Key Genomic Regions of Rice Cultivar GuiHeFeng and Its Derivatives Revealed by Genome-Wide Analysis

1. Introduction

Rice (Oryza sativa L.) is a vital food crop and the primary staple for over half of the global population [1]. It serves as a fundamental pillar of worldwide food security [2]. To address the rising food requirements of an expanding global populace, rice output has seen consistent growth over recent decades, largely attributed to the creation of new high-yielding varieties [3]. The introduction of semi-dwarf cultivars, a central element of the initial Green Revolution, led to a substantial boost in rice productivity during the 1960s. Hybrid rice varieties have further enhanced production by 9% over conventional types [4]. Nowadays rice production confronts challenges of rapid population growth, shrinking farmland, climate change and pest/disease pressure [5]. To ensure worldwide food security, there is an urgent need to create new rice varieties that offer higher yields and greater resilience to both biotic and abiotic stresses. Traditional breeding remains inefficient in developing new varieties due to limited understanding of genetic mechanisms and the time-consuming, labor-intensive process of selecting target traits [6]. According to the brand-new concept of 5G breeding, Genomic breeding (GB), which encompasses marker-assisted selection (MAS) and genomic selection (GS), appears to be a highly effective strategy for producing new high-yielding rice varieties capable of withstanding stressful conditions and unpredictable climate shifts [6].

Several key characteristics of rice are governed by genes or quantitative trait loci (QTLs) with substantial effects. Marker-assisted selection (MAS) for major-effect genes/QTLs has been widely applied to improve agronomic traits such as yield, disease resistance, and stress tolerance. However, most agronomic traits are influenced by QTLs with minor phenotypic contributions [7]. Minor-effect QTLs are restrained from utilizing in marker assisted selection (MAS), mainly because of their uncertainty in different genetic backgrounds and growing environments [8]. It is necessary to identify a robust consensus genomic region for Minor-effect QTLs to improve their effectiveness in MAS [9,10]. Analysis of the key/conserved regions that contain the excellent alleles in elite germplasms as well as the foundation parents is a good alternative to identify these consensus genomic regions relevant to the important agronomic traits [11]. Identifying key genomic regions is fundamental to understanding the genetic basis of elite traits and accelerating the breeding of improved crop varieties [12,13,14].

Backbone parents, which carry accumulated beneficial agronomic traits, such as disease resistance, high yield, and adaptability, play a crucial role in modern crop breeding programs due to their ability to transmit desirable traits to offspring through selective breeding[15,16,17].These parents are foundational in crop breeding, as evidenced by their widespread use in major Chinese rice varieties (e.g., 70% derived from 35 backbone parents between 1950–2008)[18]. A large number of superior alleles were gathered and distributed on different genomic regions, due to selective sweeps pyramid in the long-time pedigree breeding progress of the backbone parents. Through large-scale genome sequencing in combination with pedigree analysis, some key genomic regions, which can stably inherit in different genetic backgrounds of the pedigree, have been found in the rice backbone parents such as Minghui63, Huanghuazhan, Shuhui527 and Jiayu253[5,18,19,20]. These key genomic regions are important for genomic selection such as genome-wide marker assisted selection to develop new rice cultivars [11]. These four backbone parents were developed or released more than two decades ago, in 1980, 1996, 2005 and 2005, for Minghui 63, Shuhui 527 Huanghuazhan and Jiayu 253, respectively. Nevertheless, the genomic structure of rice cultivars will evolve due to shifts in their growing conditions and production objectives [6]. Only a few rice cultivars were analyzed to identify critical genomic regions associated with important traits through genome sequencing. Moreover, little is known about the key genomic regions architecture of the rice varieties developed in recent years.

GuiHeFeng is an elite conventional rice cultivar released in 2015, showing increase of yield by 12.32% compared with the control cultivars LiuShaYouZhan202 in regional test, and from which more than 10 excellent cultivars have been derived. In this study, GuiHeFeng and its 9 derivatives were selected for Whole-genome resequencing (Table 1). Using this sequence information, we were able to uncover the key genomic regions of GuiHeFeng conserved in all derivatives. We further analyzed known loci related to rice important trait or unknown QTLs by GWAS analysis, revealing the basis for the excellent performance of GuiHeFeng and all its derivatives. This comprehensive study of genomic architecture of GuiHeFeng and its derivatives will provide key genomic regions and important agronomic genes/QTLs for rice high yield breeding by genomic selection (GS).

