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Article

Comprehensive Analysis of Ghd7 Variations Using Pan-Genomics and Prime Editing in Rice

by
Jiarui Wang
1,†,
Shihang Liu
1,†,
Jisong Pu
1,
Jun Li
1,
Changcai He
1,
Lanjing Zhang
1,
Xu Zhou
1,
Dongyu Xu
1,
Luyao Zhou
1,
Yuting Guo
1,
Yuxiu Zhang
1,
Yang Wang
2,
Bin Yang
1,
Pingrong Wang
1,
Xiaojian Deng
1,* and
Changhui Sun
1,*
1
State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, China
2
Panxi Crops Research and Utilization Key Laboratory of Sichuan Province, College of Agricultural Science, Xichang University, Liangshan 615000, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2025, 16(4), 462; https://doi.org/10.3390/genes16040462
Submission received: 3 March 2025 / Revised: 10 April 2025 / Accepted: 15 April 2025 / Published: 17 April 2025
(This article belongs to the Collection Feature Papers: 'Plant Genetics and Genomics' Section)
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Abstract

:
The Ghd7 gene in rice plays a crucial role in determining heading date, plant height, and grain yield. However, the variations in Ghd7 and their functional implications across different rice accessions are not fully understood. Based on the release of a large amount of rice genome data in recent years, we investigated Ghd7 through pan-genome analysis of 372 diverse rice varieties and figured out the structural variations (SVs) in the Ghd7 locus. However, due to the high cost of pan-genomes, most genomes are based on next-generation sequencing (NGS) data now. Therefore, we developed a method for identifying SVs using NGS data and Polymerase Chain Reaction (PCR) based on the results of pan-genome analysis and identified 977 accessions carrying such SVs of Ghd7. Furthermore, we identified 46 single-nucleotide polymorphisms (SNPs) and one insertion-deletion (InDel) in the coding region of Ghd7. They are classified into 49 haplotypes. Notably, a splice-site mutation in haplotype H6 causes aberrant mRNA splicing. Using prime editing (PE) technology, we successfully restored the functional of Ghd7 in Yixiang 1B (YX1B), delaying the heading date by approximately 16 days. This modification synchronized the heading date between YX1B and the restorer line Yahui 2115 (YH2115R), enhancing the hybrid rice seed production efficiency. In conclusion, our findings highlight the potential of integrating pan-genomics and precision gene editing to accelerate crop improvement and enhance agronomic traits.

