Field Crop 2024, Vol.7 http://cropscipublisher.com/index.php/fc © 2024 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved.
Field Crop 2024, Vol.7 http://cropscipublisher.com/index.php/fc © 2024 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Publisher CropSci Publisher Editedby Editorial Team of Field Crop Email: edit@fc.cropscipublisher.com Website: http://cropscipublisher.com/index.php/fc Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada FieldCrop is an International Journal, is an open access, peer reviewed journal published online by CropSci Publisher. This journal publishes research articles of field crops, as well as innovative research conducted in the field, farm or on the land related to edible agricultural food crops. The research must be based on cropping system, crop physiology, crop genetics and breeding. Topics include (but are not limited to) different aspects like crop management, agronomy, plant pathology, entomology, soil science, vegetable and horticultural science related phenomena. All the articles published in Field Crop 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. CropSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights. CropSci Publisher is an international Open Access publisher specializing in crop science, and crops-related research registered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada.
Field Crop (online), 2024, Vol.7, No.1 http://cropscipublisher.com/index.php/fc © 2024 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content Application of Genome-wide Association Study in Crop Disease Resistance Breeding ChengFu Field Crop, 2024, Vol. 7, No. 1, 1-8 Studying the Molecular Genetic Mechanism of Barley Stress Tolerance Using GWAS HemingWei Field Crop, 2024, Vol. 7, No. 1, 9-16 Economic and Environmental Impacts of Mechanized vs. Manual Sugarcane Harvesting AmengLi Field Crop, 2024, Vol. 7, No. 1, 17-26 Impact of Climate Change on Cassava Cultivation: Genetic Adaptations and Breeding Strategies Wenzhong Huang, Zhongmei Hong Field Crop, 2024, Vol. 7, No. 1, 27-36 Sustainability in Sugarcane Processing: Integrating Environmental and Economic Perspectives Tianxia Guo Field Crop, 2024, Vol. 7, No. 1, 37-44
Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 1 Review and Progress Open Access Application of Genome-wide Association Study in Crop Disease Resistance Breeding ChengFu Hainan Key Laboratory of Crop Molecular Breeding, Sanya, 572000, Hainan, China Corresponding author email: 2397383131@qq.com Field Crop, 2024, Vol.7, No.1 doi: 10.5376/fc.2024.07.0001 Received: 05 Dec., 2023 Accepted: 08 Jan., 2024 Published: 25 Jan., 2024 Copyright © 2024 Fu, 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: Fu C., 2024, Application of genome-wide association study in crop disease resistance breeding, Field Crop, 7(1): 1-8 (doi: 10.5376/fc.2024.07.0001) Abstract Genome-wide association study (GWAS), as an effective genetic research tool, has been widely used in crop disease resistance breeding, which can identify genetic markers and genes related to disease resistance in the whole genome and provide molecular basis for breeding. This study introduced the basic principles and methods of GWAS, demonstrated the application of GWAS in crop disease resistance breeding through specific application examples, then discussed the advantages and limitations of GWAS in crop disease resistance breeding, and prospected the future development direction of GWAS in crop disease resistance breeding. This includes applications that combine high-throughput sequencing techniques, multi-omics data integration, and precision breeding techniques. GWAS provides a new research idea and method for crop disease resistance breeding, which is expected to promote the rapid cultivation of disease-resistant varieties and the sustainable development of agricultural production. Keywords Genome-wide association study; Crop disease resistance; Breeding; Genetic marker; Precision breeding The importance of crop disease resistance breeding should not be ignored, especially in the current globalized agricultural production environment, where diseases not only lead to direct loss of crop yield, but also may lead to decline in quality and market value, and even lead to food security crisis in serious cases (Mores et al., 2021). Therefore, improving crop disease resistance can effectively reduce the use of pesticides, reduce environmental pollution, and improve the economic efficiency and sustainability of agricultural production. Although traditional disease resistance breeding methods have made certain achievements in history, with the rapid evolution of pathogens and changes in ecological environment, these methods have been difficult to meet the current breeding needs, and traditional breeding methods often rely on long-term phenotypic selection, and are limited by the diversity and availability of genetic resources. Traditional methods are also limited in resolving the genetic basis of complex traits, and it is difficult to accurately locate and utilize key genes related to disease resistance (Lamichhane and Thapa, 2022). The rise of genome-wide association study (GWAS) has brought new opportunities for crop disease resistance breeding, which can explore the association between genetic variation and trait phenotype in the whole genome, and provide a powerful tool for revealing the genetic mechanism of disease resistance. Through GWAS, researchers can quickly identify genetic markers and candidate genes associated with disease resistance (Osorio-Guarin et al., 2020), and this information is of great value in guiding molecular marker-assisted breeding (MAS) and gene directed editing. This study summarized and analyzed the application and significance of genome-wide association study in crop disease resistance breeding, and discussed its application cases in different crop disease resistance breeding from the principle and method of GWAS, and analyzed its advantages and limitations. This study also looks forward to the future development of GWAS in crop resistance breeding, including the combination of multi-omics data, the use of high-throughput sequencing technology, and the integration of gene editing technology. Through this study, we hope to provide new ideas and strategies for crop disease resistance breeding, promote scientific and precise crop disease management, and improve the sustainability and stress resistance of agricultural production.
Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 2 1 Principles and Methods of GWAS 1.1 Definition and principle of GWAS Genome-wide association study (GWAS) is a genetic method used to study the relationship between genetic variation and phenotypic traits. Its principle is to explore the association between genetic variation (such as single nucleotide polymorphisms, SNPs) and specific traits through statistical analysis based on genotype and phenotypic data of a large number of individuals (Uffelmann et al., 2021). This analysis can reveal the role of genetic variation in the expression of traits and provide clues for understanding the genetic basis of traits. The key of GWAS is to conduct high density genotyping of a large number of samples to cover the variation information of the whole genome, and to evaluate the correlation between each genetic variation site and the trait phenotype through statistical tests (such as linear regression analysis). If the frequency of a mutation site is significantly different in different phenotype populations, it is considered that the site is associated with the trait phenotype. GWAS can fully explore the genome without relying on prior knowledge, reveal the polygenic genetic basis of complex traits, and provide molecular markers for breeding. 1.2 The main steps of GWAS Genome-wide association study (GWAS) is a widely used genetic tool in crop disease resistance breeding, which reveals genetic markers and genes associated with traits by analyzing the association between genetic variation and phenotypic traits. The main steps of GWAS include sample collection and genotyping, phenotypic data collection, association analysis, result validation, and biological interpretation (Figure 1). It requires collecting a sufficient number of samples and conducting genotyping to obtain genome-wide genetic variation information. The phenotypes of the samples were then recorded in detail (Belzile and Torkamaneh, 2022). These data will be used for subsequent association analysis, which will then use statistical methods to analyze the association between genotype and phenotypic data to identify genetic markers associated with specific traits. Figure 1 The steps for conducting GWAS (Uffelmann et al., 2021) The identified associated sites or candidate genes need to be verified through independent sample sets or functional verification experiments. Finally, the biological interpretation of the identified associated sites or candidate genes is carried out to explore their roles and mechanisms in phenotype formation. Through these steps, GWAS can provide valuable genetic information for crop disease resistance breeding.
Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 3 1.3 Data and technical requirements required by GWAS As a powerful genetic research tool, the application of genome-wide association study (GWAS) in crop disease resistance breeding requires certain data and technical requirements. GWAS requires large amounts of genetic variation data, typically from high-density single nucleotide polymorphism (SNP) markers or whole genome sequencing, which are used to construct genetic linkage maps (Marees et al., 2018) to identify genetic loci associated with trait phenotypes. GWAS also requires accurate phenotypic data. In crop disease resistance studies, this usually involves detailed assessment of disease responses of plants of different genotypes, and the accuracy of phenotypic data directly affects the reliability of GWAS results. GWAS also requires strong statistical analysis capabilities, association analysis requires processing large amounts of data, and mathematical control of false positive rates, the use of appropriate statistical models and correction methods (such as Bonferroni correction or false discovery rate control) is necessary (Marees et al., 2018). With the advancement of technology, the data and technical requirements of GWAS are also increasing, for example, with the reduction of sequencing costs, whole genome sequencing has gradually become an important data source for GWAS, and the development of bioinformatics tools has also provided more possibilities for the processing and analysis of GWAS data. 2 Application of GWAS in Crop Disease Resistance Breeding (A Case Study of Soybean) Soybean (Glycine max) is an important crop in the legume family, which is widely favored worldwide for its high protein and oil content. Soybean is not only an important source of human food, such as tofu, soybean milk and soybean oil, but also contains a variety of bioactive substances that are beneficial to the human body, such as isoflavones and lecithin. These ingredients have been shown to have positive effects on cardiovascular health, bone health and menopausal symptoms, and are a major component of many animal feeds, having an irreplaceable impact on the global agriculture and food industry. The origin of soybeans can be traced back to China, it has thousands of years of cultivation history, has become one of the most widely cultivated crops in the world, in agricultural production, soybeans are not only an important cash crop, but also a key component of sustainable agriculture. Major challenges include sudden death syndrome (SDS) caused by Fusarium virguliforme, one of the key diseases limiting its production. At present, the genetic mechanism of soybean resistance to SDS, especially the epistatic role between genes, is still not fully understood. Mueller and Singh's team published a paper in The Plant Journal in 2015 titled ‘Genome-wide association and epistasis studies unravel the genetic architecture of sudden death syndrome resistance in soybean’. By analyzing the genetic data of 214 soybean varieties and 31 914 single nucleotide polymorphism (SNP) markers, this study conducted a comprehensive genomic association analysis and epistatic role study (Figure 2), aiming to further explore the genetic background of soybean resistance to SDS (Zhang et al., 2015). Figure 2 Contributions of identified sudden death syndrome loci via genome-wide association studies (GWAS) and epistatic analysis to the phenotypic variance of each disease severity measurement (Zhang et al., 2015)
Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 4 Twelve key sites associated with SDS resistance and 12 interactions between SNPS and SNPS were identified. The additive and epistatic effects of these loci together contributed 24% to 52% of phenotypic variation. In the vicinity of these key SNPS, genes associated with disease resistance, pathogenesis, chitin response, and wound healing were also identified, in particular a trait associated SNP-locked-stress-induced receptor-like kinase gene 1 (SIK1) encoding a protein rich in leucine repeats. This study emphasizes that epistatic effects must be taken into account in breeding for SDS resistance in soybean to improve the explanation of phenotypic variation. Accordingly, the researchers also constructed a soybean root model for SDS pathogen defense (Figure 3). The findings of this study not only reveal the molecular mechanism of soybean resistance to SDS, but also provide a scientific basis for future anti-SDS breeding strategies based on genetic epistasis. Figure 3 Putative model for soybean defense against sudden death syndrome (SDS) based on the results of genome-wide association and epistasis studies (Zhang et al., 2015) 3 The Advantages and Limitations of GWAS in Crop Disease Resistance Breeding 3.1 The advantages of GWAS compared with traditional breeding methods Genome-wide association study (GWAS) has obvious advantages over traditional breeding methods in crop disease resistance breeding. GWAS has high efficiency, it can quickly identify the genetic markers and genes related to disease resistance within the whole genome in a short time, and accelerate the breeding process. There is no need for specific genetic background, and it can make use of existing natural population and variety resources for analysis (Tam et al., 2019), without the need to build specific genetic populations, reducing the cost and time of research. GWAS can also reveal the genetic structure of complex traits and identify multi-genes and inter-gene interactions that control complex traits, which is of great significance for understanding the genetic mechanism of crop disease resistance and guiding molecular marker-assisted breeding (MAS) (Tam et al., 2019). GWAS can also utilize existing natural population and variety resources without the need to construct large-scale genetic populations, thus reducing the cost and time of research, which makes GWAS an efficient breeding tool, especially suitable for research environments with limited resources. 3.2 Limitations and challenges of GWAS Although genome-wide association study (GWAS) has made remarkable progress in crop disease resistance breeding, it still faces some limitations and challenges. Population structure and linkage imbalance (LD) are major challenges for GWAS. Population structure refers to differences in genetic background in the sample, which can lead to false positive associations, and linkage imbalance refers to non-random associations between different loci,
Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 5 which can affect the accurate localization of related trait loci. When designing GWAS, researchers need to adopt appropriate statistical methods and correction measures to reduce the impact of these factors (Heeney, 2021). GWAS has a limited ability to detect rare variants. In crops, some important resistance traits may be controlled by lower-frequency variants, and GWAS is limited in its ability to detect these rare variants, which requires larger sample sizes and higher density of genotype data to improve detection capabilities. The biological interpretation and functional verification of GWAS results is also one of its challenges. GWAS can identify genetic markers associated with traits, but these markers may not be directly involved in the regulation of traits, which requires subsequent biological experiments to verify the function of these associated sites and reveal their mechanism of action in disease resistance (Ciochetti et al., 2023). Large amount of data and computational complexity are another difficulty in GWAS research. With the development of sequencing technology, the amount of genetic data generated is increasing, which puts higher requirements on data storage and analysis, and more efficient computational methods and software tools need to be developed to deal with these large-scale data. 3.3 Strategies to improve the efficiency and accuracy of GWAS research Improving the efficiency and accuracy of genome-wide association study (GWAS) is essential to reveal the genetic basis of complex traits such as crop disease resistance and is an important topic in current genetic research. To improve the efficiency and accuracy of GWAS research, it is necessary to comprehensively consider many factors, such as sample size, gene chip density, phenotypic data quality, population structure control, multi-omics data integration, functional validation, meta-analysis and repeated validation. By adopting these strategies, genetic variants associated with important traits such as crop disease resistance can be found more effectively, providing powerful molecular tools for crop breeding. Increasing the sample size can improve the statistical power of GWAS and help detect more genetic variation related to traits, and a large sample size can also help reduce the incidence of false positives (Spencer et al., 2009). The use of high-density gene chips can improve the coverage of genetic variation and increase the chance of detecting genetic markers associated with traits. Accurate and reliable phenotypic data are key to the success of GWAS, and improved methods for collecting and measuring phenotypic data, as well as the use of standardized phenotypic evaluation systems, can improve the accuracy of studies. Population structure and kinship can influence the results of GWAS, and the use of appropriate statistical models or methods (such as mixed linear models) to control for these factors can reduce the occurrence of false positives. Integrating multi-omics data such as transcriptomics, proteomics, and metabolomics can provide a more comprehensive biological context to help interpret GWAS results and uncover potential functional genetic variants. Functional verification of candidate genes identified by GWAS through gene editing techniques such as CRISPR/Cas9 (Laurie et al., 2010) can ensure that these genes are indeed associated with traits, thus improving the accuracy of studies. By meta-analysis of GWAS results from different studies, the reliability of the results can be improved. At the same time, repeated validation is also an important step to ensure that genetic variants of biological significance are found. 4 Future Development Direction and Prospect 4.1 Development of high-throughput sequencing techniques and bioinformatics tools The development of high-throughput sequencing techniques and bioinformatics tools has had a profound impact on the application of genome-wide association study (GWAS) in crop disease resistance breeding. High-throughput sequencing technologies, including second-generation sequencing (such as Illumina) and third-generation sequencing (such as PacBio and Oxford Nanopore), allow researchers to access vast amounts of genetic information with unprecedented speed and precision. These techniques can not only provide high density single nucleotide polymorphism (SNP) markers, but also reveal structural and rare variants, providing rich genetic variation data for GWAS (Xiao et al., 2022). High-throughput sequencing technology can also be used in transcriptomics, epigenetics and genome resequencing studies, providing a new perspective for the molecular mechanism of crop disease resistance.
Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 6 The development of bioinformatics tools supports the processing and analysis of high-throughput sequencing data. With the advent of the big data era, how to effectively manage, analyze and interpret massive biological information has become a challenge. Bioinformatics tools, including software and algorithms for sequence alignment, variation detection, gene annotation, and association analysis (Normand and Yanai, 2013), enable researchers to extract useful information from complex data. The application of machine learning and artificial intelligence technology also provides new methods for bioinformatics analysis, helping to reveal the complex genetic laws of crop disease resistance. 4.2 The application of multi-omics data integration and systems biology methods The application of multi-omics data integration and systems biology methods is an important development direction of genome-wide association study (GWAS) in crop disease resistance breeding in the future. With the progress of biotechnology, researchers can obtain the genome, transcriptomics, proteomics, metabolomics and other multi-omics data of crops, which provides comprehensive information on the physiological and molecular level of crops, and helps to deeply understand the complex mechanism of crop disease resistance. Multi-omics data integration refers to the comprehensive analysis of different levels of biological data to reveal a comprehensive picture of crop disease resistance. By integrating genomic and transcriptomic data, researchers can identify genes whose expression changes significantly during disease infection (Subramanian et al., 2020) and explore the role of these genes in disease resistance response. The integration of proteomic and metabolomic data helps to reveal changes in proteins and metabolites associated with disease resistance, providing deeper insight. The systems biology approach refers to the use of mathematical and computational models to analyze and explain complex biological systems. In crop disease resistance studies, systems biology approaches can be used to construct network models of disease resistance responses and reveal interactions between different genes, proteins, and metabolites (Pazhamala et al., 2021). This network model helps to identify the key regulatory factors and signaling pathways of disease resistance and provide targeted strategies for breeding. 4.3 The combination of precision breeding and gene editing The combination of precision breeding and gene editing technology is an important trend in contemporary crop improvement. Precision breeding, also known as molecular breeding, relies on molecular markers and genomic information to precisely select and aggregate genes associated with target traits through methods such as molecular marker-assisted selection (MAS). Gene editing technologies, such as the CRISPR/Cas9 system, can achieve precise modifications at specific sites in the crop genome to directly change the genetic characteristics of the crop (Nerkar et al., 2022), and the combination of these two technologies provides new strategies for crop disease resistance breeding. After using GWAS and other methods to identify the key genes or genetic markers related to disease resistance, these favorable genes can be aggregated into a variety through precision breeding technology to improve the disease resistance of crops. For some resistance traits that are difficult to obtain through traditional breeding methods, gene editing technology can directly introduce or modify specific resistance genes into the crop genome, thereby rapidly breeding new varieties with strong resistance to disease (Scheben and Edwards, 2017). The breeding strategy combined with precision breeding and gene editing technology can not only improve the efficiency and accuracy of breeding, but also expand the possibility of breeding, providing more choices and flexibility for crop disease resistance breeding. With the continuous development and improvement of these technologies, crop disease resistance breeding will be more efficient and accurate in the future, and is expected to make greater contributions to the sustainable development of agricultural production. 5Summary The application of genome-wide association study (GWAS) in crop disease resistance breeding has made remarkable achievements. Through GWAS, researchers can quickly and accurately identify genetic markers and genes related to disease resistance in the whole genome, providing a powerful molecular tool for crop disease
Field Crop 2024, Vol.7, No.1, 1-8 http://cropscipublisher.com/index.php/fc 7 resistance breeding. These results not only deepen our understanding of the genetic mechanism of crop resistance, but also provide a reliable basis for molecular marker-assisted breeding (MAS) and gene directed improvement. However, the application of GWAS in crop disease resistance breeding also faces some challenges. Due to the influence of population structure and linkage imbalance, GWAS may produce false positive results, affecting the accuracy of the results. Generally, GWAS can only identify large effect genes related to traits, while some small effect genes may be ignored. The biological interpretation and functional verification of GWAS results is also a complex process, which requires in-depth analysis with transcriptomic and proteomic data. With the continuous development of high-throughput sequencing technology and bioinformatics tools, the application of GWAS in crop disease resistance breeding will be more extensive and in-depth. Combined with multi-omics data and systems biology methods, GWAS will be able to reveal the complex genetic network