AUTHOR=Liang Zicong , Qi Nianhua , Li Ruoning , Gao Ruijia , Guo Ruichao , Li Jiayi , Han Yutong , Xie Nan , Zhao Wei , Yao Xingdong , Xie Futi TITLE=Identification of candidate genes and development of KASP markers for soybean pod-related traits using GWAS JOURNAL=Frontiers in Plant Science VOLUME=Volume 16 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1680918 DOI=10.3389/fpls.2025.1680918 ISSN=1664-462X ABSTRACT=Soybean (Glycine max [L.] Merr.) is a crop characterized by rich content of oil and protein in seeds, enhancing both yield and quality is considered a pressing challenge in current soybean research and production. Soybean yield is determined by individual traits, including seed number per plant, seed weight per plant, pod number per plant, pod weight per plant and 100−seed weight. Here, 338 resequenced soybean varieties (or lines) were evaluated under two planting densities for five pod−related traits. Substantial variation was detected among the 338 accessions under both densities, and all phenotypic traits followed a normal distribution. A total of 47 and 56 significant SNPs were identified respectively under high and low planting densities through genome−wide association studies (GWAS). Among them, eight SNPs were repeatedly detected across at least two planting densities or environments, and were significantly associated with the seed number per plant (SNPP), seed weight per plant (SWPP) and 100−seed weight (HSW). Based on linkage disequilibrium (LD) analysis, haplotype analysis, gene functional annotation, and qRT−PCR validation, Glyma.20G116200 and Glyma.13G162800 were identified as key genes associated with HSW and SNPP, respectively. Based on this, a KASP marker, S20_35808042 (G/C), was developed and successfully validated in 97 soybean accessions. In summary, these findings hold substantial value for soybean improvement, providing new insights into the genetic architecture of pod−related traits and establishing a conceptual foundation for marker−based selection in breeding programs.