Single-replicate field trials are common in early-stage maize hybrid screening, but raw plot observations contain field noise that can weaken genomic prediction. We evaluated whether pedigree and spatial adjustment improve the predictability of phenotypic targets and Top-N recovery, whether joint pedigree-spatial adjustment provides complementary gains, and whether target construction has a larger influence than baseline model choice. Across four sites, five agronomic traits, four phenotypic inputs, and six genomic prediction models, pedigree-corrected phenotypes (Ped) increased mean predictive ability from 0.465 ± 0.157 for raw observations (Raw) to 0.573 ± 0.131 and increased upper-tail Top-20 recall from 0.558 ± 0.146 to 0.656 ± 0.146 under a 20% predicted retention window. Spatial correction alone (Spa) was less predictable than Raw, whereas joint pedigree-spatial correction (Ped+Spa) improved over Raw but did not exceed Ped. Phenotypic input explained substantially more variation in predictive ability than model choice, whose main effect was not significant (F5428 = 1.59, p = 0.161). These results support pedigree correction as a useful target-construction strategy in the evaluated single-replicate trials while showing that spatial adjustment is conditional on model adequacy and trial structure. Top-N recall directly represents favorable direction recovery for grain yield; for grain moisture, plant height, ear height, and silking date, the archived upper-tail metric is interpreted as rank consistency because the favorable direction or ideotype is breeding-program-dependent.

Maize (Zea mays L.), a typical thermophilic crop originating from tropical regions, exhibits an inherent sensitivity to low-temperature stress. Cold stress severely restricts maize seed germination, seedling growth, the physiological metabolism, and the final grain yield, which greatly limits its geographical cultivation range and sustainable industrial development. Elucidating the molecular regulatory mechanisms underlying maize cold tolerance and excavating cold-resistant functional genes are essential for the molecular breeding of cold-tolerant maize varieties and expanding maize planting areas in high-latitude and low-temperature-prone regions. In this study, using the strongly cold-tolerant maize inbred line B144 as the experimental material, we cloned the ZmMAPKKKA gene (NCBI accession: LOC103651289) and systematically screened and verified its cold-stress-specific interacting proteins via multiple molecular biological assays. The full-length coding sequence (CDS) of ZmMAPKKKA is 1134 bp, encoding a 377-amino-acid protein with a predicted molecular weight of 40.37 kDa. The quantitative real-time PCR (qRT-PCR) results demonstrated that the ZmMAPKKKA expression was significantly upregulated by 16.56-fold in maize roots after 12 h of low-temperature treatment, indicating a tissue-specific and robust cold response in root tissues. A total of 25 interacting proteins were identified through yeast two-hybrid screening, among which three stress-responsive proteins, including a protein kinase (LOC100286253), a protein phosphatase 2C (PP2C) (LOC542176), and a NAC transcription factor (LOC118474710), were selected for subsequent verification. The Pull-Down, Co-immunoprecipitation (Co-IP), and bimolecular fluorescence complementation (BiFC) assays consistently confirmed that ZmMAPKKKA specifically interacts with these three proteins both in vitro and in vivo under cold stress conditions. This study is the first to construct a ZmMAPKKKA-centered protein interaction module in the maize mitogen-activated protein kinase (MAPK) cascade under cold stress, establishing a novel kinase-phosphatase-transcription factor regulatory cascade that improves the current understanding of cold signal transduction mechanisms in maize. Homologous genes of ZmMAPKKKA in gramineous crops including rice (Oryza sativa) and sorghum (Sorghum bicolor) have been proven to participate in diverse abiotic stress responses, suggesting the conserved functional roles of MAPKKK family genes across gramineous species. Collectively, our findings provide comprehensive insights into the molecular mechanism of the maize MAPK signaling pathway mediating cold stress adaptation and supply valuable functional gene resources for cold-tolerant maize germplasm innovation and molecular breeding.

To elucidate the mechanisms by which the rhizosphere microbial community influences cold tolerance in maize, this study employed the metagenomic technology to systematically analyze the community composition, functional characteristics, and their association with host cold tolerance in the rhizosphere of maize genotypes with different cold tolerance (cold-tolerant material B144 and cold-sensitive material Q319, among others) (n = 3 biological replicates per genotype). The results revealed that the rhizosphere microbial community of the cold-tolerant genotype B144 exhibited higher species diversity and more complex genomic features. LEfSe analysis indicated that the rhizosphere soil microbiota of B144 was significantly enriched in two major phyla, Firmicutes and Actinobacteria, as well as microbial taxa with stress tolerance potential, such as the Bacillus and Streptomyces. Further functional analysis revealed that the microbial community was specifically enriched in metabolic pathways related to glycan biosynthesis and metabolism, as well as coenzyme and vitamin metabolism. We hypothesize that the physiological stability of maize under low temperatures can be enhanced through mechanisms such as the synthesis of extracellular polysaccharides to reduce the freezing point and the provision of vitamins and antioxidant substances. In contrast, the rhizosphere microorganisms of the cold-sensitive material Q319 were more enriched in basic metabolic functions. The present study elucidates the pivotal mechanisms by which rhizosphere microorganisms facilitate maize resistance to low-temperature stress from a functional perspective. This provides theoretical support and new strategies for enhancing crop stress resistance by regulating the rhizosphere microbiome.

