Dynamic analysis of QTLs on tiller number in rice (Oryza sativa L.) with single segment substitution lines

Twelve single segment substitution lines (SSSLs) in rice, which contain quantitative trait loci (QTLs) for tiller number detected previously, were used to study dynamic expression of the QTLs in this study. These SSSLs and their recipient, Hua-Jing-Xian 74 (HJX74), were used to produce 78 crossing combinations first, and then these combinations and their parents were grown in two planting seasons with three cropping densities. Tiller number was measured at seven developmental stages. QTL effects including main effects (additive, dominance and epistasis), QTL?×?season and QTL?×?density interaction effects were analyzed at each measured stage. The additive, dominant and epistatic effects of the 12 QTLs as well as their interaction effects with the seasons and with the densities all display dynamic changes with the development. Eight QTLs are detected with significant additive effects and/or additive?×?season and/or additive?×?density interaction effects at least at one developmental stage, and all QTLs have significant dominant and epistatic effects and/or interaction effects involved in. For most of the QTLs dominant effects are much bigger than additive effects, showing overdominance. Each QTL interacts at least with eight other QTLs. Additive and dominant effects of these QTLs are mostly positive while epistatic effects are negative and minor. Most of the QTLs show significant interactions with planting seasons and cropping densities, but the additive effects of QTLs Tn3-1 and Tn3-2, the dominant effects of QTL Tn7 and Tn8, and the epistatic effects of 14 pairs of QTLs are stable across seasons and the dominant effect of QTL Tn3-3 and the epistatic effects of QTL pairs Tn2-1/Tn6-2, Tn2-1/Tn9 and Tn3-3/Tn6-3 are nearly consistent across cropping densities. This paper is the first report of dynamics on dominances and epistasis of QTLs for tiller number in rice and provides abundant information, which is useful to improve rice tiller number via heterosis and/or QTL pyramiding.     

Unconditional and conditional QTL mapping for the developmental behavior of tiller number in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A single segment substitution population of 26 lines and their recipient parent Hua-jing-xian 74 (HJX74) were selected as experimental materials for analyzing the developmental behavior of tiller number in rice. By the unconditional QTL (quantitative trait locus) mapping method, a total number of 14 SSSLs were detected with QTLs controlling rice tiller number. The number of QTLs significantly affecting tiller number and their effect values estimated differed across measuring stages. More QTLs could be detected based on time-dependent measures of different stages. By the conditional QTL mapping method, it is possible to reveal net expression of gene in a time interval. 14 QTLs on tiller number expressed their effects in dynamic patterns of themselves during whole ontogeny. They exhibited mainly negative effects within 7 days after transplanting. During 7–21 days, QTLs were in active status and expressed larger positive effects. In the mid-period of 21–35 days, they had opposite genetic effects to wither tillers. Since then these QTLs expressed positive effects again to cause the appearance of noneffective tillers. The dynamics of QTL effects was in agreement with the actual change of tillers. Mapping QTL combining unconditional with conditional analysis for time-dependent measures is helpful to understand roundly the genetic bases for the development of quantitative traits.     

Detection of QTLs with additive effects and additive-by-environment interaction effects on panicle number in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) with single-segment substitution lines

