Phenotypic Variation and Molecular Marker Network Expression of Some Agronomic Traits in Rice (Oryza sativa L.) RILS of Gr 89-1×Shuhui 527
by
,
Lu Gan
, 
Lunxiao Huang
,
Hongyu Wei
,
Fei Jiang
,
Jiajia Han
,
Jie Yu
,
Qian Liu
,
Kunchi Yu
,
Qiuyu Zhang
,
Mao Fan
and
Zhengwu Zhao
* College of Life Sciences, Chongqing Normal University/Chongqing Engineering Research Center of Specialty Crop Resources, Chongqing 401331, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 2980; https://doi.org/10.3390/agronomy12122980
Submission received: 17 October 2022
/
Revised: 20 November 2022
/
Accepted: 23 November 2022
/
Published: 27 November 2022
(This article belongs to the Section Crop Breeding and Genetics)
Abstract
:In this study, a Glutinous rice 89-1 (Gr 89-1) × Shuhui 527 recombinant inbred line population (RIL) comprising 309 F9-generations was used to screen gradient molecular markers. The phenotypic variation and distribution of eight agronomic traits obtained from multiyear and multilocation samples, as well as the network expression relationships between agronomic traits and molecular markers, were investigated. The results showed that there were 14 phenotypic lines with significant differences in the RILs, and the molecular testing results of most of the lines were consistent with the phenotype. The correlation degree between the first-level molecular markers and the eight agronomic traits was 100%. Excluding the correlations of third-level markers with grain width and grain length, the degree of correlation between molecular markers and agronomic traits decreased with an increase in marker levels. The RILs were divided into eight core populations and one approximate population, revealing genetic correspondence between agronomic traits and molecular markers.
1. Introduction
The selection of hybrid offspring is the key to rice breeding. Conventional breeding strategies directly select plants with ideal traits using the pedigree method, mixed selection method, preterm birth test method, and single-grain inheritance selection method in the segregation generation of rice sexual hybridization [1,2,3]. However, these methods are less effective in long-term breeding practices because with the continuous segmentation of genes starting from the hybrid F2 generation, some excellent traits disappear when the traits of the hybrid offspring become stable. Determining the selection period and method based on the magnitude of heritability has improved the accuracy and reliability of the selection of target traits. When heritability is high, the phenotype of the trait is highly correlated with the genotype, the effect of using the pedigree method and the mixed selection method is similar, and the selection effect in early generations is better; when heritability is low, the phenotype of the trait cannot represent its genotype, and the trade-offs rely on the offspring tested by the pedigree method or inbreeding, which were selected in later generations. Over the last two decades, molecular techniques have been used for indirect selection [4,5,6,7,8]. For example, marker-assisted selection (MAS), quantitative trait loci (QTL), and linkage disequilibrium (LD) are used to indirectly select superior traits determined by genetic factors. The combination of traditional methods and molecular biotechnology has greatly improved breeding selection efficiency.
The genetic relationship between the parents and progeny of rice is very complex. To date, the genetic processes and mechanisms of these traits have not been fully clarified. The genetic relationships between progeny and progeny, between different progeny lines, and between progeny and parents are not clear, leading to an inability to accurately grasp the selection of the hybrid progeny. In this study, 309 F9-generation RILs constructed by crossing rice (Oryza sativa L.) cultivar Gr 89-1 and Shuhui 527 were used to study the genetic relationships between parents and progeny and different lines of progeny, and the relationships between phenotypic traits and molecular markers [9]. The network expression relationship map between agronomic traits and molecular markers was constructed to provide an important reference for correlation analyses between agronomic traits and molecular markers, and improve the breeding selection efficiency.
2. Materials and Methods
2.1. Experimental Materials
In the spring of 2014, at the Bishan Rice Breeding Base of Chongqing Normal University, the rice variety Gr 89-1 was crossed with the restorer line Shuhui 527, bred by the Sichuan Agricultural University, and underwent an additional generation propagation in Hainan in the winter seasons; subsequently, the 309 F9-generation RILs were constructed in 2019.
2.2. Field Planting and Investigating Seeds
The parental and recombinant inbred lines (RILs) were planted in the Bishan Rice Breeding Base of Chongqing Normal University during the spring of 2019, in Guangpo Town, Lingshui Country, Hainan Province during the winter of the same year, and again in Laifeng Town, Bishan District, Chongqing City during the spring of 2020. Two years and three points (times) were planted in different locations in Chongqing and Hainan during different years. Each variety was planted in three rows with twelve plants in each row, and the spacing between rows was 16.7 cm × 30.0 cm. The soil fertility in the field was medium and fertilization was performed using conventional fertilization management. Strict pest and weed control were in place throughout the growth period. After maturity, the agronomic traits of each line were investigated.