of crop disease resistance in a more comprehensive way, and the combination of gene editing technology will also enable the disease resistance genes identified by GWAS to be quickly and accurately applied in breeding practice, improving the efficiency and accuracy of breeding. As a powerful genetic analysis tool, GWAS is playing an increasingly important role in crop disease resistance breeding. Future studies will further optimize GWAS analysis methods, combine multi-omics data and gene editing technology, and provide more accurate and efficient solutions for crop disease resistance breeding to meet the challenges faced by global agricultural production. References Belzile F., and Torkamaneh D., 2022, Designing a genome-wide association study: Main steps and critical decisions, In: Torkamaneh D., and Belzile F. (eds.), Genome-wide association studies, Methods in Molecular Biology, Humana, New York, USA, pp.3-12. https://doi.org/10.1007/978-1-0716-2237-7_1 Ciochetti N.P., Lugli-Moraes B., da Silva B.S., and Rovaris D.L., 2023, Genome-wide association studies: utility and limitations for research in physiology, The Journal of Physiology, 601(14): 2771-2799. https://doi.org/10.1113/JP284241 Heeney C., 2021, Problems and promises: How to tell the story of a genome wide association study? Stud. Hist. Philos. Sci., 89: 1-10. https://doi.org/10.1016/j.shpsa.2021.06.003 Lamichhane S., and Thapa S., 2022, Advances from conventional to modern plant breeding methodologies, Plant Breed. Biotech., 10: 1-14. https://doi.org/10.9787/PBB.2022.10.1.1 Laurie C.C., Doheny K.F., Mirel D.B., Pugh E.W., Bierut L.J., Bhangale T., Boehm F., Caporaso N.E., Cornelis M.C., Edenberg H.J., Gabriel S.B., Harris E.L., Hu F.B., Jacobs K.B., Kraft P., Landi M.T., Lumley T., Manolio T.A., McHugh C., Painter I., Paschall J., Rice J.P., Rice K.M., Zheng X., Weir B.S., and GENEVA Investigators, 2010, Quality control and quality assurance in genotypic data for genome-wide association studies, Genet. Epidemiol., 34(6): 591-602. https://doi.org/10.1002/gepi.20516 Marees A.T., de Kluiver H., Stringer S., Vorspan F., Curis E., Marie-Claire C., and Derks E.M., 2018, A tutorial on conducting genome-wide association studies: Quality control and statistical analysis, Int. J. Methods Psychiatr. Res., 27(2): e1608. https://doi.org/10.1002/mpr.1608 Mores A., Borrelli G.M., Laidò G., Petruzzino G., Pecchioni N., Amoroso L.G.M., Desiderio F., Mazzucotelli E., Mastrangelo A.M., and Marone D., 2021, Genomic approaches to identify molecular bases of crop resistance to diseases and to develop future breeding strategies, Int. J. Mol. Sci., 22(11): 5423. https://doi.org/10.3390/ijms22115423 Nerkar G., Devarumath S., Purankar M., Kumar A., Valarmathi R., Devarumath R., and Appunu C., 2022, Advances in crop breeding through precision genome editing, Front. Genet., 13: 880195. https://doi.org/10.3389/fgene.2022.880195 Normand R., and Yanai I., 2013, An introduction to high-throughput sequencing experiments: Design and bioinformatics analysis, In: Shomron N. (ed.), Deep sequencing data analysis, Methods in molecular biology, Humana Press, Totowa, USA, pp.1-26. https://doi.org/10.1007/978-1-62703-514-9_1 Osorio-Guarín J.A., Berdugo-Cely J.A., Coronado-Silva R.A., Baez E., Jaimes Y., and Yockteng R., 2020, Genome-wide association study reveals novel candidate genes associated with productivity and disease resistance to Moniliophthora spp. in cacao (Theobroma cacao L.), G3 Genes|Genomes|Genetics, 10(5): 1713-1725. https://doi.org/10.1534/g3.120.401153 Pazhamala L.T., Kudapa H., Weckwerth W., Millar A.H., and Varshney R.K., 2021, Systems biology for crop improvement, The Plant Genome, 14(2): e20098. https://doi.org/10.1002/tpg2.20098
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Field Crop 2024, Vol.7, No.1, 9-16 http://cropscipublisher.com/index.php/fc 9 Research Article Open Access Studying the Molecular Genetic Mechanism of Barley Stress Tolerance Using GWAS HemingWei Modern Agricultural Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author email: 2397383131@qq.com Field Crop, 2024, Vol.7, No.1 doi: 10.5376/fc.2024.07.0002 Received: 12 Dec., 2023 Accepted: 15 Jan., 2024 Published: 28 Jan., 2024 Copyright © 2024 Wei, 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: Wei H., 2024, Studying the molecular genetic mechanism of barley stress tolerance using GWAS, Field Crop, 7(1): 9-16 (doi: 10.5376/fc.2024.07.0002) Abstract Genome-wide association study (GWAS) technology has become an important means to reveal the genetic basis of crop resistance. By analyzing a large number of genetic and phenotypic data, GWAS helps to identify key genes and genetic markers related to resistance. The aim of this study was to comprehensively analyze the latest progress of molecular genetic mechanism of barley (Hordeum vulgare L.) resistance by GWAS method, introduce the application basis of GWAS technology in plant science, and focus on its application cases in the study of barley resistance to abiotic stresses such as drought tolerance, salt tolerance and low temperature tolerance. This study will also explore current research challenges and look forward to the future direction of combining multi-omics data and advanced bioinformatics tools to more in-depth analysis of genetic mechanisms of barley resistance. This study provides a new perspective and strategy for improving barley resistance by molecular genetics, which is of great significance for the sustainable development of agriculture. Keywords High-throughput sequencing; Epigenetics; Disease research; DNA methylation; Personalized medicine Barley (Hordeum vulgare L.), one of the pillars of global agriculture, plays an integral role, not only as a major