PGT_2025v16n2

Plant Gene and Trait 2025, Vol.16 http://genbreedpublisher.com/index.php/pgt © 2025 GenBreed Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved.

Plant Gene and Trait 2025, Vol.16 http://genbreedpublisher.com/index.php/pgt © 2025 GenBreed Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. GenBreed Publisher is an international Open Access publisher specializing in plant protection, plant breeding, molecular genetics, proteomics and genetic diversity registered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada. Publisher GenBreed Publisher Edited by Editorial Team of Plant Gene and Trait Email: edit@pgt.genbreedpublisher.com Website: http://genbreedpublisher.com/index.php/pgt Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Plant Gene and Trait (ISSN 1925-2013) is an open access, peer reviewed journal published online by GenBreed Publisher. The journal publishes articles that address the fundamental nature of genes and genomes at any level, either experimental or computational approaches, in plants as well as algae, including applications of novel techniques to plant biology and plant trait improvement. All papers chosen for publishing should be innovative research work in fields of plant genes or traits, plant protection, plant breeding, particular in the areas of functional genomics, genomic tools, genome technologies, transgene, genome sequencing analysis, molecular genetics, proteomics, genetic diversity, heterosis, genetic characteristics, genetic modification, genotype-phenotype relationships, stress resistance characteristics, QTL analysis, biochemistry, physiology and morphology. All the articles published in Plant Gene and Trait are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. GenBreed Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.

Plant Gene and Trait (online), 2025, Vol. 16 ISSN 1925-2013 http://genbreedpublisher.com/index.php/pgt © 2025 GenBreed Publisher, an online publishing platform of Sophia Publishing Group. All Rights Reserved. Sophia Publishing Group (SPG), founded in British Columbia of Canada, is a multilingual publisher Latest Content Dissecting Complex Traits in Rice: Insights from Recent GWAS Findings Deshan Huang, Ruchun Chen, Jianquan Li Plant Gene and Trait, 2025, Vol. 16, No. 2, 47-55 Advances in Grapevine Disease Resistance: CRISPR/Cas9 Applications Dandan Huang, Xingzhu Feng Plant Gene and Trait, 2025, Vol. 16, No. 2, 56-63 Molecular Basis of Flower Color Variation in Rapeseed: Breeding Implications Xuming Lv, Xuelian Jiang, Yeping Han Plant Gene and Trait, 2025, Vol. 16, No. 2, 64-73 The Selection and Evaluation of Excellent Varieties of Tongzi Yimucao Based on Molecular Marker Technology Yufen Wang, Jianli Lu, Lianming Zhang Plant Gene and Trait, 2025, Vol. 16, No. 2, 74-84 Effects of Different Medium Formulations on the Proliferation Rate and Growth Performance of Anoectochilus roxburghii Tissue-Cultured Seedlings Zonghui Liu, Meifang Li Plant Gene and Trait, 2025, Vol. 16, No. 2, 85-91

Plant Gene and Trait 2025, Vol.16, No.2, 47-55 http://genbreedpublisher.com/index.php/pgt 47 Research Insight Open Access Dissecting Complex Traits in Rice: Insights from Recent GWAS Findings Deshan Huang, Ruchun Chen, Jianquan Li Hier Rice Research Center, Hainan Institute of Tropical Agricultural Resources, Sanya, 572025, Hainan, China Corresponding email: jianquan.li@hitar.org Plant Gene and Trait, 2025, Vol.16, No.2 doi: 10.5376/pgt.2025.16.0006 Received: 10 Feb., 2025 Accepted: 11 Mar., 2025 Published: 20 Mar., 2025 Copyright © 2025 Huang et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Huang D.S., Chen R.C., and Li J.Q., 2025, Dissecting complex traits in rice: insights from recent GWAS findings, Plant Gene and Trait, 16(2): 47-55 (doi: 10.5376/pgt.2025.16.0006) Abstract GWAS has been proven to be a useful tool that helps promote the genetic improvement of rice. This study introduces several commonly used GWAS methods in rice, summarizes the latest GWAS achievements such as key loci related to grain size, drought resistance and protein content, and analyzes the traits related to yield through specific cases. These findings provide references for subsequent gene function verification and actual breeding. In the future, if GWAS can be combined with genomic selection and functional genomics, it is expected to accelerate the breeding of rice varieties with excellent agronomic traits. Keywords Genome-wide association studies (GWAS); Rice (Oryza sativa L.); Complex traits; Agronomic improvement; Genetic loci 1 Introduction Rice (Oryza sativa L.) is the main food crop for more than half of the global population, and its yield and quality are crucial to food security. The genetic structure of rice is complex, involving traits such as yield, flowering time, and grain quality. These traits are usually jointly controlled by multiple genes and are also affected by the environment (Huang et al., 2010; Huang et al., 2011). To cultivate high-yield and stress-resistant rice varieties, it is necessary to first clarify the genetic basis of these complex traits, which is very important for responding to the growing demand for food (Aloryi et al., 2022; Ashfaq et al., 2023). Wang et al. (2019) demonstrated that GWAS could scan the entire genome to identify single nucleotide polymorphisms (SNPs) loci related to certain traits and recognize the key regions that affect phenotypic differences. This method has achieved success in various crops such as rice and is helpful for understanding the genetic background of some important agronomic traits (Zhong et al., 2021). Singh et al. ’s research in 2022 found that the resolution and accuracy of GWAS have been significantly enhanced with the development of high-throughput sequencing technology and the improvement of statistical methods, making it easier to identify candidate genes and conduct functional studies. GWAS is very useful for understanding how complex traits such as yield, quality and disease resistance of rice are inherited (Huang et al., 2010; Wang et al., 2019). Huang et al. (2011) and Aloryi et al. (2022) have identified many important loci related to traits such as rice yield and flowering time through GWAS in their early studies and in recent years. These achievements have made the genetic structure of rice clearer and also provided more direction when choosing good varieties with molecular markers. Zuo and Li (2014) hold that it can be easier to breed new rice varieties that are both high-yielding and disease-resistant through these findings. This study summarizes the latest achievements of GWAS in rice, with a focus on its performance in the study of the genetics of complex traits. It introduces some methods used in GWAS and elaborates on the important loci and candidate genes that have been discovered so far and their roles in rice breeding and genetic research. This study hopes that GWAS can bring more help to ensuring global food security.