1. Introduction

Over the past two decades, technological advancements in sequencing and assembly have driven the rapid growth and massive expansion of plant genomics [1,2,3,4]. The emergence of third-generation sequencing platforms, such as Pacific Biosciences (Menlo Park, CA, USA) (PacBio) and Oxford Nanopore Technologies (Oxford, UK) (ONT), has further revolutionized genome assembly by producing long reads that, when combined with the accuracy of next-generation sequencing (NGS), facilitate the construction of highly contiguous and accurate genomes [5,6,7,8,9,10]. However, the availability of multiple high-quality genomes has revealed a critical limitation: a single reference genome is insufficient to capture the full genetic diversity of a species due to the extensive structural and sequence variations among accessions [11,12].
To address this limitation, the concept of the “pan-genome” was introduced. Pan-genomes represent the genomic diversity of a species and include core genes, found in all individuals, as well as variable genes, which are absent in some individuals [11,13]. Since its inception, the pan-genome framework has been refined and applied to numerous crop species, including rice, soybean, maize, and wheat [14,15,16,17,18,19,20,21,22,23]. For example, the graph-based rice pan-genome, constructed from 33 genetically diverse accessions, not only uncovered extensive genomic variations but also provided insights into their roles in genome evolution, gene expression, and environmental adaptation [24]. Moreover, the construction of a super pan-genome based on 251 high-quality rice genomes has further enhanced our ability to mine functional genes and utilize germplasm resources [25]. In soybeans, a pan-genome map was constructed by resequencing the genomes of 2898 samples and selecting 26 representative samples for genome assembly. This greatly promoted the study of soybean evolution and functional genomics [26]. In wheat, 17 representative varieties were selected from over 3500 nationally approved varieties bred in China. High-quality genomes were assembled at the chromosome level, and nearly 250,000 structural variations (SVs) were accurately identified. It also presented the history of wheat breeding in China since the 1950s at the genomic level, revealed the influence of genomic structural variation on wheat adaptability and major variety formation, and provided a new perspective and strategy for future wheat design breeding [27]. In maize, the construction of 12 genomes and analysis of heterosis mechanisms using pan-genomic and expression quantitative trait Loci (eQTL) approaches provided an important theoretical basis and genetic resources for accelerating maize improvement and breeding [28]. In diploid potatoes, the genomes of 31 representative germplasm were assembled from scratch, yielding a total of 60 haplotype assemblages. A phased pan-genome was constructed to identify a large number of genetic variants and analyze the wide genetic diversity of diploid potato germplasm [29]. In millet and setaria, 110 high-quality genomes were assembled from scratch. The first variation map of the Setaria and the first high-quality map genome of multigrain crops were constructed. Several loci and key genes related to millet domestication and breeding improvement were identified, and the genetic basis of millet domestication and improvement, as well as the role of map genomes in genetics and breeding applications, were systematically analyzed [30].
Compared with the traditional single reference genome, the pan-genome can reveal the genetic diversity of species more comprehensively, particularly in the study of SV, where it can identify a broader spectrum of structural changes [31], such as the 1.8 kb insertion in the first exon of RFT1 (LOC_Os06g06300) [25,32], the 1116 bp deletion in the DTH8 gene (LOC_Os08g07740) [33], and the 17.1 kb copy number variation (CNV) of GL7 (LOC_Os07g41200) [34]. However, the functional characterization of these SVs is often hampered by the lack of high-resolution phenotypic data and scalable validation platforms. For instance, despite large-scale studies like the rice super pan-genome [25] having mapped thousands of SVs, fewer than 5% of these variants have been experimentally linked to agronomic traits, leaving a vast reservoir of unexplored genetic potential. Moreover, the integration of pan-genomic insights into breeding programs faces systemic barriers, including the computational complexity of analyzing graph-based genomes and the limited accessibility of these tools in resource-poor regions [35]. Even when functional variants are identified, translating them into practical solutions, such as designing haplotype-specific breeding markers or editing targets, requires interdisciplinary collaboration that is still underdeveloped [36]. These limitations underscore an urgent need to bridge pan-genomics with advanced functional genomics tools, such as prime editing and high-throughput phenotyping, to unlock the full value of genetic diversity for trait optimization [37].
Addressing these challenges, we focused on Ghd7, a pivotal gene that exemplifies both the potential and limitations of pan-genomic studies in crop improvement. As a pleiotropic regulator of heading date, plant height, and grain yield, Ghd7 serves as an ideal model to investigate how SVs and functional haplotypes shape agronomic traits across diverse environments [38]. Functional alleles such as Ghd7-1 and Ghd7-3 enable rice to exploit long growing seasons in tropical regions by delaying flowering under long-day conditions, whereas loss-of-function alleles such as Ghd7-0 and Ghd7-2 facilitate adaptation to temperate climates with shorter growth durations [38]. However, despite its agricultural significance, critical gaps persist in our understanding of Ghd7 diversity. For instance, a 38.3 kb deletion spanning Ghd7 in the elite hybrid parent Zhenshan 97 (ZS97) was identified over a decade ago, yet the structural complexity of this deletion and its functional consequences remain unresolved, primarily due to the historical lack of high-quality genomic resources [38]. This case underscores a broader issue in pan-genomics: while large-scale SV detection is now feasible, linking specific SVs to phenotypic outcomes and applying these findings in breeding requires targeted integration of genomic tools and experimental validation [37].
In this study, we leveraged pan-genomic data from 372 high-quality rice genomes to systematically investigate Ghd7 SVs and other various mutation sites, along with their functional implications. By developing a novel computational pipeline that combines short-read sequencing depth analysis with a low-cost Polymerase Chain Reaction (PCR)-based validation method, we identified 997 accessions carrying large fragment deletions in Ghd7 from a dataset of 10,548 rice germplasms [39]. Furthermore, haplotype analysis of 3637 accessions revealed 46 single-nucleotide polymorphisms (SNPs) and one insertion-deletion (InDel), many of which were previously unreported. Among these, the splicing-site mutation (A to C) in the maintainer line Yixiang 1B (YX1B) stood out as a critical variant that disrupts Ghd7 function by causing aberrant mRNA splicing and premature termination of the CCT (CONSTANS, CO-like, and TOC1) domain. To validate its functional impact, we employed prime editor (PE) technology to precisely edit this mutation, restoring Ghd7 activity and delaying the heading date by approximately 16 days. This modification not only synchronized flowering between YX1B and the hybrid rice restorer line, enhancing seed production efficiency, but also established a proof-of-concept framework for functional validation of other Ghd7 haplotypes. Our findings highlight the transformative potential of integrating pan-genomics with precision gene editing, offering a robust template for dissecting the genetic basis of agronomic traits and accelerating crop improvement through targeted interventions.