food and feed source, but also in the beer and health food industries, with economic value and cultural significance across multiple sectors (Langridge, 2018). With the growth of the population and the diversification of consumption habits, the demand for barley continues to grow, and the growing environment of barley is facing unprecedented challenges. Extreme weather conditions brought about by global climate change, such as persistent droughts, frequent floods, salinized soils and abrupt temperature fluctuations, seriously threaten the growing cycle and yield of barley, thereby affecting global food security and the stability of agricultural economies. In addressing these challenges, the application of modern genetics and molecular biology techniques has provided new directions for the improvement of barley. According to the study of Uffelmann et al. (2021), genome-wide association analysis (GWAS), as a powerful genetic analysis tool, has shown its unique advantages in the study of resistance to a variety of crops. By analyzing the relationship between genetic variation and phenotype, GWAS can identify genes or gene regions associated with specific traits without prior knowledge, and the application of this method has greatly promoted researchers' understanding of crop genetic diversity and accelerated the process of agricultural breeding. A new breakthrough has been made in the study of barley resistance through GWAS technology. By analyzing barley samples from different environmental conditions, the researchers were able to identify a series of candidate genes associated with abiotic stresses such as drought tolerance, salt tolerance, and low temperature tolerance, the discovery of which not only enriched the understanding of barley stress response mechanisms, but also provided valuable resources for future molecular breeding (Gyawali et al., 2018). For example, through the functional verification of these resistance genes and the development of molecular markers, it is possible to achieve precise improvement of barley varieties for specific environmental stresses. The GWAS study also revealed the complexity of barley resistance. Many reverse resistance traits have been found to be jointly regulated by multiple genes, which play roles in different physiological pathways, such as signal transduction, osmoregulation, antioxidant defense and hormone metabolism (Gyawali et al., 2018). This
Field Crop 2024, Vol.7, No.1, 9-16 http://cropscipublisher.com/index.php/fc 10 complex genetic network requires researchers to adopt a more systematic approach in future studies. For example, GWAS and other omics data were integrated to fully analyze the molecular mechanism of barley resistance. Using GWAS to study the molecular genetic mechanism of barley resistance not only deepens the understanding of plant stress response mechanism, but also provides a new strategy for coping with global climate change and ensuring food security. With the advancement of genome sequencing technology and the development of bioinformatics tools, it is expected that more unknown resistance genes will be revealed in the future, providing a solid scientific foundation for the sustainable production of barley and other crops. 1 Overview of GWAS Technology 1.1 The fundamentals of GWAS technology Genome-wide association analysis (GWAS) techniques have become a central tool in modern genetics and genomics research, especially in revealing the genetic basis behind complex traits. The basic principle of GWAS is to analyze the association between genetic variants (especially single nucleotide polymorphisms, SNPs) and phenotypes through statistical methods in order to identify genes or genetic regions that influence specific traits. The advantage of this approach is its genome-wide analytical capability, which enables researchers to discover new relevant genetic markers without prior knowledge of the genetic control mechanisms of the target trait (Marees et al., 2018). The process of GWAS begins with precise measurements of the phenotypes and genotypes of a large number of individuals. Phenotypic data provide quantifiable information about studied traits, while genotypic data reveal the genetic variation of an individual across the whole genome, and use statistical methods to analyze these data to determine which genetic loci are significantly associated with phenotypic variation. These significantly correlated genetic loci are often considered candidate regions for influencing traits (Marees et al., 2018). Although the application of GWAS in genetic research has achieved remarkable results, the technology also faces a series of challenges. GWAS requires large sample sizes to ensure adequate statistical power because genetic control of traits often involves multiple genes, each of which may have a relatively small effect. Genetic markers discovered by GWAS often require confirmation of their function through further bioinformatic analysis and experimental validation, a process that can be complex and time-consuming (Figure 1). Figure 1 Challenges and opportunities in genome-wide association study (GWAS) (Cortes et al., 2021) 1.2 The development of GWAS technology The development of genome-wide association analysis (GWAS) was an important milestone in the field of modern genetics and genomics, marking a key step in scientists' efforts to parse the genetic basis of complex traits. Since its first successful application to human genetic research in 2005, GWAS technology has experienced rapid development and wide application, which has greatly promoted the understanding of genetic mechanisms of polygenic diseases, crop traits, and other complex traits (Figure 2) (Cortes et al., 2021).
