LGG_2025v16n1

! ! Legume Genomics and Genetics 2025, Vol.16, No.1, 1-10 http://cropscipublisher.com/index.php/lgg! ! 10! Yang J., Jiang H., Yeh C., Yu J., Jeddeloh J., Nettleton D., and Schnable P., 2015, Extreme-phenotype genome-wide association study (XP-GWAS): a method for identifying trait-associated variants by sequencing pools of individuals selected from a diversity panel, The Plant Journal, 84(3): 587-596. https://doi.org/10.1111/tpj.13029 Yoosefzadeh-Najafabadi M., Torabi S., Torkamaneh D., Tulpan D., Rajcan I., and Eskandari M., 2021, Machine-learning-based genome-wide association studies for uncovering QTL underlying soybean yield and its components, International Journal of Molecular Sciences, 23(10): 5538. https://doi.org/10.3390/ijms23105538 Yoosefzadeh-Najafabadi M., Torabi S., Tulpan D., Rajcan I., and Eskandari M., 2023, Application of SVR-mediated GWAS for identification of durable genetic regions associated with soybean seed quality traits, Plants, 12(14): 2659. https://doi.org/10.3390/plants12142659 Zhang J., Song Q., Cregan P., Nelson R., Wang X., Wu J., and Jiang G., 2015, Genome-wide association study for flowering time, maturity dates and plant height in early maturing soybean (Glycine max) germplasm, BMC Genomics, 16: 217. https://doi.org/10.1186/s12864-015-1441-4

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