2. Results

2.1. The Derivatives Exhibited Comparable Agronomic Trait Performance to GuiHeFeng

Investigation of 11 agronomic traits was conducted for all the cultivars (Figure 1 and Table S1-1). Only the plant height of HeXiFengZhan2 and GuiYaXiang was higher and lower than that of GuiHeFeng at significant level respectively, and there was no significant difference of plant height between GuiHeFeng and the other 7 cultivars (Figure 1A). The effective panicle number (EPN) of NaFengZhan and GuiYaXiang was significantly higher than that of GuiHeFeng, and the EPN of GuiNongFeng was significantly lower than that of GuiHeFeng, and there was no significant difference of plant height between GuiHeFeng and the other 6 cultivars (Figure 1B). Just as the case of plant height, only the panicle length (PL) of NaXiangSiMiao and GuiYaXiang was significantly higher and lower than that of GuiHeFeng respectively, and there was no significant difference in PL between GuiHeFeng and the other 7 cultivars (Figure 1C). There was no significant difference of number of unfilled grains （NUG) and number of filled grains (NFG) between GuiHeFeng and all the other 9 derivatives ( Figure 1D, E). The seed setting rate (SSR) of JingYouXiang 139, NaXiangSi and NaGuXiang was significantly higher than that of GuiHeFeng, and there was no significant difference of SSR between GuiHeFeng and the other 6 cultivars (Figure 1F). Only the length of flag leaf of NaFengZhan and GuiNongFeng was significantly higher than that of GuiHeFeng, without significant difference between GuiHeFeng and the other 7 cultivares (Figure 1G). Except HeFengDao445, GuiNongFeng and NaXiangSiMiao, there was no significant difference of Width of flag leaf (WFL) between GuiHeFeng and the other 5 cultivars (Figure 1H). There was no significant difference of seed weight per plant （SWPP) between GuiHeFeng and all the other 9 derivatives ( Figure 1I). There was no significant difference in thousand-kernel weight (TKW) between GuiHeFeng and the other 4 cultivars, including HeFengDao445, JingYouXiang139, GuiYaXiang and NaXiangSiMiao, respectively (Figure 1J). Ratio of length and width (RLW) of GuiHeFeng and GuiYaXiang was significantly lower than that of other 8 cultivars (Figure 1K).

In addition, for NUG, NFG and SWPP, there was no significant difference between GuiHeFeng and its all 9 derivatives; for PH, PL and LFL, there was no significant difference between GuiHeFeng and other 7 cultivars; for EPN and SSR, the number was 6 cultivars; for WFL and TKW, the number was 5 cultivars and 4 cultivars, respectively. Over half of the derivatives closely resembled GuiHeFeng in the majority of the agronomic characteristics that were evaluated.

2.2. Sequence and SNPs Information Was Produced by Whole-Genome Resequencing

GuiHeFeng and all its 9 derivatives were used to carry out whole-genome resequencing to get the basic sequence and SNPs information for further analysis. A total of 751.32 million (M) clean reads of 150 bp that include 111.18 GB data were generated from the 10 rice varieties with more than 19 × depth (Table 2). More than 98% of the clean reads of 150 bp were mapped to the Nipponbare genome, with average coverage ratio ranging from 82.42% to 89.69% (Table 2).

We used GATK v4.0 to call SNPs [21]. Overall, 6 633 507 SNPs were identified in these listed 10 cultivars. According to the genome annotation (MSU 6.1), 60.40% of all SNPs were found in intergenic regions, 5.12% in introns, 2.38% in UTRs and 3.91% in gene coding regions, 28.18% in other regions (Figure 2; Table S2).

2.3. Key GuiHeFeng Traceable Blocks Were Found in the Genome of Its Derivatives

As the method described by Zhou et al [5], the rice genome was segmented into 7471 adjacent blocks with bin size of 50 kb (Table S3). Using a cut-off of more than 85% identity between GuiHeFeng and the derivatives to exploit the GuiHeFeng traceable blocks (GTBs). As shown in Figure 4, 63.2% genomic blocks of HeFengDao445 were identified as GTBs, 59.94% for GuiFeng18, 59.73% for NaGuXiang, 59.17% for NaXiangSiMiao, 52.63% for GuiNongFeng, 51.98% for JingYouXiang139, 50.59% for HeXiFengZhan2, 50.07% for GuiYaXiang and 48.94% for NaFengZhan, respectively. There were 1310 key GTBs (kGTBs), which were derived from GuiHeFeng and found in all the 9 derivatives (Table S4). These key GTBs were unevenly distributed on the hole genome of rice, chromosome 3 with the largest number of 192, and chromosome 11 with the lowest number of 22 (Figure 3).

