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Article

Locating QTL Controlling the Yield-Related Traits in Perennial Chinese Rice “Shendao3#”

by
Yuxin Yan
1,
Jiuyan Lu
1,
Meilin Wu
1,
Tingshen Peng
1,
Lin Tan
1,
Wenbin Nan
1,
Xiaojian Qin
1,
Ming Li
1,
Junyi Gong
2 and
Yongshu Liang
1,*
1
Chongqing Key Laboratory of Plant Environmental Adaptation Biology, Chongqing Normal University, Chongqing 401331, China
2
State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute, Hangzhou 310006, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(23), 2453; https://doi.org/10.3390/agriculture15232453
Submission received: 21 September 2025 / Revised: 20 November 2025 / Accepted: 21 November 2025 / Published: 27 November 2025
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
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Abstract

Shendao3# (SD3#) exhibits perennial characteristics. Identifying the QTLs underlying the yield-related traits in SD3# provides a theoretical basis for future perennial rice breeding. In this study, SD3# and an F2 population derived from a cross between SD3# and XieqingzaoB (XQZB) and its bi-parents were selected as experimental materials. A total of fifteen yield-related traits including plant height, effective panicle per plant and thousand-grain weight in the SD3#-population and its bi-parents were investigated for both phenotypic analysis and QTL mapping. Results indicated that the fifteen yield-related traits in the SD3#-population exhibited quantitative genetic characteristics suitable for QTL analysis. Altogether, 25 QTLs underlying the yield-related traits and 26 pairs of epistatic QTLs were identified; these explained phenotypic variances ranging from 4.21% to 27.30% and 1.24% to 19.30%. Of these, nine novel QTLs underlying unfilled grain per panicle (UGP), spikelet per panicle (SP), seed setting density (SSD), grain yield per plant (GYP) and thousand-grain weight (TGW) with additive effects derived from SD3# were detected on the first, second, fourth, eighth, ninth, and tenth chromosomes. Six pleiotropic QTLs underlying two or more traits were detected on the first, fourth, seventh, eighth, and eleventh chromosomes. This work lays a good foundation for both the yield-related gene mined from SD3# and future perennial Chinese rice breeding.

1. Introduction

The main goal of rice geneticists and breeders is to pursue superior yield-related traits in rice-breeding projects. However, rice yield-related traits are controlled by quantitative trait loci (QTL) whose genetic constitutions are complex and easily affected by both genes and environmental factors. Nowadays, QTLs underlying yield-related traits in rice have been extensively identified using a series of genetic-mapping populations. Altogether, 2060 QTLs controlling yield-related traits have been identified on the whole genome of rice (www.gramene.org). Of these, some QTLs underlying yield-related traits in rice that directly or indirectly affect economic yield, including heading date, plant height, panicle traits, and grain type, have been successfully cloned and functionally analyzed. For example, the heading date of Hd6 [1], the plant height of SD1 [2], and the number of panicles of MOC1 have been successfully cloned [3]. In particular, Gn1a is the first QTL that encodes a cytokinin oxidase dehydrogenase and controld grain number [4]. LRK1 can significantly increase tillers, plant height, and grain weight per panicle via overexpression [5]. GS3 controls grain length and grain weight and explains 80–90% of the variation of grain weight and grain length in the near-isogenic lines of Minghui63# and Chuan7# [6]. Ghd7, which plays an important role in rice-yield increase and ecological adaptability, is a pleiotropic gene [7]. DEP1 is a gain-of-function mutation causing truncation of a phosphatidylethanolamine-binding protein-like domain protein, which reduces the length of the inflorescence internode, results in an increased number of grains per panicle and a consequent increase in grain yield [8]. IPA1 is a promoter that binds the protein OSSPL14, promotes primary branch differentiation, and inhibits tillering, but increases the number and weight of grains per panicle [9]. GW8 encodes a promoter-like SPB-box binding protein Osspl16 with promotion of glume cell division and grain filling, seed width, and grain weight [10]. BRD3 encodes a functional BR-degrading enzyme, cytochrome P450 monooxygenase 734A4, and enhances rice panicle branching and grain yield [11]. In summary, the successful isolation and cloning of QTLs underlying yield-related traits in rice benefited from the innovative utilization of excellent rice genetic resources for modern rice molecular breeding. Consequently, a series of unceasing excavations and innovations in rice germplasm have been involved in promoting the transformation and upgrading of the rice-breeding industry [12].
Perennial Chinese rice can survive the natural cold-winter field environment and sprout from rice tillering nodes the next spring, tiller, flower, seed, and be harvested the next autumn [13]. Compared with annual rice, the perennial Chinese rice variety exhibits the advantage of stable grain yield across multiple years and sites. It shows an average yield of 6.8 Mg ha−1 harvest−1 versus the 6.7 Mg shown by planted annual rice. It saves 58.1% of labor and 49.2% of the input costs in each regrowth cycle. Importantly, the perennial Chinese rice variety has the advantage of maintaining important ecosystem functions. Perennial crops can increase nitrogen retention and soil carbon accumulation as a result of their permanent living cover and deep root systems [14]. In agricultural production, a new perennial Chinese rice variety with “high yield, good quality, and wide adaptability” can be cultivated and commercially released to farmers. Alleviating the negative impact of land wastage on rice production caused by urbanization, industrialization, and even sharp reductions of the agricultural population has great practical significance [15].
A comparative analysis of previous studies on perennial rice reveals that progress on the genetic basis theory and perennial rice breeding is still sluggish, mostly staying in the fields of yield traits investigation and breeding research in the preliminary exploration stage. However, little is known about the genetic mechanisms of perennial Chinese rice. In particular, the understanding of the genetic loci and their modes of action for the yield-related traits in perennial Chinese rice is relatively backward. Meanwhile, it is rather difficult to integrate superior genes from different perennial rice germplasms into the new perennial rice variety. Consequently, it is very necessary to carry out studies on the genetic basis of the yield-related traits of perennial rice and dissect its genetic components into a single Mendel factor for further perennial rice breeding. SD3# can survive through the natural cold-winter field environment of Chongqing, and it exhibits perennial characteristics. Therefore, we investigated sixteen yield-related traits of SD3# between the MC and RC of 2024 and identified QTLs underlying fifteen yield-related traits with the following objectives: (1) To explore the field performance of sixteen yield-related traits in SD3# between MC and RC of 2024 to provide technical support for future perennial rice variety breeding and commercial release to farmers; (2) To identify QTLs underlying fifteen yield-related traits in SD3# to lay a good foundation for the future fine mapping, cloning, and functional research on yield-related traits genes.