Plant Gene and Trait 2025, Vol.16, No.2, 47-55 http://genbreedpublisher.com/index.php/pgt 48 2 Methodology of GWAS in Rice 2.1 Key GWAS approaches in rice research The traditional GWAS method involves conducting intensive genetic testing on many different rice individuals to identify genetic changes related to certain traits. However, this method requires testing a large number of samples and has a relatively high cost. To improve efficiency, new methods such as extreme-phenotype GWAS (XP-GWAS) have been developed. This method only detects individuals with particularly outstanding manifestations, which can save a lot of work and also identify important variations related to traits (Yang et al., 2015). PrediXcan reveals the underlying molecular mechanism by predicting the expression of genes and correlating these prediction results with traits (Gamazon et al., 2015). GCTA can be used to estimate the extent to which SNPS affect traits. 2.2 Data collection and phenotyping To better identify SNPS related to agronomic traits, researchers usually sequence many local rice varieties to establish high-density haplotype maps. Huang et al. (2010) sequenced 517 local varieties and identified approximately 3.6 million SNPS in total. These data helped researchers analyze 14 different agronomic traits and explain approximately 36% of the phenotypic differences. Phenotypic determination in this process involves meticulous measurement of traits to ensure the authenticity and reliability of the data. This comprehensive approach enables scientists to effectively match genetic variations with trait expressions, providing a lot of useful information for rice breeding (Wang et al., 2019; Huang et al., 2024). 2.3 Genotyping techniques and data analysis pipelines The second-generation genome sequencing technology is commonly used nowadays, which can provide very dense genetic information. Exome sequencing will focus on detecting genes and the regions around them. This method helped identify approximately 940 000 variations in the study of corn by Yang et al. in 2015. A similar method can also be used in the study of rice. When analyzing data, researchers usually use a variety of statistical methods to process and interpret this massive amount of information. GCTA can estimate the genetic relationships among individuals and the explanatory power of SNPS. Yang et al. (2011) and Zhang et al. (2019) demonstrated that multilocus analysis methods such as mrMLM and FASTmrEMMA could overcome some problems of traditional single-point analysis by considering the population structure and the influence of multiple genes. 3 Key Findings from Recent GWAS in Rice 3.1 Genetic loci associated with yield-related traits Huang et al. (2011) analyzed 950 rice varieties from around the world and identified 32 new loci related to flowering time and 10 grain traits, indicating that the larger the sample size, the easier it is to discover useful genetic variations. In 2021, Zhong et al. used 529 core rice varieties and identified multiple SNPS related to grain length, width, thickness, 1 000-grain weight and yield per plant. This study employed both single-site and multi-site methods and discovered a total of 20 recurrent quantitative trait nucleotides (QTNs). These traits of rice yield are very complex and are controlled by many different genes together. Kumar et al. (2021) specifically studied the performance of rice under high night temperature conditions and identified several important SNPS related to panicle length and the number of spikelets per panicle. These two traits are crucial components of biomass and harvest index. The discovery of these loci has given us a new understanding of how these traits are inherited under stress conditions. 3.2 Loci related to stress resistance traits Guo et al. (2018) conducted a GWAS study on 507 rice germplasms and identified 470 loci related to drought resistance, some of which were at the same location as the drought-resistant QTL previously found (Figure 1). Selamat and Nadarajah’s research in 2021 identified several relatively stable drought-resistant QTLS in different rice varieties and environments. They also discovered some key genes such as ABI5 and aquaporin PIP 1-2, which play significant roles in the drought resistance response of rice.

