Molecular Plant Breeding 2024, Vol.15, No.6, 371-378 http://genbreedpublisher.com/index.php/mpb 377 Fields J., Saxton A., Beyl C., Kopsell D., Cregan P., Hyten D., Cuvaca I., and Pantalone V., 2023, Seed protein and oil QTL in a prominent Glycine max genetic pedigree: enhancing stability for marker assisted selection, Agronomy, 13(2): 567. https://doi.org/10.3390/agronomy13020567 Happ M., Graef G., Wang H., Howard R., Posadas L., and Hyten D., 2021, Comparing a mixed model approach to traditional stability estimators for mapping genotype by environment interactions and yield stability in soybean [Glycine max (L.) Merr.], Frontiers in Plant Science, 12: 630175. https://doi.org/10.3389/fpls.2021.630175 PMid:33868333 PMCid:PMC8044453 Hina A., Cao Y., Song S., Li S., Sharmin R., Elattar M., Bhat J., and Zhao T., 2020, High-resolution mapping in two RIL populations refines major “QTL hotspot” regions for seed size and shape in soybean (Glycine max L.), International Journal of Molecular Sciences, 21(3): 1040. https://doi.org/10.3390/ijms21031040 PMid:32033213 PMCid:PMC7038151 Huang J., Ma Q., Cai Z., Xia Q., Li S., Jia J., Chu L., Lian T., Nian H., and Cheng Y., 2020, Identification and mapping of stable QTLs for seed oil and protein content in soybean [Glycine max (L.) Merr.], Journal of Agricultural and Food Chemistry, 68(23): 6448-6460. https://doi.org/10.1021/acs.jafc.0c01271 PMid:32401505 Izquierdo P., Kelly J., Beebe S., and Cichy K., 2023, Combination of meta‐analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean, The Plant Genome, 16(2): e20328. https://doi.org/10.1002/tpg2.20328 PMid:37082832 Khan M., Tong F., Wang W., He J., Zhao T., and Gai J., 2019, Using the RTM-GWAS procedure to detect the drought tolerance QTL-allele system at the seedling stage under sand culture in a half-sib population of soybean [Glycine max (L.) Merr.], Canadian Journal of Plant Science, 99: 801-814. https://doi.org/10.1139/cjps-2018-0309 Kim S., Tayade R., Kang B., Hahn B., Ha B., and Kim Y., 2023, Genome-wide association studies of seven root traits in soybean (Glycine max L.) landraces, International Journal of Molecular Sciences, 24(1): 873. https://doi.org/10.3390/ijms24010873 PMid:36614316 PMCid:PMC9821504 Kumar V., Vats S., Kumawat S., Bisht A., Bhatt V., Shivaraj S., Padalkar G., Goyal V., Zargar S., Gupta S., Kumawat G., Chandra S., Chalam V., Ratnaparkhe M., Gill B., Jean M., Patil G., Vuong T., Rajcan I., Deshmukh R., Belzile F., Sharma T., Nguyen H., and Sonah H., 2021, Omics advances and integrative approaches for the simultaneous improvement of seed oil and protein content in soybean (Glycine max L.), Critical Reviews in Plant Sciences, 40: 398-421. https://doi.org/10.1080/07352689.2021.1954778 Lee G., Lee S., Carter T., Shannon G., and Boerma R., 2021, Identification of soybean yield QTL in irrigated and rain-fed environments, Agronomy, 11(11): 2207. https://doi.org/10.3390/agronomy11112207 Priyanatha C., Torkamaneh D., and Rajcan I., 2022, Genome-wide association study of soybean germplasm derived from Canadian × Chinese crosses to mine for novel alleles to improve seed yield and seed quality traits, Frontiers in Plant Science, 13: 866300. https://doi.org/10.3389/fpls.2022.866300 PMid:35419011 PMCid:PMC8996715 Qin J., Wang F., Zhao Q., Shi A., Zhao T., Song Q., Ravelombola W., An H., Yan L., Yang C., and Zhang M., 2022, Identification of candidate genes and genomic selection for seed protein in soybean breeding pipeline, Frontiers in Plant Science, 13: 882732. https://doi.org/10.3389/fpls.2022.882732 PMid:35783963 PMCid:PMC9244705 Rani R., Raza G., Ashfaq H., Rizwan M., Razzaq M., Waheed M., Shimelis H., Babar A., and Arif M., 2023, Genome-wide association study of soybean (Glycine max [L.] Merr.) germplasm for dissecting the quantitative trait nucleotides and candidate genes underlying yield-related traits, Frontiers in Plant Science, 14: 1229495. https://doi.org/10.3389/fpls.2023.1229495 PMid:37636105 PMCid:PMC10450938 Ravelombola W., Qin J., Shi A., Song Q., Yuan J., Wang F., Chen P., Yan L., Feng Y., Zhao T., Meng Y., Guan K., Yang C., and Zhang M., 2021, Genome-wide association study and genomic selection for yield and related traits in soybean, PLoS One, 16(8): e0255761. https://doi.org/10.1371/journal.pone.0255761 PMid:34388193 PMCid:PMC8362977 Shook J., Zhang J., Jones S., Singh A., Diers B., and Singh A., 2021, Meta-GWAS for quantitative trait loci identification in soybean, G3: Genes, Genomes, Genetics, 11(7): jkab117. https://doi.org/10.1093/g3journal/jkab117 PMid:33856425 PMCid:PMC8495947 Stewart-Brown B., Song Q., Vaughn J., and Li Z., 2019, Genomic selection for yield and seed composition traits within an applied soybean breeding program, G3: Genes, Genomes, Genetics, 9: 2253-2265. https://doi.org/10.1534/g3.118.200917
RkJQdWJsaXNoZXIy MjQ4ODYzMg==