2. Materials and Methods

2.1. High-Quality Genomes and NGS Data Collection

We collected a comprehensive dataset of high-quality genomes and NGS data to investigate the SVs of the Ghd7 gene in rice. The high-quality genomes were sourced from multiple studies [24,25,40,41,42,43,44,45,46,47,48], with a total of 372 high-quality genomes included after excluding those with low assembly quality (Table S1). Additionally, we obtained 10,548 NGS datasets from the rice super pan-genome information resource database (RiceSuperPIRdb) [18,24,25,44,49,50,51,52,53,54,55,56,57,58,59,60,61,62].

2.2. Ghd7 SVs Identification

We downloaded the Ghd7 sequence, including the 50 kb upstream and downstream chromosome fragments, from the Nipponbare (Nip) reference genome (Michigan State University, MSU; http://rice.plantbiology.msu.edu/; accessed on 1 December 2022). For each of the 372 high-quality genomes, we used Ghd7 sequences as probes for Basic Local Alignment Search Tool (BLAST) v2.12 searches against each genome assembly. We identified 44 assemblies lacking Ghd7 sequence information. Eight high-quality genomes (ZS97, DG, D62, Y58S, II32, CN1, FS32, G46) from Ghd7-free materials were selected for detailed analysis. We used the BLAST-Like Alignment Tool (Blat) v35.1 [63] to identify homologous sequences flanking Ghd7, and the results were visualized using dot plots to represent the actual large fragment deletions.

2.3. Ghd7 Sequence Alignment and Variation Annotation

We performed multiple sequence alignment of all Ghd7 homologous sequences using MEGA v12.0.11 [64] to identify variable sites. We annotated the variation sites using ANNOVAR v2019Oct24 [65] to identify non-synonymous codons, synonymous codons, splice-site variations, and premature terminations (Table S5).

2.4. Identification of Large Fragment Deletions

To determine the large fragment deletions, we aligned 8 NGS datasets (ZS97, DG, D62, Y58S, II32, CN1, FS32, G46), which were known to contain large fragment deletions, to the Nip reference genome and identified highly conserved regions flanking the Ghd7 gene. Based on these conserved regions, we defined three key 400 bp intervals: a, b, and c. Regions a and c are located in the upstream and downstream conserved regions where large fragments are missing, respectively, while region b is at the junction of the second exon and intron of the Ghd7 gene. Then, we calculated the average sequencing depths of these three intervals. By comparing the sequencing depth of interval b to those of intervals a and c, we identified the large fragment deletion in Ghd7.

2.5. Haplotype Analysis

Using the SNPs of the Ghd7 gene region in the RiceSuperPIRdb, we removed all samples containing deletions or heterozygosity, leaving 3637 accessions. Haplotype typing was performed using the gengHapR v1.2.4 software package [66]. The phylogenetic trees of 49 haplotype SNPs were constructed using MEGA v11.0.13 [67].

2.6. Geographic Distribution

We collected and sorted the longitude and latitude data of rice materials [68]. Using R v4.3.2 language and the ggplot2 v3.5.2 package [69], we generated the world maps displaying dots of different colors to represent the geographic distribution of different Ghd7 alleles.

2.7. Prime Editor Technology

The pegRNA is the addition of a reverse transcription template (RT) and primer binding site (PBS) sequence containing new genetic information to the 3′ terminal of single-stranded guide RNA (sgRNA). Nick target sequence: GTGCTCCCACAATATGACAT; Non-nick target sequence: CATTTGCTTATGCGTACATC; RT + PBS sequence (underscore PBS sequence) as AGTTTTGCAGATGTCATATTGTGGG. These fragments were constructed into the vector PE-P3 (containing M-MLV and Cas9n (H840A)) through primer amplification and carrier ligation experiments [70]. The complex promoter-sgRNA-RT-PBS-M-MLV-Cas9 (H840A) was formed. Rice callus was cultured from wild YX1B material. The constructed gene editing vector PE-P3 was transferred into Agrobacterium EHA105 by liquid nitrogen freeze–thaw conversion and then into rice callus by Agrobacterium tumefaciens [71]. Embryogenic callus containing Agrobacterium was screened and cultured on 50 mg/L hygromycin medium to obtain resistant callus, and then the T0 generation transgenic rice was regenerated.

