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.     

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.     

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     

Dynamic QTL analysis of the Na+ content,K+ content,and Na+/K+ ratio in rice roots during the field growth under salt stress

Rice (Oryza sativa L.) is seriously impacted by global soil salinization. To determine the quantitative trait loci (QTLs) related to salt tolerance in rice roots, F_2:3 and BC₁F_2:3 populations derived from a cross between the cv. Dongnong 425 of high quality and yield and the salt-tolerant cv. Changbai 10, were studied at different development stages. Two genetic linkage maps of F_2:3 and BC1F_2:3 populations were constructed. A 66 mM NaCl solution was used to irrigate the field and to analyze the dynamic QTL of some rice root traits. Using unconditional and conditional QTL mapping methods, 30 unconditional QTLs and 16 conditional QTLs related to the 6 root traits were detected on the 9 rice chromosomes during different developmental stages. Fourteen pairs of unconditional and conditional QTLs were detected at the identical developmental stage in the identical population. A number of QTLs were detected at different developmental stages, however, many did not appear at the last stage. Remarkably, qRKC1 appeared continuously at multiple stages in both the populations suggesting its key role in regulating the salt tolerance of rice roots.     

Dynamic QTL Analysis of Rice Protein Content and Protein Index Using Recombinant Inbred Lines

Protein content (PC) and protein index (PI) play important roles in determining nutritional quality in rice (Oryza sativa L.). We used 71 lines derived from “Asominori/IR24” to analyze the developmental behavior of PC and PI through unconditional and conditional QTL mapping methods. In all, 10 unconditional QTLs and 6 conditional QTLs for PC, and 11 unconditional QTLs and 9 conditional QTLs for PI, were identified at four stages of grain filling. More were identified in the first three stages than at the final stage. Temporal patterns of gene expression for PC and PI differed over time, with several QTLs being expressed across two or three stages but many being expressed at only one stage. Some of these QTLs were closely linked with maturity QTLs reported previously. Many QTLs for PC and PI were co-localized, supporting the significant correlation found between PC and PI. Our results suggest that dynamic QTL mapping might be a valid means for revealing more genetic information about protein accumulations during seed development.     

Dynamic expression of nine QTLs for tiller number detected with single segment substitution lines in rice

Nine single segment substitution lines (SSSLs) in rice, which contain quantitative trait loci (QTLs) for tiller number on substituted segments detected in previous studies, were selected as materials to analyse dynamic expression of the QTLs in this study. These SSSLs and their recipient parent, Hua-jing-xian 74 (HJX74), were grown in four different environments and were measured for tiller number at nine different growth stages. An indirect methodology was applied in QTL mapping through analyzing multi-environment phenotypic data. Dynamics of three types of effects (including total effect, main effect, and QE interaction effect) of QTLs was released. It was shown that nine QTLs exhibited statistically significant effects only at certain stages. Effects of a QTL, although insignificant at certain stages, displayed dynamic change with the growth of rice plants. Two common features of nine QTLs were detected, one is no expression within 7 days after transplanting, and the other is opposite expression existed during the whole growth period. Nine QTLs largely focused on expression in certain stages, and accordingly were suggested to partition into three types, expression in prophase, both in prophase and in anaphase, and evenly during the whole stage. It may be reasonable explanation that dynamics of main effects of QTLs are likely due to gene expression selectly at certain times, while dynamics of QE interaction effects of QTLs might attribute to the subrogation of environmental factors. Examination of the association between QE interaction effect and specified environmental factors across stages may provide useful information on how an environmental factor regulates QTL expression. G. Liu and R. Zeng contributed equally to this work.     

