BM_2024v15n6

Bioscience Method 2024, Vol.15 http://bioscipublisher.com/index.php/bm © 2024 BioSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. BioSci Publisher is an international Open Access publisher specializing in bioscience methods, including technology, lab tool, statistical software and relative fields registered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada. Publisher BioSci Publisher Editedby Editorial Team of Bioscience Methods Email: edit@bm.bioscipublisher.com Website: http://bioscipublisher.com/index.php/bm Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Bioscience Methods (ISSN 1925-1920) is an open access, peer reviewed journal published online by BioSci Publisher. The journal publishes all the latest and outstanding research articles, letters and reviews in all areas of bioscience, the range of topics including (but are not limited to) technology review, technique know-how, lab tool, statistical software and known technology modification. Case studies on technologies for gene discovery and function validation as well as genetic transformation. All the articles published in Bioscience Methods 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. BioSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights.

Bioscience Methods (online), 2024, Vol.15, No.6 ISSN 1925-1920 https://bioscipublisher.com/index.php/bm © 2024 BioSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content The Impact of Marker-Assisted Selection on Soybean Yield and Disease Resistance Xiaomei Wang, Guohong Sun, Haide Xu, Changyuan Liu, Yanping Wang Bioscience Methods, 2024, Vol.15, No.6, 255-263 Figure Review of Genetic Approaches to Improve Yield and Starch Content in Sweet Potato Letan Luo, Yu Chen, Lin Zhao, Jiang Shi, Yanhao Zhao Bioscience Methods, 2024, Vol.15, No.6, 264-274 Advanced Processing Techniques and Applications for Value-Added Sweet Potato Products HuiqunWu Bioscience Methods, 2024, Vol.15, No.6, 275-288 Comparative Study of Rubber Biosynthesis Pathways in Eucommia ulmoides and Hevea brasiliensis Degang Zhao, Shangmei Long, Li Song, Ying Zhu, Ruoruo Wang, Dan Zhao Bioscience Methods, 2024, Vol.15, No.6, 289-301 Innate Immune Response and Pathogen Defense Mechanisms in Earwigs: A Comprehensive Molecular Biology Analysis Jun Xu, Qibin Xu Bioscience Methods, 2024, Vol.15, No.6, 302-314 Application of CRISPR/Cas9 in Wheat Genetic Improvement Yanxin Ma, Shuo Yang, Shuping Lang Bioscience Methods, 2024, Vol.15, No.6, 315-326 Case Study of Post-Harvest Processing and Value Addition in Fresh-Eating Sweet Potato Tao Chen, Jianjun Xiong, Yanlin Zhang, Renxiang Cai Bioscience Methods, 2024, Vol.15, No.6, 327-336 Advances in Agronomic Practices for High-Yield Soybean Cultivation Yuting Zhong, Shuiliang Zhong Bioscience Methods, 2024, Vol.15, No.6, 337-347

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 255 Feature Review Open Access The Impact of Marker-Assisted Selection on Soybean Yield and Disease Resistance Xiaomei Wang, Guohong Sun, Haide Xu, Changyuan Liu, Yanping Wang Heilongjiang Academy of Agricultural Sciences, Mudanjiang Branch, Mudanjiang, 157000, Heilongjiang, China Corresponding author: wyping1981@126.com Bioscience Methods, 2024, Vol.15, No.6 doi: 10.5376/bm.2024.15.0026 Received: 02 Sep., 2024 Accepted: 11 Oct., 2024 Published: 03 Nov., 2024 Copyright © 2024 Wang 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: Wang X.M., Sun G.H., Xu H.D., Liu C.Y., and Wang Y.P., 2024, The impact of marker-assisted selection on soybean yield and disease resistance, Bioscience Methods, 15(6): 255-263 (doi: 10.5376/bm.2024.15.0026) Abstract Soybean (Glycine max) is a crucial crop for global food security and agricultural sustainability, with breeding efforts focusing on improving yield and disease resistance. This study explores the role of Marker-Assisted Selection (MAS) in accelerating genetic improvement for these traits in soybean. We systematically studythe principles and types of genetic markers used in MAS, including simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs), and quantitative trait loci (QTLs), and highlight recent technological advancements such as high-throughput genotyping platforms and the integration of genomic selection (GS). Two case studies illustrate the practical impact of MAS: one on the development of high-yielding soybean varieties and another on enhancing resistance against soybean cyst nematode (SCN). While MAS has led to substantial gains in yield and resistance, its application is not without challenges, including technical, genetic, and economic constraints. This studyconcludes with a discussion on future perspectives for MAS, emphasizing the integration of emerging technologies like CRISPR/Cas9 and omics approaches. The findings indicate that MAS will continue to play a pivotal role in soybean breeding, with prospects for enhancing both yield and resilience against biotic stresses. Keywords Marker-assisted selection (MAS); Soybean breeding; Yield improvement; Disease resistance; Genetic markers 1 Introduction Soybean (Glycine max) is a globally significant crop, primarily valued for its high protein and oil content, which makes it a staple in both human and animal diets. Additionally, soybean cultivation plays a crucial role in enhancing soil fertility through nitrogen fixation, which is facilitated by symbiotic relationships with rhizobia bacteria (Ngosong et al., 2022). The crop's adaptability to various climatic conditions has led to its widespread cultivation, with significant areas dedicated to soybean farming in regions such as North and South America, Asia, and increasingly, Europe (Miller et al., 2023). Yield and disease resistance are critical factors in soybean production. High yield ensures the economic viability of soybean farming, while disease resistance minimizes losses caused by pathogens, thereby securing food supply and farmer income. Enhancing these traits is essential to meet the growing global demand for soybeans and to ensure sustainable agricultural practices. For instance, soil nutrient deficiencies and diseases can significantly constrain soybean productivity, necessitating the use of fertilizers and other interventions to maintain yield levels (Ngosong et al., 2022). Moreover, the transition to conservation and no-tillage systems has shown that while these practices can affect early plant establishment, they do not necessarily lead to major yield losses if managed correctly (Adamič and Leskovšek, 2021). Traditional breeding techniques have long been employed to improve soybean traits, but they often involve lengthy processes and are limited by the complexity of trait inheritance. Modern breeding techniques, such as genomic selection and marker-assisted selection (MAS), have revolutionized soybean improvement by enabling more precise and efficient selection of desirable traits. Genomic selection, for example, has been shown to effectively predict and enhance traits like yield, protein, and oil content in soybean breeding programs (Miller et al., 2023). MAS, in particular, leverages molecular markers linked to specific traits, allowing for the early and accurate identification of superior genotypes. This method accelerates the breeding process and increases the likelihood of developing high-yielding, disease-resistant soybean varieties (Rani et al., 2023).