Plant Gene and Trait 2025, Vol.16, No.2, 47-55 http://genbreedpublisher.com/index.php/pgt 49 Figure 1 Drought response monitored by 51 I-traits measured by RAP (Adopted from Guo et al., 2018) Image caption: (A) Raw images of a rice variety (Swarna) measured at eight time points during progressive drought stress and rewatering; (B) Soil water content measured by TDR at eight time points. Two rounds of drought stress were applied to three plants of the rice variety Swarna. Based on soil water content, the number of days of drought stress, and the stay-green level of the stressed plants after rewatering, the levels of stress, i.e. “moderate stress” (2 days for the first round of stress) and “severe stress” (3 days for the second round of stress), were determined. Error bars indicate the SE based on three biological replicates; (C) Normalized value of 51 i-traits during progressive drought stress and rewatering. The range of values at eight time points for each i-trait was transformed to 0-1 by linear normalization. y=(x-min)/(max-min) where x, y, max, and min represent raw data, normalized data, maximum, and minimum, respectively.; (D) Three temporal patterns of i-traits (GPAR, TPA, and GPA) during progressive drought stress and rewatering. GPAR, green projected area ratio; TPA, total projected area; GPA, green projected area. Error bars indicate the SE based on three biological replicates (Adopted from Guo et al., 2018) GWAS also played a significant role in identifying the genetic loci related to the disease resistance of rice. Huang et al. (2011) and Wang et al. (2019) hold that although there is no specific research on disease resistance at present, if the GWAS results are combined with the information of functional genomes, it is still possible to discover some key variations related to disease resistance traits.

Plant Gene and Trait 2025, Vol.16, No.2, 47-55 http://genbreedpublisher.com/index.php/pgt 50 Researchers have identified many gene loci related to salt tolerance in rice through GWAS, including some important positions that affect the Na+/K+ ratio and other salt tolerance indicators. Kumar et al. (2015) found in the study of 220 rice germplasm that the Saltol region on chromosome 1 was regarded as the main gene region affecting salt tolerance at the seedling stage. Kumar et al. (2021) also identified sites related to the heat tolerance of rice under stress conditions of high nocturnal temperatures. 3.3 Identification of genes influencing nutritional quality GWAS was used to study which genes in rice affect the content of proteins and minerals and to identify the gene loci related to amylose and starch composition in rice. Wang et al. (2019) demonstrated in their study that it is still possible to identify some candidate genes that affect nutritional quality by combining the results of GWAS with the information of functional genomes. Huang et al. (2010) argued that the wide application of GWAS in the genetic research of rice indicates that there have been many advancements in understanding the genetic mechanisms of traits such as amylose and the gene loci related to starch composition in rice. 3.4 Mechanistic insights from GWAS into complex trait control Guo et al. (2018) demonstrated in their study that the combination of high-density haplotype maps and functional genomic information is helpful for researchers to identify the key variations behind these traits, facilitating a better understanding of their genetic structure. Confirming the functions of these key genes through subsequent experiments also makes it clearer how these traits are controlled. Wang et al. (2019) and Kumar et al. (2021) demonstrated that these findings lay the foundation for formulating more targeted and efficient rice breeding methods. 4 Case Study: Dissecting Yield-Related Traits through GWAS 4.1 Selection of specific yield-related traits The traits such as the number of grains per panicle (GPP), 1 000-grain weight (KGW), and the number of tillers per plant (TP) are often selected for focused research because they together determine how much rice a rice plant can produce (Su et al., 2021). Traits such as grain length (GL), grain width (GW), grain thickness (GT), and 1000-grain weight (TGW) will affect the quality and yield of rice (Zhong et al., 2021). Selecting these traits for study is beneficial for a more comprehensive understanding of the genetic factors of yield, thereby formulating more targeted breeding strategies. 4.2 GWAS analysis and identification of key genetic regions Researchers have discovered many important loci related to yield by analyzing different types of rice varieties. Su et al. (2021) conducted single-site and multi-site GWAS analyses on 529 core rice varieties and identified a total of 20 quantitative trait nucleotides (QTNs) related to yield traits. Another meta-GWAS study identified 3 589 significant loci related to constituent traits, indicating that the genetic structure of rice yield is very complex. In an early study by Huang et al. (2011), 32 new loci related to flowering time and grain-related traits were identified in 950 different rice varieties through a high-density haplotype map. 4.3 Functional validation of key loci After researchers identify important gene regions related to rice yield through GWAS, they usually conduct some functional verifications such as gene expression analysis, mutant analysis, and map cloning to confirm whether these genes are really effective. Zhong et al. (2021) found that the two candidate genes LOC_Os09g02830 and LOC_Os07g31450 would respectively affect grain width (GW) and 1 000-grain weight (TGW) (Figure 2). In 2022, Aloryi et al. used meta-QTL analysis to identify 52 candidate genes that might affect grain yield, including some genes encoding cytochrome P450, zinc finger protein and MADs-box protein. 4.4 Implications for rice breeding and future applications The findings obtained from GWAS and the functional verification of key loci are of great help to rice breeding. Breeding experts can develop molecular markers for marker-assisted selection (MAS) by identifying and confirming those gene regions related to yield, thereby more effectively selecting high-yield varieties. Begum et al. (2015) applied the GWAS results to breeding projects and identified haplotypes that could be used to screen dwarf,

