Multi-environment trials (METs) are essential for maize breeding, but their implementation is often constrained by limited resources, particularly the high cost of phenotyping. To optimize resource allocation, 264 testcrosses derived from 100 inbred lines and three testers were evaluated across six locations. Four genomic prediction models, five cross-validation schemes (CV1–CV5), and five training set proportions (S1–S5) were systematically assessed for predicting grain moisture content (GMC), grain yield (GY), and plant height (PH). Together, the five cross-validation schemes and five training set proportions generated 25 sparse testing strategies. Among the four models, the reaction norm model consistently achieved the highest prediction accuracy, and was therefore selected for subsequent analyses. Averaged across S1–S5, CV1 showed the lowest prediction accuracy, with mean accuracies of 0.361 for GMC, 0.413 for GY, and 0.480 for PH. In contrast, CV2–CV5 achieved higher and comparable accuracies. Increasing the training set proportion from 1/6 (S1) to 5/6 (S5) improved prediction accuracy by 48.3% for GMC, 30.3% for GY, and 26.7% for PH, although further gains became limited once the training set proportion exceeded approximately 50% (S3). In addition, CV3 enabled reliable estimation of parental general combining ability based on predicted hybrid performance, particularly at higher training set proportions. Overall, integrating multi-environment sparse testing with genomic prediction enables efficient hybrid evaluation under limited resources, providing a cost-effective strategy to reduce phenotyping demands and accelerate breeding progress.

Vitamin E (tocochromanols), a vital lipid-soluble antioxidant, is often deficient in maize-based diets. Our objective was to identify potential genomic regions associated with tocochromanols variation and evaluate the potential of genomic predictions for its improvement. We assessed 1044 tropical maize inbreds from three panels across multiple environments, and genotyped them with high-density single-nucleotide polymorphisms. Across panels, we identified 50 causal loci, including 15 in a combined panel associated with three grain tocochromanol components. Associated loci showed strong positive allelic effects (1.12- to 2.72-fold increment due to favourable allele) for improving tocochromanol content. Notably, four loci were associated with α-tocotrienol, and three loci with α-tocopherol were found to be stable, and one pleiotropic locus influenced both. Underlying candidate genes are enriched in cellular, catalytic, biosynthetic and metabolic processes, with 14 involved in the tocochromanol biosynthesis pathway. Favourable haplotypes on chromosomes 5 and 7, notably increased α-tocopherol (6.12-16.19 µg/g) and γ-tocopherol (32.12-102.31 µg/g) levels, respectively. Genomic prediction models proved useful in predicting tocochromanols with moderate-to-high prediction accuracies (0.43-0.52), demonstrating their potential to develop elite, high-tocochromanol lines. Our results provided valuable insights into the genetic architecture of tocochromanols and support accelerating biofortification through genome-wide selection to improve vitamin E levels in tropical maize.

Fusarium ear rot (FER) caused by Fusarium verticillioides is a major constraint on global maize production. The genetic basis of FER resistance is not yet fully understood, and the development of effective breeding strategies for improving FER resistance is still a critical priority. In the present study, a collection of 254 CIMMYT tropical maize lines genotyped with 955,690 high-quality SNPs was used to conduct genome-wide association studies (GWAS), complemented by QTL (quantitative trait locus) mapping in two recombinant inbred line populations. Additionally, genomic prediction (GP) exploring various statistical models and SNP selection schemes was implemented to optimize predictive accuracy for improving FER resistance. The broad-sense heritability estimates of FER resistance were 0.69–0.86 in the CML panel across six environments and 0.39–0.69 in the two RIL populations. At a p-value threshold of 2.61 × 10−7, GWAS identified 18 SNPs significantly associated with FER resistance across six environments, and in single environment analyses, their phenotypic variance explained (PVE) values ranged from 0.68 to 13.75%, with 13 SNPs exceeding a PVE of 5%. At a p-value threshold of 1 × 10−5, an additional 37 SNPs were detected, clustering within seven environmentally stable regions identified in at least two environments. Furthermore, 13 haplotype blocks exhibiting significant phenotypic differences were identified within these stable regions, with PVE values ranging from 2.39 to 15.24%, 9 of which exceeded 5%. QTL mapping in the two RIL populations revealed 27 moderate-effect QTLs at a LOD threshold of 2.5, including four detected repeatedly across environments, though only one QTL overlapped with the GWAS-identified region. Moderate genomic prediction accuracies of FER severity were achieved across models, with GBLUP and BayesB outperforming other models, and the prediction accuracies of these two models in the three populations were all around 0.5. Integrating the significant SNP set from genetic mapping results with a 100-SNP background set enhanced the stability of cross-population predictions. These results implied that FER resistance in tropical maize is controlled by multiple genomic regions with small-to-moderate genetic effects, whereas the consistency of genomic regions detected by GWAS and QTL mapping is low. Genomic prediction incorporating regions identified across different genetic backgrounds emerges as a promising tool for accelerating FER resistance breeding.