2.8. DNA Extraction and PCR

The CTAB method was used for the isolation of genomic DNA from the fresh leaves of individual rice plants. The PCRs were conducted following the guidelines of the 2 × Taq PCR StarMix (Tsingke, Beijing, China). The PCR primers are shown in Table S3. The PCR products were examined by electrophoresis on a 0.8% (w/v) agarose gel at 120 V for 20 min. The target sequences were verified through Sanger sequencing.

2.9. Heading Date Phenotype Collection and Analysis

Heading date phenotype data were obtained from http://snp-seek.irri.org (accessed on 20 February 2023) [72] and https://ricerc.sicau.edu.cn/ (accessed on 20 February 2023) [24].

3. Results

3.1. Structural Variation in Ghd7 in High-Quality Genomes

Identifying SVs based on short-read sequencing data is challenging and often unreliable [73]. To address this, we collected high-quality genomic data from 372 genetically diverse rice cultivars, including 338 O. sativa and 34 O. rufipogon samples. We detected pervasive presence/absence variation (PAV) containing Ghd7 in 44 germplasms through sequence comparison analysis. To precisely define the deletion range of this PAV, we selected 8 high-quality genomes (ZS97, DG, D62, Y58S, II32, CN1, FS32, G46) with this variant for detailed analysis. By comparing homologous sequences flanking the Ghd7 gene, we identified a about 60 kb deletion spanning the Ghd7 region. Dot plot analysis revealed that this deletion was not continuous but intermittent, containing multiple types of SVs, including inversions, substitutions, and deletions, indicating complex chromosomal structural mutations at the Ghd7 locus (Figure 1). Compared to Nip, japonica rice showed fewer SVs in this region, while indica rice exhibited more SVs, suggesting a key role in rice subspecies differentiation.
Furthermore, the Ghd7 SVs are not completely identical, such as in ZS97 and CN1. Additionally, we observed a 1901 bp insertion SV in four materials (NH146, NH150, Aikoku, No8). Although both accessions exhibit a 60 kb deletion spanning the Ghd7 region, the specific nature and complexity of the SVs differ. In ZS97, the deletion is part of a complex chromosomal structural mutation that includes multiple types of SVs, such as inversions, substitutions, and deletions. In contrast, CN1 may have a different pattern of SVs within the same region, even though it also carries a large fragment deletion. These differences highlight the diversity of Ghd7 SVs across rice accessions and underscore the importance of detailed structural analysis in understanding the functional implications of these variations.

3.2. Identification of Large Fragment Deletions Using NGS Data

NGS data face significant challenges in the identification of SVs, particularly large fragment deletions, due to the short length of the sequencing reads. This limitation hinders the accuracy and reliability of SV detection. To address this issue, we developed a novel computational pipeline to identify large fragment deletions in the Ghd7 gene using NGS data. We applied this method to 10,548 rice germplasms from the RiceSuperPIRdb [39] and successfully identified 977 accessions carrying the Ghd7 deletion (Figure 2). We classified them as haplotype H0 (Table S2). This approach provides a new and effective strategy for SV identification using NGS data, overcoming the limitations of short-read lengths.

3.3. PCR-Based Validation of Large Fragment Deletions

To complement our computational approach for identifying large fragment deletions in the Ghd7 gene, we designed a PCR-based method using two primer pairs: G1-F/R targeting the gene’s internal sequence and G2-F/R amplifying conserved flanking regions (Table S3). PCR amplification enables precise determination of large fragment deletions in the Ghd7 region. When the Ghd7-containing fragment is deleted, G1 primers (targeting the coding sequence) fail to amplify a product, while G2 primers (anchored to conserved flanking regions) generate a distinct band. Conversely, if the Ghd7 gene is present, G1 primers amplify the expected Ghd7 fragment, while G2 primers show no amplification.
This PCR-based method provides a rapid, cost-effective strategy for validating large deletions in rice germplasms, demonstrating strong concordance with NGS results (Figure 3).