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 256 This study aims to evaluate the impact of marker-assisted selection on soybean yield and disease resistance. By integrating MAS into soybean breeding programs, the study seeks to determine its effectiveness in enhancing these critical traits. The scope of the research includes a comprehensive analysis of MAS techniques, their application in soybean breeding, and the resulting improvements in yield and disease resistance. The findings will contribute to the development of more resilient and productive soybean varieties, ultimately supporting sustainable agricultural practices and food security. 2 Marker-Assisted Selection (MAS) in Soybean Breeding 2.1 Principles of MAS and its advantages over conventional breeding Marker-assisted selection (MAS) leverages molecular markers to select desirable traits in plants, offering several advantages over conventional breeding methods. MAS is more time-efficient, cost-effective, and precise, allowing for the selection of traits at the seedling stage without the need for phenotypic evaluation of mature plants (Miedaner and Korzun, 2012; Song et al., 2023). This method is particularly beneficial for traits that are difficult to measure phenotypically, such as disease resistance and yield potential (Figure 1) (Kim et al., 2020; Hasan et al., 2021). Figure 1 The figure explains the basic procedure of marker-assisted selection (Adopted from Hasan et al., 2021) 2.2 Types of genetic markers used in soybean MAS Simple sequence repeats (SSRs), also known as microsatellites, are short, repetitive DNA sequences that are highly polymorphic and co-dominant. They are widely used in MAS due to their high reproducibility, abundance, and ease of detection (Song et al., 2023). SSRs have been successfully applied in soybean breeding programs to improve traits such as disease resistance and yield. Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in genomes. SNPs are highly suitable for high-throughput genotyping and have been extensively used in MAS for soybean breeding. The development of SNP arrays and genotyping platforms has facilitated the rapid identification and selection of beneficial alleles in soybean populations (He et al., 2014; Ludwików et al., 2015; Kim et al., 2020; Cheng, 2024).

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 257 Quantitative trait loci (QTLs) are genomic regions associated with quantitative traits, such as yield and disease resistance. Identifying QTLs linked to these traits allows breeders to select for multiple genes simultaneously, enhancing the efficiency of breeding programs. QTL mapping has been instrumental in identifying regions associated with important agronomic traits in soybeans (Miklas et al., 2006; Sebastian et al., 2010). 2.3 Technological advances in MAS for soybean High-throughput genotyping platforms, such as SNP arrays and genotyping-by-sequencing (GBS), have revolutionized MAS by enabling the rapid and cost-effective analysis of large populations. These technologies allow for the simultaneous detection of thousands of markers, facilitating the identification of beneficial alleles and accelerating the breeding process (He et al., 2014; Ludwików et al., 2015). The integration of Genomic Selection (GS) with MAS combines the strengths of both approaches, allowing for the prediction of breeding values based on genome-wide marker data. This integration enhances the accuracy of selection and accelerates the development of superior soybean cultivars with improved yield and disease resistance (Miedaner and Korzun, 2012). 2.4 Application of MAS for trait improvement in soybean MAS has been successfully applied to improve soybean yield by selecting for QTLs associated with high yield potential. Context-specific MAS (CSM) has been used to identify and select subline haplotypes with superior yield traits, resulting in significant yield gains in selected sublines (Sebastian et al., 2010). The use of high-throughput genotyping platforms has further enhanced the efficiency of yield improvement programs (He et al., 2014). MAS has been instrumental in developing soybean cultivars with enhanced disease resistance. By identifying and selecting markers linked to resistance genes, breeders have been able to develop cultivars resistant to various diseases, such as pod shattering and bacterial blight (Miklas et al., 2006; Ludwików et al., 2015; Kim et al., 2020). The use of MAS for pyramiding multiple resistance genes has also been successful, providing broad-spectrum resistance to multiple pathogens (Miklas et al., 2006; Jena and Mackill, 2008). 3 Impact of MAS on Soybean Yield Improvement 3.1 Genetic basis of yield traits in soybean The genetic basis of yield traits in soybean is complex, involving multiple quantitative trait loci (QTL) that contribute to phenotypic variation. Yield is influenced by numerous genetic factors, including genes related to plant height, seed weight, and maturity. For instance, studies have identified several QTL associated with yield and other agronomic traits, such as the E1 and E3 maturity genes and the Dt2 stem growth habit gene, which have pleiotropic effects on yield and plant height (Miedaner and Korzun, 2012; Zhu et al., 2021). The identification and understanding of these genetic components are crucial for effective marker-assisted selection (MAS) strategies aimed at yield improvement. 3.2 QTL mapping for yield-related traits QTL mapping has been instrumental in identifying loci associated with yield-related traits in soybean. For example, a study involving 875 recombinant inbred lines (RILs) from a cross between Essex and Williams 82 identified 46 yield QTLs, explaining 4.5% to 11.9% of the phenotypic variation for yield (Fallen et al., 2015). Another study mapped QTLs in a BC1 population using specific-locus amplified fragment sequencing technology, identifying 46 significant QTLs for seven yield-related traits across three environments (Mei et al., 2021). These QTLs provide valuable targets for MAS, enabling breeders to select for high-yielding genotypes more efficiently (Ludwików et al., 2015). 3.3 Case study: development of high-yielding soybean varieties using MAS A notable breeding program utilized context-specific MAS (CSM) to improve grain yield in elite soybean populations. This approach involved leveraging residual heterogeneity in elite cultivars to detect yield QTL within specific environmental contexts. The selected subline haplotypes were then compared to their mother lines in replicated yield trials across multiple environments and years (Sebastian et al., 2010). This program highlights the importance of considering both genetic and environmental contexts in MAS.