Plant height (PH) and aboveground biomass (AGB) are critical agronomic traits that determine the yield potential of maize (Zea mays L.). However, the application of genomic selection (GS) and genome-wide association studies (GWAS) in maize breeding is often hindered by the limitations of phenotypic data collection, which is typically characterized by low throughput and inadequate accuracy. To address this challenge, we employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB in a panel of 817 maize hybrids derived from 364 inbred lines over two growing seasons. Our results demonstrated that the integration of UAV-derived LiDAR point clouds with crop surface models (CSMs) enabled robust estimation of pH across multiple years (R2 > 0.90). Furthermore, a three-dimensional AGB estimation model was developed using UAV-derived PH and canopy coverage (CC), achieving high estimation accuracy (R2 > 0.83). Subsequently, the UAV-derived PH and AGB were utilized for GS and GWAS analyses. Replicated 10-fold cross-validation showed that the mean predictability was 0.504 for PH and 0.402 for AGB across eight commonly used GS models. Moreover, of the 66,066 potential crosses derived from the 364 inbred lines, the top 200 crosses selected for AGB showed up to twice the AGB of the bottom 200 crosses. Field validation demonstrated that the mean ear weight (EW) in the AGB top group was 39.0% higher than that in the bottom group. A total of 16 and 11 significant SNPs were identified by at least two GWAS methods for PH and AGB, respectively. Based on these SNPs, 81 candidate genes were functionally annotated, six of which were simultaneously associated with both traits. The candidate gene association analysis suggested that variations in the promoter region of ZmFLA9 may affect both traits. Overall, our study highlights the potential of UAV-based high-throughput phenotyping to accelerate maize genomic breeding by enabling rapid, precise, and large-scale trait assessment.

Genotyping is a key step in molecular breeding. Due to its cost-effectiveness, accuracy, and flexibility, genotyping by target sequencing (GBTS) has become a preferred technology for medium-density genotyping. In this study, a new GBTS array for fresh edible maize was developed using resequencing data from 477 lines. The array contains 5759 SNPs evenly distributed across the maize genome, with average minor allele frequency (MAF) and polymorphism information content (PIC) values of 0.40 and 0.36, respectively. These SNPs are closely associated with 1566 functional genes. Cluster analysis of 198 maize lines based on the GBTS array was consistent with their pedigree relationships. Furthermore, 277 fresh waxy maize lines were genotyped and used for genomic selection analyses of hundred-kernel weight, kernel length, and kernel width. Comparative evaluation of different models indicated that Ridge Regression Best Linear Unbiased Prediction (rrBLUP) was the optimal model, with prediction accuracies of 0.33, 0.64, and 0.36, respectively. Additional analyses using different marker densities based on the rrBLUP model showed that prediction accuracy did not increase when the number of markers exceeded 2000, indicating that this array provides sufficient marker density for genetic analysis and genomic selection. Overall, this array provides a useful tool for genetic studies of fresh edible maize and facilitates the application of genomic selection in breeding programs.