Abiotic stress constrains plant growth and productivity worldwide. To survive adverse environmental conditions, plants deploy sophisticated adaptive strategies involving transcriptional reprogramming and metabolic remodeling. Over the past decade, advancements in high-throughput sequencing and mass spectrometry have propelled transcriptomics and metabolomics as pivotal post-genomic disciplines, offering unprecedented opportunities to dissect molecular mechanisms underlying stress responses. This review synthesizes current progress in applying these omics technologies to investigate plant adaptations to key abiotic stresses (thermal, saline, water deficit/excess, and heavy metal stresses). We systematically evaluate the technical strengths and limitations of transcriptomic and metabolomic platforms, highlight recent breakthroughs in stress-responsive gene identification and metabolic pathway elucidation, and discuss emerging challenges in integrative data analysis. By bridging genotype–phenotype relationships through multi-omics approaches, this study aims to deepen our mechanistic understanding of plant stress resilience and inform the design of stress-resilient crops for sustainable agriculture.

Heterosis, a key technology in modern commercial maize breeding, is limited by the narrow genetic base which hinders breeders from developing superior hybrid varieties. By integrating big data and functional genomics technologies, it becomes possible to create new super maize inbred lines that resemble hybrid varieties through the aggregation of multiple QTL parental advantage loci. In this study, we utilized a combination of resequencing and field selfing selection methods to develop three pyramiding QTL lines (PQLs) (PQL4, 6, and 7), each containing 15, 12, and 12 QTL loci respectively. Among the three PQLs, PQL6 (266.78 cm/119.39 cm) demonstrated hybrid-like performance comparable to the hybrid (276.96 cm/127.02 cm) (P < 0.05). Testcross between PQL6 and the parental lines revealed that PQL6 had accumulated and fixed advanced parent alleles for superior traits in plant and ear height. The significant increase in PQL6 plant height primarily resulted from the aggregation of two major effective QTL (qEH2-1 and qEH8-1 on chromosomes 2 and 8), indicating that the aggregation of major effective QTL is a key selection indicator. Furthermore, PQL6 exhibited slow vegetative growth but experienced a rapid height increase during the reproductive stage, particularly in the 1–2 weeks before flowering, when its growth rate accelerated and surpassed that of the hybrid varieties. Our study explored the time period and key parameter indicators for molecular breeding of maize, providing a theoretical concept and practices for further complex multi-trait design and aggregation.

Low temperature severely restricts maize seedling establishment and yield in northern China, but the proteomic basis of low-temperature tolerance in maize remains unclear. This study used TMT-labeled quantitative proteomics combined with data-independent acquisition (DIA) and liquid chromatography–tandem mass spectrometry (LC-MS/MS) to analyze dynamic proteome changes in two maize inbred lines (low-temperature-tolerant B144 and low-temperature-sensitive Q319) at the three-leaf stage under 5 °C treatment. A total of 4367 non-redundant proteins were identified. For differentially expressed proteins (DEPs, fold change >2.0 or <0.5, ANOVA-adjusted p < 0.05, false discovery rate [FDR] < 0.05), B144 showed exclusive upregulation under stress (6 DEPs at 24 h; 16 DEPs at 48 h), while Q319 exhibited mixed regulation (9 DEPs at 24 h: 6 upregulated, 3 downregulated; 21 DEPs at 48 h: 19 upregulated, 2 downregulated). Functional annotation indicated that ribosomal proteins, oxidoreductases, glycerol-3-phosphate permease, and actin were significantly upregulated in both lines. Pathway enrichment analysis revealed associations with carbohydrate metabolism, amino acid biosynthesis, and secondary metabolite synthesis. Weighted gene co-expression network analysis (WGCNA) identified genotype-specific expression patterns: B144 showed differential expression of proteins related to acetyl-CoA synthetase and fatty acid β-oxidation at 24 h and of proteins related to D-3-phosphoglycerate dehydrogenase at 48 h; Q319 showed differential expression of proteasome-related proteins at 24 h and of proteins related to elongation factor 1α (EF-1α) at 48 h. Venn analysis found no shared DEPs between the two lines at 24 h but four overlapping DEPs at 48 h. These results clarify proteomic differences underlying low-temperature tolerance divergence between maize genotypes and provide candidate targets for molecular breeding of low-temperature-tolerant maize.