2.3. Molecular Marker Grading Screening
Through the identification and evaluation of agronomic traits in the field, eight agronomic traits with large phenotypic differences were selected: plant height (PH), panicle length (PL), grain number per panicle (GPP), seed setting rate (SSR), thousand-grain weight (TGW), flag leaf length (FLL), grain length (GL), and grain width (GW). Ten extreme lines were selected for each agronomic trait, gene pools were established, and differential molecular markers were identified. The level of grading markers was determined by comparing the differential grading markers screened out of the eight agronomic traits. The molecular markers shared by the eight agronomic traits were designated as first-level markers. The molecular markers shared by the seven agronomic traits were designated as second-level markers. The molecular markers shared by the six agronomic traits were designated as third-level markers. The molecular markers shared by the five agronomic traits were designated as fourth-level markers. The molecular markers shared by the four agronomic traits were designated as fifth-level markers. The molecular markers shared by the three agronomic traits were designated as sixth-level markers. The molecular markers shared by the two agronomic traits were designated as seventh-level markers, and the molecular markers shared by only one agronomic trait were designated as eighth-level markers.
2.4. Data Calculation and Processing
The experimental data were analyzed using Origin 8.5 and SPSS 18 statistical software and were expressed as means ± standard deviations. One-way analysis of variance (ANOVA) and Duncan multiple comparisons were used to test the significance of differences in the data between the different treatments.
3. Results
3.1. Phenotypic Variation and Trait Distribution of RILs
From 2019 to 2020, the parental Gr 89-1, parental Shuhui 527, and the 309 F9-generation RILs were planted in Laifeng Town, Bishan District, Chongqing City and Guangpo Town, Lingshui Country, Hainan Province. Eight agronomic traits, PH, PL, GPP, SSR, TGW, FLL, GL, and GW, were investigated and analyzed. As shown in Table 1, in the three environments, the phenotypic values of the eight agronomic traits were similar in the two years in Bishan, Chongqing, but smaller in Lingshui, Hainan than in the other environments. The variation range in the number of GPP and SSR was large, and the range was more than double the minimum value. The coefficient of variation of the SSR was the largest at 21.05%–22.57%, the coefficient of variation of the other traits was less than 14.47%, and the coefficient of variation of TGW was the smallest at 4.48%–5.76%. The skewness coefficient was between −0.625 and 0.783 and the kurtosis coefficient was between −0.902 and 1.117. The skewness of both PH and SSR was less than 0, showing obvious left deviation, and the kurtosis was less than 0, showing a peak. The skewness of FLL and TGW was greater than 0, indicating obvious right skewness.
The eight agronomic traits showed a normal distribution across the different years and locations. In the three environments of the RILs: PH was mainly distributed between 110.85–123.09 cm, accounting for 61.81% of the total (Figure 1a); PL was mainly distributed between 24.81 and 26.67 cm, accounting for 56.31% of the total (Figure 1b); GPP was mainly distributed between 136.73 and 157.59, accounting for 67.31% of the total (Figure 1c); SSR was mainly distributed between 63.21% and 76.91%, accounting for 55.66% of the total (Figure 1d); TGW was mainly distributed between 26.38 and 27.44 g, accounting for 60.84% of the total (Figure 1e); and GL was mainly distributed between 5.34 and 7.06 mm, accounting for 88.99% of the total (Figure 1g).
In the RILs, there were 307 lines with GW greater than 2.26 mm (three-location average of the Shuhui 527 parent), accounting for 99.35% of the total (Figure 1h), and the GW showed a normal distribution tending to Gr 89-1. There were 265 lines with FLL longer than 26.79 cm (three-location average of the Gr 89-1 parent), accounting for 85.76% of the total, and the FLL showed a normal distribution tending to Shuhui 527 (Figure 1f).
3.2. Lines Differential Expression
According to the phenotypic differences of several agronomic traits, such as plant height, panicle type, and flag leaf, 14 widely different phenotypes were found in Gr 89-1, Shuhui 527, and the 309 F9-generation RILs. No. 1, 2, 5, 12, and 14 had many awns, and No. 12 and 14 had red awns (Figure 2). No. 1, 3, 7, and 10 had lower PH than the others, with the lowest PH being 81.57 cm for No. 7, and No. 6 had a maximum PH of 122.57 cm. No. 4 had the longest FLL at 34.33 cm (Table 2). The molecular detection of 14 widely differential lines was carried out using three first-level markers: aRM85, aRM274, and aRM5414. It was found that the band types of lines No. 1, 3, 7, 9, 10, and 11 were close to the parent Gr 89-1 and the band types of lines No. 2, 4, 5, 6, and 14 were similar to that of the parent Shuhui 527. The phenotypes also tended to differ between parents, and the molecular detection results of most lines were relatively consistent with the phenotypes (Table 2; Figure 2).