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 258 Field trials conducted as part of the CSM approach demonstrated statistically significant yield gains of up to 5.8% in some selected sublines. These trials were highly replicated and spanned multiple environments and years, ensuring robust validation of the yield improvements (Sebastian et al., 2010). Additionally, another study involving 944 RILs from a diallel cross of early-maturing varieties identified major QTLs that significantly contributed to seed yield, further validating the effectiveness of MAS in yield improvement (Zhu et al., 2021). The economic and agricultural benefits of using MAS for developing high-yielding soybean varieties are substantial. By enabling the early and precise selection of superior genotypes, MAS reduces the time and cost associated with traditional breeding methods. The improved yield performance of MAS-selected varieties translates to higher productivity and profitability for farmers. Moreover, the integration of MAS with conventional breeding can accelerate the development of varieties with enhanced yield and other desirable traits, contributing to food security and sustainable agriculture (Sebastian et al., 2010; Zhu et al., 2021). 4 Impact of MAS on Disease Resistance in Soybean 4.1 Major diseases affecting soybean production Soybean cyst nematode (SCN), caused by Heterodera glycines, is one of the most destructive pests affecting soybean production globally. The integration of genetic analysis, molecular biology, and genomic approaches has significantly enhanced our understanding of the genetic control of SCN resistance. Major resistance loci such as Rhg1 and Rhg4 have been cloned, and novel resistance quantitative trait loci (QTL) have been discovered, leading to the development of gene-specific DNA markers useful for marker-assisted selection (MAS) (Figure 2) (Kim et al., 2016; Kadam et al., 2016). Figure 2 Phylogenetic tree of the Rhg1 locus constructed on the basis of 5 451 haplotypes using 19 652 accessions and the SoySNP50K (Adopted from Kadam et al., 2016) Image caption: Green diamond shaped bullets showthe high copies of the Rhg1 allele present in the known soybean lines from maturity groups III to V; pink diamond shaped bullets show the low copies of the Rhg1 allele present in the known soybean lines from maturity groups III to V; and light blue hexagon shaped bullets showing the resistant lines (Adopted from Kadam et al., 2016) Phytophthora root rot (PRR), caused by Phytophthora sojae, is another significant disease limiting soybean yield. Resistance to PRR is complex and involves both major resistance genes (Rps) and QTL for partial resistance. Recent advancements in genetic mapping and sequencing have identified several Rps genes and QTL, facilitating the development of diagnostic markers and MAS strategies for breeding PRR-resistant soybean cultivars (Zhong et al., 2017; Jiang et al., 2020; Karhoff et al., 2022; Chandra et al., 2022).