2. Materials and Methods

2.1. Plant Materials

Perennial Chinese rice SD3# withstood cold to −1 °C as the daily minimum temperature for one day, 0 °C as the daily minimum temperature for four days, and 1 °C as the daily minimum temperature for four days in January 2021 (Supplementary Table S1), and safely survived through the natural cold-winter field environment of Chongqing (29°32′ N, 106°32′ E) Municipality, China [16]. It also sprouted from rice tillering nodes in March 2021, tillered, flowered, set seed, and was harvested in August 2021, thereby exhibiting perennial characteristics; it was selected as the female parent. XQZB is sensitive to low temperature but widely used in rice “three-line” commercial breeding. It cannot survive through the natural cold-winter field environment of Chongqing. The seeds of both SD3# and XQZB were collected from the China National Rice Research Institute (CNRRI). In March 2024, SD3# survived through the cold-winter field environment of Chongqing and sprouted from rice tillering nodes (Figure 1A–F), exhibiting perennial characteristics.
In mid-July of 2022, the hybrid combination of SD3#/XQZB was constructed at the Rice Biotechnology breeding Station of Chongqing Normal University (CQNU) at an altitude of 285.8 m, Chongqing (29°32′ N, 106°32′ E), China. In late August of 2022, the seed hybrid F0 was planted at the Southern Breeding Station of the Sichuan Academy of Agricultural Sciences (SAAS), Yingzhou town, Lingshui County, Hainan Province, China. In late December of 2022, the F2 population seeds from F1 plants were collected to construct the molecular-linkage map and identify the QTLs underlying the yield-related traits in SD3#.

2.2. Phenotyping Experiment

In mid-March 2023, field experiments were conducted at the Rice Biotechnology Testing Station of CQNU at the altitude of 285.8 m, University Town, Shapingba district, Chongqing (29°32′ N, 106°32′ E), China. Seeds of the SD3#-population and its bi-parents were sown on 15 March 2023. The 35-day-old seedlings of the SD3#-population and its bi-parents were transplanted into single rows of 10 plants, leaving a 20 cm gap between plants within each row and a 25 cm gap between rows. A total of 400 individual plants of the SD3#-population were planted. Water and fertilizer management and pest control were the same as in conventional fields [17]. At the tiller stage of the rice, the leaves of SD3#-population individuals and their bi-parents were sampled to construct the molecular linkage map of rice.
After the autumn harvest in August of 2023, the rice straw of SD3# was exposed to the natural cold-winter field environment, and it safely survived through the cold-winter field environment. In March of 2024, the SD3# sprouted from rice tillering node, tillered, flowered, set seed, and was continuously harvested in August of 2024, thus exhibiting perennial characteristics (Figure 1A–F). In mid-March 2024, the SD3# was planted at the Rice Biotechnology Testing Station of CQNU. The planting technique of SD3# in 2024 was consistent with that of the biparental planting in 2023. The yield-related traits of SD3# in MC and RC of 2024 were sampled for phenotypic analysis.

2.3. Trait Measurement

Referring to the method of rice seed testing described by Shen [18], at the maturity stage, a total of 88 F2 individuals from the SD3#-population and five individual plants of both SD3# and XQZB were sampled at random for the yield-related traits measurement. Altogether, 15 yield-related traits including plant height (PH, in cm), effective panicles per plant (EPP), panicle length (PL, in cm), filled grains per panicle (FGP), unfilled grains per panicle (UGP), spikelet per panicle (SP), seed setting rate (SSR), seed setting density (SSD), grain weight per panicle (GWP, in g), grain yield per plant (GYP, in g), grain length (GL, in mm), grain width (GW, in mm), length to width ratio (LWR), grain thickness (GT, in mm), and thousand-grain weight (TGW, in g) were measured for both phenotypic analysis and QTL mapping. Meanwhile, five individual plants of SD3# in both the MC and RC of 2024 were sampled at random for the yield-related traits measurement. A total of 16 yield-related traits including heading date (HD, in d), plant height (PH, in cm), and thousand-grain weight (TGW, in g) were measured for phenotypic analysis.

2.4. Data Analysis

All measurements of the yield-related traits and phenotypic data of the SD3#-population, bi-parents, and SD3# in both the MC and RC of 2024 were inputted into Microsoft Excel 2010. With the same software, three indices of standard derivation (SD), mean value, and coefficient of variation (CV) were calculated. GraphpadPrism 5.0 software [19] was employed to calculate the t-test values and phenotypic correlation coefficients (PCCs), as well as draw the histograms. QTLIciMapping version 4.20 [20] software was employed to identify both main-QTL and epistatic-QTL.

2.5. Linkage Map and QTL Analysis

A linkage map containing 112 pairs of simple sequence repeats (SSR) markers with 28% polymorphism between the bi-parents and covering a genomic length of 1950.72 cM with an average interval of 17.14 cM between markers was drawn using QTLIcimapping version 4.20. It was also used to identify QTLs underlying the yield-related traits. Inclusive composite interval mapping (ICIM) for QTL analysis in the SD3#-population was performed using QTLIciMapping 4.20 software. The probability of marker variables entering the model (PIN) of 0.01 and walking speed of 1 cM was set. An LOD threshold was set at 1000 permutations to advocate QTLs. An LOD score of ≥3.0 was used to declare a significant major QTL. The contribution rate (%) was estimated as the percentage of variance explained by each locus in proportion to the total phenotypic variance. Digenic epistasis QTLs in the SD3#-population were identified by the ICIM-EPI mapping of QTLIciMapping version 4.20 software with mapping parameters of 1 cM steps and 0.001 probabilities in a stepwise regression. Genome-wide threshold F-values (F = 0.001) were estimated from 1000 permutations to indicate the presence of epistasis. QTL nomenclature followed the principle suggested by McCouch [21].

3. Results

3.1. Phenotype of 16 Yield-Related Traits of SD3# in Both MC and RC of 2024

The 16 yield-related traits of perennial Chinese rice SD3# across the MC and RC of 2024 exhibited an irregular variance in terms of field phenotypic values and were easily affected by field environmental factors (Figure 1G–Q, Table 1). Within five individuals of SD3# in both the MC and RC of 2024, the 16 yield-related traits in the SD3# exhibited wide phenotypic variation, with CV% values that ranged from 1.23% for SSR to 37.25% for GYP in 2024 MC, 1.11% for HD to 25.11% for EPP in 2024 RC. In particular, both the GYP and PP of SD3# in both the MC and RC exhibited maximum variances of 37.25%, 27.68%, 22.20%, and 25.11%, respectively.
The 16 yield-related traits of SD3# exhibited a wide phenotypic variation between the MC and RC of 2024 and significantly different responses to environmental factors, with t-test values that ranged from 0.06 to 13.86. In particular, the SD3# had significantly higher phenotypic values on PH (121.42 ± 2.71 cm) in the MC than those (110.40 ± 5.31 cm) of the RC, with t-test value of 7.08. However, the SD3# had significantly higher phenotypic values on HD (127 ± 1.41 d) and UGP (48.80 ± 7.91) in the RC than those measurements (108.50 ± 1.71) and (13.33 ± 2.29) in the MC, with t-test values of 13.86 and 8.67. The HD, PH, and UGP in SD3# exhibited significant differences in field phenotypic values between the MC and RC of 2024 and inconsistent field adaptability. The remaining thirteen yield-related traits in SD3# exhibited insignificant differences of field phenotype and consistent field adaptability between the MC and RC.