A novel population consisting of 35 single-segment substitution lines (SSSLs) originating from crosses between the recipient parent, Hua-jing-xian 74 (HJX74), and 17 donor parents was evaluated in six cropping season environments to reveal the genetic basis of genetic main effect (G) and genotype-by-environment interaction effect (GE) for panicle number (PN) in rice. Subsets of lines were grown in up to six environments. An indirect analysis method was applied, in which the total genetic effect was first partitioned into G and GE by using the mixed linear-model approach, and then QTL (quantitative trait locus) analyses on these effects were conducted separately. At least 18 QTLs for PN in rice were detected and identified on 9 of 12 rice chromosomes. A single QTL effect (a + ae) ranging from −1.5 to 1.2 was divided into two components, additive effect (a) and additive × environment interaction effect (ae). A total number of 9 and 16 QTLs were identified with a ranging from −0.4 to 0.6 and ae ranging from −1.0 to 0.6, respectively, the former being stable but the latter unstable across environments. Three types of QTLs were suggested according to their effects expressed. Two QTLs (Pn-1b and Pn-6d) expressed stably across environments due to the association with only a, nine QTLs (Pn-1a, Pn-3c, Pn-3d, Pn-4, Pn-6a, Pn-6b, Pn-8, Pn-9 and Pn-12) with only ae were unstable, and the remaining seven of QTLs were identified with both a and ae, which also were unstable across environments. This is the first report on the detection of QE (QTL-by-environment interaction effect) of QTLs with SSSLs. Our results illustrate the efficiency of characterizing QTLs and analyzing action of QTLs through SSSLs, and further demonstrate that QE is an important property of many QTLs. Information provided in this paper could be used in the application of marker-assisted selection to manipulate PN in rice. Guifu Liu and Zemin Zhang contributed equally to this work.     

Quantitative trait loci analysis for the developmental behavior of tiller number in rice (Oryza sativa L.)

 A doubled-haploid rice population of 123 lines from Azucena/IR64 was used for analyzing the developmental behavior of tiller number by conditional and unconditional QTL mapping methods. It was indicated that the number of QTLs significantly affecting tiller number was different at different measuring stages. Many QTLs controlling tiller growth identified at the early stages were undetectable at the final stage. Only one QTL could be detected across the whole growth period. By conditional QTL mapping, more QTLs for tiller number could be detected than that by unconditional mapping. The temporal patterns of gene expression for tiller number could be different at different stages. Even an individual gene or genes at the same genomic region might have opposite genetic effects at various growth stages. Received: 7 July 1997 / Accepted: 10 February 1998     

Identification and pyramiding of QTLs for cold tolerance at the bud bursting and the seedling stages by use of single segment substitution lines in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Cold stress is one of the main constraints in rice production, and damage from cold can occur at different developmental stages in rice. Understanding the genetic basis of cold tolerance is the key for breeding cold-tolerant variety. In this study, we used single segment substitution lines (SSSLs) derived from a cross between cold-tolerant japonica variety “Nan-yang-zhan” and a popular indica variety “Hua-jing-xian 74” to detect and pyramid QTLs for cold tolerance at the bud bursting and the seedling stages. Evaluation of cold tolerance of these SSSLs and their recurrent parent helped identify two cold-tolerant QTLs (qCTBB-5 and qCTBB-6) at the bud bursting stage and two cold-tolerant QTLs (qCTS-6 and qCTS-12) at the seedling stage. The SSSLs carrying these QTLs showed stronger cold tolerance than their recurrent parent HJX74 did in three independent experiments. The qCTBB-6 and qCTS-6 were mapped to the same chromosomal region. QTL pyramiding was performed by intercrossing of SSSLs carrying the respective QTLs for cold tolerance at the bud bursting stage and the seedling stage and marker-assisted selection (MAS). The selected pyramiding line SC1-1 with different cold-tolerant QTLs showed cumulative effects on cold tolerance. Our results suggest that different genes (QTLs) control cold tolerance at bud bursting and seedling stages, and pyramiding of stable expression QTLs for cold tolerance at different developmental stages through MAS is a good strategy to prevent cold damage in rice.     