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 259 Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is a devastating foliar disease. Although not explicitly covered in the provided data, the principles of MAS for disease resistance in other pathogens can be applied to ASR. The identification and deployment of resistance genes through MAS can significantly enhance resistance to ASR. 4.2 Identification and deployment of disease resistance genes The identification of disease resistance genes involves high-resolution mapping, genomic sequencing, and the development of molecular markers. For instance, the identification of candidate genes for PRR resistance has been achieved through high-resolution mapping and RNA-seq analysis, pinpointing specific genes that can be targeted for MAS (Jiang et al., 2020; Karhoff et al., 2022). Similarly, the development of SNP markers for SCN resistance loci Rhg1 and Rhg4 has facilitated the differentiation of resistant and susceptible genotypes, accelerating the breeding of resistant cultivars (Kadam et al., 2016). 4.3 Case study: utilization of MAS for enhancing SCN resistance in soybean Screening for SCN resistance involves evaluating a large number of soybean accessions for resistance traits. For example, the use of the SoySNP50K iSelect BeadChip has enabled the evaluation of phylogenetic diversity and the identification of novel sources of SCN resistance. The integration of resistance genes into elite soybean cultivars is achieved through MAS. Gene-specific markers for Rhg1 and Rhg4 have been developed, allowing for the precise selection and incorporation of these resistance genes into breeding programs (Kim et al., 2016; Kadam et al., 2016). The stability of resistance and its impact on yield are critical factors in the success of MAS. Studies have shown that resistance alleles can significantly increase yield in disease-prone fields without negatively affecting yield in less disease-prone environments (Karhoff et al., 2022). Continuous evaluation and breeding efforts are necessary to ensure the durability and effectiveness of resistance genes (Kim et al., 2016). 4.4 Strategies for combining disease resistance and yield traits Combining disease resistance with high yield traits is a major goal in soybean breeding. The identification of QTL associated with both yield and disease resistance can facilitate the development of high-yielding, disease-resistant cultivars. For instance, the identification of yield QTL and their integration with disease resistance genes through MAS can enhance both yield and resistance in soybean (Fallen et al., 2015). Advanced genomic approaches, such as genomic selection and genome editing, offer promising strategies for achieving this goal (Chandra et al., 2022). 5 Challenges and Limitations of MAS in Soybean Breeding 5.1 Technical and operational challenges Marker-assisted selection (MAS) in soybean breeding faces several technical and operational challenges. One significant issue is the complexity of traits such as yield, which are controlled by multiple quantitative trait loci (QTL) with small individual effects. This complexity makes it difficult to identify and utilize effective markers for MAS (Fallen et al., 2015). Additionally, the accuracy of MAS can be compromised by the presence of residual heterogeneity within elite soybean populations, which affects the detection and selection of yield QTL (Sebastian et al., 2010). Another operational challenge is the labor-intensive and time-consuming nature of phenotypic evaluations required for traits like pod shattering resistance, which complicates the integration of MAS into breeding programs (Kim et al., 2020). 5.2 Genetic and Environmental Interactions The effectiveness of MAS is often limited by genetic and environmental interactions. For instance, the expression of QTL can vary significantly across different environments, making it challenging to identify stable markers that are effective in diverse conditions (Fallen et al., 2015). This context-specific variability necessitates the development of models that can predict genotype performance within specific environmental contexts, which adds another layer of complexity to the breeding process (Sebastian et al., 2010). Moreover, the interaction between different resistance genes, such as those for soybean mosaic virus (SMV), can lead to unexpected susceptibility in certain genetic backgrounds, further complicating the use of MAS (Maroof et al., 2008).

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 260 5.3 Cost and resource constraints The cost of genotyping and the resources required for MAS are significant constraints. Although the cost of genotyping has decreased over time, it still represents a substantial investment, particularly for large-scale breeding programs (Sebastian et al., 2010). The need for high-throughput genotyping platforms and the development of diagnostic markers also adds to the financial burden. Additionally, the integration of MAS into conventional breeding programs requires substantial resources in terms of both time and expertise, which can be a limiting factor for many breeding programs (Jena and Mackill, 2008; Miedaner and Korzun, 2012). 5.4 Potential solutions and future prospects Despite these challenges, there are several potential solutions and future prospects for improving the effectiveness of MAS in soybean breeding. Advances in genomic selection (GS) offer promising alternatives by utilizing a broader range of markers across the genome, which can improve prediction accuracy and selection efficiency (Arruda et al., 2016). The development of high-throughput genotyping platforms and the use of chip-based technologies can also reduce costs and streamline the MAS process (Miedaner and Korzun, 2012). Additionally, the identification of broad-spectrum resistance genes and the pyramiding of multiple resistance genes through MAS can enhance the durability and effectiveness of resistance traits (Maroof et al., 2008; Ludwików et al., 2015). Future research should focus on improving the integration of MAS with conventional breeding methods and developing more robust models to account for genetic and environmental interactions (Jena and Mackill, 2008; Sebastian et al., 2010). By addressing these challenges and leveraging new technologies, MAS can become a more effective tool for improving soybean yield and disease resistance, ultimately contributing to more resilient and productive soybean cultivars. 6 Future Perspectives of MAS in Soybean Breeding 6.1 Integration of MAS with emerging technologies The integration of CRISPR/Cas9 genome editing with marker-assisted selection (MAS) holds significant promise for soybean breeding. CRISPR/Cas9 allows for precise modifications at specific genomic loci, which can be used to introduce or enhance traits identified through MAS. This combination can accelerate the development of soybean varieties with improved yield and disease resistance. For instance, CRISPR/Cas9 can be used to target and modify genes associated with pod shattering resistance, as identified by MAS, to create more robust soybean cultivars (Kim et al., 2020). The use of omics technologies, such as genomics and transcriptomics, can greatly enhance the effectiveness of MAS in soybean breeding. Genomic data can provide a comprehensive understanding of the genetic architecture of important traits, while transcriptomic data can reveal gene expression patterns associated with these traits. By integrating these data with MAS, breeders can more accurately select for complex traits like yield and disease resistance. For example, the identification of yield QTLs through genomic analysis can be combined with MAS to improve soybean yield across different environments (Sebastian et al., 2010; Fallen et al., 2015). 6.2 Digital phenotyping and precision agriculture in MAS Digital phenotyping and precision agriculture technologies can revolutionize MAS by providing high-throughput, accurate phenotypic data. These technologies can monitor plant growth, health, and yield in real-time, allowing for more precise selection of desirable traits. The integration of digital phenotyping with MAS can enhance the selection process for traits like disease resistance and yield, making it more efficient and cost-effective. For instance, digital phenotyping can be used to assess the effectiveness of MAS in selecting for resistance to soybean cyst nematode, thereby improving the overall efficiency of breeding programs (Santana et al., 2014). 6.3 International collaborations and data sharing International collaborations and data sharing are crucial for the advancement of MAS in soybean breeding. By sharing genetic and phenotypic data across borders, researchers can build more comprehensive databases, which can be used to identify and validate markers for important traits. Collaborative efforts can also facilitate the development of standardized protocols and tools for MAS, making it more accessible and effective globally. For example, the success of MAS in breeding programs for other crops, such as wheat and barley, can provide