3.2. Correlations of 16 Yield-Related Traits of SD3# in Both MC and RC of 2024

A total of 58 pairs of significant correlation values among the 16 yield-related traits of SD3# across the MC and RC of 2024 were estimated (Figure 2, Table 2). In the 2024 MC, there were 24 pairs of significant PCC values, including 17 pairs of positive correlations and 7 pairs of negative correlations calculated in SD3# (Figure 2A). In particular, GYP had significantly negative correlations with SP, SSD, GWP, GL, GW, LWR, and GT, with correlation coefficients that ranged from −0.88 to −1.00. GWP had significantly positive correlations with SSD, GL, GW, LWR, and GT, with correlation coefficients that ranged from 0.99 to 1.00, but had significantly negative correlations with GYP, with a correlation coefficient of −1.00. SSD had significantly positive correlations with GWP, GL, GW, LWR, and GT, with correlation coefficients that ranged from 0.97 to 0.99, but had significantly negative correlations with GYP, with a correlation coefficient of −0.99.
In the 2024 RC, there were 34 pairs of significant PCC values among the 16 yield-related traits in SD3#, including 23 pairs of positive correlations and 11 pairs of negative correlations (Figure 2B). In particular, GYP had significantly negative correlations with PL, with a correlation coefficient of −0.92. GWP had significantly positive correlations with SP, SSD, GL, GW, LWR, and GT, with correlation coefficients that ranged from 0.95 to 1.00, but had significantly negative correlations with PH, with a correlation coefficient of −0.92. SSD had significantly positive correlations with SP, GWP, GL, GW, LWR, and GT, with correlation coefficients that ranged from 0.94 to 1.00, but had significantly negative correlations with PH, with a correlation coefficient of −0.92. In summary, the number, strength, and direction of correlations among the 16 yield-related traits in SD3# across the MC and RC of 2024 exhibited significant differences. Therefore, the 16 yield-related traits in SD3# exhibited uncertain phenotypic differences in ecological adaptability.

3.3. Phenotypic Variation for Yield-Related Traits in SD3#-Population and Its Bi-Parents

A significant phenotypic difference was observed between SD3# and XQZB for the fifteen yield-related traits, except for UGP and TGW (Table 3). It showed wide variety of t-test values ranging from 1.26 to 30.35. Meanwhile, SD3# had significantly higher phenotypic values on PH, FGP, SP, SSR, SSD, GWP, GYP, GW, and GT than those of XQZB. However, the XQZB had significantly higher phenotypic values on EPP, PL, GL and LWR than those of SD3#. All of the measured traits in the SD3#-population showed tremendous bidirectional segregation beyond its bi-parents, except for EPP, FGP, and SSR, which displayed a wide coefficient of variation ranging from 7.02% for GL to 67.52% for UGP (Figure 3). They exhibited continuously normal distribution and belonged to quantitative inheritance, which was suited to QTL mapping.

3.4. Correlation of the 15 Yield-Related Traits in the SD3#-Population

There were 44 significant correlation pairs for the 15 yield-related traitsestimated in the SD3#-population (Figure 4, Table 4). Of these, there were 33 significantly positive correlation pairs and 11 significantly negative correlation pairs calculated for all measured traits.
GYP showed significantly positive correlations with PH, EPP, PL, FGP, SP, SSR, GWP and TGW with correlation coefficients that ranged from 0.27 to 0.80. GWP showed significantly positive correlations with PL, FGP, SP, SSR, SSD, and TGW with correlation coefficients that ranged from 0.28 to 0.78, but a significantly negative correlation with UGP with a correlation coefficient of −0.26. PH showed significantly positive correlations with EPP, PL, UGP, SP, and GYP with coefficients that ranged from 0.30 to 0.37, but a significantly negative correlation with UGP with correlation coefficient of −0.22. PL showed significantly positive correlations with PH, EPP, FGP, UGP, SP, GWP, GYP, and LWR with correlation coefficients that ranged from 0.21 to 0.45. SP showed significantly positive correlations with PH, PL, FGP, UGP, SSD, GWP, and GYP with correlation coefficients that ranged from 0.21 to 0.89, but significantly negative correlations with SSR and TGW with correlation coefficients of −0.25 and −0.27. TGW showed significantly negative correlations with UGP, SP, and SSD with coefficients that ranged from −0.27 to −0.36, but showed significantly positive correlations with GWP and GYP with correlation coefficients that ranged from 0.32 to 0.27. PH, PL, GWP and GYP were regarded as the most important determinants in the yield-related traits of the SD3# population. All in all, the yield-related trait in the SD3# population was a very complex characteristic. It showed complex correlations among all of the measured traits that directly or indirectly determined the formation of yield traits.

3.5. QTLs for Yield-Related Traits

Altogether, 25 QTLs underlying 15 yield-related traits were detected on the first, second, third, fourth, fifth, seventh, eighth, ninth, tenth, and eleventh chromosomes. LOD values ranged from 3.00 to 6.06, which explained the wide phenotypic variation ranging from 4.21% for qGW2a to 27.30% for qPH1 (Figure 5, Table 5). For PH, only one QTL (qPH1) was detected on the first chromosome, and it explained a phenotypic variation of 27.30%. Moreover, the SD3# allele increased PH at qPH1. For the number of panicles per plant, four QTLs (qEPP1, qEPP3, qEPP7, and qEPP11) were detected on the first, third, seventh, and eleventh chromosomes, respectively, which explained the phenotypic variation that ranged from 7.33% to 10.82%. Among them, the SD3# alleles increased the number of effective panicles per plant at qEPP3, but the XQZB allele increased EPP at qEPP1, qEPP7, and qEPP11. For panicle length, three QTLs (qPL1, qPL2, and qPL11) were detected on the first, second, and eleventh chromosomes, respectively. They explained the phenotypic variation ranging from 6.07% to 8.23%. The SD3# allele increased PL at qPL1, but the XQZB alleles increased PL at both qPL2 and qPL11. For the number of empty grains per panicle, only one QTL (qUGP8) was detected on the eighth chromosome. It explained 14.32% of the phenotypic variation. The SD3# allele increased UGP at qUGP8. For the number of spikelets per panicle, only one QTL (qSP1) was detected on the first chromosome. It explained 26.56% of the phenotypic variation. The SD3# allele increased SP at qSP1. For the seed setting rate, two QTLs (qSSR4 and qSSR8) were detected on the fourth and eighth chromosomes, which explained 4.61% and 5.40% of the phenotypic variation, respectively. The SD3# allele increased SSR at qSSR4, but the XQZB allele increased SSR at qSSR8. For seed setting density, two QTLs (qSSD1 and qSSD2) were detected on the first and second chromosome, which explained 14.11% and 20.65% of the phenotypic variation, respectively. The SD3# alleles increased SSD at both qSSD1 and qSSD2. For the grain yield per plant, four QTLs (qGYP7, qGYP9, qGYP10a, and qGYP10b) were detected on the seventh, ninth, and tenth chromosomes, respectively. They explained 5.16% to 9.07% of the phenotypic variation. The XQZB allele increased GYP at qGYP7, but the SD3# alleles increased GYP at qGYP9, qGYP10a, and GYP10b, respectively. For grain width, four QTLs (qGW2a, qGW2b, qGW11a, and qGW11b) were detected on the second and eleventh chromosomes, which explained 4.76% to 5.08% of the phenotypic variation. The SD3# alleles increased GW at qGW2a, qGW2b, qGW11a, and qGW11b, respectively. For grain thickness, only one QTL (qGT5) was detected on the fifth chromosome. It explained 9.30% of the phenotypic variation. The XQZB allele increased GT at qGT5. For thousand-grain weight, two QTLs (qTGW1 and qTGW4) were detected on the first and fourth chromosomes, which explained 4.76% and 5.08% of the phenotypic variation, respectively. The SD3# alleles increased TGW at both qTGW1 and qTGW4.