水稻株高上位性效应和ＱＥ互作效应的ＱＴＬ遗传研究

利用基因混合模型的ＱＴＬ定位方法研究了由籼稻品种ＩＲ６４和粳稻品种Ａzucena杂交衍生的ＤＨ群体在４个环境中的ＱＴＬ上位性效应和环境互作效应,结果表明,上位性是数量性状的重要遗传基础,并揭示了上位性的几个重要特点,所有的ＱＴＬ都参与了上位性效应的形成,６４％的ＱＴＬ还具有本身的加性效应,因此传统方法对ＱＴＬ加性效应的估算会由于上位性的影响而有偏,其他３６％的ＱＴＬ没有本身的加性效应,却参与了４８％的上位性互作用,这些位点可能通过诱发和修饰其他位点而起作用,上位性的特点还包括,经常发现了一个ＱＴＬ与多个ＱＴＬ发生互作;大效应的ＱＴＬ也参与上位性互作;上位性互作受环境影响,ＱＴＬ与环境的互效应比ＱＴＬ的主效应更多地被检测到,表明数量性状基因的表达易受环境影响。     

上位性和QTL×环境互作对水稻(Oryza sativa L.)抽穗期的影响

水稻抽穗期是重要的农艺性状之一,对水稻品种的地理分布和适应性起到关键性作用。适宜的抽穗期是获得高产的前提。因此确定水稻抽穗期的遗传基础在育种计划中具有重要的意义。本研究用一套来源于亲本IR64/Azucena的双单倍体（DH）群体在两个种植季节的试验资料,用基于混合线性模型的复合区间作图方法,对水稻抽穗期QTL的加性、上位性及其与环境互作效应进行了研究。结果表明共有14个QTL影响水稻抽穗期,它们分布在除第5和第9条染色体外的10条染色体上,有8个位点携带单位点效应,5对位点携带双位点互作效应,2个单位点和1对双位点存在与环境的互作,所有效应值介于1.179～2.549天之间,相应的贡献率为1.04%～4.84%。基于所估算的QTL效应值,本研究预测了两个亲本和两个极端型品系的遗传效应值,并讨论了影响遗传效应值与实际观测值偏差的可能原因,以及研究群体所具有的遗传潜力。对水稻抽穗期QTL的定位结果与前人研究基本一致,并进一步证实了上位性和QE互作效应是水稻抽穗期的重要遗传基础。     

上位性和QTL×环境互作对水稻(Oryza sativa L.)抽穗期的影响

水稻抽穗期是重要的农艺性状之一,对水稻品种的地理分布和适应性起到关键性作用。适宜的抽穗期是获得高产的前提。因此确定水稻抽穗期的遗传基础在育种计划中具有重要的意义。本研究用一套来源于亲本IR64/Azucena的双单倍体(DH)群体在两个种植季节的试验资料,用基于混合线性模型的复合区间作图方法,对水稻抽穗期QTL的加性、上位性及其与环境互作效应进行了研究。结果表明共有14个QTL影响水稻抽穗期,它们分布在除第5和第9条染色体外的10条染色体上,有8个位点携带单位点效应,5对位点携带双位点互作效应,2个单位点和1对双位点存在与环境的互作,所有效应值介于1.179～2.549天之间,相应的贡献率为1.04%～4.84%。基于所估算的QTL效应值,本研究预测了两个亲本和两个极端型品系的遗传效应值,并讨论了影响遗传效应值与实际观测值偏差的可能原因,以及研究群体所具有的遗传潜力。对水稻抽穗期QTL的定位结果与前人研究基本一致,并进一步证实了上位性和QE互作效应是水稻抽穗期的重要遗传基础。     

Mapping QTLs with digenic epistasis under multiple environments and predicting heterosis based on QTL effects

Mixed linear model approach was proposed for mapping QTLs with the digenic epistasis and QTL by environment (QE) interaction as well as additive and dominant effects. Monte Carlo simulations indicated that the proposed method could provide unbiased estimations for both positions and genetic main effects of QTLs, as well as unbiased predictions for QE interaction effects. A method was suggested for predicting heterosis based on individual QTL effects. The immortalized F₂ (IF₂) population constructed by random mating among RI or DH lines is appropriate for mapping QTLs with epistasis and their QE interaction. Based on the models and methodology proposed, we developed a QTL mapping software, QTLMapper 2.0 on the basis of QTLmapper 1.0, which is suitable for analyzing populations of DH, RIL, F₂ and IF₂. Data of thousand grain weight of IF₂ population with 240 lines derived from elite hybrid rice Shanyou 63 were analyzed as a worked example.     