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 261 valuable insights and methodologies that can be adapted for soybean breeding (Francia et al., 2005; Miedaner and Korzun, 2012). 7 Concluding Remarks The research on the impact of marker-assisted selection (MAS) on soybean yield and disease resistance has yielded significant insights. Several studies have identified quantitative trait loci (QTL) associated with yield and disease resistance, demonstrating the potential of MAS in soybean breeding. For instance, a study identified 46 yield QTL, with five being novel, explaining 4.5% to 11.9% of the phenotypic variation for yield. Another study identified four QTL associated with resistance to sudden death syndrome (SDS), accounting for 65% of the phenotypic variability in disease incidence. Additionally, the accelerated yield technology™ (AYT™) approach has been effective in combining forward selection for simple traits with context-specific MAS for complex traits like yield. The validation of MAS for pod shattering resistance showed high prediction accuracy, confirming its applicability in breeding programs. Furthermore, genomic selection (GS) has been shown to be as effective as phenotypic selection for yield, with the potential for greater efficiency if marker assay costs are reduced. The findings from these studies have several implications for soybean breeding programs. The identification of specific QTL for yield and disease resistance traits provides valuable markers that can be used to enhance selection efficiency and accuracy. For example, the use of markers for SDS resistance can significantly improve the selection of resistant genotypes, thereby protecting yield in infested fields. The integration of MAS and GS into breeding programs can streamline the selection process, reducing the time and resources required for developing high-yielding, disease-resistant soybean varieties. The successful application of MAS for traits like pod shattering resistance and SCN resistance further underscores its utility in addressing specific breeding challenges. Overall, these advancements can lead to the development of soybean varieties with improved yield potential and resilience to biotic and abiotic stresses. Future research should focus on several key areas to further enhance the impact of MAS on soybean breeding. First, there is a need for continued identification and validation of QTL associated with important agronomic traits across diverse genetic backgrounds and environments. This will ensure the robustness and applicability of MAS in different breeding contexts. Second, the development of cost-effective genotyping methods will be crucial for the widespread adoption of GS and MAS in breeding programs. Third, research should explore the integration of MAS with other advanced breeding techniques, such as genome editing, to accelerate the development of superior soybean varieties. Finally, breeding programs should prioritize the pyramiding of multiple resistance genes to develop cultivars with broad-spectrum and durable resistance to various diseases, as demonstrated in the case of SMV resistance. By addressing these areas, future research can significantly contribute to the sustainability and productivity of soybean agriculture. Acknowledgments We are grateful to Dr. Zhao for critically reading the manuscript and providing valuable feedback that improved the clarity of the text. We express our heartfelt gratitude to the two anonymous reviewers for their valuable comments on the manuscript. 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 Adamič S., and Leskovšek R., 2021, Soybean (Glycine max (L.) Merr.) growth, yield, and nodulation in the early transition period from conventional tillage to conservation and no-tillage systems, Agronomy, 11(12): 2477. https://doi.org/10.3390/agronomy11122477 Arruda M., Lipka A., Brown P., Krill A., Thurber C., Brown-Guedira G., Dong Y., Foresman B., and Kolb F., 2016, Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivumL.), Molecular Breeding, 36: 1-11. https://doi.org/10.1007/s11032-016-0508-5 Chandra S., Choudhary M., Bagaria P., Nataraj V., Kumawat G., Choudhary J., Sonah H., Gupta S., Wani S., and Ratnaparkhe M., 2022, Progress and prospectus in genetics and genomics of Phytophthora root and stem rot resistance in soybean (Glycine max L.), Frontiers in Genetics, 13: 939182. https://doi.org/10.3389/fgene.2022.939182