Figure 3. These key GTBs exhibited a non-uniform genomic distribution across the rice genome.(chr.1:181; chr.2:138; chr.3:192; chr.4:118; chr.5:112; chr.6:85; chr.7: 181; chr.8:85; chr.9:94; chr.10:67; chr.11:22; chr.12: 35).

Figure 3. These key GTBs exhibited a non-uniform genomic distribution across the rice genome.(chr.1:181; chr.2:138; chr.3:192; chr.4:118; chr.5:112; chr.6:85; chr.7: 181; chr.8:85; chr.9:94; chr.10:67; chr.11:22; chr.12: 35).

Figure 4. Key genomic regions of GuiHeFeng. The key genomic regions of GuiHeFeng stably inherited by its elite derivatives are represented in orange. The derivative name and the similarity of each derivative to the GuiHeFeng genome is shown in the left side.

Figure 4. Key genomic regions of GuiHeFeng. The key genomic regions of GuiHeFeng stably inherited by its elite derivatives are represented in orange. The derivative name and the similarity of each derivative to the GuiHeFeng genome is shown in the left side.

2.4. Key Genomic Regions Were Selected from kGTBs and Selection Sweeps

Selective sweeps are the genomic region which probably contain excellent alleles relevant to the important agronomic traits and preferably selected by the breeder selective sweeps (SSWs) [11]. To exploit the selective sweeps (SSWs) of GuiHeFeng and its derivatives, θπ, θw and Tajima’s D [22] were calculated with sliding window of 50 kb across 12 chromosomes with Variscan [23], with a cut-off of 5% of Tajima’s D test ( Tajima’s D≥1.94).We found 375 SSWs, totaling 18.75 Mb, distributing on all chromosomes (Figure 5; Table S5). Furthermore, we found only 20 SSWs were included in kGTBs, indicating all these 20 keys genomic regions were important for rice breeding and preferably selected by different breeders.

2.5. Excellent Alleles Were Exploited from kGTBs and Key Genomic Region

Beyond key genomic regions, rice breeders are particularly interested in superior alleles located within these areas. To find the excellent alleles on kGTBs, adjacent SNPs with the same segregation pattern were combined to form a marker for association test with agronomic traits by PLINK analysis. As results (Table 3, Table S6) , 20 alleles including 17 genes were found on the kGTBs, 2 genes Rd and OsCYP704A3, associated with seed morphology, 2 genes D2 and TAC1, linked to plant architecture, 6 genes (Gnla, Rf3, OsLG3, DPL2, GLW7 and HSA1b) related to yield, 2 genes Hd7 and Hd1, involved in heading date, 4 genes (BET1, OsJAZ1, bZIP73 and LHCB5) for biotic stress, and one gene OsUGT707A2 for secondary metabolism, respectively. There was the largest number of genes involved in yield regulation, while only one gene related to secondary metabolism. The SNPs polymorphism consistency between GuiHeFeng and all the derivatives of the 17 genes between were reconfirmed by gene chip analysis (Table S7-1). However, the derivatives showed difference between GuiHeFeng at some genes, such as ALK, Badh2 and Rf2 (Table S7-2, Table S7-3). We found no important genes on the 20 key genomic regions by PLINK analysis. So, we directly found the loci for key genomic regions (kGRs) in Nipponbare genome IRGSP-1.0 on Rice Gene Index (RGI; https://riceome.hzau.edu.cn) platform. As shown in Table 4 and Table S5, there were 38 genes on the key genomic regions, except the key genomic regions on chr.12. To our surprise, among the 38 genes, 29 genes are involved in the defense responses against biotic/abiotic stress, 4 genes for fertility, only 1 gene for yield components, and 4 genes for other functions (Table 4; Table S8).

2.6. More SNPs (QTLs) Related to Important Traits Were Detected by GWAS Analysis

To determine more SNPs related to putative traits, we tested the association between SNPs and the mean data of agronomic traits collected from early season of 2024 (Table S1-2), late season of 2024(Table S1-3) and early season of 2025(Table S1-4) and the average(Table S1-1) in Nanning using compressed general linear model (GLM) and mixed linear model (MLM) implemented in TASSEL.