3.3. Molecular Network Expression
F9-generation RILs of 309 lines constructed by crossing Gr 89-1 and Shuhui 527 were selected for eight agronomic traits (PH, PL, GPP, SSR, TGW, FLL, GL, and GW). Gene pools were established for ten extreme lines of the eight agronomic traits. From the 589 initially screened markers, 196 polymorphic markers (Table S1 for detailed primer information) were obtained, including 3 first-level markers, 6 second-level markers, 11 third-level markers, 17 fourth-level markers, 21 fifth-level markers, 28 sixth-level markers, 40 seventh-level markers, and 70 eighth-level markers (Table 3). The network expression relationship map was constructed to explain the correspondence between the agronomic traits and molecular markers. The three first-level markers showed polymorphism in all eight agronomic traits with corresponding network lines. As the level of markers increased, fewer agronomic traits showed polymorphism in each molecular marker. In the eighth-level markers, only one agronomic trait showed polymorphism in each molecular marker, and there was a single network line between markers and agronomic traits, which corresponded one to one (Figure 3). For the correlation between the eight grades of molecular markers and the eight agronomic traits, as can be seen in Table 4, the correlation degree between the first-level markers and the eight agronomic traits was 100%. Except for the correlation between the third-level markers and GW and GL, the correlations between molecular markers and agronomic traits decreased gradually with the increase of marker levels. Moreover, the correlations between the molecular markers and agronomic traits of the eighth-level markers were the lowest, and the correlation degrees with GW, SSR, TGW, PL, GPP, PH, GL, and FLL were 8.57%, 25.71%, 11.43%, 5.71%, 7.14%, 10.00%, 11.43%, and 20.00%, respectively. Three first-level markers were used to detect the 309 F9-generation RILs, and 76 resources were found to have molecular differences, from the first-level marker to the eighth-level marker, step-by-step and filter-by-level. Finally, 70 eighth-level markers were used to detect the RILs, and, as a result, 28 markers showed polymorphism, and only seven lines showed differences. Moreover, all of these showed differences in the level 1-8 grade markers and formed the first core group with a relatively distant genetic relationship. The 13 lines selected by the seventh-level markers constituted the second core group; the 17 lines selected by the sixth-level markers constituted the third core group; and the 76 lines selected by the first-level markers comprised the eighth core group (Table 5). The remaining 233 lines showed no difference in each level marker, belonged to an approximate group, and had a close genetic relationship.
4. Discussion
The key to breakthroughs in rice breeding is the exploration and application of new genes [10,11,12]. New gene mining and functional research and utilization are currently research hotspots [13,14,15,16,17,18]. Chen et al. [19] discovered a natural C2H2 transcription factor allele in rice. Zhou Wenbin’s team [20] found a high-yield gene (OsDREB1C) in rice. The key to creating new gene resources is to clarify the genetic relationships and choose hybrid offspring [21,22,23,24]. Hybrid offspring are the foundation of gene resource innovation, and excellent gene resources are sources of various innovations [25,26,27,28]. It is difficult to select the population separated by parents, especially in the early generation, mainly because the selection in the field can only be judged by phenotype and not by genotype. Most traits in rice are quantitative and controlled by multiple QTL genes. The research results from interval mapping, composite interval mapping, inclusive composite interval mapping, and genome-wide composite interval mapping have been successfully used to map quantitative trait loci (QTLs) in bi-parental segregation populations, laying a good foundation for molecular-marker-assisted selection, and improving the accuracy of hybrid offspring selection [29,30,31]. Based on single gene marker analysis, this study studied the relationship between molecular markers and agronomic traits and proposed a new method for the selection of hybrid offspring.
The relationship between phenotypic traits and their corresponding genes is the core of the discovery, creation, and selection of excellent gene resources [32]. In this study, 309 F9-generation RILs were constructed by crossing Gr 89-1 with Shuhui 527 and were used as materials to study the phenotypic variation and distribution of eight agronomic traits over many years and time points, and 14 phenotypes with significant differences were found. Subsequently, according to the phenotypic traits of the extreme lines, gradient grading markers were screened, and the RILs were graded and identified. Furthermore, according to the number of differences detected by gradient markers at different levels, the RILs were divided into eight core groups and one approximate group to evaluate their genetic relationship at the molecular level. Through molecular network expression, genetic correspondence between agronomic traits and molecular markers was revealed, which provided a reference for the genetic selection of hybrid offspring.
There are abundant rice germplasm resources in the world, but their utilization rate is not high. One reason for this is that the traditional classification and research evaluation of rice germplasm resources are limited to the simple description and identification of morphological characteristics. Although there are enzyme markers that were developed more recently than traditional techniques, owing to the influence of quantity, environment, and biological growth and development stage, the exact identification and evaluation of rice germplasm resources cannot be carried out, hence their application scope is limited to a certain extent. Molecular markers can reveal differences in rice seed resource materials at the DNA molecular level and become a reliable and efficient tool for the identification and analysis of germplasm resources. At present, molecular markers are widely used in indica and japonica subspecies, main cultivars, landraces, backbone parents of hybrid rice, and genetic diversity of rice seed resources [33]. Many studies have been conducted on the classification and identification of rice seed resources using genetic markers. Yang et al. [34] used 48 SSR molecular markers to analyze the genetic similarity of ninety rice varieties from nine different countries. Shu et al. [35] used 34 pairs of SSR primers to conduct genetic similarity and cluster analyses of 313 japonica rice varieties from different geographical sources. Li et al. [36] analyzed the polymorphisms of 33 American rice and 13 Chinese rice germplasm resources using 24 pairs of SSR primers. As there are only a few polymorphic markers in these studies, the identification results are often not ideal, especially for resources with close relationships. The results of this research can also be used to identify the genetic relationships among different resources. Through the relationships between phenotype and molecular markers and different genetic groups, we can improve the accuracy and reliability of germplasm resource identification, compensate for the shortcomings of traditional phenotypic identification techniques, employ techniques that play an important role in the rational utilization of resources, and promote new breakthroughs in crop breeding.