Field Crop 2024, Vol.7, No.1, 9-16 http://cropscipublisher.com/index.php/fc 11 Figure 2 Genome-wide association study methods for improving computational speed and statistical power (Cortes et al., 2021) GWAS technology was born from several key technological advances: The development of high-throughput genotyping techniques made it possible to analyze genome-wide genetic variation in large numbers of samples at a reasonable cost. The creation of public databases and genetic information resources, such as the human genome project (HGP) and the HapMap Project, has provided GWAS with essential reference sequence and background genetic variation information. Advances in bioinformatics and statistical methods have provided tools for processing large-scale genetic data and complex statistical analyses. Early GWAS research focused on human genetic diseases, successfully identifying multiple disease-related genetic markers, such as susceptibility genes for type 2 diabetes, coronary artery disease, and multiple cancers, and these findings not only revealed the genetic basis of disease, but also provided new ideas for disease prediction, prevention, and treatment. Later, the application of GWAS technology was gradually expanded to the agricultural field, and important agronomic traits of crops, such as yield, resistance and quality, were studied (Cortes et al., 2021). In crop research, GWAS has helped scientists identify key genes and genetic markers associated with traits, providing targets for crop molecular breeding and gene editing. In recent years, with the further development of sequencing technology and the reduction of costs, GWAS has begun to evolve towards whole genome sequencing, which can capture genetic variation more comprehensively, improve the ability to find rare variants and resolve the genetic structure of complex traits, and integrate GWAS with other omics data, such as transcriptomic, proteomic and epigenetic data. It has become a new trend in current research. This multi-omics integration analysis is expected to further improve the analytical power of GWAS and reveal more complex genetic regulatory networks. 1.3 Application of GWAS in plant genetics The application of genome-wide association analysis (GWAS) in the field of plant genetics has become an important means to reveal the genetic basis of plant traits. Since the first successful application of GWAS technology in the study of human genetic diseases, its application in plant science has expanded rapidly, and now covers a wide range of research fields from crop yield and quality improvement to stress tolerance. By analyzing correlations between genetic variation and phenotypic traits, this technique enables genome-wide identification of genes or genetic markers associated with important agronomic traits. In terms of crop yield and quality improvement, GWAS has successfully identified many key genes that affect the yield and quality of food crops such as rice, wheat, maize, etc. These genes are involved in many aspects such as photosynthetic efficiency, nutrient absorption and utilization, grain size and component accumulation, and through the discovery and functional research of these key genes, scientists can carry out targeted molecular breeding to improve crop yield and quality (Liu and Yan, 2019). In the study of stress tolerance, GWAS technology also shows its strong potential. With the intensification of global climate change, crops are facing increasing biological and abiotic stress, such as drought, salinity, low temperature, pests and diseases. By identifying genes associated with these stress responses, GWAS technology
Field Crop 2024, Vol.7, No.1, 9-16 http://cropscipublisher.com/index.php/fc 12 provides new ideas for revealing crop stress response mechanisms and developing stress-tolerant varieties (Lafarge et al., 2017). For example, using GWAS analysis, scientists identified key genes associated with drought response, salt stress tolerance, and cold resistance in multiple crops. GWAS technology has also been widely used in the study of plant growth and development, secondary metabolite synthesis, resistance to pests and diseases, etc. These studies not only enrich our understanding of plant physiological and ecological functions, but also provide scientific basis for crop genetic improvement and ecological environment protection. 2 Genetic Basis of Barley Resistance 2.1 Classification and genetic characteristics of inverse tolerance In nature, plants are confronted with various biological and abiotic adversities, which seriously affect the growth, development and yield of plants. In this context, understanding plant resistance, especially for important crops such as barley, has become a key area of agricultural science research. Adverse tolerance can be divided into two main categories: abiotic and biological stress tolerance. Abiotic stresses include extreme climatic conditions such as drought, salinity, high or low temperatures, and heavy metal contamination in soil, while biological stresses involve the invasion of pathogens and pests (Singh et al., 2019). A plant's ability to respond and adapt to these adversities, known as resilience, is controlled by complex genetic mechanisms, often involving the interaction of multiple genes. The genetic characteristics of reverse tolerance are characterized by polygenic control, variable gene effect sizes, environmental dependence, epistasis and phenotypic plasticity, which imply that reverse tolerance is a quantitative trait that is jointly influenced by multiple genes and environmental factors (Andersen et al., 2016). With the development of molecular biology techniques, especially the application of advanced methods such as genome-wide association analysis (GWAS), scientists are gradually revealing the genetic basis that controls resistance to reverse-resistance. These studies not only deepen researchers' understanding of the mechanisms by which plants survive and adapt to stress conditions, but also provide important molecular markers and candidate genes for the development of more resistant crop varieties. 