3.6. Pleiotropic QTLs for Yield-Related Traits

The chromosomal locations of all QTLs identified in the SD3#-population revealed six genomic regions on the first, fourth, seventh, eighth, and eleventh chromosomes, where clustered QTLs affected two yield traits (Figure 5, Table 6). For the first chromosome, two QTLs’ (qSP1 and qSSD1) genomic regions underlying spikelet per panicle and seed setting density were flanked repeatedly by RM265 and RM3738 on the first chromosome. The SD3# alleles increased SP at qSP1 and SSD at qSSD1. Two QTLs (qEPP1 and qPL1) genomic regions affecting panicle per plant and panicle length were flanked repeatedly by RM3642 and RM600 on the first chromosome. The SD3# allele increased EPP at qEPP1, and XQZB allele increased PL at qPL1. For the fourth chromosome, two QTL (qSSR4 and qTGW4) genomic regions were flanked repeatedly by RM7051 and RM5633, controlling seed setting rate and thousand-grain weight. The SD3# allele increased SSR at qSSR4 and TGW at qTGW4. For the seventh chromosome, two QTLs (qEPP7 and qGYP7) genomic regions were flanked repeatedly by RM1135 and RM5793 and repeatedly affected panicle per plant and grain yield per plant, respectively. The XQZB allele increased EPP at qEPP7 and GYP at qGYP7. For the eighth chromosome, two QTL (qUGP8 and qSSR8) genomic regions were flanked repeatedly by RM1111 and RM3702, which simultaneously affected empty grain per panicle and seed setting rate. The SD3# allele increased UGP at qUGP8, and the XQZB allele increased SSR at qSSR8. For the eleventh chromosome, two QTLs’ (qEPP11 and qGW11a) genomic regions were flanked repeatedly by RM7120 and RM6293 and simultaneously affected the number of panicles per plant and grain width. The XQZB allele increased EPP at qEPP11, and the SD3# allele increased GW at qGW11a. For the pleiotropic QTLs that controlled two or more yield-related traits, they were located repeatedly at the same marker interval. Thus, pleiotropic QTLs should be given more attention in future rice genetic and breeding projects. It is twice as efficient for multi-yield traits to be synchronously improved through one yield trait being genetically improved in future rice genetic and breeding projects.

3.7. Digenic Epistatic QTLs for Yield-Related Traits

Altogether, 26 epistatic QTL pairs for 15 yield traits were detected on the whole genome of rice, except for the sixth and eleventh chromosomes in the SD3# population. They accounted for an extensive phenotypic variation ranging from 1.15% to 19.30%, with wide-ranging LODs from 5.01 to 8.74 (Table 7). Among them, eight epistatic QTL pairs were found for SSR, six for EPP, five for GYP, three for PL, and one each for PH, UGP, GW, and GT. For all epistatic QTL pairs, four interaction patterns, including additive × additive, additive × dominant, dominant × additive, and dominant × dominant, were identified in the SD3#-population. There, 11 out of 24 genomic regions of the QTLs interacted repeatedly with two or more marker intervals. For example, the genomic interval RM3642-RM600 underlying EPP and PL on the 1st chromosome interacted with the genomic interval RM6519-RM5651 on 2nd chromosome and the genomic interval RM8019-RM6990 on the 8th chromosome. The genomic interval RM250-RM3763 underlying PL and GW on the 2nd chromosome interacted with the interval RM1352-RM3199 on the 3rd chromosome and the genomic interval RM559-RM5979 on the 4th chromosome. The genomic interval RM7051-RM5633 underlying SSR on the 4th chromosome interacted with the genomic interval RM7637-RM5812 on the 2nd chromosome, the genomic interval RM317-RM7051 underlying both SSR and GW on the 4th chromosome, and the genomic interval RM24085-RM160 on the 9th chromosome. The genomic interval RM1135-RM5793 underlying EPP and GYP on the 7th chromosome interacted with the interval RM3555-RM5481 on the 7th chromosome, the genomic interval RM1111-RM3702 on the 8th chromosome, and the genomic interval RM202-RM7120 on the 11th chromosome. The genomic interval RM1111-RM3702 underlying UGP and SSR on the 8th chromosome interacted with the genomic interval RM5633-RM401 on the 4th chromosome, the genomic interval RM5793-RM432 on the 7th chromosome, the genomic interval RM1135-RM5793 underlying EPP and GYP on the 7th chromosome, the genomic intervals RM257-RM5661 and RM24085-RM160 on the 9th chromosome, and the genomic interval RM5708-RM3882 on the 10th chromosome. The genomic interval RM24085-RM160 underlying GYP on the 9th chromosome interacted with the genomic interval RM7051-RM5633 underlying SSR on the 4th chromosome and the genomic interval RM1111-RM3702 underlying SSR on the 8th chromosome. The genomic interval RM5708-RM3882 underlying GYP on the 10th chromosome interacted with the interval RM85-RM3856 underlying EPP on the 3rd chromosome, the genomic interval RM317-RM7051 underlying SSR on the 4th chromosome, and the genomic interval RM1111-RM3702 underlying SSR on the 8th chromosome. The genomic interval RM202-RM7120 on the 11th chromosome interacted with the interval RM3738-RM8084 underlying PH on the 1st chromosome, the genomic interval RM1135-RM5793 underlying EPP and GYP on the 7th chromosome, and the genomic interval RM5708-RM3882 underlying GYP on the 10th chromosome. The genomic interval RM7120-RM6293 on the 11th chromosome interacted with the genomic interval RM559-RM5979 underlying SSR on the 4th chromosome and the genomic interval RM24085-RM160 underlying GYP on the 9th chromosome. The remaining 11 epistatic QTL pairs involved only one interaction pair.

4. Discussion

4.1. The Significance of the Research on Perennial Rice Germplasm

As we look back on the research and development process of rice genetic and breeding in China, the discovery of excellent rice germplasm and the creation of rice breeding materials are important prerequisites for breeding breakthrough rice varieties and publishing papers in world-class journals. Especially in rice production, the exploration and innovative utilization of excellent rice germplasm, including dwarf rice [22], male sterility rice [23], photoperiod thermo-sensitive male sterile rice [24], and restorer rice [25], has significantly increased the per-unit yield of rice. Meanwhile, a series of important agronomic genes including rice blast Pigm [26], bacterial blight Xa23 [27], brown planthopper BPH9 [28], and high-temperature resistant QT12 [29] have been successfully cloned for future rice molecular breeding. Consequently, to explore excellent rice germplasm is of great scientific significance for accelerating research into rice genetics and breeding. Similarly, exploring and innovatively utilizing the perennial rice germplasm is crucial to perennial rice variety breeding. During the course of perennial rice variety breeding, any perennial rice variety that must exhibit stable field performance on the yield-related traits across both MC and RC will be considered for commercial releasing to farmers [30]. Therefore, it is necessary to clarify the field phenotypic characteristics of the yield-related traits of perennial rice germplasm between MC and RC.
This study found that the field phenotypic values on heading date, plant height, unfilled grain per panicle in the perennial rice Shendao3# from northern China showed significant differences across MC and RC of 2024. The remaining 13 yield-related traits in SD3# showed insignificant difference between MC and RC of 2024. Meanwhile, SD3# exhibited significant differences in correlation coefficients among the 16 yield-related traits across the MC and RC of 2024. This indicated that the field phenotypes of different yield-related traits of SD3# displayed different responses to the field climate environment between the MC and RC. Therefore, in future research on the breeding, demonstration, and promotion of new perennial rice varieties, particular attention should be paid to the stable consistency of the yield-related traits in perennial rice across different rice planting ecological environments, especially for the stable consistency of heading date across the MC and RC.