Genetic dissection of interactions between wheat flour starch and its components in two populations using two QTL mapping methods

Starch content and its components are important for determining wheat end-use quality and yield. However, little information is available about their interactions at the QTL/gene level in more than one population using different QTL mapping methods. Therefore, to dissect these interactions, two mapping populations from two locations over 2 years were used. The QTLs for the populations were analyzed by unconditional and conditional QTL mapping by two different analysis methods. In the two populations, there were a total of 24 unconditional additive QTLs detected for flour amylose (FAMS), flour amylopectin (FAMP), flour total starch (FTSC), and the ratio of FAMS to FAMP using ICIMapping4.1 methods, but 26 unconditional QTLs were found using QTLNetwork2.0 methods. Of these QTLs, 10 stable major additive QTLs were identified in more than one environment, mainly distributed on chromosomes 3B, 4A, 5A, and 7D. The maximum percentage of phenotypic variance explained (PVE) reached 54.31%. Two new unconditional major additive QTLs on chromosome 3B (Qftsc3B and Qfamp3B) were found. A total of 23 and 19 conditional additive QTLs were identified in the two populations using two different methods, respectively. Of which, eight and six stable major conditional QTLs were detected on chromosomes 3B, 4A, and 7D, respectively. New repressed QTLs were identified, such as Qftsc/fams5B-1 and Qftsc/fams5B-2. There were 20 epistatic unconditional and 15 conditional QTLs detected. In all, important QTLs on chromosomes 3B, 4A, and 7A were found in both populations. However, the number of important QTLs in the special recombinant inbred line (RIL) population was higher than that in the double haploid (DH) population, especially on chromosomes 7D and 5B. Moreover, the QTLs on chromosomes 4A, 7A, and 7D were close to the Wx-1 loci in the RIL population. These indicated better results can be obtained by a special population to target traits than by a common population. The important QTLs on key chromosomes can always be detected no matter what kinds of populations are used, such as the QTLs on chromosome 4A. In addition, QTL clusters were found on chromosomes 4A, 3B, 7A, 7D, and 5A in the two populations, indicating these chromosome regions were very important for starch biosynthesis.     

QTL detected for grain-filling rate in maize using a RIL population

The grain-filling rate plays an important role in determining grain yield. To elucidate the genetic basis of the grain-filling rate, a set of 203 recombinant inbred lines was evaluated at two locations over 2 years. Quantitative trait loci (QTL) for grain-filling rate were detected using conditional and unconditional QTL analysis of genetic linkage maps comprising 217 SSR markers. The results showed that the grain-filling rate increased between 15 and 35 days after pollination, then decreased at the last two sampling times. Hybrids with high grain-filling rates determined the grain yield in those areas with a short growth season for maize. A total of 23 unconditional QTL for grain-filling rate were detected using the 100-kernel weight as the input data at different sampling stages. They were distributed on 10 chromosomes (except chromosome 9), and some QTL were detected at different sampling stages. In addition, nine conditional QTL were identified using the average increase in 100-kernel weight of per day between two sampling times, and six conditional QTL were detected simultaneously using the unconditional QTL mapping strategy. The QTL mapping results demonstrated that the grain-filling rate is controlled by a complex genetic mechanism, and the QTL detected at different sampling stages might be important contributors to grain yield in maize.     

不同发育阶段大豆株高和茎粗QTL的动态分析

利用中豆29×中豆32的重组自交系,以复合区间作图法对不同发育阶段的大豆株高和茎粗同时进行非条件和条件QTL定位,在11个连锁群检测到18个株高QTL,在9个连锁群检测到19个茎粗QTL。不同发育时期影响大豆株高和茎粗QTL的数量、加性效应和贡献率均不相同,QTL表达具有时序性和选择性,有些QTL仅表达1次,有些可多次连续表达。有3个株高QTL和1个茎粗QTL在3个年度重复表达,有6个株高QTL和2个茎粗QTL在2个年度重复表达。F连锁群上株高和茎粗QTL存在共位性,R1～R4期均有株高和茎粗QTL同时表达,但株高和茎粗QTL的增效基因不同,株高QTL表达次数多而茎粗QTL表达次数较少,前期(V4～R3)QTL表达数量多而后期(R4～R5)表达数量较少。株高和茎粗QTL的动态变化与表型相关分析结果一致,对于适期选择粗秆抗倒的高产材料具有指导作用。     

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

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

Quantitative trait loci analysis for the developmental behavior of Soybean (Glycine max L. Merr.)