5. Conclusions
Eight gradient molecular markers were screened using a Glutinous rice 89-1 (Gr 89-1) × Shuhui 527 recombinant inbred line population (RILs) comprising 309 F9-generations. The RILs were divided into eight core populations and one approximate population. Molecular network expression revealed genetic correspondence between agronomic traits and molecular markers.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12122980/s1; Table S1: sequences of the primers used in this study.
Author Contributions
L.G. and Z.Z. conceived and designed the study. L.G., Z.Z., L.H., H.W., F.J. and J.H. performed the experiments. J.Y., Q.L., K.Y., Q.Z., M.F. and Z.Z. analyzed all experimental data. L.G., Z.Z. and L.H. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
The National Natural Science Foundation of China (31670326); the Program for Innovative Research Team in University, Chongqing (CXTDX201601018).
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Multiyear and multisite phenotypic interval distribution of the eight agronomic traits in F9 RILS of Gr 89-1 × Shuhui 527: (a) PH interval, (b) PL interval, (c) GPP interval, (d) SSR interval, (e) TGW interval, (f) FLL interval, (g) GL interval, and (h) GW interval.
Figure 1.
Multiyear and multisite phenotypic interval distribution of the eight agronomic traits in F9 RILS of Gr 89-1 × Shuhui 527: (a) PH interval, (b) PL interval, (c) GPP interval, (d) SSR interval, (e) TGW interval, (f) FLL interval, (g) GL interval, and (h) GW interval.
Figure 2.
Main stem, panicle, flag leaf, and corresponding molecular markers of 14 different plant types in F9 RILs of Gr 89-1 × Shuhui 527; (a) main stem; (b) panicle; (c) flag leaf; P1 represents Gr 89-1; P2 represents Shuhui 527; the numbers (1–14) represent 14 different plant types in F9 RILS of Gr 89-1 × Shuhui 527.
Figure 2.
Main stem, panicle, flag leaf, and corresponding molecular markers of 14 different plant types in F9 RILs of Gr 89-1 × Shuhui 527; (a) main stem; (b) panicle; (c) flag leaf; P1 represents Gr 89-1; P2 represents Shuhui 527; the numbers (1–14) represent 14 different plant types in F9 RILS of Gr 89-1 × Shuhui 527.
Figure 3.
Network diagram of the relationship between eight agronomic traits and gradient molecular markers in F9 RILS of Gr 89-1 × Shuhui 527.
Figure 3.
Network diagram of the relationship between eight agronomic traits and gradient molecular markers in F9 RILS of Gr 89-1 × Shuhui 527.
Table 1.
Phenotypic variation of 8 agronomic traits in different years and locations in F9 RILS of Gr 89-1 × Shuhui 527.
Table 1.
Phenotypic variation of 8 agronomic traits in different years and locations in F9 RILS of Gr 89-1 × Shuhui 527.
| Trait | Environment | Parents | RIL Population | |||||
|---|---|---|---|---|---|---|---|---|
| Gr 89-1 | Shuhui 527 | Mean ± SE | Range (%) | CV (%) | Skewness | Kurtosis | ||
| PH/cm | E1 | 113.68 | 121.92 | 110.96 ± 0.69 a | 80.25~135.33 | 11.04 | −0.583 ± 0.138 | −0.438 ± 0.276 |
| E2 | 105.88 | 117.32 | 108.03 ± 0.61 b | 82.39~129.64 | 10.01 | −0.516 ± 0.138 | −0.561 ± 0.276 | |
| E3 | 112.57 | 120.37 | 110.22 ± 0.63 a | 82.69~132.45 | 10.13 | −0.477 ± 0.138 | −0.5 ± 0.276 | |