3.4. Haplotype Analysis of Ghd7

The Ghd7 gene coordinately regulates heading date and yield-related traits in response to photoperiod. Analyzing the natural allelic variations in the Ghd7 gene in different rice germplasms can systematically reveal the mechanism of its coordinated regulation of photoperiod response, heading date, and yield-related traits, and provide key targets for molecular design breeding. Given the rapid development of rice germplasm resources and genomic sequencing, previous analyses of Ghd7 variation types have become insufficiently comprehensive. Therefore, we conducted a comprehensive and systematic analysis of Ghd7 genomic variations. Building on the identification of large fragment deletions, we performed a comprehensive haplotype analysis of the Ghd7 gene using 10,548 NGS datasets from rice germplasm resources. After excluding samples with missing data or heterozygosity, we obtained 3637 high-quality germplasm datasets. We identified 211 SNPs in the Ghd7 genomic sequence and excluded those located in synonymous codon positions or introns. Ultimately, we identified 46 SNPs and one InDel variant, including 42 nonsynonymous codon variants, one insertion at a conserved amino acid site, two splicing site variants, and two premature stop mutations (Table S4). Haplotype analysis revealed that indica rice subgroups were primarily distributed in haplotypes H0, H1, H3, H5, H6, and H8, while japonica rice subgroups were mainly in H2, H4, and H7. Splicing site variations were found in indica rice varieties, while premature stop mutations were detected in japonica rice varieties, indicating distinct selective pressures on Ghd7 in different rice subspecies (Figure 4A).

3.5. Geographical Distribution of Ghd7 Haplotypes

The geographic distribution of Ghd7 haplotypes reveals how natural allelic variation aligns with regional adaptation. Based on the 49 haplotypes identified, we constructed a global distribution map of Ghd7 variants, including loss-of-function alleles (e.g., splice-site and premature termination mutations) and functional haplotypes (Figure 4B). The results showed that deletion, termination, and splicing site variants were distributed across different regions. Deletion types were present in indica, japonica, and aus subspecies, indicating widespread distribution. Splicing site variants were predominantly found in Southeast Asia, China, India, and Iraq, mainly in indica rice. Premature stop mutations were primarily in Northeast China and Jiangsu, all in temperate japonica varieties. The geographic distribution of Ghd7 allelic diversity highlights its divergent roles in rice populations and demonstrates that natural variation in this gene is driven by ecological selection pressures. These findings provide a genetic foundation for tailoring rice varieties to specific climatic zones through allele-specific breeding strategies.

3.6. Validation of Splicing Site Variation and Improvement of Heading Date

Building on the foundation of the numerous variant sites identified, we intend to utilize PE technology for validation. In this study, we targeted a splice site variant at genomic position 9,152,733 in haplotype H6. Compared to the reference genome Nip, YX1B exhibited a mutation where adenine (A) at the first 3′ splice site of the second exon of Ghd7 was changed to cytosine (C). This mutation led to aberrant mRNA splicing, resulting in a 34 bp deletion and a frameshift mutation that caused premature termination of the CCT domain (Figure 5A,B). Although this site has been reported, its function has not been validated to date [74]. To validate the functional impact of this variant, we applied PE technology to edit the splice-site mutation in YX1B. Primers flanking the splice site were used to confirm successful prime editing in T0 transgenic lines. The edited plants exhibited a delayed heading date of approximately 16 days, confirming that the splice-site variant indeed causes early heading (Figure 5C).
One of the main constraints in hybrid rice seed production is the asynchronous heading date between the parental lines, which hinders the mechanization of seed production. This modification synchronized flowering between YX1B and the hybrid rice restorer line Yahui 2115 (YH2115R), enabling the same sowing and planting and enhancing seed production efficiency (Figure 5D). Yield comparisons between the edited hybrid rice Yixiangyou 2115 (Ghd72088C-A) and the control Yixiangyou2115 showed no significant difference, indicating that the editing did not negatively affect yield (Figure 5E). This study demonstrates the potential of integrating pan-genomics and precision gene editing to improve agronomic traits and accelerate crop improvement. The successful editing of this splice-site variant in YX1B provides a new strategy for the improvement and application of heading date in rice varieties.

