Maize (Zea mays L.) germplasm in China Summer maize ecological region (CSM) or central corn-belt of China is diverse but has not been systematically characterized at molecular level. In this study, genetic variation, genome diversity, linkage disequilibrium patterns, population structure, and characteristics of different heterotic groups were studied using 525,141 SNPs obtained by Genotyping-By-Sequencing (GBS) for 490 inbred lines collected from researchers at CSM region. The SNP density is lower near centromere, but higher near telomere region of maize chromosome, the degree of linkage disequilibrium (r²) vary at different chromosome regions. Majority of the inbred lines (66.05%) show pairwise relative kinship near zero, indicating a large genetic diversity in the CSM breeding germplasm. Using 4849 tagSNPs derived from 3618 haplotype blocks, the 490 inbred lines were delineated into 3 supergroups, 6 groups, and 10 subgroups using ADMIXTURE software. A procedure of assigning inbred lines into heterotic groups using genomic data and tag-SNPs was developed and validated. Genome differentiation among different subgroups measured by F_st, and the genetic diversity within each subgroup measured by GD are both large. The share of heterotic groups that have significant North American germplasm contribution: P, SS, IDT, and X, accounts about 54% of the CSM breeding germplasm collection and has increased significantly in the last two decades. Two predominant types of heterotic pattern in CSM region are: M-Reid group × TSPT group, and X subgroup × Local subgroups.

Plant height (PH) and ear height (EH) are important traits associated with biomass, lodging resistance, and grain yield in maize. There were strong effects of genotype x environment interaction (GEI) on plant height and ear height of maize. In this study, 203 maize inbred lines were grown at five locations across China’s Spring and Summer corn belts, and plant height (PH) and ear height (EH) phenotype data were collected and grouped using GGE biplot. Five locations fell into two distinct groups (or mega environments) that coincide with two corn ecological zones called Summer Corn Belt and Spring Corn Belt. In total, 73,174 SNPs collected using GBS sequencing platform were used as genotype data and a recently released multi-environment GWAS software package IIIVmrMLM was employed to identify QTNs and QTN x environment (corn belt) interaction (QEIs); 12 and 11 statistically significant QEIs for PH and EH were detected respectively and their phenotypic effects were further partitioned into Add*E and Dom*E components. There were 28 and 25 corn-belt-specific QTNs for PH and EH identified, respectively. The result shows that there are a large number of genetic loci underlying the PH and EH GEIs and IIIVmrMLM is a powerful tool in discovering QTNs that have significant QTN-by-Environment interaction. PH and EH candidate genes were annotated based on transcriptomic analysis and haplotype analysis. EH related-QEI S10_135 (Zm00001d025947, saur76, small auxin up RNA76) and PH related-QEI S4_4 (Zm00001d049692, mads32, encoding MADS-transcription factor 32), and corn-belt specific QTNs including S10_4 (Zm00001d023333, sdg127, set domain gene127) and S7_1 (Zm00001d018614, GLR3.4, and glutamate receptor 3.4 or Zm00001d018616, DDRGK domain-containing protein) were reported, and the relationship among GEIs, QEIs and phenotypic plasticity and their biological and breeding implications were discussed.

Southern corn rust (SCR) caused by Puccinia polysora Underw is a major disease leading to severe yield losses in China Summer Corn Belt. Using six multi-locus GWAS methods, we identified a set of SCR resistance QTNs from a diversity panel of 140 inbred lines collected from China Summer Corn Belt. Thirteen QTNs on chromosomes 1, 2, 4, 5, 6, and 8 were grouped into three types of allele effects and their associations with SCR phenotypes were verified by post-GWAS case-control sampling, allele/haplotype effect analysis. Relative resistance (RR_R) and relative susceptibility (RRs) catering to its inbred carrier were estimated from single QTN and QTN-QTN combos and epistatitic effects were estimated for QTN-QTN combos. By transcriptomic annotation, a set of candidate genes were predicted to be involved in transcriptional regulation (S5_145, Zm00001d01613, transcription factor GTE4), phosphorylation (S8_123, Zm00001d010672, Pgk2- phosphoglycerate kinase 2), and temperature stress response (S6_164a/S6_164b, Zm00001d038806, hsp101, and S5_211, Zm00001d017978, cellulase25). The breeding implications of the above findings were discussed.

Although phenotypic plasticity facilitates the understanding of trait variation, its study remains limited. To study phenotypic plasticity, in this study, 203 inbred maize lines were phenotyped for flowering time and plant height in Zhengzhou, Ningjin, Tieling, and Binxian and genotyped with 121,871 high-quality SNPs. The regression slopes and intercepts of flowering time and plant height on four meteorological factors in their corresponding, most significant correlation windows were used to estimate the phenotypic plasticity of the above traits and to further indirectly detect the interactions between quantitative trait nucleotides and meteorological factors. Of the two known and seven candidate genes identified in this study, ZmCCT, GRMZM2G035417, GRMZM2G069651, and GRMZM2G359322 can be used to explain why spring maize has a longer flowering time than summer maize, as these genes delay pollen development and flowering regulators under long day and low temperature; while ZmPIN1b, GRMZM2G062045, GRMZM2G370777, GRMZM2G077752, and GRMZM2G126397 can be used to explain why Tieling has higher plant height than other regions, as these genes enhance auxin transport and suppress dwarfing genes under increased precipitation and low temperature. This study explains the phenotypic plasticity of these traits.

Despite the extensive utilization of heterosis in modern breeding, its genetic architecture remains incompletely understood. This study aimed to decipher the genetic basis of heterosis in maize. Four heterosis indices derived from seven traits in a 2023 maize NCII breeding population were analyzed in association with 36,671,063 genetic markers. Among the 119 candidate genes identified through multi-omics integration and haplotype analyses, 101 exhibited consistent and significant haplotype-heterosis associations across both the 2019 and 2023 NCII breeding populations. Five high-confidence candidate genes were highlighted, with HDA108, MADS3, and Zm00001eb080510 repeatedly detected across multiple environments and populations. eGWAS linked the known heterosis-related genes ZAR1, bZIP29, and ACCO2 to one, two, and two candidate genes, respectively. Five candidate genes showed co-localization of eQTLs and heterosis-associated QTNs within linkage disequilibrium windows (2.60 to 8.63 kb). Integration of multi-dimensional networks using a graph neural network framework identified six hub genes, including HDA108 (degree = 224). Notably, complementary genetic backgrounds of parental lines enabled more complete gene functionality in hybrid offspring. Candidate genes exhibited substantially more significant haplotype-trait associations in F₁ hybrids (98) than in parental lines (35), and 97 candidates showed significant allele frequency divergence (Fst > 0.2) between parental groups. The number of superior haplotypes was significantly correlated with both best-parent and mid-parent heterosis across the two breeding populations. These findings demonstrate that heterosis-related genes exert stronger functional effects in hybrids than in inbred parents, thereby enhancing trait performance in offspring. Furthermore, variation in the number of superior haplotypes directly contributed to heterosis differences among hybrids. All heterosis-associated QTNs primarily influenced trait phenotypes through epistatic interactions, which emerged as the predominant genetic driver of heterosis. This study has identified some novel candidate genes and elucidated some key genetic mechanisms underlying heterosis, providing some valuable insight and resource for precision hybrid breeding.