Acetylation is one of the ubiquitous modifications of cell wall polysaccharides, which affects cell wall structure and function. Acetyltransferase and acetyl esterase mediate the process of polysaccharides acetylation and are responsible for the dynamic modification of acetyl. Currently, three classes of acetyltransferases (reduced wall acetylation 2, RWA2; trichome birefringence-like, TBL; altered xyloglucan 9, AXY9) and two classes of acetyl esterases (pectin acetylesterase, PAE; brittle leaf sheath 1, BS1) have been identified. Despite the established importance of TBL-mediated acetylation in plant cell wall integrity and immunity in several species (e.g., Arabidopsis, rose, and rice), the ZmTBLs family members implicated in immune responses in maize remain largely unexplored in terms of their genomic distribution, functional diversification, and mechanistic roles. In this study, we identified 66 ZmTBLs, which shared high sequence and structural similarity and not inclined to have non-synonymous substitution. Nearly all ZmTBLs harbor a PC-Esterase domain and conserved GDS- and DxxH-related motifs, with the additional presence of a DCxH motif suggesting a higher level of sequence conservation within the ZmTBLs family. According to the transcriptome data, some ZmTBLs may participate in resistance to multiple pathogens. RT-qPCR showed ZmTBL20 was highly induced during necrotrophic pathogen Bipolaris maydis infection, and inoculation assay further validated the function of ZmTBL20 as a positive regulator in maize response to B. maydis. Moreover, this modified resistance was achieved by changing the cell wall acetylation levels. These results suggest that TBLs may play a significant role in maintaining resistance to southern corn leaf blight in maize.

Genomic selection (GS) holds great promise for accelerating breeding progress in plants, and the advancement of across-population GS is essential to realize its full potential. However, conventional across-population GS heavily relies on precisely aligned markers across dense genotypes, whereas the feasibility of using flexible low-density markers remains underexplored. This study developed GS-Impute, a residual convolutional denoising autoencoder-based neural network framework that enables accurate genotype imputation for low-density across-population GS. A key breakthrough of GS-Impute is an automatic matching algorithm that resolves the persistent challenge of targeted training in the presence of both sporadic and systematic missing data. Additionally, GS-Impute incorporates a data augmentation strategy and several advanced techniques to enhance imputation accuracy, including residual blocks, dynamic learning-rate optimization, and layer normalization. Comprehensive evaluations across rice and maize breeding populations demonstrated that GS-Impute outperforms the latest versions of established benchmark tools, including Beagle5.4, Minimac4, and STICI. Importantly, the results indicate that GS-Impute makes across-population GS feasible with low-density markers, establishing a resource-efficient strategy with the potential to transform genomic breeding programs.

Photoperiod sensitivity poses a major obstacle to the expansion, breeding, and production of maize (Zea mays) in temperate regions. While the photoperiod-dependent FLOWERING LOCUS T (FT)/ZCNs pathway modulates floral development, the mechanism by which crops perceive specific light wavelengths and regulate flowering remains largely unknown. In this study, we demonstrate that the rhythmic expression of the blue light receptor FLAVIN-BINDING KELCH REPEAT F-BOX 1a (ZmFKF1a) is finely controlled by the Evening Complex (EC) components of LUX ARRHYTHMO 2 (ZmLUX2). ZmFKF1a interacts with GIGANTEA 1 (ZmGI1), stabilizing it and promoting its nuclear localization via a blue light-dependent mechanism. In the nucleus, ZmGI1 directly binds and activates Zea mays MADS-box 4 (ZMM4), a MADS-box gene specifically expressed in the shoot apical meristem, which drives floral transition. Genetic analyses revealed that ZmGI1 is epistatic to ZmFKF1a in promoting shoot apex development and accelerating flowering in maize. Our findings elucidate a ZmLUX2-ZmFKF1a-ZmGI1-ZMM4 regulatory module that fine-tunes photoperiodic flowering of day-neutral temperate maize lines, functioning independently of ZEA CENTRORADIALISs (ZCNs). Furthermore, transgenic maize overexpressing ZmFKF1a exhibited accelerated flowering and enhanced yield specifically in photoperiod-sensitive tropical maize lines under extreme natural long-day conditions, underscoring its potential application in improving maize production through precise manipulation of flowering traits. These insights advance our understanding of how blue light signaling orchestrates flowering time in maize and offer a promising strategy for optimizing crop performance in diverse environments.