Plant Gene and Trait 2025, Vol.16, No.2, 47-55 http://genbreedpublisher.com/index.php/pgt 51 early-flowering and high-yielding varieties. Su et al. (2021) held that component traits such as the number of grains per spike (GPP) and the number of tillers (TP), because they have a positive impact on yield, can be regarded as indirect indicators of yield and also provide a basis for designing the ideal plant type. Gene editing tools such as CRISPR/Cas9 may be used in the future to precisely modify these key genes, thereby enhancing the yield potential of rice. If GWAS can be continuously combined with functional genomics and breeding practices, it will bring new hope for cultivating high-yield rice and solving global food problems. Figure 2 (a) Local linkage disequilibrium for qTGW-7-1. (b) Box plot of TGW traits about four haplotypes of LOC_Os07g31450. The x-axis represents four haplotypes of LOC_Os07g31450 and the y-axis represents 1000-grain weight. The table below is the detailed information of four haplotypes.(c) Heatmap of the expression pattern of LOC_Os07g31450 in various tissues among three local rice species. The y-coordinate indicates three species and relative expression, and x-coordinate indicates 39 different parts and development stages of rice tissue. Red represents higher gene expression and green indicates lower gene expression level, the gene expression levels are log2 transformed (Adopted from Zhong et al., 2021) 5 Challenges and Limitations in GWAS for Rice 5.1 Population structure and confounding factors If the group structure is not handled properly, false associations may arise. Population structure refers to the different subpopulations included in the study. Because they have different ancestors, the frequencies of some alleles are inherently different, and this difference has nothing to do with the traits themselves. Sul et al. (2016) and Sul et al. (2018) indicated that if not distinguished, incorrect results might be obtained. It seems that a certain variation is related to traits, but in fact, it is only due to different population structures. To reduce this impact, scientists have developed hybrid models that can take into account both group structure and kinship during analysis. However, this type of model is rather complex to calculate and not easy to operate (Jiang et al., 2019). 5.2 Limitations in phenotypic data and measurement Because many traits are rather complex and the measurement requires a very high degree of accuracy, the determination of phenotypes often encounters difficulties. In rice research, traits such as yield, flowering time and

Plant Gene and Trait 2025, Vol.16, No.2, 47-55 http://genbreedpublisher.com/index.php/pgt 52 grain quality are often influenced by multiple genes and environmental factors, which makes it difficult to obtain unified and accurate data (Huang et al., 2011; Korte and Farlow, 2013). Phenotypic determination in large-scale rice populations is costly and labor-intensive, and may also limit the scale of research. As a result, it becomes difficult to discover those truly useful genetic variations (Yano et al., 2016). 5.3 Environmental variability and its impact on GWAS findings Changes in conditions such as temperature, humidity, soil type and moisture will all affect the expression of traits and their relationship with genes. Wang et al. (2019) argued that the gene-by-environment interaction (GEI) would make the results of GWAS more difficult to interpret, because the same genetic variation might have different effects in different environments. It is not easy to control these environmental differences. If not, it may lead to inconsistent research results and even be difficult to replicate (Sul et al., 2016). 5.4 Integration of GWAS with other genomic tools If GWAS can be combined with other genomic tools, the results will be clearer and more explanatory. Cai et al. ’s research in 2023 indicates that fine positioning technology can take advantage of genetic differences among different varieties, prioritize the study of those variations that might truly play a role, and also reduce errors caused by interference. Using GWAS together with single-cell data or functional genomic information is also useful for a deeper understanding of the biological mechanisms behind these traits. However, Huang et al. (2011) proposed that such comprehensive methods require very complex computing tools and a large amount of detailed data, but these conditions are not always available. 6 Future Prospects of GWAS in Rice Improvement 6.1 Combining GWAS with genomic selection (GS) GWAS can provide a lot of detailed information about genes and traits. If GS uses this information, it can predict more accurately which varieties are better. The BLUPGA model incorporates the results of GWAS into the genomic relationship matrix and performs more accurately than traditional methods when predicting multiple traits of rice. Using trait marker selection and prediction models that can take into account different environments, there is also better performance in predicting complex traits such as drought resistance. This combination approach can help breed new rice varieties with high yield and stress resistance more quickly, promoting the sustainable development of agriculture. 6.2 Functional genomics and GWAS for trait dissection Huang et al. (2011) identified many candidate genes related to important agronomic traits by using high-density haplotype maps and sequence-based GWAS. These genes can be verified experimentally for their roles in trait expression. Some important genes found through GWAS were later confirmed in experiments, indicating that this method is very useful in the study of traits (Wang et al., 2019). Scientists can gain a clearer understanding of how traits are regulated by genes through the combination of GWAS and functional genomes, and it also provides the possibility of precisely intervening in key genes in the future. 6.3 Translating GWAS findings into breeding programs Applying the research results of GWAS to actual rice breeding is an important step for genetic research to truly play a role. Finding gene loci related to important traits provides very useful resources for marker-assisted selection (MAS) and genomic selection (GS) (Huang et al., 2010). Spindel et al. (2015) argued that using the results of GWAS to improve the GS model could make trait prediction more accurate and enhance the efficiency of breeding. Spindel et al. (2016) demonstrated that incorporating the latest GWAS findings into the GS model could further optimize breeding strategies and enable useful new genetic variations to be better integrated into excellent breeding materials. These advancements can shorten the breeding time and enhance the genetic improvement effect of rice. 6.4 Opportunities for enhancing GWAS methodologies in rice High-density SNP chips and next-generation sequencing technology enable more genotypes to be studied (Korte and Farlow, 2013). Some new statistical methods, such as the mixed model, can reduce false positive results and