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 262 Cheng F., 2024, Application of genome-wide association study in crop disease resistance breeding, Field Crop, 7(1): 1-8. https://doi.org/10.5376/fc.2024.07.0001 Fallen B., Allen F., Kopsell D., Saxton A., McHale L., Shannon J., Kantartzi S., Cardinal A., Cregan P., Hyten D., and Pantalone V., 2015, Selective genotyping for marker assisted selection strategies for soybean yield improvement, Plant Genetics, Genomics, and Biotechnology, 2(1): 95-119. https://doi.org/10.5147/PGGB.V2I1.156 Francia E., Tacconi G., Crosatti C., Barabaschi D., Bulgarelli D., Dall’Aglio E., and Vale G., 2005, Marker assisted selection in crop plants, Plant Cell, Tissue and Organ Culture, 82: 317-342. https://doi.org/10.1007/s11240-005-2387-z Hasan N., Choudhary S., Naaz N., Sharma N., and Laskar R. A., 2021, Recent advancements in molecular marker-assisted selection and applications in plant breeding programmes, Journal of Genetic Engineering and Biotechnology, 19(1): 128. https://doi.org/10.1186/s43141-021-00231-1 He J., Zhao X., Laroche A., Lu Z., Liu H., and Li Z., 2014, Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding, Frontiers in Plant Science, 5: 484. https://doi.org/10.3389/fpls.2014.00484 Jena K., and Mackill D., 2008, Molecular markers and their use in marker-assisted selection in rice, Crop Science, 48(4): 1266-1276. https://doi.org/10.2135/CROPSCI2008.02.0082 Jiang B., Cheng Y., Cai Z., Li M., Jiang Z., Ma R., Yuan Y., Xia Q., and Nian H., 2020, Fine mapping of a Phytophthora-resistance locus RpsGZ in soybean using genotyping-by-sequencing, BMC Genomics, 21: 1-11. https://doi.org/10.1186/s12864-020-6668-z Kadam S., Vuong T., Qiu D., Meinhardt C., Song L., Deshmukh R., Patil G., Wan J., Valliyodan B., Scaboo A., Shannon J., and Nguyen H., 2016, Genomic-assisted phylogenetic analysis and marker development for next generation soybean cyst nematode resistance breeding, Plant Science, 242: 342-350. https://doi.org/10.1016/j.plantsci.2015.08.015 Karhoff S., Vargas-Garcia C., Lee S., Mian M., Graham M., Dorrance A., and McHale L., 2022, Identification of candidate genes for a major quantitative disease resistance locus from soybean PI 427105B for resistance to Phytophthora sojae, Frontiers in Plant Science, 13: 893652. https://doi.org/10.3389/fpls.2022.893652 Kim J., Kim K., Jung J., Kang B., Lee J., Ha B., and Kang S., 2020, Validation of marker-assisted selection in soybean breeding program for pod shattering resistance, Euphytica, 216: 1-9. https://doi.org/10.1007/s10681-020-02703-w Kim K., Vuong T., Qiu D., Robbins R., Shannon J., Li Z., and Nguyen H., 2016, Advancements in breeding, genetics, and genomics for resistance to three nematode species in soybean, Theoretical and Applied Genetics, 129: 2295-2311. https://doi.org/10.1007/s00122-016-2816-x Ludwików A., Cieśla A., Arora P., Das G., Rao G., and Das R., 2015, Molecular marker assisted gene stacking for biotic and abiotic stress resistance genes in an elite rice cultivar, Frontiers in Plant Science, 6: 698. https://doi.org/10.3389/fpls.2015.00698 Maroof M., Jeong S., Gunduz I., Tucker D., Buss G., and Tolin S., 2008, Pyramiding of soybean mosaic virus resistance genes by marker‐assisted selection, Crop Science, 48(2): 517-526. https://doi.org/10.2135/CROPSCI2007.08.0479 Mei H., Liu Y., Cui C., Hu C., Xie F., Zheng L., Du Z., Wu K., Jiang X., Zheng Y., and Ma Q., 2021, QTL mapping of yield-related traits in sesame, Molecular Breeding, 41(7): 43. https://doi.org/10.1007/s11032-021-01236-x Miedaner T., and Korzun V., 2012, Marker-assisted selection for disease resistance in wheat and barley breeding, Phytopathology, 102(6): 560-566. https://doi.org/10.1094/PHYTO-05-11-0157 Miklas P., Kelly J., Beebe S., and Blair M., 2006, Common bean breeding for resistance against biotic and abiotic stresses: from classical to MAS breeding, Euphytica, 147: 105-131. https://doi.org/10.1007/s10681-006-4600-5 Miller M., Song Q., and Li Z., 2023, Genomic selection of soybean (Glycine max) for genetic improvement of yield and seed composition in a breeding context, The Plant Genome, 16(4): e20384. https://doi.org/10.1002/tpg2.20384 Ngosong C., Tatah B., Olougou M., Suh C., Nkongho R., Ngone M., Achiri D., Tchakounté G., and Ruppel S., 2022, Inoculating plant growth-promoting bacteria and arbuscular mycorrhiza fungi modulates rhizosphere acid phosphatase and nodulation activities and enhance the productivity of soybean (Glycine max), Frontiers in Plant Science, 13: 934339. https://doi.org/10.3389/fpls.2022.934339 Rani R., Raza G., Tung M., Rizwan M., Ashfaq H., Shimelis H., Razzaq M., and Arif M., 2023, Genetic diversity and population structure analysis in cultivated soybean (Glycine max [L.] Merr.) using SSR and EST-SSR markers, PLoS One, 18(5): e0286099. https://doi.org/10.1371/journal.pone.0286099 Santana F., Silva M., Guimarães J., Ferreira M., Pereira W., Piovesan N., and Barros E., 2014, Marker-assisted selection strategies for developing resistant soybean plants to cyst nematode, Crop Breeding and Applied Biotechnology, 14: 180-186. https://doi.org/10.1590/1984-70332014V14N3A27

Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 263 Sebastian S., Streit L., Stephens P., Thompson J., Hedges B., Fabrizius M., Soper J., Schmidt D., Kallem R., HindsMark A., Feng L., and Hoeck J., 2010, Context-specific marker-assisted selection for improved grain yield in elite soybean populations, Crop Science, 50(4): 1196-1206. https://doi.org/10.2135/CROPSCI2009.02.0078 Song L., Wang R., Yang X., Zhang A., and Liu D., 2023, Molecular markers and their applications in marker-assisted selection (MAS) in bread wheat (Triticum aestivumL.), Agriculture, 13(3): 642. https://doi.org/10.3390/agriculture13030642 Zhong C., Sun S., Li Y., Duan C., and Zhu Z., 2017, Next-generation sequencing to identify candidate genes and develop diagnostic markers for a novel Phytophthora resistance gene, RpsHC18, in soybean, Theoretical and Applied Genetics, 131: 525-538. https://doi.org/10.1007/s00122-017-3016-z Zhu X., Leiser W., Hahn V., and Würschum T., 2021, Identification of QTL for seed yield and agronomic traits in 944 soybean (Glycine max) RILs from a diallel cross of early‐maturing varieties, Plant Breeding, 140(2): 254-266. https://doi.org/10.1111/PBR.12900

