × Environment interactions.     

利用BC2F2高代回交群体定位水稻籽粒大小和形状QTL

以我国优良籼稻恢复系蜀恢527为轮回亲本, 以来自菲律宾的Milagrosa为供体亲本, 培育了样本容量为199株的BC₂F₂高代回交群体。选取85个均匀分布在12条染色体上的多态性SSR标记进行基因型分析, 同时对粒长、粒宽、长宽比和千粒重4种性状进行了表型鉴定。采用性状－标记间的单向和双向方差分析对上述性状进行了QTL定位。单向方差分析(P<0.01)共检测到了10个控制粒长、粒宽、长宽比和千粒重的QTL, 其中有3个具有多效性。由于粒长和长宽比的高度相关性, 控制长宽比的2个QTL均能在粒长QTL中检测到。位于第3染色体着丝粒区域的qgl3b是一个控制粒长、长宽比和千粒重的主效QTL, 它可以分别解释粒长、长宽比和千粒重表型变异的29.37%、26.15%和17.15%。该QTL对于粒长、长宽比和千粒重均表现较大的加性效应(来自蜀恢527的等位基因为增效)和负向超显性。位于第8染色体的qgw8位点是一个控制粒宽的主效QTL, 同时也是控制千粒重的微效QTL, 能解释粒宽表型变异的21.47%和千粒重表型变异的5.16%。该QTL对粒宽和千粒重均具有较大的加性效应(来自蜀恢527的等位基因为增效)和正向部分显性。双向方差分析(P<0.005)共检测到61对显著的上位性互作, 涉及54个QTL, 其中23个是能同时影响2~4个性状的多效位点, 且有8个位点与单向方差分析检测到的相同。控制长宽比的13对上位性互作位点中, 与控制粒长的上位性互作位点完全相同的有8对。以上结果为进一步开展水稻籽粒大小和形状有利基因的精细定位、克隆和分子设计育种奠定了基础。     

Identification and multiple comparisons of QTL and epistatic interaction conferring high yield under boron and phosphorus deprivation in Brassica napus

Boron (B) and phosphorus (P) are two essential nutrients for plants. To unravel the genetic basis of B and P efficiency in Brassica napus, quantitative trait locus (QTL) and epistatic interaction analysis for yield and yield-related traits under contrast B and P conditions were performed using two mapping populations across various environments. Main effect QTLs were detected by QTLNetwork and QTL Icimapping (ICIM), and were compared with our previously reported main effect QTLs identified by QTLCartographer. Epistatic QTLs were identified by QTLNetwork, ICIM and Genotype matrix mapping (GMM), and multiple comparisons of main effect QTLs and epistatic QTLs were conducted. For the two mapping populations, 51 main effect QTLs were identified by QTLNetwork, 106 by ICIM. Among them, 35 main effect QTLs were simultaneously identified by three programs. Moreover, 578, 18 and 62 epistatic QTLs were identified by GMM, QTLNetwork and ICIM, respectively. Interestingly, a total of 235 epistatic QTLs identified by GMM were associated with 50 main effect QTLs identified by three programs. However, only nine epistatic QTLs identified by QTLNetwork and ICIM were involved in main effect QTLs. Twenty-two main effect QTLs in the BERIL population overlapped with 20 main effect QTLs for the same traits in the BQDH population, but no main effect QTLs were detected both under P and B stress environments, indicating the genetic differences in B and P homeostasis in B. napus. By in silico mapping, 29 candidate genes were located in the consensus QTL intervals. This study suggested the availability of dissecting genetic basis for complex traits under B/P deficiency by analyzing main effect QTLs and epistatic QTLs using multiple programs across different environments. The robust main effect QTLs and epistatic QTLs associated could be useful in breeding B and P efficient cultivars of B. napus.     