A total of 255 significant association sites were detected using GLM (Table S9). Among these sites, 130 SNPs were found to be associated with PH, with 128 identified in the early season of 2024 and 2 in the early season of 2025. For EPN, WFL, and TKW, the numbers of associated SNPs were 10, 48, and 49, respectively, all detected in the early season of 2024. Additionally, 58 SNPs were found to be associated with SSR in the early season of 2025. Interestingly, 5 associated sites were identified across 4 principal areas of the GuiHeFeng traceable block (kGTBs) (Table S9). During the early part of the 2024 season, the SNP positions chr04:23737663 and chr04:23737670, which are associated with plant height, were found to be situated on the gene Os4BGlu11, and chr07: 23610137 located on LOC_Os07g39410 (Figure 6A). In early-season of 2025, SNPs position chr06: 12623369 was located on LOC_Os06g21850.1, associated with SSR (Figure 6B). No gene was found for the position of chr07:4127113, which was associated with WFL in early season of 2024. Os4BGlu11 encodes β-Glucosidases, which hydrolyzes abscisic acid glucose ester (ABA-GE), regulating the development of root [24]. LOC_Os06g21850.1 encodes conserved hypothetical protein, and LOC_Os07g39410 encodes retrotransposon protein, both with unknown functions. No significant association sites were in MLM and GLM analysis of late-season of 2024 and the average of the 3 seasons.

3. Discussion

3.1. Important Genes Were Identified from GuiHeFeng and Its Derivatives

Genes relevant to the critical agronomic traits play important role in rice breeding. For example, the ‘Green Revolution’ gene sd1 has been used to develop a lot of rice cultivars and made a significant contribution increases in rice yields [25]. Exploiting and utilizing important genes from elite germplasm is the permanent target for rice breeders. Important genes such as Xa21[26], Gn1a [27], Wx [28], GS5 [29]and IPA1[30] for resistance, grain yield, quality and plant type, were identified in an elite rice HuangHuaZhan through whole genome sequencing and pedigree analysis [5]. The important gene TAC1[31] was also found in HuangHuaZhan [6]. Six important genes—sd1[32], LP [33], GW5[34], BC10[35], RL14[36]and OsNAC6[37]—were discovered in another elite rice variety, 9311[19]. We identified 17 important genes in the kGTBs, which existed in both GuiHeFeng and the other 9 derivatives (Table 3). Among these 17 genes, 2 genes Gn1a and TAC1 were also found the 7 genes identified in HuangHuaZhan. Gn1a, the first major QTL implicated in grain-number regulation per panicle, explained 44% of the phenotypic variance.[27]. TAC1 is a major quantitative trait locus, positive controlling tiller angle in rice [31]. D2 identified in GuiHeFeng also plays an important role in the regulation of tiller angle [38]. It seems that grain number per panicle and tiller angle, controlled by Gn1a and TAC1/D2, is a part of the most critical agronomic traits for rice breeder during breeding selection. Rd controls red coat of seed [39], and CYP704A3 negatively controls the length of rice seed [40]. Hd1[41]and DTH2[42] both can delay heading date under long-day conditions. Longer heading date results in more biomass and higher yield. Maybe, this is the reason to explain the preference of breeder for Hd1 and DTH2 in rice breeding practice. GLW7 increases both length and weight of rice grain [43]. Three seed production genes were found in GuiHeFeng and all the derivatives. Rf3[44] positively regulated the restoration of fertility, but DPL2[45] and HSA1b [46] both control hybrid incompatibility. The function of Rf3 contradicts the function of DPL2 and HSA1b. However, GuiHeFeng and all the derivatives had high seed-setting rate, ranging from 80.5% to 88.2% (Table S1-2). Moreover, GuiHeFeng shows strong compatibility for both two-line and three-line male sterile line (Data unpublished). It is needed to carry out more research to illustrate the seed reproduction mechanism of the 3 genes for GuiHeFeng and the derivatives.

Previous research showed biotic and abiotic stress related genes were favored by breeders [5,6]. Our results were consentaneous with these previous findings. Among the 17 genes, 5 were stress related genes, OsLG3[47], BET1[48], OsJAZ1[49], bZIP73[50], LHCB5[51]. In addition, among the 38 genes in the key genomic regions, 29 genes are involved in the defense responses against biotic/abiotic stress (Table 4). Our results support the proposal：To maintain high yield and good quality of the target cultivars wherever cultivated, stress related genes would be spontaneously selected by different breeders to respond to varied environments in rice breeding.