Bioscience Methods 2024, Vol.15, No.6, 264-274 http://bioscipublisher.com/index.php/bm 264 Feature Review Open Access Figure Review of Genetic Approaches to Improve Yield and Starch Content in Sweet Potato LetanLuo1,YuChen 2, LinZhao1, JiangShi 1 , Yanhao Zhao3 1 Crop (Ecology) Research Institute of Hangzhou Academy of Agricultural Sciences, Hangzhou, 310021, Zhejiang, China 2 Science and Technology Bureau of Lin'an District, Lin'an, 311300, Zhejiang, China 3 Tonglu County Agricultural Technology Extension Center, Tonglu, 311500, Zhejiang, China Corresponding author: tomatoman@126.com Bioscience Methods, 2024, Vol.15, No.6 doi: 10.5376/bm.2024.15.0027 Received: 05 Sep., 2024 Accepted: 16 Oct., 2024 Published: 06 Nov., 2024 Copyright © 2024 Luo 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: Luo L.T., Chen Y., Zhao L., Shi J., and Zhao Y.H., 2024, Figure review of genetic approaches to improve yield and starch content in sweet potato, Bioscience Methods, 15(6): 264-274 (doi: 10.5376/bm.2024.15.0027) Abstract Sweet potato (Ipomoea batatas) is a globally significant crop for both food and industrial use, with high yield and starch content playing crucial roles in meeting demands for food, feed, and bioenergy. However, improving sweet potato yield and starch content poses challenges due to its genetic complexity and environmental sensitivity. This study summarizes genetic improvement methods for enhancing sweet potato yield and starch content, focusing on traditional breeding, marker-assisted selection (MAS), genomic selection (GS), gene editing, and multi-omics integration strategies. In recent years, MAS and GS have shown distinct advantages in accelerating the selection of high-yield and high-starch traits in sweet potato. Gene editing technologies, such as CRISPR/Cas9, provide precise approaches for the targeted regulation of key genes. Additionally, multi-omics techniques, including transcriptomics, metabolomics, and proteomics, help elucidate the biological pathways and regulatory mechanisms that influence yield and starch synthesis, offering strong support for optimizing breeding strategies. This study provides a clear direction for sweet potato breeding research, advancing progress toward high-yield and high-starch content varieties and carrying profound implications for global agricultural production and sustainability. Keywords Sweet potato; Genetic improvement; Yield; Starch content; Gene editing 1 Introduction Sweet potato (Ipomoea batatas) is a vital crop with significant economic and nutritional value worldwide. It is a staple food in many developing countries and serves as a crucial source of carbohydrates, vitamins, and minerals. The crop's adaptability to diverse climatic conditions and its ability to thrive in poor soils make it an essential food security crop, particularly in regions prone to food scarcity. Additionally, sweet potato is increasingly recognized for its industrial applications, including its use in food derivatives, dietary supplements, and as a raw material in various industrial processes (Lyu et al., 2021). Despite its importance, sweet potato cultivation faces several challenges, particularly in terms of yield and starch content. Traditional breeding methods have been employed to address these issues, focusing on traits such as root yield, starch content, and disease resistance (Kar et al., 2022). However, the complex polyploid nature of sweet potato and its long breeding cycle have limited the effectiveness of these conventional approaches (Lyu et al., 2021). Recent advancements in genetic engineering and biotechnological techniques, such as CRISPR/Cas9-mediated genome editing, have shown promise in overcoming these challenges by enabling precise modifications to the sweet potato genome (Wang et al., 2019; Lyu et al., 2021). Enhancing the yield and starch content of sweet potato is crucial for meeting the growing global food demand and supporting sustainable agriculture. Increased yield and improved starch quality can significantly contribute to food security, particularly in regions where sweet potato is a staple crop (Lamaro et al., 2023). Moreover, higher starch content and better starch properties can enhance the crop's industrial value, making it more suitable for various applications, including biofuel production and food processing (Lyu et al., 2021). Genetic improvements that increase yield and starch content can also help in developing sweet potato varieties that are more resilient to environmental stresses, thereby supporting sustainable agricultural practices (Ren et al., 2018; Fan et al., 2021).