4.2. Comparison with Previous QTL Mapping

The cardinal goal of any study on the genetic mechanisms of quantitatively inherited traits is to identify the major QTLs hidden in different rice germplasm resources and finally mine the genes of interest for molecular rice breeding [31]. In this study, the SD3# was selected as plant material to identify QTLs underlying yield-related traits and thus lay a good foundation for future perennial Chinese rice molecular breeding. The F2 population was a temporary population, whose contemporary genotypes differed from offspring genotypes, but exhibited two advantages of both its the complete genotype and short development cycle, and so it is a preferred genetic population widely used in identifying markers linked with QTLs underlying important yield-related traits in rice quantitative genetic projects [32].
In this study, a total of 25 QTLs underlying the 15 yield-related traits were detected on the whole genome (except for the sixth and eleventh chromosomes) using the SD3#-population. Of these, six major QTLs can explain more than 10% of phenotypic variation. Six pleiotropic QTL genomic regions controlling two or more yield-related traits were detected on the first, fourth, seventh, eighth, and eleventh chromosomes. All of the QTLs followed the genetic model of additive, dominant, and epistatic effects of additive by additive, additive by dominant, dominant by additive, and dominant by dominant in the SD3# population. However, the remaining 13 QTL genomic regions such as qEPP3, qPL2, qPL11, qGYP9, qGYP10a, and qGYP10b were detected for only a single yield trait. Among the 25 QTLs, 14 QTLs were located the same genomic region and even cloned. Accordingly, we searched their alignment QTLs identified in the same chromosomal region through the publicly available QTL database (www.gramene.org) published by previous researchers. That qPH1 underlies PH was previously reported in the Yangdao6#/Lvhan1# RIL population [33]. That qEPP1 relates to EPP was previously detected on the first chromosome in the Zhongjiazao17/D50 F2 population [34]. That qEPP3 is related to EPP was detected on the third chromosome in the Zhaiyeqin8/Jingxi17 DH population [35]. That qEPP11 is related to EPP was detected on the eleventh chromosome in the Oryza sativa × Oryza rufipogon BC2F2 population [36]. That qPL1 is related to PL was detected on the first chromosome in the Zhongjiazao17/D50 DH population [37]. That qPL2 is related to PL was detected on the second chromosome in the O.rufipogon/Jefferson BC2F2 population [38]. That qPL11 relates to PL was detected on the eleventh chromosome in the Lemont/Teqing RIL population [39]. That qSSR4 underlies SSR was previously reported in both the Zhaiyeqing8/Jingxi17 DH [35,40] and Zhongjiazao17/D50 F2 population [34]. That qSSR8 relates to SSR was detected on the eighth chromosome in the Nipponbare/Zhongjiazao17 RIL population [41]. That qGW2a and qGW2b are related to the GW was detected on the second chromosome in the Nipponbare/Zhongjiazao17 RIL population [42]. That qGW11a is related to GW was detected on the eleventh chromosome in the O.sativa/O.glaberrima DH population [43]. That qGW11b relates to GW was detected on the eleventh chromosome in the IR64/Azucena DH population [44]. That qGT5 relates to GW was detected on the eleventh chromosome in the four populations of Tesanai2/CB F2 and F2:3 [41], O.rufipogon/V20A BCF2 [45], and Oryza rufipogon Griff/Guichao2# ILs [46]. However, 11 novel QTLs associated with the yield-related traits were primarily mapped in the SD3#-population. Among them, for qEPP7 related to effective panicle per plant and qGYP7 underlying the grain yield per plant, the XQZB alleles increased EPP at qEPP7 and increased GYP at qGYP7. For the remaining nine QTLs related to the yield-related traits, the SD3# alleles increased UGP at qUGP8, SP at qSP1, SSD at qGSSD1 and qSSD2, GYP at qGYP9, qGYP10a, and qGYP10b, and TGW at qTGW1 and qTGW4. Nine novel QTLs (qUGP8, qSP1, and qTGW4) underlying the yield-related traits might exhibit a unique genetic model of SD3#. They may also provide a novel genetic resource for perennial Chinese rice variety breeding. Therefore, special efforts should be devoted to fine mapping of these newly discovered QTLs and studying their positional cloning for perennial Chinese rice molecular breeding.
Six QTL chromosomal regions associated with two or more yield-related traits were detected repeatedly in the SD3#-population. For example, qEPP1 and qPL1 were flanked repeatedly at the marker interval of RM1135-RM57930 on the first chromosome, and qEPP7 and qGYP7 were detected repeatedly at the marker interval of RM1135-RM57930 on the seventh chromosome. Similarly, the clustered distribution of QTL has also been reported in previous studies and other grasses [47,48]. Consequently, paying more attention to pleiotropic QTLs (chromosome regions) in future rice genetic and breeding projects is necessary. It would be twice as efficient for multi-yield traits to be synchronously improved through one genetically improved yield trait in future rice genetic and breeding projects.

5. Conclusions

Perennial Chinese rice Shendao3# can survive through the cold-winter field environment of Chongqing and sprout from rice tillering nodes. Meanwhile, SD3# exhibited a stable field performance on the yield-related traits between MC and RC of 2024, and indicats that it is a good germplasm for future perennial rice breeding. Altogether, 25 QTLs and 26 epistatic QTLs were detected on the whole genome, except for the sixth and twelfth chromosomes. They explained phenotypic variations ranging from 4.21% to 27.30% and 1.15% to 19.30%. Nine novel QTLs and six pleiotropic QTLs underlying the yield-related traits were detected for the future elucidation of the yield-related traits in SD3#. This study laid a good foundation for yield-related gene mining and its utilization in the sustainable development of future agriculture.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15232453/s1, Table S1: The daily maximum/minimum temperature (°C) in University Town, Shapingba district in January 2021, Chongqing, China.

Author Contributions

Y.Y.: Investigation, Data curation, and Formal analysis; J.L.: Investigation and Methodology; M.W., T.P., and L.T.: Investigation and Data curation; W.N.: Data curation and Validation; X.Q. and M.L.: Software and Formal analysis; J.G.: Resources and Methodology; Y.L.: Conceptualization, Funding acquisition, Supervision, Writing—original draft, and Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China (SKL-KF202226) and Chongqing Natural Science Foundation of China (cstc2021jcyj-msxmX0007), and the Open Project Program of State Key Laboratory of Rice Biology (20190202).