Chilling injury during the germination stage (CIGS) of maize significantly hinders production, particularly in middle- and high-latitude regions, leading to slow germination, seed decay, and increased susceptibility to pathogens. This study dissects the genetic architecture of CIGS resistance expressed in terms of the relative germination rate (RGR) in maize through association mapping using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). A natural panel of 287 maize inbred lines was evaluated across multiple environments. The results revealed a broad-sense heritability of 0.68 for chilling tolerance, with 12 significant QTLs identified on chromosomes 1, 3, 5, 6, and 10. A genomic prediction analysis demonstrated that the rr-BLUP model outperformed other models in accuracy, achieving a moderate prediction accuracy of 0.44. This study highlights the potential of genomic selection (GS) to enhance chilling tolerance in maize, emphasizing the importance of training population size, marker density, and significant markers on prediction accuracy. These findings provide valuable insights for breeding programs aimed at improving chilling tolerance in maize.

Abiotic stress is a significant factor restricting the normal growth and development of plants. Under abiotic stress, plants maintain their life and continuous growth by reconfiguring transcriptional regulation and metabolic networks. In recent years, with the development of sequencing technology and mass spectrometry technology, transcriptomics and metabolomics have emerged as new disciplines following genomics and proteomics, which are conducive to identifying metabolites and regulatory genes in plants under abiotic stress. In this review, we mainly analyzes the technical characteristics, advantages, and disadvantages of transcriptomics and metabolomics, focusing on reviewing the research progress in the field of plant responses to abiotic stress (temperature stress, salt stress, water stress, and heavy metal stress) using transcriptomics and metabolomics both domestically and internationally in recent years. It also provides a prospective view on current issues, which helps accelerate the understanding of the mechanisms of plant responses to abiotic stress and provides new ideas for breeding stress-resistant varieties in the future.

A stable genomic region conferring FSR resistance at 250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10–7 to 8.27 × 10–13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30

Maize (Zea mays L.) is highly sensitive to temperature during its growth and development stage. A 1 °C drop in temperature can delay maturity by 10 days, resulting in a yield reduction of over 10%. Low-temperature tolerance in maize is a complex quantitative trait, and different germplasms exhibit significant differences in their responses to low-temperature stress. To explore the differences in gene expression and metabolites between B144 (tolerant) and Q319 (susceptible) during germination under low-temperature stress and to identify key genes and metabolites that respond to this stress, high-throughput transcriptome sequencing was performed on the leaves of B144 and Q319 subjected to low-temperature stress for 24 h and their respective controls using Illumina HiSeqTM 4000 high-throughput sequencing technology. Additionally, high-throughput metabolite sequencing was conducted on the samples using widely targeted metabolome sequencing technology. The results indicated that low-temperature stress triggered the accumulation of stress-related metabolites such as amino acids and their derivatives, lipids, phenolic acids, organic acids, flavonoids, lignin, coumarins, and alkaloids, suggesting their significant roles in the response to low temperature. This stress also promoted gene expression and metabolite accumulation involved in the flavonoid biosynthesis pathway. Notably, there were marked differences in gene expression and metabolites related to the glyoxylate and dicarboxylate metabolism pathways between B144 and Q319. This study, through multi-omics integrated analysis, provides valuable insights into the identification of metabolites, elucidation of metabolic pathways, and the biochemical and genetic basis of plant responses to stress, particularly under low-temperature conditions.

Background: Cold damage of maize during germination is a global problem; it occurs frequently in northeast China, and leads to a large-scale reduction in yield. Low temperature tolerance of maize in germination is a complex quantitative trait controlled by multigenes, and no major QTLs or key genes have been identified. Results: An F2 isolation population with S319 and R144 as parents was constructed. The bulked segregant analysis (BSA) and specific-locus amplified fragment-sequencing (SLAF-seq) methods were applied to locate the chromosomal association regions related to low-temperature tolerance of maize during germination. Sequencing obtained 221.72 Gbp clean data, with an average sequencing depth of 25.96X. Four candidate regions associated with low-temperature tolerance trait of maize in germination were obtained, with a total length of 25.71 Mb and 1513 annotated genes, including 456 nonsynonymous mutant genes and 111 frameshift mutant genes.