Plant Gene and Trait 2025, Vol.16, No.2, 47-55 http://genbreedpublisher.com/index.php/pgt 53 improve the discovery ability of GWAS. Cortes et al. ’s research in 2021 indicates that future studies can focus more on new directions such as analyzing rare variations and using synthetic association methods, or continue to optimize the GWAS model. If these methodological issues can be resolved, it will make GWAS more accurate, which is helpful for better identifying key variations and gaining a deeper understanding of the genetic structure of complex traits in rice. 7 Conclusion Genome-wide association studies (GWAS) have advanced people's understanding of the genetic mechanisms of complex traits in rice and have played a significant role in identifying gene loci related to important agronomic traits such as flowering time and grain yield over the past decade. Researchers discovered millions of SNPS through high-density haplotype maps and whole-genome sequencing, and established detailed genetic maps. The combination of GWAS and functional genomics has helped scientists identify many key genes and study their functions, facilitating a deeper understanding of the genetic structure of rice. Statistical methods such as linear mixed models, by considering population structure and other interfering factors, have improved the accuracy and analytical ability of GWAS. The recent research results of GWAS have had a significant impact on rice breeding. By identifying the genetic variations and loci related to the ideal traits, breeders can select these traits more specifically during the breeding process and accelerate the breeding speed of new varieties. Identifying candidate genes and conducting functional verification have also helped develop more accurate molecular markers and improved the efficiency of marker-assisted selection. Understanding the genetic mechanisms of complex traits enables researchers to regulate multiple genes simultaneously and cultivate rice varieties that are more productive, more stress-resistant and have better nutrition. Future GWAS studies on rice can expand the diversity of rice germplasm resources, which is useful for better discovering new genetic variations related to traits. Combining multiple omics data such as transcriptomics and metabolomics with GWAS is helpful for understanding the molecular mechanisms behind complex traits from multiple perspectives. The development and use of more complex statistical models can better handle the problems of multi-gene control and the interaction between genes, making the results of GWAS clearer and easier to understand. The application of these research results in rice breeding requires the joint efforts of geneticists, breeding experts and other relevant personnel to ensure that this genetic information is truly useful for increasing rice yield and ensuring food security. Acknowledgments We would like to express our heartfelt gratitude to the advisor, Professor Tang, for his invaluable guidance and strong support throughout this research. We also thank our lab colleagues for their assistance during the writing process. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Reference Aloryi K., Okpala N., Amo A., Bello S., Akaba S., and Tian X., 2022, A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice, Frontiers in Plant Science, 13: 1035851. https://doi.org/10.3389/fpls.2022.1035851 Ashfaq M., Rasheed A., Zhu R., Ali M., Javed M., Anwar A., Tabassum J., Shaheen S., and Wu X., 2023, Genome-wide association mapping for yield and yield-related traits in rice (Oryza sativa L.) using SNPs markers, Genes, 14(5): 1089. https://doi.org/10.3390/genes14051089 Begum H., Spindel J., Lalusin A., Borromeo T., Gregorio G., Hernandez J., Virk P., Collard B., and McCouch S., 2015, Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa), PLoS One, 10(3): e0119873. https://doi.org/10.1371/journal.pone.0119873 Cai M., Wang Z., Xiao J., Hu X., Chen G., and Yang C., 2023, XMAP: cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias, Nature Communications, 14: 6870. https://doi.org/10.1038/s41467-023-42614-7

