An appropriate flowering period is an important selection criterion in maize breeding. It plays a crucial role in the ecological adaptability of maize varieties. To explore the genetic basis of flowering time, GWAS and GS analyses were conducted using an associating panel consisting of 379 multi-parent DH lines. The DH population was phenotyped for days to tasseling (DTT), days to pollen-shedding (DTP), and days to silking (DTS) in different environments. The heritability was 82.75%, 86.09%, and 85.26% for DTT, DTP, and DTS, respectively. The GWAS analysis with the FarmCPU model identified 10 single-nucleotide polymorphisms (SNPs) distributed on chromosomes 3, 8, 9, and 10 that were significantly associated with flowering time-related traits. The GWAS analysis with the BLINK model identified seven SNPs distributed on chromosomes 1, 3, 8, 9, and 10 that were significantly associated with flowering time-related traits. Three SNPs 3_198946071, 9_146646966, and 9_152140631 showed a pleiotropic effect, indicating a significant genetic correlation between DTT, DTP, and DTS. A total of 24 candidate genes were detected. A relatively high prediction accuracy was achieved with 100 significantly associated SNPs detected from GWAS, and the optimal training population size was 70%. This study provides a better understanding of the genetic architecture of flowering time-related traits and provides an optimal strategy for GS.

Introduction: Maize ranks among the most essential crops globally, yet its growth and yield are significantly hindered by salt stress, posing challenges to agricultural productivity. To utilize saline-alkali soils more effectively and enrich maize germplasm resources, identifying salt-tolerant genes in maize is essential.Methods: In this study, we used a salt-tolerant maize inbred line, SPL02, and a salt-sensitive maize inbred line, Mo17. We treated both lines with 180 mmol/L sodium chloride (NaCl) for 0 days, 3 days, 6 days, and 9 days at the three-leaf growth stage (V3). Through comprehensive morphological, physiological, and transcriptomic analyses, we assessed salt stress effects and identified hub genes and pathways associated with salt tolerance.Results: Our analysis identified 25,383 expressed genes, with substantial differences in gene expression patterns across the salt treatment stages. We found 8,971 differentially expressed genes (DEGs)—7,111 unique to SPL02 and 4,791 unique to Mo17—indicating dynamic gene expression changes under salt stress. In SPL02, the DEGs are primarily associated with the MAPK signaling pathway, phenylpropanoid biosynthesis, and hormone signaling under salt treatment conditions. In Mo17, salt stress responses are primarily mediated through the abscisic acid-activated signaling pathway and hormone response. Additionally, our weighted gene co-expression network analysis (WGCNA) pinpointed five hub genes that likely play central roles in mediating salt tolerance. These genes are associated with functions including phosphate import ATP-binding protein, glycosyltransferase, and WRKY transcription factors.Discussion: This study offers valuable insights into the complex regulatory networks governing the maize response to salt stress and identifies five hub genes and pathways for further investigation. These findings contribute valuable knowledge for enhancing agricultural resilience and sustainability in saline-affected environments.

The leaf area index (LAI) is a direct indicator of crop canopy growth and serves as an indirect measure of crop yield. Unmanned aerial vehicles (UAVs) offer rapid collection of crop phenotypic data across multiple time points, providing crucial insights into the evolving dynamics of the LAI essential for crop breeding. In this study, the variation process of the maize LAI was investigated across two locations (XD and KZ) using a multispectral sensor mounted on a UAV. During a field trial involving 399 maize inbred lines, LAI measurements were obtained at both locations using a random forest model based on 28 variables extracted from multispectral imagery. These findings indicate that the vegetation index computed by the near-infrared band and red edge significantly influences the accuracy of the LAI prediction. However, a prediction model relying solely on data from a single observation period exhibits instability (R² = 0.34–0.94, RMSE = 0.02–0.25). When applied to the entire growth period, the models trained using all data achieved a robust prediction of the LAI (R² = 0.79–0.86, RMSE = 0.12–0.18). Although the primary variation patterns of the maize LAI were similar across the two fields, environmental disparities changed the variation categories of the maize LAI. The primary factor contributing to the difference in the LAI between KZ and XD lies in soil nutrients associated with carbon and nitrogen in the upper soil. Overall, this study demonstrated that UAV-based time-series phenotypic data offers valuable insight into phenotypic variation, thereby enhancing the application of UAVs in crop breeding.

Heat stress threatens maize production. Through a genome-wide association study using pollen viability decline rate as a phenotype, we identified ZmHEAT1, encoding ZmKAKU41, a nuclear envelope protein known to regulate nuclear morphology in both maize and Arabidopsis, as a key regulator of thermotolerance. ZmHEAT1 positively regulates heat tolerance at seedling and reproductive stages, with its overexpression improving thermotolerance and its knockout reducing pollen viability and yield under high temperatures. ZmHEAT1 interacts with its homolog ZmKAKU42 and the nucleoskeleton protein ZmNCH1 to form a complex at the nuclear envelope. Mutants lacking ZmHEAT1 or ZmKAKU42 exhibit increased nuclear envelope damage under heat stress, along with repressed expression of key heat shock protein genes HSP90-2 and HSP17.4. This work establishes the ZmHEAT1/ZmKAKU42-ZmNCH1 complex as a critical guardian of nuclear integrity and a modulator of the heat-responsive transcriptional program, providing a valuable genetic resource for breeding heat-tolerant maize varieties.

Plant architecture-related traits are key agronomic traits affecting crop growth and yield. To unravel the genetic architecture of plant height (PH), ear height (EH), tassel length (TL), and tassel primary branch number (TPBN), 379 DH lines derived from 21 maize hybrids were used for genome-wide association study (GWAS) and genomic selection (GS) analyses. Although plant architecture-related traits were significantly influenced by genotype and genotype-by-environment interactions, moderate to high broad-sense heritability was observed for PH (81.3%), EH (79.6%), TL (86.4%), and TPBN (82.5%). Using six different models for GWAS, seven unique SNPs on chromosomes 1, 2, and 3 were identified for PH, 92 unique SNPs located on chromosomes 1 to 9 were identified for EH, three unique SNPs on chromosome 6 were detected for TL, and 18 unique SNPs located on chromosomes 1, 4, 5, 8, and 10 were identified for TPBN at the p-value threshold of 7.42 × 10⁻⁶. A few hotspot genomic regions conferring plant architecture-related traits were identified, located in bins 2.07, 4.07, 8.03, 6.01, and 10.00. A total of 144 putative candidate genes were identified, which were enriched in endocytosis and lipid biosynthetic process, electron carrier activity, chloroplast stroma, and plastid stroma. The prediction accuracy evaluated through 5-fold cross-validation was 0.44 for PH, 0.43 for EH, 0.31 for TL, and 0.30 for TPBN. When the training population size (TPS) reached 60–70% or marker density (MD) reached 3000, the prediction accuracy tends to stabilize, indicating that the optimum size of TPS and MD were 60–70% and 3000 for GS, respectively. The highest prediction accuracy evaluated by using 30–5000 significant SNPs corresponding to the lowest p-value was 0.70 for PH, 0.85 for EH, 0.58 for TL, and 0.75 for TPBN, with an increase in accuracy of 59.1% to 150.0%. These results demonstrate that integrating GS with a subset of highly significant SNPs can substantially enhance prediction efficiency, thereby facilitating the selection of superior genotypes and accelerating the breeding of maize varieties with optimized plant architecture. This study has further elucidated the genetic basis of maize architecture-related traits and provided valuable information on how to implement GS to breed novel maize varieties with optimized plant types.