| PL/cm | E1 | 23.19 | 27.68 | 25.43 ± 0.11 a | 21.17~29.46 | 7.35 | −0.251 ± 0.138 | −0.693 ± 0.276 |
| E2 | 22.26 | 26.03 | 24.54 ± 0.09 b | 21.09~28.69 | 6.28 | 0.02 ± 0.138 | −0.74 ± 0.276 | |
| E3 | 24.03 | 28.05 | 25.49 ± 0.09 a | 21.68~29.17 | 6.26 | −0.11 ± 0.138 | −0.484 ± 0.276 | |
| GPP | E1 | 123.76 | 165.34 | 136.09 ± 1.12 a | 84.56~178.46 | 14.47 | −0.49 ± 0.138 | −0.094 ± 0.276 |
| E2 | 119.63 | 153.14 | 131.63 ± 0.89 b | 89.16~164.10 | 12.92 | −0.625 ± 0.138 | −0.035 ± 0.276 | |
| E3 | 125.84 | 168.37 | 134.4 ± 1.07 a | 86.12~177.64 | 14.08 | −0.576 ± 0.138 | 0.003 ± 0.276 | |
| SSR/% | E1 | 90.2 | 88.12 | 61.18 ± 0.78 a | 28.97~90.12 | 22.57 | −0.287 ± 0.138 | −0.575 ± 0.276 |
| E2 | 87.63 | 85.46 | 61.15 ± 0.73 a | 30.13~87.63 | 21.05 | −0.223 ± 0.138 | −0.667 ± 0.276 | |
| E3 | 89.03 | 88.96 | 61.23 ± 0.74 a | 28.98~90.61 | 21.39 | −0.229 ± 0.138 | −0.444 ± 0.276 | |
| TGW/g | E1 | 25.3 | 30.53 | 26.55 ± 0.09 a | 23.08~31.69 | 5.76 | 0.323 ± 0.138 | 0.45 ± 0.276 |
| E2 | 24.96 | 29.19 | 26.34 ± 0.07 a | 22.13~30.33 | 4.48 | 0.174 ± 0.138 | 1.117 ± 0.276 | |
| E3 | 25.2 | 30.79 | 26.43 ± 0.08 a | 22.16~31.49 | 5.6 | 0.31 ± 0.138 | 0.5 ± 0.276 | |
| FLL/cm | E1 | 26.7 | 35.15 | 31.05 ± 0.23 a | 23.45~42.68 | 13.33 | 0.459 ± 0.138 | −0.902 ± 0.276 |
| E2 | 25.77 | 36.17 | 30.04 ± 0.2 b | 24.28~38.92 | 11.72 | 0.783 ± 0.138 | −0.477 ± 0.276 | |
| E3 | 27.9 | 36.28 | 31.22 ± 0.22 a | 22.37~40.19 | 12.43 | 0.369 ± 0.138 | −0.799 ± 0.276 | |
| GL/mm | E1 | 4.6 | 7.31 | 6 ± 0.04 a | 4.21~7.52 | 11.83 | −0.213 ± 0.138 | −0.68 ± 0.276 |
| E2 | 4.57 | 7.08 | 5.82 ± 0.04 b | 4.10~7.36 | 12.20 | −0.219 ± 0.138 | 0.463 ± 0.276 | |
| E3 | 4.65 | 7.26 | 5.85 ± 0.04 b | 4..05~7.92 | 13.85 | −0.092 ± 0.138 | −0.215 ± 0.276 | |
| GW/mm | E1 | 2.87 | 2.28 | 2.57 ± 0.01 a | 2.27~2.96 | 6.23 | 0.136 ± 0.138 | −0.901 ± 0.276 |
| E2 | 2.8 | 2.19 | 2.56 ± 0.01 a | 2.19~2.93 | 5.86 | 0.00 ± 0.138 | −0.751 ± 0.276 | |
| E3 | 2.85 | 2.31 | 2.57 ± 0.01 a | 2.12~2.96 | 6.23 | −0.087 ± 0.138 | −0.615 ± 0.276 | |
E1: 2019 Chongqing Bishan Spring (F9); E2: 2019 Hainan Lingshui Winter (F10); E3: 2020 Chongqing Bishan Spring (F11); the different letters in the same column of the table indicate that the same trait is significantly different at the 0.05 level.
Table 2.
Phenotypes of 14 different lines.
| Numbering | Code | A | B | C | PH (cm) | PL (cm) | GPP | SSR (%) | TGW (g) | FLL (cm) | GL (mm) | GW (mm) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Gr 89-1 | 113.71 ± 4.22 | 23.16 ± 0.89 | 123.08 ± 3.16 | 88.95 ± 1.29 | 25.15 ± 0.17 | 26.79 ± 1.07 | 5.61 ± 0.04 | 2.84 ± 0.04 | ||||
| Shuhui 527 | 119.87 ± 2.34 | 27.25 ± 1.08 | 162.28 ± 8.06 | 87.51 ± 1.83 | 30.17 ± 0.86 | 35.87 ± 0.62 | 7.22 ± 0.12 | 2.36 ± 0.06 | ||||
| 1 | 5 | 87.77 ± 1.33 | 25.66 ± 1.07 | 105.91 ± 2.32 | 57.23 ± 3 | 26.15 ± 0.72 | 25.42 ± 1.03 | 6.07 ± 0.28 | 2.61 ± 0.27 | |||
| 2 | 26 | 121.17 ± 5.91 | 28.37 ± 0.9 | 165.65 ± 6.13 | 44.48 ± 6.24 | 28 ± 0.31 | 33.17 ± 1.3 | 6.18 ± 0.22 | 2.53 ± 0.03 | |||
| 3 | 137 | 83.43 ± 1.1 | 27.83 ± 1.33 | 96.44 ± 2.14 | 82.71 ± 1.3 | 26.18 ± 0.21 | 26.95 ± 2.25 | 5.46 ± 0.28 | 2.61 ± 0.03 | |||
| 4 | 84 | 115.57 ± 3.9 | 28.01 ± 0.71 | 133.97 ± 3.98 | 42.65 ± 3.56 | 26.25 ± 0.3 | 34.33 ± 1.81 | 7.49 ± 0.19 | 2.34 ± 0.03 | |||