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Plant Gene and Trait 2025, Vol.16, No.2, 56-63 http://genbreedpublisher.com/index.php/pgt 56 Research Insight Open Access Advances in Grapevine Disease Resistance: CRISPR/Cas9 Applications Dandan Huang, Xingzhu Feng Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China Corresponding email: xingzhu.feng@hibio.org Plant Gene and Trait, 2025, Vol.16, No.2 doi: 10.5376/pgt.2025.16.0007 Received: 23 Feb., 2025 Accepted: 25 Mar., 2025 Published: 03 Apr., 2025 Copyright © 2025 Huang and Feng, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Huang D.D., and Feng X.Z., 2025, Advances in grapevine disease resistance: CRISPR/Cas9 applications, Plant Gene and Trait, 16(2): 56-63 (doi: 10.5376/pgt.2025.16.0007) Abstract This study collates the new progress in grape disease resistance, explains how CRISPR/Cas9 is used in the research and improvement of disease resistance genes, summarizes the pathogenesis of common grape diseases, and which disease resistance genes have been discovered and studied. It also analyzes some examples of improving grape disease resistance using CRISPR technology. This study also elaborates on the possible problems that CRISPR may encounter in the application of grapes, proposes some improvement methods for these problems, and discusses how gene editing can be combined with traditional breeding, how multiple traits can be improved simultaneously in the future, and how to better promote the market. This research aims to provide a scientific basis for the study of grape disease resistance and the breeding of more disease-resistant and market-popular grape varieties. Keywords Grapes; Disease resistance; CRISPR/Cas9; Gene editing; Multi-omics integration; Breeding improvement 1 Introduction The economic value of grapevine (Vitis vinifera) is very high. The grapevine industry holds a very important position in global agriculture. The production of wine, table grapes and raisins all contribute greatly to economic development. However, grapevines are vulnerable to various diseases, especially powdery mildew caused by the fungus Erysiphe necator, which is the most serious one. This disease can cause a significant decrease in yield. Many times, chemical fungicides are needed for control, but these agents may have adverse effects on the environment (Borrelli et al., 2018). Disease-resistant breeding is a crucial step in making grapevine cultivation more stable and environmentally friendly. The traditional breeding methods are slow and not accurate enough. It is not easy to cultivate disease-resistant grapevine varieties. Erdoğan et al. (2023) found that genome editing technologies, especially the emergence of CRISPR/Cas9, have brought new solutions to this problem. This technology can directly and precisely modify the genes of grapevines, enhancing their disease resistance. Ren et al. (2022) believe that it is useful for reducing reliance on chemical agents and promoting more environmentally friendly agricultural methods. The CRISPR/Cas9 technology is changing the way crops are bred by rapidly and precisely improving genes. This technology has been successfully used in grapevine research to edit susceptibility genes like MLO, making grapevines more resistant to powdery mildew. CRISPR/Cas9 is flexible and accurate, and has become a powerful tool for improving disease resistance and other important agronomic traits. Wan et al. (2020) also developed a method called “traceless editing”, which does not leave exogenous DNA and can also reduce regulatory issues related to genetically modified organisms, enabling this technology to be more widely applied. This study will elaborate on the application of CRISPR/Cas9 technology in enhancing the disease resistance of grapevines, focusing on whether targeted mutations in susceptible genes can increase the resistance of grapevines to powdery mildew. This work is very important for grapevine cultivation. It can not only reduce the use of chemicals, but also possibly increase the yield and is more conducive to environmental protection. This research aims to provide assistance for the future cultivation of grapevine varieties that are more disease-resistant and more adaptable to the environment.