Impact of epistasis and QTL×environment interaction on the developmental behavior of plant height in rice (Oryza sativa L.)

QTLs with epistatic effects and environmental interaction effects for the developmental behavior of plant height in rice were studied by conventional and conditional methods for quantitative trait loci (QTLs) by mapping with a doubled-haploid population of 123 lines from IR64/Azucena in three environments. The results showed that epistatic effects were important and most epistasis could be detected only by conditional QTL mapping, while most non–epistatic QTLs could be detected by both conventional and conditional methods. Many modificative QTLs showed only epistatic effects without their own additive effects at some stages. QTL×environment (QE) interaction effects were detected more often than QTL main effects for plant-height behavior, which might indicate that gene expression could be greatly affected by the environment. No QTLs had effects during the whole of ontogeny. Conditional QTL mapping might be a valid way to reveal dynamic gene expression for the development of quantitative traits, especially for epistatic effects. Received: 19 May 2000 / Accepted: 27 October 2000     

Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions. Received: 23 October 1998 / Accepted: 11 May 1999     

Detection of epistatic interactions of three QTLs for heading date in rice using single segment substitution lines

A library consisting of 1123 single segment substitution lines (SSSLs) in the same genetic back-ground of an elite rice variety Huajingxian74 (HJX74) was evaluated for heading date. From this library, two SSSLs, W23-03-8-9-1 and W15-03-1-31, with substitution segments on chromosome 3 and 2, respectively, were found to have significantly different heading date from the recipient HJX74. For genetic dissection and epistatic interaction of quantitative trait loci (QTLs) for heading date in two SSSLs, three secondary SSSLs with smaller substitution segments and genic pyramiding lines (GPLs) were developed from an F₂ segregating population of a cross between the two donor SSSLs, W23-03-8-9-1 and W15-03-1-31, using marker-assisted selection (MAS). The QTL analysis revealed that QTL for heading date detected in SSSL W23-03-08-9-1 was genetically dissected into two QTLs, qHD3-1 and qHD3-2, by overlapping mapping. At the same time, one QTL, qHD2-1 in the donor SSSL W15-03-1-31 was also identified. Analysis of GPLs for heading date showed epistatic interactions between qHD3-1 and qHD3-2, between qHD3-1 and qHD2-1, and between qHD3-2 and qHD2-1. These QTLs and epistatic interactions were confirmed in three cropping seasons under different natural daylength conditions, and their physiological functions for heading date were performed.     

Locating genes associated with root morphology and drought avoidance in rice via linkage to molecular markers

This research was undertaken to identify and map quantitative trait loci (QTLs) associated with five parameters of rice root morphology and to determine if these QTLs are located in the same chromosomal regions as QTLs associated with drought avoidance/tolerance. Root thickness, root:shoot ratio, root dry weight per tiller, deep root dry weight per tiller, and maximum root length were measured in three replicated experiments (runs) of 203 recombinant inbred lines grown in a greenhouse. The lines were from a cross between indica cultivar Co39 andjaponica cultivar Moroberekan. The 203 RI lines were also grown in three replicated field experiments where they were drought-stressed at the seedling, early vegetative, and late-vegetative growth stage and assigned a visual rating based on leaf rolling as to their degree of drought avoidance/tolerance. The QTL analysis of greenhouse and field data was done using single-marker analysis (ANOVA) and interval analysis (Mapmaker QTL). Most QTLs that were identified were associated with root thickness, root/shoot ratio, and root dry weight per tiller, and only a few with deep root weight. None were reliably associated with maximum root depth due to genotype-by-experiment interaction. Root thickness and root dry weight per tiller were the characters found to be the least influenced by environmental differences between greenhouse runs. Correlations of root parameters measured in greenhouse experiments with field drought avoidance/tolerance were significant but not highly predictive. Twelve of the fourteen chromosomal regions containing putative QTLs associated with field drought avoidance/tolerance also contained QTLs associated with root morphology. Thus, selecting for Moroberekan alleles at marker loci associated with the putative root QTLs identified in this study may be an effective strategy for altering the root phenotype of rice towards that commonly associated with drought-resistant cultivars.     