Quantitative trait loci mapping of forage agronomic traits in six mapping populations derived from European elite maize germplasm

Four agronomic traits were analysed including dry matter concentration (DMC) and dry matter yield (DMY) for stover, plant height (PHT) and days from planting to silking (DPS). We mapped quantitative trait loci (QTL) in three populations with doubled haploid lines (DHL), one RIL population and two testcross (TC) populations derived from crosses between two of the four populations mentioned above to elite tester lines, based on field phenotyping at multiple locations and years for each; 146–168 SSRs were used for genotyping of the four mapping populations. Significant high phenotypic and genotypic correlations were found for all traits at two locations, while DMC was negatively correlated with the other traits. A total of 42, 41, 54, and 45 QTL were identified for DMC, DMY, PHT, and DPS, respectively, with 9, 7, 12, and 7 major QTL for each trait. Most detected QTL displayed significant interactions with environment. Major QTL detected in more than two populations will contribute to marker‐assisted breeding and also to fine mapping candidate genes associated with maize agronomic traits.     

Identification of quantitative trait loci underlying seed protein and oil contents of soybean across multi‐genetic backgrounds and environments

Seed protein and oil contents are important quantitative traits in soybean. Previously, quantitative trait loci (QTL) associated with seed protein and oil were mostly identified in single genetic background. The objective of this work was to identify QTL and their epistatic effects underlying seed protein and oil contents in three recombinant inbred line populations (two of them used one common female parent) across eight environments by composite interval mapping. Forty QTL underlying protein content and 35 QTL underlying oil content were identified. Among them, nine were universal QTL underlying protein content and four were universal QTL underlying oil content. Epistatic interactions between QTL underlying seed protein/oil and different genetic backgrounds were detected. Different pairs of epistatic interactions were observed in diverse genetic backgrounds across multi‐environments. Common marker intervals were observed to simultaneously underlie seed protein and oil contents with different epistatic interactions. The results in this study suggested that a specific genotype with high oil content and low protein content might significantly affect the selection of soybean lines for high seed protein.     

Comparison and analysis of main effects,epistatic effects,and QTL × environment interactions of QTLs for agronomic traits using DH and RILs populations in rice

Two genetic linkage maps based on doubled haploid (DH) and recombinant inbred lines (RILs) populations, derived from the same indica-japonica cross ‘Samgang × Nagdong’, were constructed to analyze the quantitative trait loci (QTLs) affecting agronomic traits in rice. The segregations of agronomic traits in RILs population showed larger variations than those in DH population. A total of 10 and 12 QTLs were identified on six chromosomes using DH population and seven chromosomes using RILs population, respectively. Three stable QTLs including pl9.1, ph1.1, and gwp11.1 were detected through different years. The percentages of phenotypic variation explained by individual QTLs ranged from 8 to 18% in the DH population and 9 to 33% in the RILs population. Twenty-three epistatic QTLs were identified in the DH population, while 21 epistatic QTLs were detected in the RILs population. Epistatic interactions played an important role in controlling the agronomic traits genetically. Four significant main-effect QTLs were involved in the digenic interactions. Significant interactions between QTLs and environments (QE) were identified in two populations. The QTLs affecting grain weight per panicle (GWP) were more sensitive to the environmental changes. The comparison and QTLs analysis between two populations across different years should help rice breeders to comprehend the genetic mechanisms of quantitative traits and improve breeding programs in marker-assisted selection (MAS).     

Mapping quantitative trait loci for yield-related traits in Chinese cabbage (Brassica rapa L. ssp. pekinensis)

Chinese cabbage (Brassica rapa L. ssp. pekinensis) is one of the most important vegetables in China. However, the inheritance of yield-related traits in Chinese cabbage is poorly understood to date. To map quantitative trait loci (QTL) for yield-related traits in Chinese cabbage, a genetic linkage map was constructed with 192 doubled haploid (DH) lines. The genetic map was constructed based on 190 sequence-related amplified polymorphisms and 43 simple sequence repeats. QTL mapping was conducted for 11 yield-related traits in 170 DH lines derived from a cross between two diverse Chinese cabbage lines, ‘WZ’ and ‘FT’, under different environmental conditions. A total of 46 main QTL (M-QTL) and 7 epistatic QTL (E-QTL) were identified. The phenotypic variation explained by each M-QTL and E-QTL ranged from 4.85 to 25.06 % and 1.85 to 13.29 %, respectively. The QTL-by-environment interactions were detected using the QTLNetwork 2.0 program in joint analyses of multi-environment phenotypic values. The phenotypic variation explained by each QTL and by QTL × environment interaction was 1.14–4.24 % and 0.00–1.26 %, respectively. Our results provide a better understanding of the genetic factors controlling leaf and head-related traits in Chinese cabbage.