为了完善玉米自交系萌发期耐盐性的鉴定评价标准,筛选耐盐性强的玉米种质资源,解析玉米自交系萌发期耐盐性遗传机制,为玉米萌发期耐盐性育种提供理论依据和技术支撑.对390份玉米自交系耐盐性相关的10个生理指标进行测定,利用主成分分析确定为5个综合指标,即7d发茅率、芽干重、芽鲜重、根干重和根长,可作为玉米萌发期耐盐性鉴定指标.根据隶属函数分析确定各材料的耐盐综合评价指数,并通过聚类分析将材料萌发期耐盐性分为6个等级,其中高度盐敏感型材料298份,中度盐敏感型材料49份,盐敏感型材料27份,耐盐型材料9份,中度耐盐型材料5份,高度耐盐型材料2份.

玉米是全球最主要的粮食作物之一,提高玉米籽粒品质是当今世界玉米育种领域高度关注的问题.因为传统常规育种方法具有育种时间长且转化率低等限制因素,所以解决这一问题最经济有效的方法就是利用分子标记进行辅助选择育种.为了给今后玉米品质性状的分子设计育种提供参考,本研究总结了国内外玉米籽粒品质性状的QTL定位、分子标记辅助改良和候选基因克隆及转基因技术应用的相关研究进展.指出玉米优质基因资源的利用还不够充分,现有分子标记技术在玉米育种中的应用还不够广泛,今后应改进育种方法和品质鉴定技术,以缩短玉米育种周期.

利用杂种优势可显著提高大豆单产,但亲本的创制及杂交组合的配制缺乏有效的理论指导,导致杂交种组配工作存在盲目性,强优势大豆杂交种的产出周期较长.针对上述问题,以中国东北部和国外的43份亲本为材料,利用经过筛选的14个大豆产量性状相关SSR分子标记进行遗传多样性分析和杂种优势类群划分.结果发现,所用SSR标记共扩增出31个等位变异位点,平均每个标记检测到2.2143个等位变异位点,变幅为2.0000～4.0000;主效基因频率为0.4419～0.9302,平均为0.6817;基因遗传多样性指数为0.1298～0.6101,平均为0.4043;多态性信息含量为0.1214～0.5272,平均为0.3280.根据遗传距离将43份亲本材料划分为2个类群;25个杂交种的35份亲本材料分属于2个类群;且27个杂交种的39份亲本材料为国内和国外材料之间配制的杂交组合,反映出中国东北与国外材料之间存在较强的杂种优势.对本研究所用的杂交种亲本间遗传距离分析发现,遗传距离在0.4～0.6时杂种优势利用效率较高.上述结果为优异亲本的选育方向和强优势杂交种亲本的合理组配提供参考.

Background: Cold injury is one of the most important limiting factors for maize production in mid-high latitude regions in the world. A total of 314 QTLs for maize low temperature tolerance have been identified in different populations using different statistical methods. However, few identical QTLs have been identified in different research studies. Results: A consensus map of QTLs related to maize low temperature tolerance was constructed, based on the public genetic map, IBM2 2008 Neighbors as a reference map, along with a set of 314 QTLs reported in the literature over the past 20 years. A total of 187 QTLs were projected onto the IBM2 2008 Neighbors by software BioMercator. Forty-seven consensus QTLs were detected. The confidence interval at all sites ranged from 0.04 cM to 102.73 cM, and the proportion of the phenotypic variance associated with each of them ranged from 3.32% to 20.11%. Major chromosomal sites were identified on Chr.6 (MQTL29, MQTL30, and MQTL31). Conclusions: This study provides further insights into the genetic basis of maize low temperature tolerance. Moreover, the MQTLs reported here could be harnessed for functional marker development and candidate gene mining of maize low temperature tolerance. How to cite: Yu T, Zhang J, Cao J, et al. A meta-analysis of low temperature tolerance QTL in maize. Electron J Biotechnol 2022;58. https://doi.org/10.1016/j.ejbt.2022.05.002 ?? 2022 Pontificia Universidad Cat??lica de Valpara??so. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

萌发期冷害是限制我国东北地区玉米生产的重要因素之一,开展玉米种质材料的耐冷性鉴定是提高耐冷玉米品种选育效率和创新玉米耐冷种质资源的基础.本研究以279份玉米自交系为试验材料,利用人工气候箱开展萌发期耐冷性鉴定,采用主成分分析、隶属函数分析及聚类分析等方法进行萌发耐冷性评价.主成分分析结果表明,7 d发芽率、7 d芽鲜重、7 d芽干重可作为玉米萌发期耐冷性鉴定的指标.进一步通过综合耐冷性D值及聚类分析,将279份玉米自交系的耐冷性程度划分为5个等级.其中耐冷性差的材料为21份,耐冷性一般的材料为54份,耐冷性中等的材料为58份,耐冷性较强的材料为116份,耐冷性强的材料为30份.耐冷性强的材料主要有FS1、FS102、FS122等,其中FS122的D值最高,为0.91.