Bioscience Methods 2024, Vol.15, No.6, 264-274 http://bioscipublisher.com/index.php/bm 265 This study will analyze the advancements in genetic improvement techniques for enhancing sweet potato yield and starch content, delving into recent achievements in genetic engineering, genome editing, and traditional breeding methods. It aims to provide a comprehensive overview of the current state of research while identifying potential future directions. The ultimate goal is to highlight effective strategies for increasing sweet potato yield and starch content, thereby contributing to food security and sustainable agriculture. 2 Genetic Basis of Sweet Potato Yield and Starch Content 2.1 Genetic background of yield traits in sweet potato The yield traits in sweet potato are influenced by multiple quantitative trait loci (QTL) and genetic factors. For instance, a study identified two major QTL on linkage groups 3 and 12 that affect starch content, β-carotene, dry matter, and flesh color. These QTL regions act pleiotropically, reducing starch content while increasing β-carotene in genotypes carrying specific haplotypes (Gemenet et al., 2019). Another study using a polyploid genome-wide association study (GWAS) identified significant SNPs associated with starch content, dry matter, and storage root fresh weight, highlighting the complex genetic architecture of these traits (Haque et al., 2023). Additionally, QTL mapping in potato, a close relative of sweet potato, has revealed that QTLs for tuber yield and starch content are often linked, suggesting shared genetic control. 2.2 Starch synthesis pathways and related genes Starch synthesis in sweet potato involves several key biochemical pathways and genes. The primary enzymes include ADP-glucose pyrophosphorylase (AGPase), soluble starch synthase (SSS), and starch branching enzyme (SBE). AGPase catalyzes the first step in starch biosynthesis, converting glucose-1-phosphate and ATP to ADP-glucose, which is then used by SSS to elongate the starch chain. SBE introduces branch points into the starch molecule, creating a more complex structure (Menéndez et al., 2002). In sweet potato, genes such as granule-bound starch synthase I (IbGBSSI) have been identified as crucial for amylose biosynthesis, with consistent expression during starch accumulation (Haque et al., 2023). Additionally, the physical linkage of phytoene synthase with sucrose synthase has been shown to negatively correlate β-carotene and starch content, indicating a complex interplay between these pathways (Gemenet et al., 2019) 2.3 Gene expression and environmental interactions Environmental factors such as soil quality and climate significantly influence gene expression, impacting yield and starch content in sweet potato. High heritability and genetic advance for traits like vine length, number of branches, and root yield per plant suggest that these traits are less influenced by environmental factors and are governed by additive genes (Kar et al., 2022). However, the expression of genes involved in starch metabolism can be modulated by environmental conditions. For example, cold storage conditions in potato tubers lead to the accumulation of reducing sugars due to the activity of genes like invertase, which are also relevant in sweet potato (Li et al., 2005). Furthermore, QTLs for traits like cold-induced sweetening and reconditioning in potato have been mapped to specific chromosomes, indicating that environmental interactions can have a significant genetic basis (Xiao et al., 2018). 3 Role of Traditional Breeding in Sweet Potato Improvement 3.1 Phenotypic selection and hybrid breeding Traditional breeding methods, such as phenotypic selection and hybrid breeding, have been instrumental in improving sweet potato yield and starch content. Phenotypic selection involves choosing plants with desirable traits based on observable characteristics. This method has been effective in identifying high-yield and high-starch varieties, as demonstrated by the significant genetic variability and potential for genetic gains in sweet potato populations (Otoboni et al., 2020; Vargas et al., 2020). For instance, the study by Otoboni et al. (2020) showed that 81.25% of the traits had genotypic coefficients of variation above 20%, indicating favorable conditions for selection with considerable genetic advances. However, phenotypic selection has its limitations. The process is labor-intensive and time-consuming, requiring multiple generations to achieve significant improvements. Additionally, the selection is often influenced by

Bioscience Methods 2024, Vol.15, No.6, 264-274 http://bioscipublisher.com/index.php/bm 266 environmental factors, which can mask the true genetic potential of the plants. The study by Visalakshi et al. (2021) highlighted the high variability in traits such as vine length, number of branches per plant, and root yield per plant, which are influenced by environmental conditions, making it challenging to achieve consistent results. 3.2 Mutation breeding Mutation breeding is another traditional approach that has been used to create genetic diversity in sweet potato. This method involves inducing mutations through physical or chemical agents to generate new genetic variations. Mutation breeding has been successful in developing sweet potato varieties with improved starch properties. For example, the study by Katayama et al. (2006) demonstrated that crossing and mutagenesis could increase the variations of amylose content in sweet potato, leading to the selection of variants with low or high amylose content. The application of mutation breeding has led to the development of new sweet potato cultivars with desirable traits. The "Quick Sweet" cultivar, developed through mutation breeding, features low gelatinization temperatures and altered starch fine structure, making it suitable for various industrial applications (Katayama et al., 2006). This cultivar's starch properties, such as lower gelatinization temperatures and higher proportions of short amylopectin chains, provide excellent cold storage stability, which is beneficial for food processing industries. 3.3 Successful cases: examples and applications of high-yield and high-starch sweet potato variety development Several successful cases of high-yield and high-starch sweet potato variety development have been reported. One notable example is the study by Lin et al. (2007), which investigated the maternal effects on yield and quality traits in sweet potato through reciprocal crosses. The results showed significant positive correlations between top weight, storage root weight, and starch content, indicating the potential for selecting high-yield and high-starch varieties through hybrid breeding. Another successful case is the identification of promising sweet potato genotypes with high genetic variability and potential for selection gains. The study by Otoboni et al. (2020) identified genotypes CERAT31-01, CERAT21-02, and CERAT51-30 as the most promising for high yield and starch content. Similarly, the study by Vargas et al. (2020) recommended the VR13-61 accession for root production and VR13-11 and VR13-22 for dual-aptitude, highlighting the effectiveness of traditional breeding methods in improving sweet potato traits. 4 Application of Marker-Assisted Selection (MAS) in Sweet Potato Breeding 4.1 Principles of marker-assisted selection and its advantages in efficient sweet potato breeding Marker-assisted selection (MAS) is a modern plant breeding technique that leverages molecular markers to select desirable traits in crops. The principle behind MAS is the identification and use of DNA markers that are closely linked to genes of interest, allowing breeders to select plants with favorable traits at the seedling stage, thus bypassing the need for phenotypic selection in mature plants (Francia et al, 2005; Collard and Mackill, 2008; Singh and Singh, 2015). This method significantly accelerates the breeding process by enabling the selection of traits that are difficult to measure directly, such as yield and stress tolerance (Collard and Mackill, 2008; Singh and Singh, 2015). MAS offers several advantages over traditional breeding methods. It allows for the precise transfer of genomic regions of interest, improving the efficiency of breeding programs (Babu et al., 2004; Huang and Hong, 2024). Additionally, MAS can be used for both simply inherited traits and complex polygenic traits, although its application in the latter has been more challenging (Babu et al., 2004; Francia et al, 2005). The integration of MAS with conventional breeding can lead to the development of new cultivars with improved traits in a shorter time frame (Collard and Mackill, 2008; Singh and Singh, 2015). 4.2 Important molecular markers related to yield and starch accumulation and their applications The identification of key molecular markers associated with yield and starch accumulation is crucial for the successful application of MAS in sweet potato breeding. Quantitative trait loci (QTL) mapping studies have

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