Genetic mapping of QTL controlling kernel protein content in maize using multiple DH populations

Maize kernel protein content is a key determinant of nutritional quality for food and feed. However, the relatively low protein level in conventional maize endosperm remains a major constraint that reduces the bioavailability of grain nutrients for human and livestock consumption. The genetic basis underlying kernel protein content still needs further dissection using stable and efficient mapping populations. Three DH populations were constructed from four elite maize inbred lines with contrasting protein contents. Phenotypic analysis revealed that kernel protein content displayed a continuous, approximately normal distribution with broad-sense heritability of 70.17% to 73.93%, suggesting that genetic factors contributed predominantly to phenotypic variation. Using 5730 SNP markers, three genetic linkage maps were constructed with an average interval of 1.20 cM. A total of 14 significant QTLs were detected across the three populations, with individual PVE ranging from 3.70% to 18.04%. Notably, stable and overlapping QTLs were identified across populations, including a prominent cluster on chromosome 7. Genome-wide two-locus scanning detected 35 significant epistatic pairs, with epistatic interactions accounting for up to 58.78% of phenotypic variance in C8806 and showing highly variable genetic contributions across populations. Comparative mapping indicated that 12 of 14 QTLs colocalized with known protein and amino acid related QTLs, validating the reliability and conservation of these genomic regions. Within these intervals, seven key candidate genes were identified, which participate in nitrate uptake, nitrogen signal transduction, amino acid transport, and the synthesis, accumulation and transcriptional regulation of storage proteins. This study reveals the polygenic genetic architecture of maize kernel protein content. The stable QTLs, overlapping loci, and key candidate genes identified provide a foundation for marker-assisted selection and map-based cloning. These findings will support molecular breeding toward improving protein content and nutritional quality in maize.