2.2 Genetic markers associated with reverse tolerance in barley In agricultural genetics and breeding research, identifying genetic markers associated with crop resistance is key to improving crop resilience and yield. Barley (Hordeum vulgare L.) is an important food and feed crop in the world, and its resistance to reverse-resistance has attracted extensive attention. Genetic markers related to resistance to adverse stress can not only help researchers understand the molecular mechanism of barley response to stress (Binott et al., 2017), but also provide a powerful tool for molecular-assisted breeding, especially in improving abiotic stress tolerance of barley such as drought, salinity and low temperature. With the advancement of molecular biology techniques, especially the application of genome-wide association analysis (GWAS) and gene mapping techniques, researchers have successfully identified multiple genetic markers associated with resistance in the barley genome. These markers are usually located near key stress response genes or gene clusters, covering multiple levels such as signal transduction, gene expression regulation and metabolic pathway regulation. For example, some studies have found SNP markers related to drought tolerance in barley through GWAS analysis, which are located at or near known stress response genes. For example, genes in ABA (abscisic acid) signaling pathway, as well as some antioxidant oxidase genes, etc. (Tarawneh et al., 2020). In addition to abiotic stresses, the resistance of barley to pathogens is also an important aspect in the study of resistance to reverses. By locating genetic markers associated with resistance to specific diseases, the researchers were able to identify key genes that control barley disease resistance traits, such as resistance to rust, downy mildew and leaf spot. 2.3 Key resistance genes identified in previous studies In the field of crop resistance research, previous studies have successfully identified several key genes that play a critical role in the response and adaptation mechanisms of plants in the face of abiotic and biological stresses, especially in important crops such as barley. For example, the DREB gene family, as transcription factors, plays a
Field Crop 2024, Vol.7, No.1, 9-16 http://cropscipublisher.com/index.php/fc 13 key role in regulating plant response to drought, low temperature and saline-alkali stress, and enhances plant adaptation by activating downstream stress response gene expression (Garrett et al., 2017). CBL/CIPK signaling system plays an important role in maintaining electrolyte balance and improving salt and drought tolerance in plant cells through its unique ion regulation mechanism. Members of the LEA protein gene family play a protective role in plant resistance to drought and low temperature stress, and reduce the damage under adverse conditions by maintaining the water content of cells and the stability of biological macromolecules. In rice, the study of OsNHX1 gene reveals how plants respond to salt stress by regulating Na+/H+ exchange, which provides an important reference for the study of salt tolerance in other crops. The proline synthesis pathway involved in AtP5CS gene also shows its importance in plant resistance to drought and salt stress. By increasing proline synthesis, plants are able to enhance their osmoregulatory capacity and thus enhance stress tolerance. 3 Application of GWAS in the Study of Barley Resistance 3.1 A case study of barley drought tolerance tolerance by GWAS In one study, scientists collected several barley germplasm resources and evaluated their phenotypic performance under drought conditions, including drought response traits such as leaf water potential, yield, and root structure. Illumina 9k single nucleotide polymorphism (SNP) chips were used for genome-wide SNP analysis of these barley germplasm. Through GWAS analysis, the researchers identified multiple gene loci associated with drought tolerance in barley. These gene loci are located in different regions of the barley genome, some of which are associated with genes related to stress response such as root growth and ABA signaling pathways, and through further functional validation experiments, the researchers confirmed the importance of some candidate genes for drought tolerance in barley (Sallam et al., 2019). The results of this study show that GWAS technology can help identify key genetic factors in barley drought tolerance and provide potential candidate genes for the future development of more drought-tolerant barley varieties, and the study also provides important clues for further understanding of barley drought response mechanisms. This case study shows that GWAS techniques have great potential for studying drought tolerance in barley and provide an important scientific basis for addressing environmental stress challenges such as drought. It is important to note that the GWAS findings require further validation and functional analysis to confirm the exact mechanism of action of candidate genes on drought tolerance in barley. 3.2 A case study of barley salt stress tolerance by GWAS To carry out the GWAS study of salt tolerance in barley, the scientists collected a number of barley germplasm resources, which included barley species of different geographical origin and cultivation purposes. By conducting multiple phenotypic assessments under salt stress, such as growth indicators, biomass, ion concentration, etc., the researchers determined the level of tolerance of barley under salt stress. A high density single nucleotide polymorphism (SNP) chip was used to analyze the whole genome SNP of these barley germplasm. Through GWAS analysis, the researchers found multiple gene loci related to barley salt stress tolerance, which were distributed in different regions of the barley genome (Fan et al., 2016), and through further functional verification experiments, the researchers identified some candidate genes. These genes are involved in stress response pathways related to salt stress response, ion balance and osmotic regulation. This case study shows that GWAS techniques can help reveal the genetic basis of salt stress tolerance in barley and provide important clues for the future development of barley varieties with greater salt stress tolerance. The study also provides an important scientific basis for further understanding of barley's adaptation to salt stress, but the GWAS findings require further validation and functional analysis to confirm the exact mechanism of action of candidate genes on barley salt stress tolerance.
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