3.2. kGTBs and Key Genomic Region Is Useful for Modern Rice Breeding

Marker-assisted selection (MAS) has been successfully utilized to pyramid elite allele of important genes, improving the yield, quality and resistance of rice cultivars [52,53]. It is critical to assess the performance of target allele before its utilization in MAS. Due to uncertainty of genetic backgrounds and growing environments, it is difficult to detect the minor effect QTLs, especially for the abiotic stress related to QTLs, by traditional QTL analysis method [54,55]. A method named Meta-QTL analysis has been invented the detect the key genomic region, which contain the target allele and stably inherit in different genetic backgrounds and growing environments [9]. The emergence of high-throughput genome sequencing and the availability of pedigree analysis makes the finding of such key genomic region more precise and higher efficiency, and key genomic regions related to important agronomic traits have been found in the rice backbone parents, such as Minghui63, Huanghuazhan, Shuhui527 and Jiayu253 [5,18,19,20]. In the present study, 1310 key GuiHeFeng traceable blocks (Table S4), 375 selective sweeps, and 20 key genomic regions (Table S5) were identified from GuiHeFeng and the derivatives. Moreover 17 important genes were found on the kGTBs (Table 3), and 38 found on the 20 key genomic regions (Table 4). These key genomic regions could be used as important blocks for genomic selection (GS) in the future of rice breeding.

Some important genes, for example, most NLR genes are positionally clustered in a genomic region [13]. Some abiotic stress related QTLs are also clustered on the genomic region [56,57]. we found 3 alleles of Gn1a clustered on chr.1, 2 alleles of OsLG3 clustered on chr.3 (Table 3). As shown in Table 4, 4 abiotic stress related genes OsABA1, OsAP37, OsPT17 and OsPP65 were clustered on chr.4; two resistance related genes OsWAK54 and OsWAK55 on chr.4; two resistance related genes OsRRK1 and OsLRR-RLK1 on chr.4 and 6; meanwhile, 9 abiotic stress related genes clustered on chr.9. It suggests that alleles of the same gene or QTLs, as well as gene/QTLs with similar functions, frequently cluster within specific genomic regions. In comparison with handling and utilizing individual alleles of gene/QTLs, key genomic regions that contain clusters of multiple elite alleles demonstrate greater effectiveness in rice breeding, particularly for minor-effect QTLs.

3.3. GuiHeFeng Is a Backbone Parent for Rice Breeding

Backbone parents, as the carrier of multiple beneficial agronomic traits, are critical for rice breeding [18]. GuiHeFeng is typically a high yield rice cultivar, showing increase of yield by 12.32% in comparison with the control cultivars. So, it was used widely by different breeders to develop new rice cultivars. The percentage of GuiHeFeng traceable blocks in the derivatives ranged from 48.94% to 63.20%. However, more than half of the derivatives closely resembled GuiHeFeng in the majority of the agronomic traits that were evaluated (Figure 1). In addition, no derivative showed significant increase of seed weight per plant (SWPP) than that of GuiHeFeng. The results indicated that GuiHeFeng was dominant at large number of yield-related genes/QTLs, showing high heritability in yield performance. In addition to the maintaining of high yield performance of GuiHeFeng, 4 derivatives GuiNongFeng, NaXiangSiMiao, GuiYaXiang and JingYouXiang139 showed improvement of quality with fragrance Badh2 (Table S7-1). It is feasible to use GuiHeFeng as high yield backbone parent, to cross with the other unique parent to improve the quantity or resistance of rice cultivar.

Currently, biotic and abiotic stress tolerance has become a primary objective for rice breeding programs [6]. Our results showed that 18 of the 20 key genomic regions, which were identified from GuiHeFeng, contained more than one biotic or abiotic resistance related genes(Table S8). The results indicate that GuiHeFeng could be used as a stress resistance parent to develop new high-yield varieties of rice with resistance to stressful environments and unpredictable climate changes.