Quantitative trait loci (QTLs) identified so far in soybean were mainly derived in the final stage of plant development, which did not apply to the exploitation of genetic effects that were expressed during a specific developmental stage. Thus, the aim of this study was to identify conditional QTLs associated with yield traits at a specific developmental interval of soybean plant. The 143 recombinant inbred lines developed from the cross of soybean cultivars ‘Charleston’ and ‘Dongnong 594’ were used for the developmental QTLs analysis of pod number in the main stem and plant height by composite interval mapping method combined with mixed genetic model. The results indicated that the number and type of QTLs and their genetic effects for the two agronomic traits were different in a series of measuring stages. A total of 10 unconditional QTLs in 6 linkage groups and 5 conditional QTLs in 3 linkage groups were identified for the pod number of the main stem, while 13 unconditional QTLs in 7 linkage groups and 12 conditional QTLs in 6 linkage groups were identified for plant height. Many QTLs that were detected in the early stages were different from those detected at the later stages. Some QTLs existed only at one stage and others existed across two or three stages. Five marker intervals (satt509-satt251, sat_099-sat_113, sat_113-OPAW19_4, satt457-OPC10_85, sat_095-OPBA08_5) were proven to be associated both with the development of pod number in the main stem and the development of plant height. The present study suggested that the development of pods and plant height in soybean were governed by time-dependent gene expression.     

水稻叶绿素含量动态QTL分析

为剖析水稻不同生育时期叶绿素含量的变化动态及遗传机制,以‘Sasanishiki’（粳稻）、‘Habataki’（籼稻）及其杂交衍生的85个回交重组自交系（BILs）群体为材料,对控制水稻叶片叶绿素含量的数量性状基因位点（QTL）变化动态进行了分析。共检测到39个QTL,包括26个非条件QTL和13个条件QTL,分布在除第7和第11号染色体以外的10条染色体上,平均每个时期检测到3．25个非条件QTL。其中生育前期和生育中后期检测到的QTL位点较少,仅为1—3个;在生育中期（盛期）检测到的QTL位点相对较多,一般为4～5个。在生育期的中期和后期均能在第1和2号染色体上检测到控制叶绿素含量的QTL,并且这些QTL位点在1和2号染色体上呈现聚集现象。本研究同时发现了一些新的QTL位点,这些QTL将有助于我们更全面地了解叶绿素在不同发育时期的遗传基础。     

QTLs and epistasis for aluminum tolerance in rice (Oryza sativa L.) at different seedling stages

To investigate the genetic background for aluminum (Al) tolerance in rice, a recombinant inbred (RI) population, derived from a cross between an Al-sensitive lowland indica rice variety IR1552 and an Al-tolerant upland japonica rice variety Azucena, was used in culture solution. A molecular linkage map, together with 104 amplified fragment length polymorphism (AFLP) markers and 103 restriction fragment length polymorphism (RFLP) markers, was constructed to map quantitative trait loci (QTLs) and epistatic loci for Al tolerance based on the segregation for relative root length (RRL) in the population. RRL was measured after stress for 2 and 4 weeks at a concentration of 1mM of Al³⁺ and a control with a pH 4.0, respectively. Two QTLs were detected at both the 2nd and the 4th weeks on chromosomes 1 and 12 from unconditional mapping, while the QTL on chromosome 1 was only detected at the 2nd stress week from conditional mapping. The effect of the QTL on chromosome 12 was increased with an increase of the stress period from 2 to 4 weeks. The QTL on chromosome 1 was expressed only at the earlier stress, but its contribution to tolerance was prolonged during growth. At least one different QTL was detected at the different stress periods. Mean comparisons between marker genotypic classes indicated that the positive alleles at the QTLs were from the Al-tolerant upland rice Azucena. An important heterozygous non-allelic interaction on Al tolerance was found. The results indicated that tolerance in the younger seedlings was predominantly controlled by an additive effect, while an epistatic effect was more important to the tolerance in older seedlings; additionally the detected QTLs may be multiple allelic loci for Al tolerance and phosphorus-uptake efficiency, or for Al and Fe²⁺ tolerance. Received: 29 July 1999 / Accepted: 13 October 1999     

