Molecular Plant Breeding 2024, Vol.15, No.6, 371-378 http://genbreedpublisher.com/index.php/mpb 376 consistent effects across different studies (Shook et al., 2021). Similarly, another study reported stable QTLs for seed yield in at least three out of four environments, highlighting their robustness (Ayalew et al., 2022). These stable QTLs can be used to develop soybean varieties that perform well across diverse environmental conditions, ensuring reliable yield improvements. 5.4 Challenges and potential solutions for incorporating GWAS findings into breeding Incorporating GWAS findings into breeding programs presents several challenges, including the complexity of trait inheritance, genotype-environment interactions, and the need for high-throughput phenotyping. One challenge is the limited statistical power of conventional GWAS methods, which can be addressed by integrating machine learning algorithms to enhance QTL detection (Yoosefzadeh-Najafabadi et al., 2021; Yoosefzadeh-Najafabadi et al., 2023). Another challenge is the negative correlation between important traits, such as seed protein and oil content, which requires careful selection strategies to balance these traits (Fields et al., 2023). Additionally, the integration of multi-environment trial data can help identify stable QTLs, reducing the impact of genotype-environment interactions (Shook et al., 2021). By addressing these challenges, breeders can effectively utilize GWAS findings to develop high-yielding, resilient soybean varieties (Stewart-Brown et al., 2019). 6 Concluding Remarks Looking forward, the application of GWAS in soybean breeding presents numerous opportunities for yield improvement. The continuous refinement of GWAS methodologies, including the incorporation of machine learning techniques, will likely enhance the precision of QTL identification. Future research should focus on validating the identified QTLs and understanding their functional mechanisms to develop more effective breeding strategies. Additionally, expanding the genetic diversity of soybean germplasm panels and conducting multi-environment trials will help identify QTLs that are stable across different conditions, thereby improving the resilience and adaptability of soybean cultivars. The integration of genomic selection (GS) with GWAS also holds promise for accelerating the breeding cycle and achieving higher genetic gains in soybean yield and related traits. Acknowledgments The authors extend heartfelt thanks to Dr. X. Fang, Director of the Hainan Institute of Tropical Agricultural Resources and an expert in soybean molecular genetics, for thoroughly reviewing the initial draft of this paper and providing comprehensive revision suggestions. The authors also express gratitude to the two anonymous peer reviewers for their valuable comments and suggestions on the manuscript. Funding This research is funded by the Heilongjiang Agricultural Science and Technology Innovation Leapfrog Program (CX22YQ02) and the Scientific Research Operation Fund Project of Heilongjiang Provincial Research Institutes (CZKYF2023-1-C002). 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. References Ayalew H., Schapaugh W., Vuong T., and Nguyen H., 2022, Genome‐wide association analysis identified consistent QTL for seed yield in a soybean diversity panel tested across multiple environments, The Plant Genome, 15(4): e20268. https://doi.org/10.1002/tpg2.20268 PMid:36258674 Bhat J., Adeboye K., Ganie S., Barmukh R., Hu D., Varshney R., and Yu D., 2022, Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.), Frontiers in Genetics, 13: 953833. https://doi.org/10.3389/fgene.2022.953833 PMid:36419833 PMCid:PMC9677453
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