QTL x environment interactions in rice. I. heading date and plant height

One hundred twenty six doubled-haploid (DH) rice lines were evaluated in nine diverse Asian environments to reveal the genetic basis of genotype × environment interactions (GEI) for plant height (PH) and heading date (HD). A subset of lines was also evaluated in four water-limited environments, where the environmental basis of G × E could be more precisely defined. Responses to the environments were resolved into individual QTL × environment interactions using replicated phenotyping and the mixed linear-model approach. A total of 37 main-effect QTLs and 29 epistatic QTLs were identified. On average, these QTLs were detectable in 56% of the environments. When detected in multiple environments, the main effects of most QTLs were consistent in direction but varied considerably in magnitude across environments. Some QTLs had opposite effects in different environments, particularly in water-limited environments, indicating that they responded to the environments differently. Inconsistent QTL detection across environments was due primarily to non- or weak-expression of the QTL, and in part to significant QTL × environment interaction effects in the opposite direction to QTL main effects, and to pronounced epistasis. QTL × environment interactions were trait- and gene-specific. The greater GEI for HD than for PH in rice were reflected by more environment-specific QTLs, greater frequency and magnitude of QTL × environment interaction effects, and more pronounced epistasis for HD than for PH. Our results demonstrated that QTL × environment interaction is an important property of many QTLs, even for highly heritable traits such as height and maturity. Information about QTL × environment interaction is essential if marker-assisted selection is to be applied to the manipulation of quantitative traits.Communicated by G. Wenzel     

Molecular dissection of developmental behavior of tiller number and plant height and their relationship in rice (Oryza sativa L.)

Plant height and tiller number are two important characters related to yield in rice (Oriza sativa L.). Zhenshan97 x Minghui63 recombinant inbred lines were employed to dissect the genetic basis of development of plant height and tiller number using conditional and unconditional composite interval mapping approaches. The traits were normally distributed with transgressive segregation in both directions. Increasingly negative correlations were observed between tiller number and plant height at five consecutive growth stages. A total of 23 and 24 QTL were identified for tiller number and plant height, respectively. More QTL were detected by conditional mapping than by conventional mapping. Different QTL/genes apparently controlled the traits at different developmental stages. Three genomic regions were identified as putative co-located QTL, which showed opposite additive effects on tiller number and plant height. Furthermore, in the period reaching maximum tiller number, the expression of QTL for tiller number was active, whereas that of QTL for plant height was inactive. These facts provided a possible genetic explanation for the negative correlations between the traits. The research demonstrates conditional mapping to be superior to conventional mapping for this type of research. Implications of the results for hybrid rice improvement are discussed.     

利用染色体单片段代换系定位水稻株高QTL

株高是典型的数量性状,易受遗传背景的干扰和环境因素的影响,利用单片段代换系（single segment substitution lines,SSSLs）能减少遗传背景的干扰。以85个单片段代换系为材料,其受体亲本为籼稻广陆矮4号（Oryzasativassp.in-dica）,供体亲本为粳稻日本晴（Oryza sativa ssp.japonica）。通过单因素方差分析和Dunnett’s多重比较,分析单片段代换系与受体亲本之间株高的差异,对代换片段上的株高QTL进行鉴定。以P≤0.001为阈值共检测到24个株高QTLs,分别分布于除第10染色体外的其它11条染色体上,其中3个QTLs的加性效应表现为减效作用,另外21个株高QTLs的加性效应表现为增效作用。QTLs加性效应变化范围为-6.5-31.74,加性效应百分率的变化范围为-8.81%-41.96%。该研究对进一步发掘和利用新的矮秆或半矮秆基因资源具有重要意义。     

Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits

The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F₂ (IF₂) to the two parents, from random-cross of IF₂ or from selfing of IF₂ population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF₂ population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.     