Conditional QTL mapping of protein content in wheat with respect to grain yield and its components

Grain protein content in wheat (Triticum aestivum L.) is generally considered a highly heritable character that is negatively correlated with grain yield and yield-related traits. Quantitative trait loci (QTL) for protein content was mapped using data on protein content and protein content conditioned on the putatively interrelated traits to evaluate possible genetic interrelationships between protein content and yield, as well as yield-related traits. Phenotypic data were evaluated in a recombinant inbred line population with 302 lines derived from a cross between the Chinese cultivar Weimai 8 and Luohan 2. Inclusive composite interval mapping using IciMapping 3.0 was employed for mapping unconditional and conditional QTL with additives. A strong genetic relationship was found between protein content and grain yield, and yield-related traits. Unconditional QTL mapping analysis detected seven additive QTL for protein content, with additive effects ranging in absolute size from 0.1898% to 0.3407% protein content, jointly accounting for 43.45% of the trait variance. Conditional QTL mapping analysis indicated two QTL independent from yield, which can be used in marker-assisted selection for increasing yield without affecting grain protein content. Three additional QTL with minor effects were identified in the conditional mapping. Of the three QTLs, two were identified when protein content was conditioned on yield, which had pleiotropic effects on those two traits. Conditional QTL mapping can be used to dissect the genetic interrelationship between two traits at the individual QTL level for closely correlated traits. Further, conditional QTL mapping can reveal additional QTL with minor effects that are undetectable in unconditional mapping.     

Identification of genomic regions determining flower and pod numbers development in soybean （Glycine max L.）

Flower and pod numbers per plant are important agronomic traits underlying soybean yield.So far quantitative trait loci (QTL) detected for flower and pod-related traits have mainly focused on the final stage,and might therefore have ignored genetic effects expressed during a specific developmental stage.Here,dynamic expressions of QTL for flower and pod numbers were identified using 152 recombinant inbred lines (RILs) and a linkage map of 306 markers.Wide genetic variation was found among RILs;17 unconditional and 18 conditional QTL were detected for the two traits at different developmental stages over two years.Some QTL were detected only at one stage and others across two or more stages,indicating that soybean flower and pod numbers development may be governed by time-dependent gene expression.Three main QTL (qfn-Chr18-2,qfn-Chr20-1,and qfn-Chr19) were detected for flower number,and two main QTL (qpn-Chr11 and qpn-Chr20) were detected for pod number.The phenotypic variation explained by them ranged from 6.1% to 34.7%.The markers linked to these QTL could be used in marker-assisted selection for increasing soybean flower and pod numbers,with the ultimate aim of increasing soybean yield.Comparison of the QTL regions for flower and pod numbers traits with the related genes reported previously showed that seven and four related genes were located in the QTL regions of qfn-Chr11 and qfn-Chr19,respectively.Tbese results provide a basis for fine mapping and cloning of flower and pod development-related genes.     

Kernel weight per spike: what contributes to it at the individual QTL level?

Spike length (SL), spikelet number (SPN) per spike, kernel number per spike (KNPS), and thousand-kernel weight (TKW) have strong genetic associations with kernel weight per spike (KWPS) in wheat. To investigate their genetic relationships at the individual quantitative trait locus (QTL) level, both unconditional and conditional QTL mapping for KWPS with respect to SL, SPN, KNPS, and TKW were conducted. Two related F_8:9 recombinant inbred line populations, comprising 485 and 229 lines, respectively, were used. The trait phenotypic performances of each population were evaluated in four different environments. Unconditional QTL mapping analysis identified 22 putative additive QTL for KWPS, five of which were stable QTL, and only QKwps-WJ-1B.2 showed significant additive-by-environment interaction effects. In comparison with unconditional QTL mapping analysis, conditional QTL mapping analysis indicated that, at the QTL level, KNPS and TKW contributed more to KWPS than did SL and SPN. Any unconditional QTL for KWPS detected in this study was associated with at least one of its four related traits. The present study will provide assistance in the understanding of the genetic relationships between KWPS and its related traits.     