Data Availability Statement

The data supporting this study finding’s are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Agriculture 15 02453 g001aAgriculture 15 02453 g001b
Figure 1. The field performance of perennial Chinese rice SD3# in both MC and RC of 2024. (A–F) Perennial Chinese rice SD3# sprouted from rice tillering nodes in April 2024, (G–L) The field performance of SD3# in MC of 2024, (M–Q) The field performance of SD3# in RC of 2024. SD3#: shendao3#; MC: major crop; RC: ratooning crop.
Figure 1. The field performance of perennial Chinese rice SD3# in both MC and RC of 2024. (A–F) Perennial Chinese rice SD3# sprouted from rice tillering nodes in April 2024, (G–L) The field performance of SD3# in MC of 2024, (M–Q) The field performance of SD3# in RC of 2024. SD3#: shendao3#; MC: major crop; RC: ratooning crop.
Agriculture 15 02453 g001aAgriculture 15 02453 g001b
Agriculture 15 02453 g002
Figure 2. Correlation of the 16 yield-related traits of SD3# in both MC and RC of 2024. (A) Correlation of 16 yield-related traits of SD3# in 2024 MC. (B) Correlation of 16 yield-related traits of SD3# in 2024 RC. Notice: SD3#: shendao3#; MC: major crop; RC: ratooning crop. * Represent the significant difference at level of 5%.
Figure 2. Correlation of the 16 yield-related traits of SD3# in both MC and RC of 2024. (A) Correlation of 16 yield-related traits of SD3# in 2024 MC. (B) Correlation of 16 yield-related traits of SD3# in 2024 RC. Notice: SD3#: shendao3#; MC: major crop; RC: ratooning crop. * Represent the significant difference at level of 5%.
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Agriculture 15 02453 g003
Figure 3. Frequency distribution of 15 yield-related traits in the SD3#-population and its bi-parents. Notice: PH: plant height; EPP: effective panicle per plant; PL: panicle length; FGP: filled grain per panicle; UGP: unfilled grain per panicle; SP: spikelet per panicle; SSR: seed setting rate; SSD: seed setting density; GWP: grain weight per panicle; GYP: grain yield per plant; GL: grain length; GW: grain width; GT: grain thickness; LWR: length to width ratio; TGW: thousand-grain weight. SD3#: shendao3#; XQZB: xieqingzaoB.
Figure 3. Frequency distribution of 15 yield-related traits in the SD3#-population and its bi-parents. Notice: PH: plant height; EPP: effective panicle per plant; PL: panicle length; FGP: filled grain per panicle; UGP: unfilled grain per panicle; SP: spikelet per panicle; SSR: seed setting rate; SSD: seed setting density; GWP: grain weight per panicle; GYP: grain yield per plant; GL: grain length; GW: grain width; GT: grain thickness; LWR: length to width ratio; TGW: thousand-grain weight. SD3#: shendao3#; XQZB: xieqingzaoB.
Agriculture 15 02453 g003
Agriculture 15 02453 g004
Figure 4. Correlation of the 15 yield-related traits in the SD3#-population. * Represent the significant difference at level of 5% and 1%.
Figure 4. Correlation of the 15 yield-related traits in the SD3#-population. * Represent the significant difference at level of 5% and 1%.
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Figure 5. QTL underlying the 15 yield-related traits detected in the SD3#-population.
Figure 5. QTL underlying the 15 yield-related traits detected in the SD3#-population.
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Table 1. Phenotype of 16 yield-related traits of SD3# in both MC and RC of 2024.
Table 1. Phenotype of 16 yield-related traits of SD3# in both MC and RC of 2024.
TraitsShendao3#t-Test
Value
Major CropRatooning Crop
Means ± SDCV (%)Means ± SDCV (%)
HD (d)108.50 ± 1.711.58127 ± 1.411.1113.86 **
PH (cm)121.42 ± 2.712.23110.40 ± 5.314.817.08 **
EPP18.50 ± 5.1227.6819.00 ± 4.7725.110.37
PL (cm)19.07 ± 1.095.7220.52 ± 0.673.272.66
FGP180.83 ± 5.553.07205.20 ± 27.4413.371.88
UGP13.33 ± 2.2917.1848.80 ± 7.9116.218.67 **
SP167.50 ± 4.542.71156.40 ± 27.4917.580.82
SSR (%)92.64 ± 1.141.2375.86 ± 4.976.555.96
SSD9.52 ± 0.666.939.99 ± 1.2412.410.49
GWP (g)3.63 ± 0.184.963.77 ± 0.4812.730.91
GYP (g)49.83 ± 18.5637.2543.74 ± 9.7122.200.98
GL (mm)7.05 ± 0.243.407.34 ± 0.172.321.73
GW (mm)3.08 ± 0.134.223.03 ± 0.144.620.20
LWR2.29 ± 0.062.622.43 ± 0.072.880.06
GT (mm)2.25 ± 0.062.672.12 ± 0.094.251.30
TGW (g)24.83 ± 1.074.3124.20 ± 2.148.840.16
Notice: HD: heading date; PH: plant height; EPP: effective panicle per plant; PL: panicle length; FGP: filled grain per panicle; UGP: unfilled grain per panicle; SP: spikelet per panicle; SSR: seed setting rate; SSD: seed setting density; GWP: grain weight per panicle; GYP: grain yield per plant; GL: grain length; GW: grain width; GT: grain thickness; LWR: length to width ratio; TGW: thousand-grain weight; MC: major crop; RC: ratooning crop; SD: standard deviation; CV: coefficient of variation. ** represent the significant difference at level of 5% and 1% (α0.05 = 2.57; α0.01 = 4.03).
Table 2. Correlations of the 16 yield-related traits of SD3# in both MC and RC of 2024.
Table 2. Correlations of the 16 yield-related traits of SD3# in both MC and RC of 2024.
TraitsHDPHEPPPLFGPUGPSPSSRSSDGWPGYPGLGWLWRGTTGW
HD1.000.97 *0.450.55−0.93 *0.22−0.90 *0.550.860.870.340.770.840.840.860.67
PH0.011.000.550.63−0.90 *0.26−0.91 *0.45−0.92 *−0.92 *0.450.85−0.90 *−0.90 *−0.92 *0.56
EPP0.290.851.00−0.97 *0.530.040.460.090.730.720.870.800.770.740.750.59
PL0.310.030.091.000.560.140.610.040.830.82−0.92 *0.89 *0.870.840.840.71
FGP0.640.140.030.121.000.150.690.770.740.750.250.650.720.720.750.64
UGP0.520.160.070.280.881.000.610.640.440.440.410.460.420.460.420.10
SP0.290.430.400.670.620.261.000.120.94 *0.95 *0.570.89 *0.92 *0.94 *0.94 *0.61