| 5 | 111 | 120.74 ± 2.38 | 26.43 ± 0.98 | 151.25 ± 8.72 | 66.59 ± 2.77 | 26.33 ± 0.35 | 27.78 ± 1.39 | 6.05 ± 0.39 | 2.36 ± 0.05 | |||
| 6 | 122 | 122.57 ± 2.31 | 27.06 ± 0.65 | 90.1 ± 5.41 | 72.48 ± 2.9 | 26.81 ± 0.6 | 30.56 ± 2.71 | 5.66 ± 0.15 | 2.65 ± 0.04 | |||
| 7 | 210 | 81.57 ± 1.47 | 26.82 ± 0.56 | 113.57 ± 3.76 | 83.96 ± 1.52 | 25.42 ± 0.65 | 27.97 ± 2.52 | 5.36 ± 0.24 | 2.82 ± 0.06 | |||
| 8 | 256 | 119.72 ± 2.61 | 28.3 ± 0.83 | 140.27 ± 5.35 | 61.05 ± 2.14 | 25 ± 0.85 | 25.88 ± 4.05 | 5.33 ± 0.3 | 2.66 ± 0.07 | |||
| 9 | 274 | 113.37 ± 1.68 | 22.76 ± 1.52 | 144.72 ± 3.14 | 45.55 ± 2.08 | 28.37 ± 0.48 | 30.93 ± 4.6 | 6.14 ± 0.34 | 2.66 ± 0.04 | |||
| 10 | 303 | 86.34 ± 5.24 | 24.4 ± 0.22 | 98.97 ± 10.66 | 74.24 ± 2.39 | 26.79 ± 0.72 | 27.14 ± 1.47 | 4.95 ± 0.41 | 2.73 ± 0.03 | |||
| 11 | 288 | 114.19 ± 1.76 | 25.87 ± 0.44 | 134.66 ± 2.45 | 59.48 ± 3.43 | 26.02 ± 0.27 | 28.01 ± 3.18 | 5.89 ± 0.25 | 2.40 ± 0.04 | |||
| 12 | 176 | 113.29 ± 2.05 | 25.57 ± 1.06 | 128.3 ± 1.34 | 76.36 ± 1.16 | 26.43 ± 0.68 | 34.67 ± 1.74 | 5.46 ± 0.31 | 2.66 ± 0.03 | |||
| 13 | 53 | 115.82 ± 1.89 | 24.46 ± 0.63 | 119.19 ± 6.43 | 47.99 ± 1.29 | 25.92 ± 1.22 | 37.94 ± 1.41 | 5.93 ± 0.16 | 2.88 ± 0.03 | |||
| 14 | 234 | 111.24 ± 8.89 | 26.03 ± 0.34 | 123.88 ± 3.73 | 70.87 ± 2.59 | 27.2 ± 0.24 | 26.92 ± 0.46 | 5.58 ± 0.43 | 2.39 ± 0.04 | |||
A represents aRM85, B represents aRM274, and C represents aRM5414. The gray, red, and black boxes denote the genotypes of Gr 89-1, Shuhui 527, and the heterozygote, respectively.
Table 3.
Molecular markers of eight different agronomic traits.
| Trait | Differential Molecular Markers (Table S1 for Detailed Primer Information) |
|---|---|
| PH | aRM85, aRM274, aRM5414; bRM13, bRM17, bRM298, bRM449, bRM1195; cOSR28, cRM39, cRM71, cRM155, cRM162, cRM190, cRM598; dRM142, dRM221, dRM267, dRM292, dRM304, dRM311, dRM337, dRM497, dRM508, dRM599, dRM1163, dRM7102; eRM18, eRM50, eRM103, eRM154, eRM159, eRM185, eRM329, eRM334, eRM411, eRM524, eRM16844, eRM24614; fRM111, fRM213, fRM223, fRM232, fRM327, fRM481, fRM521, fRM23331, fRM23340, fRM26563; gRM1, gRM140, gRM195, gRM205, gRM214, gRM250, gRM258, gRM315, gRM350, gRM21587, gRM22319; hRM120, hRM255, hRM296, hRM542, hRM589, hRM12498, hRM27513 |
| PL | aRM85, aRM274, aRM5414; bRM17, bRM35, bRM298, bRM449, bRM1195; cRM19, cRM39, cRM71, cRM87, cRM155, cRM162, cRM175, cRM8277; dRM209, dRM221, dRM231, dRM267, dRM292, dRM337, dRM472, dRM497, dRM508, dRM18383, dRM24291; eRM22, eRM50, eRM154, eRM159, eRM172, eRM202, eRM411, eRM500, eRM524, eRM19417, eRM26811; fRM208, fRM327, fRM463, fRM2615, fRM12850, fRM23359; gRM140, gRM210, gRM278, gRM307, gRM423, gRM551, gRM3417, gRM6172, gRM7446; hRM7, hRM129, hRM236, hRM309 |
| GPP | aRM85, aRM274, aRM5414; bRM13, bRM17, bRM35, bRM449, bRM1195; cOSR28, cRM71, cRM87, cRM175, cRM190, cRM598, cRM8277; dRM142, dRM209, dRM231, dRM304, dRM337, dRM472, dRM497, dRM599, dRM1163, dRM7102, dRM18383; eRM18, eRM22, eRM72, eRM103, eRM159, eRM185, eRM329, eRM334, eRM573, eRM16844, eRM19417, eRM26811; fRM111, fRM213, fRM232, fRM259, fRM273, fRM306, fRM331, fRM336, fRM341, fRM481, fRM23331, fRM23340, fRM23520, fRM26796; gRM21, gRM113, gRM161, gRM176, gRM212, gRM214, gRM3148, gRM6172, gRM16937, gRM21587; hRM137, hRM146, hRM257, hRM284, hRM332 |