为加快玉米萌发期耐冷性遗传育种研究,在对玉米重组自交系群体进行萌发期耐冷性鉴定的基础上,采用分离群体混合分析(Bulked Segregant Analysis,BSA)方法,对2亲本和2混池进行特异位点扩增片段测序(Specific-locus Amplified Fragment-sequencing,SLAF),初步定位了玉米萌发期耐冷性相关的染色体区段,并对关联区域内的基因进行初步富集分析.结果表明:研究获得了282563个SLAF标签,每个SLAF标签平均父本测序深度为27.06×,母本平均测序深度为31.85×,耐冷池和冷敏感池的测序深度分别为36.72×和34.17×.获得多态性SLAF标签47624个,多态性比例为16.85％.通过BSA关联分析在玉米第9号染色体上定位到了一个91.06 Mb大小的玉米萌发期耐冷性关联区域,区域内共关联到3170个基因.

Maize (Zea mays L.) is a thermophilic plant and a minor drop in temperature can prolong the maturity period. Plants respond to cold stress through structural and functional modification in cell membranes as well as changes in the photosynthesis and energy metabolism. In order to understand the molecular mechanisms underlying cold tolerance and adaptation, we employed leaf transcriptome sequencing together with leaf microstructure and relative electrical conductivity measurements in two maize inbred lines, having different cold stress tolerance potentials. The leaf physiological and transcriptomic responses of maize seedlings were studied after growing both inbred lines at 5 °C for 0, 12 and 24 h. Differentially expressed genes were enriched in photosynthesis antenna proteins, MAPK signaling pathway, plant hormone signal transduction, circadian rhythm, secondary metabolites related pathways, ribosome, and proteasome. The seedlings of both genotypes employed common stress responsive pathways to respond to cold stress. However, the cold tolerant line B144 protected its photosystem II from photooxidation by upregulating D1 proteins. The sensitive line Q319 was unable to close its stomata. Collectively, B144 exhibited a cold tolerance owing to its ability to mediate changes in stomata opening as well as protecting photosystem. These results increase our understanding on the cold stress tolerance in maize seedlings and propose multiple key regulators of stress responses such as modifications in photosystem II, stomata guard cell opening and closing, changes in secondary metabolite biosynthesis, and circadian rhythm. This study also presents the signal transduction related changes in MAPK and phytohormone signaling pathways in response to cold stress during seedling stage of maize.

Common rust is one of the major foliar diseases in maize, leading to significant grain yield losses and poor grain quality. To dissect the genetic architecture of common rust resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid (DH) population, DH1, were used to perform GWAS and linkage mapping analyses. The GWAS results revealed six single-nucleotide polymorphisms (SNPs) significantly associated with quantitative resistance of common rust at a very stringent threshold of P- value 3.70 × 10 –6 at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04. Linkage mapping identified five quantitative trait loci (QTL) at bins 1.03, 2.06, 4.08, 7.03, and 9.00. The phenotypic variation explained (PVE) value of each QTL ranged from 5.40 to 12.45%, accounting for the total PVE value of 40.67%. Joint GWAS and linkage mapping analyses identified a stable genomic region located at bin 4.08. Five significant SNPs were only identified by GWAS, and four QTL were only detected by linkage mapping. The significantly associated SNP of S10_95231291 detected in the GWAS analysis was first reported. The linkage mapping analysis detected two new QTL on chromosomes 7 and 10. The major QTL on chromosome 7 in the region between 144,567,253 and 149,717,562 bp had the largest PVE value of 12.45%. Four candidate genes of GRMZM2G328500 , GRMZM2G162250 , GRMZM2G114893 , and GRMZM2G138949 were identified, which played important roles in the response of stress resilience and the regulation of plant growth and development. Genomic prediction (GP) accuracies observed in the GWAS panel and DH1 population were 0.61 and 0.51, respectively. This study provided new insight into the genetic architecture of quantitative resistance of common rust. In tropical maize, common rust could be improved by pyramiding the new sources of quantitative resistance through marker-assisted selection (MAS) or genomic selection (GS), rather than the implementation of MAS for the single dominant race-specific resistance gene.

Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.