4. Discussion

The advancement of sequencing technology and the significant reduction in sequencing costs have revolutionized plant genomics, enabling the assembly of genomes for an increasing number of species [4]. The pan-genome, which encompasses all sequences for a species, including consensus sequences, large SVs, and small variations such as SNPs and InDels, has emerged as a critical tool for genomic research. A key challenge now is how to effectively utilize the pan-genome to mine genetic variants and present them graphically [12].
In this study, we leveraged high-quality genomes from 372 genetically diverse rice varieties to analyze natural allelic variation at the Ghd7 locus, which plays a crucial role in rice ecological adaptation. Using Nip as the reference genome and through multiple sequence alignment (Table S5), we identified a total of 42 variation sites (excluding introns), including 13 insertion-deletions (InDels) and 27 nonsynonymous codons, as well as one premature termination mutation and one previously unreported splice-site mutation. Compared to analyses using NGS data alone, our pan-genomic approach identified more indels and confirmed most previously reported variants, demonstrating the reliability of the pan-genomic analysis. However, the limited number of high-quality genomes restricts the identification of specific variants, highlighting the need for more comprehensive pan-genomic resources to fully explore gene function.
Our SVs analysis revealed that 977 accessions harbor large fragment deletions in Ghd7, indicating that structural variants affecting this gene are widespread across diverse genetic backgrounds. This aligns with previous findings that allelic variation at Ghd7 is a major contributor to natural variation in heading date among rice cultivars [38]. Notably, functional validation of the Ghd7 H6 haplotype remains relatively scarce in crop species. Fortunately, PE accurately corrects single-base substitutions without introducing double-strand breaks, providing a powerful tool for confirming the phenotypic effects of subtle genomic alterations [70,75]. In this case, we succeeded in the restoration of the wild-type splicing site in H6 in the maintainer line YX1B, which led to a delay in the heading date by approximately 16 days, confirming the functional importance of the splice-site mutation in disrupting Ghd7 activity.
While our findings provide compelling evidence linking this haplotype H6 to the altered Ghd7 heading date function, we acknowledge certain mechanistic limitations. Specifically, we did not assess Ghd7 protein abundance, subcellular localization, or downstream DNA-binding activity, all of which could provide additional support for the causal role of the identified variant. Previous studies have shown that proper nuclear localization and repression function of Ghd7 require an intact CCT domain [38]. It remains unclear whether the truncated protein resulting from aberrant splicing in YX1B is subject to proteolytic degradation, mislocalization, or loss of function. Future investigations employing Western blotting, confocal microscopy, or interactome profiling could elucidate the downstream consequences of such mutations at the protein level.
Another limitation is the underexplored role of regulatory variation in the promoter region of Ghd7. Transcription of Ghd7 is tightly regulated by photoperiodic and circadian signals, and cis-regulatory elements within its promoter have been implicated in shaping diurnal expression patterns and adaptation to different latitudinal zones [76]. SVs or SNPs in these noncoding regulatory regions could modulate expression levels, timing, or epigenetic configuration, thereby influencing heading date independently of coding sequence variation. Integrating transcriptomic and epigenomic datasets, such as RNA-seq, ATAC-seq, or ChIP-seq, across different haplotypes would be a valuable next step in characterizing the transcriptional landscape and regulatory architecture of Ghd7.
Moreover, from a breeding perspective, our study demonstrates the practical utility of pan-genome-guided genome editing. Large fragment deletions and the splice site of H6 were primarily found in three-line and two-line sterile lines, while strong functional Ghd7 was concentrated in restorer lines. Varieties such as Kitaake, Chimao, and KY131 exhibit premature termination mutations, suggesting a breeding tendency for Ghd7 haplotype selection. By correcting the Ghd7 splice site of H6 in YX1B using PE, we synchronized its flowering with the restorer line YH2115R. This synchronization enables efficient mechanized seed production and enhances the efficiency of the hybrid seed set. This “genotype-by-editing” strategy, where pan-genomic variation informs precise genome editing interventions, could be broadly extended to other agronomic traits such as plant height, stress resistance, and yield components. Our work not only deepens the functional understanding of Ghd7 variation but also illustrates a translational approach to precision breeding through the convergence of pan-genomics and genome editing technologies.

5. Conclusions

In conclusion, our study demonstrates that leveraging high-quality genomes and extensive genomic data to analyze functional gene variants and validate them using PE technology is an effective approach for identifying superior allelic variations. This method not only provides a powerful tool for gene discovery and functional validation but also offers a template for future research. By integrating advanced genomic technologies with precise gene editing, our study highlights the potential to accelerate crop improvement and enhance agronomic traits. The successful application of this approach in the analysis and validation of Ghd7 variants sets a precedent for similar studies in other crops and genes, paving the way for more efficient and targeted genetic enhancement in plant breeding programs.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/genes16040462/s1, Table S1: List of high-quality genomic information; Table S2: 4613 pieces of material information; Table S3: List of primers used in this study; Table S4: Haplotype analysis of Ghd7; Table S5: Haplotype analysis of high-quality genomes.

