Genetic mapping with testcrossing associations and F2:3 populations reveals the importance of heterosis in chilling tolerance at maize seedling stage

Genome-Wide Association Study for Maize Hybrid Performance in a Typical Breeder Population

Maize is one of the major crops that has demonstrated success in the utilization of heterosis. Developing high-yield hybrids is a crucial part of plant breeding to secure global food demand. In this study, we conducted a genome-wide association study (GWAS) for 10 agronomic traits using a typical breeder population comprised 442 single-cross hybrids by evaluating additive, dominance, and epistatic effects. A total of 49 significant single nucleotide polymorphisms (SNPs) and 69 significant pairs of epistasis were identified, explaining 26.2% to 64.3% of the phenotypic variation across the 10 traits. The enrichment of favorable genotypes is significantly correlated to the corresponding phenotype. In the confident region of the associated site, 532 protein-coding genes were discovered. Among these genes, the Zm00001d044211 candidate gene was found to negatively regulate starch synthesis and potentially impact yield. This typical breeding population provided a valuable resource for dissecting the genetic architecture of yield-related traits. We proposed a novel mating strategy to increase the GWAS efficiency without utilizing more resources. Finally, we analyzed the enrichment of favorable alleles in the Shaan A and Shaan B groups, as well as in each inbred line. Our breeding practice led to consistent results. Not only does this study demonstrate the feasibility of GWAS in F1 hybrid populations, it also provides a valuable basis for further molecular biology and breeding research.

Chilling Tolerance in Maize: Insights into Advances—Toward Physio-Biochemical Responses’ and QTL/Genes’ Identification

Maize, a major staple cereal crop in global food supply, is a thermophilic and short-day C4 plant sensitive to low-temperature stress. A low temperature is among the most severe agro-meteorological hazards in maize-growing areas. This review covers the latest research and progress in the field of chilling tolerance in maize in the last 40 years. It mainly focuses on how low-temperature stress affects the maize membrane and antioxidant systems, photosynthetic physiology, osmoregulatory substances and hormone levels. In addition, the research progress in identifying cold-tolerance QTLs (quantitative trait loci) and genes to genetically improve maize chilling toleranceis comprehensively discussed. Based on previous research, this reviewprovides anoutlook on potential future research directions and offers a reference for researchers in the maize cold-tolerance-related field.

Recent Advances in the Analysis of Cold Tolerance in Maize

Maize (Zea mays L.) is an annual grass that originated in tropical and subtropical regions of the New World. Maize is highly sensitive to cold stress during seed gemination and the seedling phase, which can lead to reductions in plant vigor and grain production. There are large differences in the morphological and physiological changes caused by cold stress among maize varieties. In general, cold tolerant varieties have a stronger ability to maintain such changes in traits related to seed germination, root phenotypes, and shoot photosynthesis. These morphological and physiological characteristics have been widely used to evaluate the cold tolerance of maize varieties in genetic analyses. In recent years, considerable progress has been made in elucidating the mechanisms of maize in response to cold tolerance. Several QTL, GWAS, and transcriptomic analyses have been conducted on various maize genotypes and populations that show large variations in cold tolerance, resulting in the discovery of hundreds of candidate cold regulation genes. Nevertheless, only a few candidate genes have been functionally characterized. In the present review, we summarize recent progress in molecular, physiological, genetic, and genomic analyses of cold tolerance in maize. We address the advantages of joint analyses that combine multiple genetic and genomic approaches to improve the accuracy of identifying cold regulated genes that can be further used in molecular breeding. We also discuss the involvement of long-distance signaling in plant cold tolerance. These novel insights will provide a better mechanistic understanding of cold tolerance in maize.