BACKGROUND: H2B histones play crucial roles in plant responses to biotic stress. However, to date, most research on H2B histones has focused on their roles in post-translational modification, and studies specifically investigating the intrinsic properties of these histones remain relatively limited. Here we identified the ZmH2B in maize (Zea mays) and investigated its role in the response of maize to infection by the Southern corn leaf blight pathogen Bipolaris maydis. RESULT: In this study, a nucleus-localized ZmH2B was identified from maize. To characterize the role of this histone in disease resistance, we employed virus-induced gene silencing (VIGS) and transient overexpression (VOX) to generate ZmH2B-silenced (FoMV:ZmH2B) and ZmH2B-overexpressing (FoMV:ZmH2B-VOX) lines. FoMV:ZmH2B lines showed enhanced B. maydis infection and an inhibited chitin-induced reactive oxygen species burst, whereas FoMV:ZmH2B-VOX lines exhibited the opposite effects. Furthermore, ZmH2B overexpression induced the expression of various pathogenesis-related genes, suggesting that these genes enhance resistance against B. maydis. Transcriptome analysis of ZmH2B-silenced plants revealed that the differentially expressed genes were predominantly enriched in photosynthesis-related pathways, pointing to a role for photosynthesis in B. maydis resistance. CONCLUSIONS: These results suggest that ZmH2B positively regulates maize resistance to B. maydis.

Fusarium ear rot (FER), caused by Fusarium verticillioides, results in substantial yield losses and poses a significant threat to maize production worldwide. However, the genetic basis of FER resistance remains poorly understood. Utilizing QTL-seq and association analysis, we identified a gene encoding UDP-ᴅ-glucuronate 4-epimerase 1 (ZmGAE1). A 141-base pair insertion was revealed as the natural functional variation in the promoter of ZmGAE1, which decreases its expression and enhances resistance to FER. Functional validation confirmed that ZmGAE1 acts as a negative regulator of maize resistance to FER. Notably, reduced ZmGAE1 accumulation not only improved FER resistance but also lowered fumonisin content. This effect was attributed to increased cell density within the down-placenta chalaza region, accompanied by the accumulation of galacturonic acid and pectin. Crucially, lines lacking ZmGAE1 exhibited no adverse effects on key agronomic traits and showed resistance to multiple diseases, including maize stalk rot, southern leaf blight, and seed rot. These findings highlight ZmGAE1 as a promising candidate for improving FER resistance in maize, offering a novel approach for crop protection and sustainable agriculture.

Maize (Zea mays L.) is an important food crop throughout the world and is also one of the earliest crops to use heterosis. In this study, we evaluated the genetic diversity, population structure, and selective sweep of 100 elite inbred maize lines collected from the current breeding program in Sichuan province, Southwest China, using 5,261,175 high-quality single nucleotide polymorphisms (SNPs). We discovered an abundance of genetic diversities and classified them into four groups. By combining kinship relationships, these groups were further divided into Tropic-local A, Improved-tropic, Tropic-local B, and Improved-local. Genomic differentiation was assessed using Fst values (0.21–0.44) as well as genetic diversity (π = 6.07 × 10−4 − 6.61 × 10−4). We generated 900 (90 × 10) hybrids using 90 and 10 inbred maize lines from 100 diverse maize germplasms. All hybrids were evaluated for 10 traits in three replicate tests across two locations. We found that the patterns of G1 × G3, G1 × G4, G2 × G3, and G3 × G4 exhibited significant heterosis in yield-related traits and have been used in commercial breeding. In addition, we also explored the relationship between 10 traits of hybrid offspring and the number of heterozygous SNP. Under most heterosis modes, the best linear unbiased estimation (BLUE) value of the trait was highly consistent with the trend of deleterious SNPs, but there was a deviation in the G1 × G3 mode. Taken together, the results provide insight into the utilization of the current maize germplasm in Sichuan province to improve hybrid breeding.