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

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

Conditional and unconditional QTL mapping of drought-tolerance-related traits of wheat seedling using two related RIL populations

For discovering the quantitative trait loci (QTLs) contributing to early seedling growth and drought tolerance during germination, conditional and unconditional analyses of 12 traits of wheat seedlings: coleoptile length, seedling height, longest root length, root number, seedling fresh weight, stem and leaves fresh weight, root fresh weight, seedling dry weight, stem and leaves dry weight, root dry weight, root to shoot fresh weight ratio, root-to-shoot dry weight ratio, were conducted under two water conditions using two F_8:9 recombinant inbred line (RIL) populations. The results of unconditional analysis are as follows: 88 QTLs accounting for 3.33–77.01% of the phenotypic variations were detected on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B and 7D. Among these QTLs, 19 were main-effect QTLs with a contribution rate greater than 10%. The results of the conditional QTL analysis of 12 traits under osmotic stress on normal water conditions were as follows: altogether 22 QTLs concerned with drought tolerance were detected on chromosomes 1B, 2A, 2B, 3B, 4A, 5D, 6A, 6D, 7B, and 7D. Of these QTLs, six were main-effect QTLs. These 22 QTLs were all special loci directly concerned with drought tolerance and most of them could not be detected by unconditional analysis. The finding of these QTLs has an important significance for fine-mapping technique, map-based cloning, and molecular marker-assisted selection of early seedling traits, such as growth and drought tolerance.     

Mapping of QTL for tiller number at different stages of growth in wheat using double haploid and immortalized F<Subscript>2</Subscript> populations

Effective tiller number is one of the most important traits for wheat (Triticum aestivum L.) yield, but the inheritance of tillering is poorly understood. A set of 168 doubled haploid (DH) lines derivatives of a cross between two winter wheat cultivars (Huapei 3 and Yumai 57), and an immortalized F₂ (IF₂) population generated by randomly permutated intermating of these DHs were investigated, and QTLs of tillering related to the maximum tillering of pre-winter (MTW), maximum tillering in spring (MTS), and effective tillering in harvest (ETH) were mapped. Phenotypic data were collected for the two populations from two different environments. Using inclusive composite interval mapping (ICIM), a total of 9 and 18 significant QTL were detected across environments for tillering in the DH and IF₂ populations, respectively. Four QTLs were common between two populations. A major QTL located on the 5D chromosome with the allele originating from Yumai 57 was detected and increased 1.92 and 3.55 tillers in MTW and MTS, respectively. QTLs (QMts6D, QEth6D) having a neighbouring marker interval at Xswes679.1 and Xcfa2129 on chromosome 6D was detected in MTS and ETH. These results provide a better understanding of the genetic factors for selectively expressing the control of tiller number in different growth stages and facilitate marker-assisted selection strategy in breeding.     

Genetic Relationships Among Panicle Characteristics of Rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) Using Unconditional and Conditional QTL Analyses

Using mixed-model-based composite interval mapping and conditional statistical methods, we studied quantitative trait loci (QTLs) with epistatic effects and QTLs by environment interaction effects for rice seed set percent (SSP), filled grain number per plant (FGP), and panicle length (PL). A population of 241 recombinant inbred lines was used which was derived from a cross between “Zhenshan 97” and “Minghui 63.” Its linkage map included 221 molecular markers. Our QTL analysis detected 28, 25, and 32 QTLs for SSP, FGP, and PL, respectively. Each QTL explained 1.37%∼13.19% of the mean phenotypic variation. A comparison of conventional and conditional mapping provided information about the genetic control system involved in the synthetic process of SSP, FGP, and PL at the level of individual QTLs. Conditional QTLs with reduced (or increased) effects were identified for SSP, which were significantly influenced by FGP or PL. Some QTLs could express independently for the given traits, thereby providing possibilities for simultaneous improvement of SSR and PL, and SSR and FGP. Epistasis was more sensitive to environmental conditions than were additive effects.