| SSR | aRM85, aRM274, aRM5414; bRM13, bRM35, bRM298, bRM449, bRM1195; cRM19, cRM39, cRM71, cRM87, cRM155, cRM162, cRM175, cRM190, cRM598; dRM209, dRM221, dRM231, dRM267, dRM304, dRM311, dRM472, dRM508, dRM7102, dRM24291; eRM22, eRM72, eRM103, eRM172, eRM185, eRM202, eRM334, eRM411, eRM573, eRM1141, eRM24614; fRM114, fRM208, fRM327, fRM331, fRM463, fRM519, fRM23359, fRM26563; gRM113, gRM176, gRM195, gRM230, gRM345, gRM423, gRM438, gRM493, gRM551, gRM3417; hRM10, hRM108, hRM207, hRM217, hRM252, hRM266, hRM287, hRM316, hRM406, hRM424, hRM480, hRM526, hRM571, hRM5384, hRM12051, hRM14429, hRM18353, hRM24874 |
| TGW | aRM85, aRM274, aRM5414; bRM13, bRM17, bRM35, bRM298, bRM449, bRM1195; cRM19, cOSR28, cRM87, cRM155, cRM162, cRM190, cRM8277; dRM142, dRM221, dRM267, dRM292, dRM304, dRM311, dRM337, dRM599, dRM1163, dRM18383, dRM24291; eRM18, eRM50, eRM154, eRM500, eRM1141, eRM16844, eRM24614, eRM26811; fRM114, fRM213, fRM223, fRM253, fRM306, fRM341, fRM470, fRM481, fRM521, fRM23331, fRM23520, fRM26796; gRM1, gRM109, gRM131, gRM205, gRM224, gRM230, gRM250, gRM278, gRM302, gRM307, gRM315, gRM443; hRM29, hRM215, hRM235, hRM270, hRM339, hRM590, hRM3331, hRM15811 |
| FLL | aRM85, aRM274, aRM5414; bRM13, bRM17, bRM35, bRM298, bRM449, bRM1195; cRM19, cOSR28, cRM39, cRM71, cRM175, cRM190, cRM598, cRM8277; dRM142, dRM209, dRM231, dRM292, dRM311, dRM472, dRM508, dRM599, dRM18383; eRM18, eRM72, eRM185, eRM329, eRM500, eRM573, eRM1141, eRM16844, eRM19417, eRM26811; fRM111, fRM208, fRM219, fRM253, fRM259, fRM273, fRM306, fRM331, fRM470, fRM519, fRM2615, fRM12850, fRM23340; gRM443, gRM493, gRM567, gRM1282, gRM3148, gRM7446, gRM21605, gRM22319, gRM26547; hRM48, hRM229, hRM248, hRM251, hRM279, hRM288, hRM401, hRM432, hRM455, hRM490, hRM1942, hRM11982, hRM12140, hRM15857 |
| GL | aRM85, aRM274, aRM5414; bRM13, bRM17, bRM35, bRM298, bRM449; cRM19, cOSR28, cRM39, cRM87, cRM155, cRM162, cRM175, cRM190, cRM598, cRM8277; dRM209, dRM304, dRM337, dRM472, dRM497, dRM508, dRM599, dRM1163, dRM7102, dRM18383, dRM24291; eRM50, eRM72, eRM172, eRM202, eRM411, eRM500, eRM524, eRM573, eRM24614; fRM219, fRM223, fRM259, fRM336, fRM341, fRM463, fRM521, fRM2615, fRM23520; gRM21, gRM161, gRM210, gRM350, gRM438, gRM1282, gRM3763, gRM16937; hRM102, hRM234, hRM276, hRM343, hRM440, hRM6621, hRM7245, hRM22187 |
| GW | aRM85, aRM274, aRM5414; bRM13, bRM17, bRM35, bRM298, bRM1195; cRM19, cOSR28, cRM39, cRM71, cRM87, cRM155, cRM162, cRM175, cRM598, cRM8277; dRM142, dRM221, dRM231, dRM267, dRM292, dRM311, dRM497, dRM1163, dRM7102, dRM24291; eRM22, eRM103, eRM154, eRM159, eRM172, eRM202, eRM329, eRM334, eRM524, eRM1141, eRM19417; fRM114, fRM219, fRM232, fRM253, fRM273, fRM336, fRM470, fRM519, fRM12850, fRM23359, fRM26563, fRM26796; gRM109, gRM131, gRM17, gRM212, gRM224, gRM258, gRM302, gRM345, gRM567, gRM3763, gRM21605, gRM26547; hRM49, hRM239, hRM289, hRM348, hRM561, hRM12299 |
Superscripts in the table represent the marker levels; a: first-level markers, b: second-level markers, c: third-level markers, d: fourth-level markers, e: fifth-level markers, f: sixth-level markers, g: seventh-level markers, and h: eighth-level markers.