4. Materials and Methods

4.1. Plant Materials

A total of 10 rice varieties were used for analysis in this study (Table 1). GuiHeFeng was one of the two parents of the other 9 derivatives. HeFengDao445 and HeXiFengZhan2Hao were collected from Hechi Agricultural Science Research Institute, JingYouXiang139 from Guangxi Boshiyuan Seed Industry Co., Ltd., GuiHeFeng and 9 derivatives from Rice Research Institute of Guangxi Academy of Agricultural Sciences. All varieties were planted in the experimental field of the Rice Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, China in early-season and Late-season of 2024, and early-season of 2025. Each variety was planted in three plots, 5 rows for each plot and 10 plants for each plot. The spacing between plants and plots was 20 cm×20 cm and 30 cm×30 cm respectively. The plots of all varieties were arranged in randomized complete block design.

4.2. Genome Resequencing and SNP Calling

A single individual of each variety was selected for whole genome resequencing. Genomic DNA was extracted from young leaves using a DNA Extraction Kit (Qiagen, Hilden, Germany), sequenced on the Illumina X10 platform (150 bp reads and 300–500 bp insert). We removed the low-quality paired reads, including those (putative PCR duplicates, with >10 nucleotides aligned to the adapter, with ≥10% unidentified nucleotides (N) and >50% bases having phred quality <10) [58]. The clean reads were mapped to the reference genome of Nipponbare (MSU v7.0) by using Burrows–Wheeler Alignment (BWA) software (v0.7.12) [59]. The sequencing depth, genome coverage, and other information of each sample were calculated were calculated by SAMtools software [60]. GATK v4.0 software was used for identifying SNPs [21]. The SNPs were annotated using SnpEff (version 4.1) [61].

4.3. Construction of Genome Bins, Identification of Key Genomic Region and Selection Sweep Region

The genome was segmented into non-overlapping bins of 50 kb length. The similarity between each sample of the 9 derivatives and GuiHeFeng is calculated to obtain the similarity matrix for each bin. If identity of according to bin of tested derivative and GuiHeFeng is larger than or equals to 0.85, then it was deemed as conserved blocks (GuiHeFeng traceable blocks, GTBs). Such GTBs found in all the 9 derivatives was considered as key GTB (kGTBs). To identify the selection sweeps (SSWs), θw and Tajima’s D [22] were calculated with sliding window of 50 kb across 12 chromosomes with Variscan [23] using SNPs identified from resequencing. We used 5% as a cut-off of Tajima’s D test (Tajima’s D≥1.94) to identify top selective sweeps with high significance. Regions found in both kGTBs and top selective sweeps were identified as key genomic regions (kGRs) for rice breeding. Figures of key blocks or selection sweep regions were drawn using Perl script with GD module (www.perl.org).

4.4. Association Test and Gene Chip Analysis

For kGTBs, adjacent SNPs with the same segregation pattern were combined to form a marker for association test [5]. PLINK was used to analyze the association between these markers and 11 agronomic traits in a linear model [62]. Important loci for agronomic traits were determined as those with FDR p-values less than 0.0001 from 100,000 permutation tests. The important loci for key genomic regions (kGRs) were found in Nipponbare genome IRGSP-1.0 on Rice Gene Index (RGI; https://riceome.hzau.edu.cn) platform. The whole-genome SNP array GSR40K was employed to analyze the variations of 164 functional genes. GSR40K analysis was performed at Wuhan Greenfafa Institute of Novel Gene chip R&D Co., LTD (Wuhan, China) (https : //green fafa.com/), according to the Infinium HD Assay Ultra Protocol (HYPERLINK: https://www.illumina.com).

4.5. Association Test and Gene Chip Analysis

To determine more SNPs related to putative traits, we tested the association between SNPs and the mean data of agronomic traits collected from early season of 2024, late season of 2024 and early season of 2025 in Nanning using compressed general linear model (GLM) and mixed linear model (MLM) implemented in TASSEL.

4.6. Agronomic Trait Investigation

Agronomic traits, including plant height (PH), effective panicle number (EPN) , panicle length (PL), number of unfilled grains (NUG), number of filled grains (NFG)，seed setting rate (SSR), length of flag leaf (LFL), width of flag leaf (WFL), seed weight per plant （SWPP), thousand-kernel weight (TKW), ratio of length and width (RLW) were investigated during all growth seasons. Statistical analysis was performed using LSD software.

5. Conclusions

Through in-depth analysis of key genomic regions in Guifeng rice using SNP data, this study integrated kGTB and SSW strategies to pinpointe critical genomic regions and identify superior alleles. It elucidated the potential molecular basis for yield traits and key regions underpinning stable inheritance in GuiHeFeng rice. These findings provide new insights for advancing molecular design, breeding and genomic selection in the GuiHeFeng rice background.