Soil salinization poses a major challenge to global food security, affecting over one billion hectares of arable land and severely constraining crop productivity. As the primary interface between plants and soil, roots play a pivotal role in sensing and adapting to salinity stress through remarkable structural and functional plasticity. This review integrates recent advances in root system architecture (RSA) dynamics, suberin biosynthesis, hormonal regulation, and microbiome interactions to elucidate how plants achieve salinity resilience. We discuss key genes and regulatory modules controlling primary root elongation, lateral root patterning, and barrier formation, emphasizing transcriptional networks involving MYB, NAC, and WRKY families and their coordination with ABA, auxin, and ethylene signaling. Special attention is given to the biosynthesis and deposition of suberin as a dynamic ion-selective barrier governed by hormonal crosstalk and lipid metabolism. We further highlight how beneficial microbes such as Azospirillum, Bacillus, and arbuscular mycorrhizal fungi enhance salt tolerance by modulating phytohormones, antioxidant systems, and ionic homeostasis. Integrating multi-omics and CRISPR-based tools with microbiome engineering offers new avenues to design salt-resilient root ideotypes. We propose a conceptual framework linking molecular regulation, hormonal dynamics, and rhizosphere ecology to root system plasticity, providing a blueprint for engineering next-generation crops capable of maintaining growth and productivity in saline environments.

Introduction:Leaf midrib architecture (LMA) in maize plays essential roles in supporting leaf structure and facilitating photosynthetic performance. Despite its importance for plant architecture and yield potential, the genetic basis underlying key midrib traits remains poorly understood. Methods:To dissect the genetic architecture of LMA, we performed a genome-wide association study (GWAS) using a diverse panel of 508 maize inbred lines representing tropical, subtropical, and temperate germplasm. Three traits-midrib width (MW), total midrib thickness (TMT), and midrib basal thickness (MBT)-were measured across two environments in Liaoning Province, China. Phenotypic data were analyzed using mixed linear models with population structure and kinship correction. Significant loci were identified under a Bonferroni-adjusted threshold, and candidate genes were functionally annotated and examined for expression patterns using publicly available transcriptomic data. Results:All LMA traits exhibited continuous variation and moderate heritability (H^2 = 0.33-0.61), with significant positive correlations among them. GWAS identified six SNPs significantly associated with LMA on chromosomes 5, 7, and 8, corresponding to 97 genes in adjacent genomic intervals. Among these, 27 annotated genes were enriched in functions related to transcriptional regulation, hormone signaling, cytoskeleton organization, and cell development. Key candidate genes included GRMZM2G074124 (YABBY-domain factor), GRMZM2G130953 and GRMZM2G332390 (auxin-related), GRMZM2G079185 (LOB-domain protein), and GRMZM2G407517 (Actin7). Correlation analyses further revealed that LMA traits are significantly associated with yield-related parameters, including cob diameter and grain weight. Discussion:Our findings demonstrate that LMA in maize is a complex quantitative trait governed by multiple genes with diverse biological functions. Several candidate genes involved in auxin response, cytoskeletal dynamics, and lateral organ development play crucial roles in midrib formation. The observed associations between midrib traits and yield components suggest that LMA can serve as a valuable target for improving canopy structure and productivity in maize breeding. These results provide novel insights and candidate loci for molecular dissection and genetic improvement of leaf midrib traits.