Table 4.
Correlation between different grade markers and agronomic traits.
| Marker Level | NTM | GW | SSR | TGW | PL | GPP | PH | GL | FLL | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NAM | CD (%) | NAM | CD (%) | NAM | CD (%) | NAM | CD (%) | NAM | CD (%) | NAM | CD (%) | NAM | CD (%) | NAM | CD (%) | ||
| 1 | 3 | 3 | 100.00 | 3 | 100.00 | 3 | 100.00 | 3 | 100.00 | 3 | 100.00 | 3 | 100.00 | 3 | 100.00 | 3 | 100.00 |
| 2 | 6 | 5 | 83.33 | 5 | 83.33 | 6 | 100.00 | 5 | 83.33 | 5 | 83.33 | 5 | 83.33 | 5 | 83.33 | 6 | 100.00 |
| 3 | 11 | 10 | 90.91 | 9 | 81.82 | 7 | 63.64 | 8 | 72.73 | 7 | 63.64 | 7 | 63.64 | 10 | 90.91 | 8 | 72.73 |
| 4 | 17 | 10 | 58.82 | 10 | 58.82 | 11 | 64.71 | 11 | 64.71 | 11 | 64.71 | 12 | 70.59 | 11 | 64.71 | 9 | 52.94 |
| 5 | 21 | 11 | 52.38 | 11 | 52.38 | 8 | 38.10 | 11 | 52.38 | 12 | 57.14 | 12 | 57.14 | 9 | 42.86 | 10 | 47.62 |
| 6 | 28 | 12 | 42.86 | 8 | 28.57 | 12 | 42.86 | 6 | 21.43 | 14 | 50.00 | 10 | 35.71 | 9 | 32.14 | 13 | 46.43 |
| 7 | 40 | 11 | 27.50 | 10 | 25.00 | 12 | 30.00 | 9 | 22.50 | 10 | 25.00 | 11 | 27.50 | 8 | 20.00 | 9 | 22.50 |
| 8 | 70 | 6 | 8.57 | 18 | 25.71 | 8 | 11.43 | 4 | 5.71 | 5 | 7.14 | 7 | 10.00 | 8 | 11.43 | 14 | 20.00 |
Note: number of total markers of the level (NTM), number of associated markers of the level (NAM), and the ratio of NAM to NTM of the corresponding marker level is the correlation degree (CD).
Table 5.
Molecular detection results of 309 F9 RILs derived from Gr 89-1 × Shuhui 527 by eight-grade markers.
Table 5.
Molecular detection results of 309 F9 RILs derived from Gr 89-1 × Shuhui 527 by eight-grade markers.
| Marker Level | Label Number | Number of Detection Resources | Number of Differential Resources | Number of Polymorphic Markers |
|---|---|---|---|---|
| First-level marker | 3 | 309 | 76 | 3 |
| Second-level marker | 6 | 76 | 53 | 5 |
| Third-level marker | 11 | 53 | 41 | 8 |
| Fourth-level marker | 17 | 41 | 28 | 11 |
| Fifth-level marker | 21 | 28 | 21 | 13 |
| Sixth-level marker | 28 | 21 | 17 | 15 |
| Seventh-level marker | 40 | 17 | 13 | 21 |
| Eighth-level marker | 70 | 13 | 7 | 26 |
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Gan, L.; Huang, L.; Wei, H.; Jiang, F.; Han, J.; Yu, J.; Liu, Q.; Yu, K.; Zhang, Q.; Fan, M.; et al. Phenotypic Variation and Molecular Marker Network Expression of Some Agronomic Traits in Rice (Oryza sativa L.) RILS of Gr 89-1×Shuhui 527. Agronomy 2022, 12, 2980. https://doi.org/10.3390/agronomy12122980
AMA Style
Gan L, Huang L, Wei H, Jiang F, Han J, Yu J, Liu Q, Yu K, Zhang Q, Fan M, et al. Phenotypic Variation and Molecular Marker Network Expression of Some Agronomic Traits in Rice (Oryza sativa L.) RILS of Gr 89-1×Shuhui 527. Agronomy. 2022; 12(12):2980. https://doi.org/10.3390/agronomy12122980
Chicago/Turabian StyleGan, Lu, Lunxiao Huang, Hongyu Wei, Fei Jiang, Jiajia Han, Jie Yu, Qian Liu, Kunchi Yu, Qiuyu Zhang, Mao Fan, and et al. 2022. "Phenotypic Variation and Molecular Marker Network Expression of Some Agronomic Traits in Rice (Oryza sativa L.) RILS of Gr 89-1×Shuhui 527" Agronomy 12, no. 12: 2980. https://doi.org/10.3390/agronomy12122980
APA StyleGan, L., Huang, L., Wei, H., Jiang, F., Han, J., Yu, J., Liu, Q., Yu, K., Zhang, Q., Fan, M., & Zhao, Z. (2022). Phenotypic Variation and Molecular Marker Network Expression of Some Agronomic Traits in Rice (Oryza sativa L.) RILS of Gr 89-1×Shuhui 527. Agronomy, 12(12), 2980. https://doi.org/10.3390/agronomy12122980
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