Plant Gene and Trait 2025, Vol.16, No.2, 56-63 http://genbreedpublisher.com/index.php/pgt 57 2 Current Status of Grape Disease Resistance Research 2.1 Major diseases of grapes and their pathogenic mechanisms Grapevines are often threatened by fungal diseases such as Plasmopara viticola and Erysiphe necator during the cultivation process, and these diseases can affect the yield and quality of grapevines. Harper et al. (2023) demonstrated in their study that downy mildew often occurs in humid weather, causing leaves to be injured or fall off. Powdery mildew can infect the green parts of grapevines, leaving a layer of white powder on the surface, which looks like mold and affects the photosynthesis of the plants. Botrytis bunch rot caused by Botrytis cinerea can also cause economic losses. At present, it is mostly controlled by spraying fungicides, but this will make the germs drug-resistant. Fassolo et al. (2022) hold that to effectively prevent and control these diseases, it is necessary to understand how the pathogens invade grapevines and the defense mechanisms of the grapevines themselves. 2.2 Advances in research on disease resistance-related genes in grapes Researchers have discovered many genetic loci related to the resistance of grapevines to downy mildew and powdery mildew in recent years. Liu et al. (2023) discovered that Rpv36 and Rpv37 are associated with downy mildew, and Ren14 and Ren15 are related to powdery mildew. These new loci are all concentrated in some genomic regions rich in disease-resistant related genes. Through transcriptome analysis, scientists have also identified different genes expressed when grapevines interact with pathogens, providing people with new insights into some secondary disease-resistant genes in grapevines. 2.3 Applications and limitations of conventional breeding techniques for disease resistance improvement Traditional breeding methods often introduce disease-resistant genes from wild grapevines into cultivated varieties to enhance their disease resistance. However, this method has the problem of a long breeding cycle, and the expression of disease-resistant traits is greatly affected by environmental and pathogen changes, with a relatively complex phenotype (Ricciardi et al., 2024). Possamai and Wiedemann-Merdinoglu (2022) hold that some pathogens will gradually adapt to disease-resistant genes, which will affect the sustainability of the disease-resistant effect. At this point, long-term tracking and the adoption of multiple management methods to deal with it become necessary. 3 Application of CRISPR/Cas9 Technology in Grape Disease Resistance 3.1 Research on disease resistance gene mutation and regulation using CRISPR The MLO gene family is the gene that makes grapevines prone to powdery mildew. Ahmad et al. (2020) edited these genes using CRISPR/Cas9 in their study. After adding some minor deletions or insertions to the two genes VvMLO3 and VvMLO4, the resistance of some grapevine varieties to powdery mildew has become stronger. Paul et al. (2021) conducted a knockout experiment on the VvPR4b gene, which is related to the resistance of grapevines to downy mildew, using CRISPR/Cas9. They found that once this gene was knocked out, grapevines became more susceptible to downy mildew infection, indicating that VvPR4b is crucial in the disease resistance process (Figure 1). 3.2 Role of gene editing in functional studies of disease resistance-related genes Gene editing with CRISPR/Cas9 is useful for a clearer understanding of the role of a certain gene in disease resistance. Researchers can observe whether the disease resistance of plants has changed by creating mutations with functional deficiencies and determine whether this gene is related to disease resistance. Alphonse et al. (2021) edited the VvPR4b gene and found that once this gene was knocked out, grapevines became more prone to downy mildew and the reactive oxygen species in their bodies also decreased, indicating that this gene is very important in disease prevention. 3.3 Case studies: improving grape disease resistance through gene editing In 2022, Jiao et al. ’s research significantly enhanced the resistance of grapevines to powdery mildew through targeted mutations in the VvMLO3 and VvMLO4 genes. These mutated grapevines exhibit more cell death and cell wall deposition, which is a response of plant defense. Another study used the CRISPR immune system to combat grapevine leafroll-associated virus 3 (GLRaV-3). Li et al. (2020) successfully cultivated transgenic grapevine varieties with strong antiviral capabilities by using CRISPR/FnCas9 and LshCas13a.

Plant Gene and Trait 2025, Vol.16, No.2, 56-63 http://genbreedpublisher.com/index.php/pgt 58 Figure 1 Workflow of CRISPR/Cas system in plants (A) and fungi (B); Agrobacterium-mediated transformation is a common method for genome modification in plants, including CRISPR/Cas system delivery (Adopted from Paul et al., 2021) 4 Technical Optimization and Challenges 4.1 Technical bottlenecks in applying CRISPR/cas9 technology to grapes Najafi et al. (2022) demonstrated that the fact that grapevines are woody plants makes gene editing more complex, especially in multicellular explants such as somatic cell embryos, where there is still a lack of efficient gene transfer methods, which is one of the main obstacles. Unstable editing efficiency is also a big problem. Ren et al. (2024) found that the editing efficiency of the same CRISPR/Cas9 system might vary from 0% to 38.5% in different experiments. Such a significant difference indicates that the transmission mode and expression system of CRISPR still need to be further improved to enable it to exert a more stable effect in grapevines. 4.2 Off-target effects and genetic stability issues in gene editing Off-target effects are often encountered in the application of CRISPR/Cas9 technology. Sometimes the system may act in places where it shouldn't be edited, which may cause genes to become unstable. Such mistakes will affect the development of disease-resistant grapevines, as inaccurate editing may cause side effects. Osakabe et al. (2018) attempted to solve this problem by using shorter sgRNA or dual Cas9 nickases, and these methods have been proven to reduce unexpected modifications. Researchers also pay attention to whether the edited plants are stable, because some edited grapevines may have problems such as yellowing and necrosis of leaves, which affect the breeding effect. 4.3 Methods to improve the efficiency of gene editing Researchers have tried many methods to achieve better gene editing effects of CRISPR/Cas9 in grapevines. Olivares et al. ’s research in 2021 demonstrated that the use of geminivirus-based replicons can enhance the expression efficiency of CRISPR/Cas9 and achieve better editing results in multicellular explants. The method of directly delivering CRISPR/Cas9 ribonucleoproteins (RNPs) into protoplasts is considered to have great development prospects. It does not require the integration of exogenous DNA and can avoid regulatory issues related to genetically modified organisms. Nyu (2024) holds that adjusting the ratio of Cas9 to sgRNA is also crucial. An appropriate ratio can enhance the transformation efficiency of protoplasts and the success rate of targeted mutations (Figure 2) (Olivares et al., 2021).