High-zinc maize offers a promising solution to alleviate this micronutrient malnutrition, particularly in the Global South. Maize, as a C4 crop, shows potential in addressing declining zinc levels in the face of environmental stressors, but achieving optimal zinc concentrations in maize kernels requires targeted breeding efforts. This chapter highlights the genetic variability of kernel zinc concentration in maize germplasm, considering the complexities of trait inheritance and the influence of genotype-environment interactions. Conventional breeding strategies, alongside modern tools such as QTL mapping, genome-wide association studies (GWAS), and genomic selection (GS), have been instrumental in developing high-zinc varieties. Data from efforts in Latin America and Africa reveal that these varieties can perform competitively in terms of yield and agronomic traits. The bioavailability of zinc in biofortified maize is discussed, alongside the nutritional benefits it provides, particularly in traditional maize-based diets. Key challenges, such as potential yield penalties and the need for consumer acceptance, are explored, with emphasis on combining nutritional improvement with essential agronomic traits like disease resistance and yield stability. The broader opportunities for scaling high-zinc maize are also considered, underscoring the role of collaborative efforts and public-private partnerships in ensuring the sustainable adoption of these nutritionally enhanced varieties.

Fusarium ear rot (FER) caused by Fusarium species severely reduces grain yield and quality of maize. Genome prediction (GP), a promising tool for quantitative trait breeding in plants and animals, uses molecular markers for capturing quantitative trait loci and predicting the genetic value of candidates for selection. In the present study, different subsets of markers and statistical methods for GP accuracy were tested in diverse inbred populations for FER resistance using a five-fold cross-validation approach. The prediction accuracy increased with an increase in the number of random markers; however, an increase in number beyond 10K did not increase the prediction accuracy. The prediction accuracy of selected markers was higher than that of random markers, and 500–1000 selected markers had the highest prediction accuracy, beyond which it slowly decreased. Although there was no difference among statistical methods when using selected markers at high prediction accuracy, significant differences were observed when using random markers. On this basis, a liquid chip named FER0.4K (liquid chip for genomic prediction of FER) containing 381 SNPs was developed for low-cost, high-throughput genotyping, with a prediction of approximately 0.82. The statistical method of genome prediction was compiled into a web-based, easy-to-use statistical analysis software using the “shiny” package in R. In summary, this study provides a foundation for FER resistance breeding in maize and offers new insights into the genetic improvement of other complex quantitative traits in plants.

Integrating multiple modern breeding techniques in maize has always been challenging. This study aimed to address this issue by applying a flexible sparse partial diallel cross design composed of 945 maize hybrids derived from 266 inbred lines across different heterotic groups. The research integrated genome-wide association studies, genomic selection and genomic evaluation of parental inbred lines to accelerate the breeding process for developing single-cross hybrids. Significant associations were identified for 7-25 stable single nucleotide polymorphisms (SNPs) associated with the general combining abilities (GCAs) of nine yield-related traits. Using the maizeGDB and NCBI databases, 264 candidate genes were screened and functionally annotated based on significant SNPs detected by at least three statistical methods. The marker set developed from these GCA SNPs significantly improved the prediction accuracy of hybrids across all traits. The GCA estimates of the inbred lines involved in the top 100 and bottom 100 hybrids consistently ranked at the top and bottom, thereby confirming the accuracy of the predictions. Furthermore, the top 100 crosses selected using BayesB, GBLUP and LASSO showed a 105.4-108.6% increase in average ear weight compared to the bottom 100 crosses in field validation, demonstrating strong selection gains. Notably, amongst the top 100 hybrids, A017/A037 and A037/A169, each containing six superior genotypes were registered as Suyu 161 and Tongyu 1701, respectively, by the National Crop Variety Approval Committee in China. These results highlight the effectiveness of genomic selection and provide valuable insights for advancing genomic hybrid breeding in maize.

This commentary on Scharwies et al. (2025, Science) discusses maize root branching in response to moisture gradients and highlights research gaps in investigation of the role of soil type and soil properties in driving weak or strong root hydropatterning in maize.