SSR0.640.660.590.250.750.760.011.000.150.150.350.020.120.100.150.29
SSD0.100.720.830.340.480.370.820.201.001.00 *0.750.99 *1.00 *1.00 *1.00 *0.66
GWP0.080.720.790.460.440.270.870.260.99 *1.000.740.98 *1.00 *1.00 *1.00 *0.68
GYP0.070.690.780.480.460.28−0.88 *0.24−0.99 *−1.00 *1.000.850.790.780.760.55
GL0.100.670.740.520.480.270.91 *0.220.97 *1.00 *−1.00 *1.000.99 *0.99 *0.99 *0.65
GW0.050.670.780.490.470.300.88 *0.220.99 *1.00 *−1.00 *0.99 *1.001.00 *1.00 *0.70
LWR0.080.720.810.420.450.310.860.240.99 *1.00 *−1.00 *0.99 *1.00 *1.001.00 *0.68
GT0.040.680.800.440.470.330.850.220.99 *1.00 *−1.00 *0.99 *1.00 *1.00 *1.000.69
TGW0.100.770.350.110.430.610.250.690.240.310.290.300.240.290.231.00
Notice: HD: heading date; PH: plant height; EPP: effective panicle per plant; PL: panicle length; FGP: filled grain per panicle; UGP: unfilled grain per panicle; SP: spikelet per panicle; SSR: seed setting rate; SSD: seed setting density; GWP: grain weight per panicle; GYP: grain yield per plant; GL: grain length; GW: grain width; GT: grain thickness; LWR: length to width ratio; TGW: thousand-grain weight; SD3#: shendao3#; MC: major crop; RC: ratooning crop. * represents a significant difference at level of 5%, PCCs above the right corner are for the 16 yield-related traits of SD3# in MC of 2024; PCCs down the left corner are for the 16 yield-related traits of SD3# in RC of 2024.
Table 3. Field performance of yield traits in SD3#-population and its bi-parents.
Table 3. Field performance of yield traits in SD3#-population and its bi-parents.
TraitsBi-ParentsSD3#-Population
SD3#XQZBt-Test ValueMeans ± SDRangeCV (%)
PH (cm)102.8082.4013.07 **137.96 ± 16.36100.00–167.009.78
EPP9.8013.203.30 *7.73 ± 2.863.00–17.0037.80
PL (cm)17.6022.047.89 **28.33 ± 3.1516.20–34.0011.78
FGP157.2096.606.13 **193.29 ± 62.4627.00–379.0032.12
UGP6.2010.001.2699.02 ± 68.0212.00–296.0067.52
SP163.40106.604.56 **292.32 ± 72.86143.00–502.0024.17
SSR (%)96.2590.882.74 *67.00 ± 0.198.36–94.5927.74
SSD9.284.837.85 **10.32 ± 2.256.27–17.1622.07
GWP (g)3.992.716.29 **5.26 ± 1.472.32–8.9427.88
GYP (g)33.0424.274.43 *34.38 ± 15.845.57–88.8447.17
GL (mm)6.779.7525.49 **10.16 ± 9.497.60–10.727.02
GW (mm)3.092.4311.63 **2.87 ± 0.232.17–3.717.81
LWR2.194.0030.35 **3.56 ± 3.352.18–4.5411.78
GT (mm)2.192.073.36 *1.99 ± 0.181.17–2.218.70
TGW (g)24.6026.802.4224.70 ± 0.3713.50–33.9015.15
Notice: PH: plant height; EPP: effective panicle per plant; PL: panicle length; FGP: filled grain per panicle; UGP: unfilled grain per panicle; SP: spikelet per panicle; SSR: seed setting rate; SSD: seed setting density; GWP: grain weight per panicle; GYP: grain yield per plant; GL: grain length; GW: grain width; GT: grain thickness; LWR: length to width ratio; TGW: thousand-grain weight. SD3#: Shendao3#; XQZB: xieqingzaoB; SD: standard deviation; CV: coefficient of variation. *, ** represent the significant difference at level of 5% and 1% (α0.05 = 2.57; α0.01 = 4.03).
Table 4. Correlation of the 15 yield-related traits in the SD3#-population.
Table 4. Correlation of the 15 yield-related traits in the SD3#-population.
TraitsPHEPPPLFGPUGPSPSSRSSDGWPGYPGLGWLWRGTTGW
PH1.00
EPP0.32 *1.00
PL0.37 *0.26 *1.00
FGP0.040.120.26 *1.00
UGP0.30 *0.080.25 *−0.38 *1.00
SP0.31 *0.190.45 *0.50 *0.61 *1.00
SSR−0.22 *−0.02−0.060.68 *−0.90 *−0.25 *1.00
SSD0.160.08−0.000.42 *0.56 *0.89 *−0.26 *1.00
GWP0.120.190.35 *0.78 *−0.26 *0.43 *0.49 *0.28 *1.00
GYP0.32 *0.80 *0.38 *0.49 *−0.120.31 *0.28 *0.150.60 *1.00
GL0.090.160.20−0.070.23 *0.16−0.160.070.020.091.00
GW0.130.07−0.12−0.200.31 *0.12−0.29 *0.19−0.19−0.09−0.031.00
LWR0.080.160.21 *−0.050.210.15−0.140.050.030.091.00 *−0.101.00
GT−0.070.020.04−0.09−0.12−0.190.03−0.24 *0.150.070.020.110.011.00
TGW−0.090.130.120.00−0.29 *−0.27 *0.19−0.36 *0.32 *0.27 *−0.04−0.16−0.030.47 *1.00
Notice: PH: plant height; EPP: effective panicle per plant; PL: panicle length; FGP: filled grain per panicle; UGP: unfilled grain per panicle; SP: spikelet per panicle; SSR: seed setting rate; SSD: seed setting density; GWP: grain weight per panicle; GYP: grain yield per plant; GL: grain length; GW: grain width; GT: grain thickness; LWR: length to width ratio; TGW: thousand-grain weight. * represents the significant difference at level of 5%.
Table 5. QTLs underlying the yield-related traits in the SD3#-population.
Table 5. QTLs underlying the yield-related traits in the SD3#-population.
TraitQTLChromosomeGenomic PositionMarker IntervalLODAdditiveDominantR2 (%)Favorable Allele
PHqPH1134902085-37261443RM3738-RM80846.069.937.2127.30SD3#
EPPqEPP119463544-24866202RM3642-RM6003.06−3.02−4.808.06XQZB
qEPP334333680-13933574RM489-RM60803.023.84−5.567.33SD3#
qEPP7716932001-17489638RM1135-RM57935.23−3.58−6.5210.82XQZB
qEPP111111763775-2888052RM7120-RM62934.28−3.04−5.098.53XQZB
PLqPL1127925715-32774365RM3642-RM6003.006.054.196.07SD3#
qPL2213481661-19677083RM250-RM37634.67−6.673.948.05XQZB
qPL11111124242-4773752RM1341-RM34283.96−3.115.538.23XQZB
UGPqUGP8835196573-37261443RM1111-RM37023.1049.48−132.6214.32SD3#
SPqSP1113059580-24116775RM265-RM37385.5248.1233.1426.56SD3#
SSRqSSR4411389704-20800963RM7051-RM56333.880.02−0.324.61SD3#
qSSR8816932001-17489638RM1111-RM37025.90−0.170.465.40XQZB
SSDqSSD1110811135-19788247RM265-RM37384.530.471.8214.11SD3#
qSSD222722348-14527760RM324-RM2625.251.49−3.8020.65SD3#
GYPqGYP772722348-13761888RM1135-RM57933.32−14.32−23.547.25XQZB
qGYP994407860-23568212RM24085-RM1603.4121.40−14.195.79SD3#
qGYP10a1011389704-15894177RM5708-RM38824.2417.86−13.649.07SD3#
qGYP10b1019677083-28788052RM3882-RM82013.7419.60−23.565.16SD3#