Thermal Stresses in Maize: Effects and Management Strategies

Climate change can decrease the global maize productivity and grain quality. Maize crop requires an optimal temperature for better harvest productivity. A suboptimal temperature at any critical stage for a prolonged duration can negatively affect the growth and yield formation processes. This review discusses the negative impact of temperature extremes (high and low temperatures) on the morpho-physiological, biochemical, and nutritional traits of the maize crop. High temperature stress limits pollen viability and silks receptivity, leading to a significant reduction in seed setting and grain yield. Likewise, severe alterations in growth rate, photosynthesis, dry matter accumulation, cellular membranes, and antioxidant enzyme activities under low temperature collectively limit maize productivity. We also discussed various strategies with practical examples to cope with temperature stresses, including cultural practices, exogenous protectants, breeding climate-smart crops, and molecular genomics approaches. We reviewed that identified quantitative trait loci (QTLs) and genes controlling high- and low temperature stress tolerance in maize could be introgressed into otherwise elite cultivars to develop stress-tolerant cultivars. Genome editing has become a key tool for developing climate-resilient crops. Moreover, challenges to maize crop improvement such as lack of adequate resources for breeding in poor countries, poor communication among the scientists of developing and developed countries, problems in germplasm exchange, and high cost of advanced high-throughput phenotyping systems are discussed. In the end, future perspectives for maize improvement are discussed, which briefly include new breeding technologies such as transgene-free clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas)-mediated genome editing for thermo-stress tolerance in maize.

Genome-Wide Association Study of Maize Aboveground Dry Matter Accumulation at Seedling Stage

Dry matter accumulation and partitioning during the early phases of development could significantly affect crop growth and productivity. In this study, the aboveground dry matter (DM), the DM of different organs, and partition coefficients of a maize association mapping panel of 412 inbred lines were evaluated at the third and sixth leaf stages (V3 and V6). Further, the properties of these phenotypic traits were analyzed. Genome-wide association studies (GWAS) were conducted on the total aboveground biomass and the DM of different organs. Analysis of GWAS results identified a total of 1,103 unique candidate genes annotated by 678 significant SNPs (P value < 1.28e-6). A total of 224 genes annotated by SNPs at the top five of each GWAS method and detected by multiple GWAS methods were regarded as having high reliability. Pathway enrichment analysis was also performed to explore the biological significance and functions of these candidate genes. Several biological pathways related to the regulation of seed growth, gibberellin-mediated signaling pathway, and long-day photoperiodism were enriched. The results of our study could provide new perspectives on breeding high-yielding maize varieties.

Seed Weight as a Covariate in Association and Prediction Studies for Biomass Traits in Maize Seedlings

BACKGROUND

The seedling stage has received little attention in maize breeding to identify genotypes tolerant to water deficit. The aim of this study is to evaluate incorporation of seed weight (expressed as hundred kernel weight, HKW) as a covariate into genomic association and prediction studies for three biomass traits in a panel of elite inbred lines challenged by water withholding at seedling stage.

METHODS

109 genotyped-by-sequencing (GBS) elite maize inbreds were phenotyped for HKW and planted in controlled conditions (16/8 day/night, 25 °C, 50% RH, 200 µMol/m2/s) in trays filled with soil. Plants in control (C) were watered every two days, while watering was stopped for 10 days in water withholding (WW). Fresh weight (FW), dry weight (DW), and dry matter content (DMC) were measured.

RESULTS

Adding HKW as a covariate increased the power of detection of associations in FW and DW by 44% and increased genomic prediction accuracy in C and decreased in WW.

CONCLUSIONS

Seed weight was effectively incorporated into association studies for biomass traits in maize seedlings, whereas the incorporation into genomic predictions, particularly in water-stressed plants, was not worthwhile.

Dissecting the genetics of cold tolerance in a multiparental maize population

We identify the largest amount of QTLs for cold tolerance in maize; mainly associated with photosynthetic efficiency, which opens new possibilities for genomic selection for cold tolerance in maize. Breeding for cold tolerance in maize is an important objective in temperate areas. The objective was to carry out a highly efficient study of quantitative trait loci (QTLs) for cold tolerance in maize. We evaluated 406 recombinant inbred lines from a multi-parent advanced generation intercross (MAGIC) population in a growth chamber under cold and control conditions, and in the field at early and normal sowing. We recorded cold tolerance-related traits, including the number of days from sowing to emergence, chlorophyll content and maximum quantum efficiency of photosystem II (F_v/F_m). Association mapping was based on genotyping with near one million single nucleotide polymorphism (SNP) markers. We found 858 SNPs significantly associated with all traits, most of them under cold conditions and early sowing. Most QTLs were associated with chlorophyll and F_v/F_m. Many candidate genes coincided between the current research and previous reports. These results suggest that (1) the MAGIC population is an efficient tool for identifying QTLs for cold tolerance; (2) most QTLs for cold tolerance were associated with F_v/F_m; (3) most of these QTLs were located in specific genomic regions, particularly bin 10.04; (4) the current study allows genetically improving cold tolerance with genome-wide selection.

MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database

MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.