## 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]. [PERSON] et al. [19] discovered a natural C\({}_{2}\)H\({}_{2}\) transcription factor allele in rice. [PERSON]'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  
Figure 3: Network diagram of the relationship between eight agronomic traits and gradient molecular markers in F\({}_{9}\) RIL\({}_{5}\) of Gr 89-1 \(\times\) Shuhui 527. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Marker Level** & **Label Number** & \begin{tabular}{c} **Number of Detection** \\ **Resources** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Differential Resources** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Polymorphic Markers** \\ \end{tabular} \\ \hline 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 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Molecular detection results of 309 F\({}_{9}\) RIL\({}_{5}\) derived from Gr 89-1 \(\times\) Shuhui 527 by eight-grade markers. F\({}_{9}\)-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.