Author Contributions

Conceptualization, X.D.; data curation, J.L. and J.P.; formal analysis, Y.W., B.Y. and P.W.; funding acquisition, C.S.; investigation, J.W., S.L., L.Z. (Luyao Zhou), J.P., Y.G., Y.Z., L.Z. (Lanjing Zhang), X.Z. and D.X.; methodology, C.S.; resources, C.H.; supervision, Y.W., B.Y., P.W., X.D. and C.S.; writing—original draft, J.W. and S.L.; writing—review and editing, S.L., X.D. and C.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by grants from the National Natural Science Foundation of China (32172022, 31371602, 31401358, and 32260502) and the Natural Science Foundation of Sichuan Province (2023NSFSC0213, 2024NSFTD0022, and 2022NSFSC1748).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors.

Acknowledgments

Thank you to all the individuals and institutions that contributed to this research and to the funding organizations that provided financial support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Genes 16 00462 g001
Figure 1. Dot plot of 12 materials compared to Nipponbare (Nip) sequences. The horizontal coordinate is the physical location of the Nip genome, and the vertical coordinate is the physical location of the genome of the comparison variety. The completeness of the long diagonal line in the middle is positively correlated with the matching degree of the two genomes. The scattered dots and short lines on both sides of the diagonal line represent homologous sequences with different physical locations, and the red scattered dots and short lines represent homologous sequences with inversion between the two. The blue dotted line represents the position of Ghd7 in Nip. The more scattered points and short lines indicate that there are more homologous sequences with nonlinear relationships, and the greater the variation between varieties.
Figure 1. Dot plot of 12 materials compared to Nipponbare (Nip) sequences. The horizontal coordinate is the physical location of the Nip genome, and the vertical coordinate is the physical location of the genome of the comparison variety. The completeness of the long diagonal line in the middle is positively correlated with the matching degree of the two genomes. The scattered dots and short lines on both sides of the diagonal line represent homologous sequences with different physical locations, and the red scattered dots and short lines represent homologous sequences with inversion between the two. The blue dotted line represents the position of Ghd7 in Nip. The more scattered points and short lines indicate that there are more homologous sequences with nonlinear relationships, and the greater the variation between varieties.
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Figure 2. Identification of large block missing using short-read sequencing data. (A) The sequences of Nip and CN1 are compared in the dot plot, the horizontal coordinate is the sequence of Nip, the vertical coordinate is the sequence of CN1, the blue dotted line represents the position of Ghd7 in Nip, and the three red arrows represent the three designed intervals of 400 bp length. The scattered dots and short lines on both sides of the diagonal line represent homologous sequences with different physical locations, and the red scattered dots and short lines represent homologous sequences with inversion between the two. (B) The two exons (indicated in the black rectangle) and the intron (indicated in the gray line) of Ghd7 are shown in the graphics. The blue dotted rectangle indicates the position of interval b in Ghd7. (C) Comparison of short-read sequences mapping to different locations of the reference genome.
Figure 2. Identification of large block missing using short-read sequencing data. (A) The sequences of Nip and CN1 are compared in the dot plot, the horizontal coordinate is the sequence of Nip, the vertical coordinate is the sequence of CN1, the blue dotted line represents the position of Ghd7 in Nip, and the three red arrows represent the three designed intervals of 400 bp length. The scattered dots and short lines on both sides of the diagonal line represent homologous sequences with different physical locations, and the red scattered dots and short lines represent homologous sequences with inversion between the two. (B) The two exons (indicated in the black rectangle) and the intron (indicated in the gray line) of Ghd7 are shown in the graphics. The blue dotted rectangle indicates the position of interval b in Ghd7. (C) Comparison of short-read sequences mapping to different locations of the reference genome.
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Figure 3. Identification of large fragment deletion by PCR. (A) Dot plot of G46 compared to Nip sequences. The red arrows indicate the locations of the two primers. The scattered dots and short lines on both sides of the diagonal line represent homologous sequences with different physical locations, and the red scattered dots and short lines represent homologous sequences with inversion between the two, the blue dotted line represents the position of Ghd7 in Nip. (B) PCR results of the two primers of Nip and G46.