GWqGW2a23073406-27342022RM5651-RM37323.040.34−0.374.21SD3#
qGW2b211389704-15894177RM5812-RM3244.070.28−0.265.38SD3#
qGW11a1111763775-28788053RM7120-RM62933.820.27−0.355.34SD3#
qGW11b1119677083-28788052RM6293-RM13413.460.28−0.355.03SD3#
GTqGT553073406-27342022RM405-RM263.98−0.160.289.30XQZB
TGWqTGW119463544-24866202RM3642-RM6004.150.440.324.76SD3#
qTGW4413059580-24116775RM7051-RM56334.430.380.575.08SD3#
Notice: PH: plant height; EPP: effective panicle per plant; PL: panicle length; UGP: unfilled grain per panicle; SP: spikelet per panicle; SSR: seed setting rate; SSD: seed setting density; GYP: grain yield per plant; GW: grain width; GT: grain thickness; TGW: thousand-grain weight. Genomic position (bp) referenced to the genome sequence of Nipponbare. R2 (%): the total phenotypic variation explained by single QTL.
Table 6. Pleiotropic QTLs underlying the yield-related traits in the SD3#-population.
Table 6. Pleiotropic QTLs underlying the yield-related traits in the SD3#-population.
TraitsQTLChromosomeMarker IntervalLOD ValueAdditiveDominantR2 (%)
SPqSP11RM265-RM37385.5248.1233.1426.56
SSDqSSD11RM265-RM37384.530.471.8214.11
EPPqEPP11RM3642-RM6003.06−3.02−4.808.06
PLqPL11RM3642-RM6003.006.054.196.07
SSRqSSR44RM7051-RM56333.880.02−0.324.61
TGWqTGW44RM7051-RM56334.430.380.575.08
EPPqEPP77RM1135-RM57935.23−3.58−6.5210.82
GYPqGYP77RM1135-RM57933.32−14.32−23.547.25
UGPqUGP88RM1111-RM37023.1049.48−132.6214.32
SSRqSSR88RM1111-RM37025.90−0.170.465.40
EPPqEPP1111RM7120-RM62934.28−3.04−5.098.53
GWqGW11a11RM7120-RM62933.820.27−0.355.34
Notice: EPP: effective panicle per plant; PL: panicle length; UGP: unfilled grain per panicle; SP: spikelet per panicle; SSR: seed setting rate; SSD: seed setting density; GYP: grain yield per plant; GW: grain width; TGW: thousand-grain weight. R2 (%): the total phenotypic variation explained by single QTL.
Table 7. Epistatic QTL underlying the yield-related traits in the SD3#-population.
Table 7. Epistatic QTL underlying the yield-related traits in the SD3#-population.
TraitsChr aMarker IntervalChr aMarker IntervalLOD ValueAdd bAddDom cDomAdd × AddAdd × DomDom × AddDom × DomR2 (%) d
PH1RM3738-RM808411RM202-RM71205.014.82−8.370.49−28.138.08−21.17−5.6330.5119.30
EPP1RM3642-RM6002RM6519-RM56517.08−1.971.90−3.211.56−3.383.300.42−0.592.21
3RM3513-RM13525RM405-RM265.030.91−1.033.501.48−0.70−2.765.63−7.371.49
3RM85-RM385610RM5708-RM38826.40−0.341.584.550.98−1.782.012.20−8.641.98
7RM3555-RM54817RM1135-RM57936.13−3.07−1.71−2.01−6.460.692.891.554.182.19
7RM5793-RM4328RM1111-RM37025.080.410.360.671.550.39−5.504.95−6.191.61
7RM1135-RM579311RM202-RM71206.840.55−2.71−1.063.880.89−4.193.52−7.082.20
PL1RM3642-RM6008RM8019-RM69905.48−4.58−3.57−2.01−2.00−2.566.121.037.146.28
2RM250-RM37633RM1352-RM31995.10−5.300.774.326.971.585.93−1.27−9.504.94
2RM7637-RM58124RM7051-RM56335.490.910.343.403.33−2.59−3.69−0.77−5.135.13
UGP8RM1111-RM37029RM257-RM56615.117.29−9.84−88.52−27.85−52.25−51.5945.48−29.791.74
SSR4RM317-RM70514RM7051-RM56335.94−0.010.070.07−0.100.04−0.360.09−0.141.53
4RM5633-RM4018RM1111-RM37027.420.070.00−0.330.21−0.01−0.04−0.150.341.45
4RM7051-RM56339RM24085-RM1605.690.000.02−0.41−0.130.020.13−0.100.561.24
4RM317-RM705110RM5708-RM38827.220.120.150.090.12−0.22−0.030.02−0.381.49
4RM559-RM597911RM7120-RM62936.72−0.030.020.00−0.240.24−0.22−0.120.471.54
8RM1111-RM37029RM24085-RM1605.32−0.040.16−0.12−0.090.040.16−0.430.341.30
8RM1111-RM370210RM5708-RM38827.95−0.060.200.28−0.010.180.18−0.17−0.011.50
8RM1111-RM370211RM6293-RM13416.80−0.050.160.26−0.060.190.12−0.130.071.52
GYP7RM3555-RM54817RM1135-RM57937.0114.80−1.7417.34−5.760.78−25.4021.76−22.252.28
7RM1135-RM57938RM1111-RM37025.681.06−18.79−18.91−8.21−8.948.788.106.612.52
7RM1135-RM579311RM202-RM71205.87−2.54−3.60−3.5012.75−12.4724.777.82−26.832.28
9RM24085-RM16011RM7120-RM62935.319.01−13.9511.16−14.50−12.05−4.5710.83−2.132.36
10RM5708-RM388211RM202-RM71205.407.41−13.271.7215.35−2.4418.9520.25−31.152.94
GW2RM250-RM37634RM559-RM59796.250.12−0.010.460.280.180.000.34−0.792.49
GT5RM405-RM2611RM202-RM71208.74−0.040.110.17−0.250.150.18−0.100.262.65
Notice: PH: plant height; EPP: effective panicle per plant; PL: panicle length; UGP: unfilled grain per panicle; SSR: seed setting rate; GYP: grain yield per plant; GL: grain length; GW: grain width; GT: grain thickness. a Chromosome, b Additive effect, c Dominant effect, d R2 (%) the total phenotypic variation explained by single epistatic QTL pairs.
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Yan, Y.; Lu, J.; Wu, M.; Peng, T.; Tan, L.; Nan, W.; Qin, X.; Li, M.; Gong, J.; Liang, Y. Locating QTL Controlling the Yield-Related Traits in Perennial Chinese Rice “Shendao3#”. Agriculture 2025, 15, 2453. https://doi.org/10.3390/agriculture15232453

AMA Style

Yan Y, Lu J, Wu M, Peng T, Tan L, Nan W, Qin X, Li M, Gong J, Liang Y. Locating QTL Controlling the Yield-Related Traits in Perennial Chinese Rice “Shendao3#”. Agriculture. 2025; 15(23):2453. https://doi.org/10.3390/agriculture15232453

Chicago/Turabian Style

Yan, Yuxin, Jiuyan Lu, Meilin Wu, Tingshen Peng, Lin Tan, Wenbin Nan, Xiaojian Qin, Ming Li, Junyi Gong, and Yongshu Liang. 2025. "Locating QTL Controlling the Yield-Related Traits in Perennial Chinese Rice “Shendao3#”" Agriculture 15, no. 23: 2453. https://doi.org/10.3390/agriculture15232453

APA Style

Yan, Y., Lu, J., Wu, M., Peng, T., Tan, L., Nan, W., Qin, X., Li, M., Gong, J., & Liang, Y. (2025). Locating QTL Controlling the Yield-Related Traits in Perennial Chinese Rice “Shendao3#”. Agriculture, 15(23), 2453. https://doi.org/10.3390/agriculture15232453

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