Stability of QTLs for rice grain dimension and endosperm chalkiness characteristics across eight environments

Rice appearance quality, including traits specifying grain dimension and endosperm chalkiness, represents a major problem in many rice-producing areas of the world. In this study, the genetic basis of six appearance quality traits of milled rice was dissected into quantitative trait loci (QTL) main effects, and the stability of these QTLs was assessed in a population of 66 chromosome segment substitution lines (CSSLs) across eight environments. The CSSLs showed transgressive segregation for many of the traits, and significant correlations were detected among most of the traits. Twenty-two QTLs were identified on eight chromosomes, and numerous QTLs affecting related traits were mapped in the same regions, probably reflecting pleiotropic effects. Nine QTLs, namely qGL-1,qGL-3, qGW-5,qLWR-3, qLWR-5,qPGWC-8, qPGWC-9, qACE-8, and qDEC-8, were consistently detected across the eight environments. The additive main effect and multiplicative interaction (AMMI) analysis showed that genotype (G) × environment (E) interaction was significant for all six traits, with the first three iPCA terms accounting for over 80% of the G × E variance. Both D_I values and the iPCA1-iPCA2 biplots showed that the CSSLs harboring the nine QTL alleles were more stable than those carrying any of the additional 13 QTL alleles, thereby confirming their environmental stability and pointing to their appropriateness as targets for marker-assisted selection for high-quality rice varieties.     

Functional mapping of quantitative trait loci associated with rice tillering

Several biologically significant parameters that are related to rice tillering are closely associated with rice grain yield. Although identification of the genes that control rice tillering and therefore influence crop yield would be valuable for rice production management and genetic improvement, these genes remain largely unidentified. In this study, we carried out functional mapping of quantitative trait loci (QTLs) for rice tillering in 129 doubled haploid lines, which were derived from a cross between IR64 and Azucena. We measured the average number of tillers in each plot at seven developmental stages and fit the growth trajectory of rice tillering with the Wang–Lan–Ding mathematical model. Four biologically meaningful parameters in this model––the potential maximum for tiller number (K), the optimum tiller time (t ₀), and the increased rate (r), or the reduced rate (c) at the time of deviation from t ₀––were our defined variables for multi-marker joint analysis under the framework of penalized maximum likelihood, as well as composite interval mapping. We detected a total of 27 QTLs that accounted for 2.49–8.54% of the total phenotypic variance. Nine common QTLs across multi-marker joint analysis and composite interval mapping showed high stability, while one QTL was environment-specific and three were epistatic. We also identified several genomic segments that are associated with multiple traits. Our results describe the genetic basis of rice tiller development, enable further marker-assisted selection in rice cultivar development, and provide useful information for rice production management.     

Characterization of the main effects,epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice

Main effects, epistatic effects and their environmental interactions of QTLs are all important genetic components of quantitative traits. In this study, we analyzed the main effects, epistatic effects of the QTLs, and QTL by environment interactions (QEs) underlying four yield traits, using a population of 240 recombinant inbred lines from a cross between two rice varieties tested in replicated field trials. A genetic linkage map with 220 DNA marker loci was constructed. A mixed linear model approach was used to detect QTLs with main effects, QTLs involved in digenic interactions and QEs. In total, 29 QTLs of main effects, and 35 digenic interactions involving 58 loci were detected for the four traits. Thirteen QTLs with main effects showed QEs; no QE was detected for the QTLs involved in epistatic interactions. The amount of variations explained by the QTLs of main effect were larger than the QTLs involved in epistatic interactions, which in turn were larger than QEs for all four traits. This study illustrates the ability of the analysis to assess the genetic components underlying the quantitative traits, and demonstrates the relative importance of the various components as the genetic basis of yield traits in this population.