Figure 3. Identification of large fragment deletion by PCR. (A) Dot plot of G46 compared to Nip sequences. The red arrows indicate the locations of the two primers. The scattered dots and short lines on both sides of the diagonal line represent homologous sequences with different physical locations, and the red scattered dots and short lines represent homologous sequences with inversion between the two, the blue dotted line represents the position of Ghd7 in Nip. (B) PCR results of the two primers of Nip and G46.
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Figure 4. Haplotype distribution frequency and geographical distribution of Ghd7. (A) Distribution frequency of the Ghd7 haplotype in different subspecies. GJ (Japonica), Or1 (Oryza rufipogon1), Or2 (Oryza rufipogon2), XI (Indica). (B) Geographical distribution of materials with different variant forms of Ghd7. The colored dots represent the classification of different variants.
Figure 4. Haplotype distribution frequency and geographical distribution of Ghd7. (A) Distribution frequency of the Ghd7 haplotype in different subspecies. GJ (Japonica), Or1 (Oryza rufipogon1), Or2 (Oryza rufipogon2), XI (Indica). (B) Geographical distribution of materials with different variant forms of Ghd7. The colored dots represent the classification of different variants.
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Figure 5. Detection and functional verification of splicing site variation. (A) Ghd7 gene structure and Ghd7 genome sequence comparison between Yixiang 1B (YX1B) and Nip. The blue dotted frame is the first 3’ splice site of the second exon of Ghd7. The two exons (the first exon is denoted by the black rectangle and the second by the gray rectangle) and the intron (indicated in the gray line) of Ghd7 are shown in the graphics. The red frame is the splicing site identified by the Nip and the red dotted frame is the splicing site identified by YX1B. (B) Ghd7 gene structure and Ghd7 cDNA sequence comparison between YX1B and Nip. The two exons (the first exon is denoted by the black rectangle and the second by the gray rectangle) of Ghd7 are shown in the graphics. (C) YX1B-Ghd72088C-A late heading phenotype. (D) Comparison of the heading date of YX1B, YX1B-Ghd72088C-A, and YH2115R. (E) Comparison of yield per plant between hybrid rice Yixiangyou 2115 and Yixiangyou 2115 (Ghd72088C-A). p values for One-Way ANOVA; (ns) no significance.
Figure 5. Detection and functional verification of splicing site variation. (A) Ghd7 gene structure and Ghd7 genome sequence comparison between Yixiang 1B (YX1B) and Nip. The blue dotted frame is the first 3’ splice site of the second exon of Ghd7. The two exons (the first exon is denoted by the black rectangle and the second by the gray rectangle) and the intron (indicated in the gray line) of Ghd7 are shown in the graphics. The red frame is the splicing site identified by the Nip and the red dotted frame is the splicing site identified by YX1B. (B) Ghd7 gene structure and Ghd7 cDNA sequence comparison between YX1B and Nip. The two exons (the first exon is denoted by the black rectangle and the second by the gray rectangle) of Ghd7 are shown in the graphics. (C) YX1B-Ghd72088C-A late heading phenotype. (D) Comparison of the heading date of YX1B, YX1B-Ghd72088C-A, and YH2115R. (E) Comparison of yield per plant between hybrid rice Yixiangyou 2115 and Yixiangyou 2115 (Ghd72088C-A). p values for One-Way ANOVA; (ns) no significance.
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Wang, J.; Liu, S.; Pu, J.; Li, J.; He, C.; Zhang, L.; Zhou, X.; Xu, D.; Zhou, L.; Guo, Y.; et al. Comprehensive Analysis of Ghd7 Variations Using Pan-Genomics and Prime Editing in Rice. Genes 2025, 16, 462. https://doi.org/10.3390/genes16040462

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Wang J, Liu S, Pu J, Li J, He C, Zhang L, Zhou X, Xu D, Zhou L, Guo Y, et al. Comprehensive Analysis of Ghd7 Variations Using Pan-Genomics and Prime Editing in Rice. Genes. 2025; 16(4):462. https://doi.org/10.3390/genes16040462

Chicago/Turabian Style

Wang, Jiarui, Shihang Liu, Jisong Pu, Jun Li, Changcai He, Lanjing Zhang, Xu Zhou, Dongyu Xu, Luyao Zhou, Yuting Guo, and et al. 2025. "Comprehensive Analysis of Ghd7 Variations Using Pan-Genomics and Prime Editing in Rice" Genes 16, no. 4: 462. https://doi.org/10.3390/genes16040462

APA Style

Wang, J., Liu, S., Pu, J., Li, J., He, C., Zhang, L., Zhou, X., Xu, D., Zhou, L., Guo, Y., Zhang, Y., Wang, Y., Yang, B., Wang, P., Deng, X., & Sun, C. (2025). Comprehensive Analysis of Ghd7 Variations Using Pan-Genomics and Prime Editing in Rice. Genes, 16(4), 462. https://doi.org/10.3390/genes16040462

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