Iron (Fe) deficiency significantly constrains maize productivity and nutritional quality, particularly in alkaline soils. To address this challenge, a genome-wide association study (GWAS) was performed using the BLINK model on 93 diverse maize inbred lines genotyped with 230,657 SNPs. Eight morphological and biomass partitioning traits were evaluated under both optimum and Fe-deficient stress (Fe-DS) conditions over two growing seasons. Population structure analysis revealed five distinct genetic subpopulations. A total of 546 and 187 moderately significant marker-trait associations (MTAs) were identified under optimum and Fe-DS conditions, respectively, with chromosomes 1 and 2 harboring the highest density of associations. Harvest index and thousand-grain weight (TGW) exhibited the strongest genetic signals. Across all traits, a total of 24 and 31 highly significant MTAs were detected under optimumt and Fe-DS conditions, respectively. Notably, seven stable MTAs for TGW were consistently identified on chromosome 1. Furthermore, 630 novel pleiotropic genomic regions containing co-localized MTAs for multiple traits were discovered, primarily on chromosomes 1, 2, and 6, indicating shared genetic regulation. These identified MTAs and quantitative trait loci provide valuable targets for marker-assisted breeding to enhance Fe-deficiency tolerance and nutrient-use efficiency in maize, though further validation across diverse genetic backgrounds remains essential for breeding applications.
Hosseinzade et al. (2026) studied this question.