The understanding of the characteristics and metabolite changes in waxy and normal maize kernels after cooking is rather limited. This study was designed to meticulously analyze the differences in characteristics and metabolites of these kernels before and after steaming. To cut environmental impacts, samples were obtained by pollinating one ear with mixed pollen. Non-targeted metabolomics was used to analyze metabolites comprehensively. The results demonstrated that a total of 4043 annotated metabolites were identified. Principal component analysis (PCA) indicated distinct variances between kernels before and after steaming and between the two maize types. Steaming led to an increase in differential metabolites (DEMs) for both maize varieties, noticeably in waxy maize. In waxy maize, the down-regulated DEMs were associated with lipid metabolism, while the up-regulated ones were related to amino acid, phenylpropanoid, and flavone metabolism. Compared to steamed normal maize kernels, waxy maize had more DEMs in purine and steroid pathways, fewer in fatty acid, α-linolenic acid, and phenylpropanoid ones, with marked differences in secondary metabolites like those in amino acid metabolism. This study offers a vital foundation and direction for future research on metabolic pathways regarding maize quality improvement and flavor regulation.

IntroductionMaize (Zea mays L.) is one of the most important crops worldwide, the kernel size-related traits are the major components of maize grain yield.MethodsTo dissect the genetic architecture of four kernel-related traits of 100-kernel weight, kernel length, kernel width, and kernel diameter, a genome-wide association study (GWAS) was conducted in the waxy and sweet maize panel comprising of 447 maize inbred lines re-sequenced at the 5× coverage depth. GWAS analysis was carried out with the mixed linear model using 1,684,029 high-quality SNP markers.ResultsIn total, 49 SNPs significantly associated with the four kernel-related traits were identified, including 46 SNPs on chromosome 3, two SNPs on chromosome 4, and one SNP on chromosome 7. Haplotype regression analysis identified 338 haplotypes that significantly affected these four kernel-related traits. Genomic selection (GS) results revealed that a set of 10,000 SNPs and a training population size of 30% are sufficient for the application of GS in waxy and sweet maize breeding for kernel weight and kernel size. Forty candidate genes associated with the four kernel-related traits were identified, including both Zm00001d000707 and Zm00001d044139 expressed in the kernel development tissues and stages with unknown functions.DiscussionThese significant SNPs and important haplotypes provide valuable information for developing functional markers for the implementation of marker-assisted selection in breeding. The molecular mechanism of Zm00001d000707 and Zm00001d044139 regulating these kernel-related traits needs to be investigated further.

Genomic prediction enables rapid selection of maize varieties with low kernel water content (KWC), facilitating the development of mechanized maize harvesting and reducing costs. This study evaluated and characterized the KWC and grain yield (GY) of hybrid maize in northern China and used genomic prediction to identify superior hybrid combinations with low kernel water content at maturity (MKWC) and high GY adapted to northern China. A total of 285 hybrids obtained from single crosses of 34 inbred lines from Stiff Stalk and Non-Stiff Stalk heterotic groups were used for genomic prediction of KWC and GY. We tested 20 different statistical prediction models considering additive effects and evaluating the impact of dominance and epistasis on prediction accuracy. Employing 10-fold cross-validation, it showed that the average prediction accuracy ranged drastically from 0.386 to 0.874 across traits and models. Eight linear statistical methods displayed a very similar prediction accuracy for each trait. The average prediction accuracy of machine learning methods was lower than that of linear statistical methods for KWC-related traits, but the random forest model had a high prediction accuracy of 0.510 for GY. When genetic effects were incorporated into the prediction model, the prediction accuracy for each trait was improved. Overall, the model with dominant and epistatic effects (G:AD(AA)) performed best. For the same number of markers, predictions using trait-specific markers resulted in higher prediction accuracy than randomly selected markers. When the number of trait-specific SNPs was set to 100, the prediction accuracy of GY increased by 33.27%, from 0.406 to 0.541. Out of all the 561 potential hybrids, the TOP 30 hybrids selected by genomic prediction would lead to a 1.44% decrease in MKWC compared with Xianyu335, a hybrid with a fast kernel water dry-down, and these hybrids also had higher GY simultaneously. Our results confirm the value of genomic prediction for hybrid breeding low MKWC suitable for maize mechanized harvesting in northern China. In conclusion, this study highlights the potential of genomic prediction to optimize maize hybrid breeding, enhancing efficiency and providing insights into genotype-accuracy relationships. The findings offer new strategies for hybrid design and advancing mechanized harvesting in northern China.