Plant Gene and Trait 2025, Vol.16, No.2, 56-63 http://genbreedpublisher.com/index.php/pgt 59 Figure 2 CRISPR/Cas 9-mediated gene editing in grapes using Agrobacterium (Adopted from Olivares et al., 2021) 5 Discovery and Application Prospects of Disease Resistance Genes in Grapes 5.1 Multi-omics studies and integrated analysis of disease resistance genes Karn et al. (2021) discovered some genes with significant expression changes in Vitis vinifera through transcriptome sequencing, and identified pathways related to disease resistance such as phenylpropane biosynthesis and the MAPK signaling pathway. Another study has discovered a disease-resistant gene locus called REN12 in Vitis amurensis. It can prevent the spread of the disease when the pathogen just begins to infect and shows strong resistance. These studies indicate that combining different types of data is useful for discovering more genes involved in the disease resistance process, especially those secondary resistance genes (Su et al., 2023). 5.2 Phenotypic validation and molecular mechanism analysis of edited disease resistance genes Confirming whether disease-resistant genes are truly effective usually requires phenotypic verification, that is, testing the disease-resistant performance of these genes under different conditions. Sapkota et al. (2023) recently found in their research that plants’ resistance to powdery mildew increased after overexpressing the VqSERK3/BAK1 gene in Arabidopsis thaliana, as this gene can regulate cell death and stomatal immune responses.

Plant Gene and Trait 2025, Vol.16, No.2, 56-63 http://genbreedpublisher.com/index.php/pgt 60 The REN11 disease resistance site fromVitis aestivalis has shown stable powdery mildew resistance under various environmental conditions. Conducting phenotypic verification not only makes the mechanism of action of these genes clearer, but also enables them to be better applied to grapevine breeding. 5.3 Functional stability studies of disease resistance genes across different grape cultivars The key to whether disease-resistant genes can play a stable role in different grapevine varieties for a long time lies in whether they can be truly applied in breeding. Yin et al. (2022) found that resistance sites like REN11 remain effective in various tissues and environments, indicating that it holds promise for long-term and stable disease-resistant breeding. Yan et al. (2017) indicated that some disease-resistant genes are only effective in specific tissues or specific environments, and certain QTLS related to fungal diseases exhibit such characteristics in grapevines. For disease-resistant genes to be truly widely applied, more comprehensive studies on their stability and adaptability are needed. 6 Future Directions for CRISPR/Cas9 in Grape Breeding 6.1 Integration of gene editing with conventional breeding strategies The combination of CRISPR/Cas9 gene editing technology and traditional breeding methods brings great hope for the improvement of grapevine varieties (Zhong, 2024). This method can cultivate new varieties with strong disease resistance, good fruit quality and high yield more quickly. It can precisely modify specific genes and retain the genetic diversity and stress resistance brought about in traditional breeding (Wang et al., 2017). Ren et al. (2019b) argued that CRISPR/Cas9 can also specifically control those genes that cause adverse traits to make up for the insufficiently precise selection process in traditional breeding. 6.2 Development of gene editing techniques for multi-trait improvement The CRISPR/Cas9 technology can now edit multiple genes simultaneously, which is crucial for improving the complex traits controlled by multiple genes in grapevines. Fizikova et al. ’s research in 2021 found that multiple editing techniques can jointly enhance traits such as disease resistance, fruit quality, and stress tolerance. In grapevine research, there have been successful examples of developing efficient multi-editing systems, which makes it possible to simultaneously improve multiple target traits and cultivate excellent grapevine varieties with multiple advantages at the same time. 6.3 Public policy and market promotion potential for gene-edited grapes Whether gene-edited grapevines can be widely accepted and commercialized largely depends on policy support and market promotion methods. The resolution of related regulatory and public awareness issues has become increasingly important with the development of CRISPR/Cas9 technology. Zhou et al. (2020) demonstrated that “traceless” editing technologies like CRISPR/Cas9 ribonucleoprotein do not introduce exogenous DNA and are expected to bypass some regulatory obstacles related to genetically modified organisms. Good communication methods are also needed. Only by making the public aware of the benefits and safety of gene-edited grapevines can we win their trust and make it easier for these new varieties to enter the market. 7 Data Integration and Collaborative Mechanisms 7.1 Sharing and integration of multi-omics data in disease resistance research Chao et al. (2023) proposed in their study that methods such as genomics, transcriptomics, and metabolomics are beneficial for a more comprehensive understanding of plant traits and their interactions. A large amount of data has been accumulated at present, but due to different sources and the lack of unified standards for metadata, it is still very difficult to fully integrate these data. Conesa and Beck (2019) discovered that the TransMetaDb database in the Vitis Visualization platform is promoting data sharing and integration. These platforms enable researchers to conduct more in-depth analyses such as studies on the relationships between genes and between genes and metabolites, providing strong support for scientific research and grapevine breeding. 7.2 Importance of international collaboration in grape disease resistance research Grape cultivation is important in many parts of the world, and similar problems are also encountered in disease resistance research. Through international cooperation, researchers can share resources, technologies and data

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