QTL Mapping of Kernel Number-Related Traits and Validation of One Major QTL for Ear Length in Maize

The additive function of YIGE2 and YIGE1 in regulating maize ear length

Ear length (EL) is a key trait that greatly contributes to yield in maize. Although dozens of EL quantitative trait loci have been mapped, very few causal genes have been cloned, and the molecular mechanisms remain largely unknown. Our previous study showed that YIGE1 is involved in sugar and auxin pathways to regulate ear inflorescence meristem (IM) development and thus affects EL in maize. Here, we reveal that YIGE2, the paralog of YIGE1, regulates maize ear development and EL through auxin pathway. Knockout of YIGE2 causes a significant decrease of auxin level, IM length, floret number, EL, and grain yield. yige1 yige2 double mutants had even shorter IM and ears implying that these two genes redundantly regulate IM development and EL. The genes controlling auxin levels are differential expressed in yige1 yige2 double mutants, leading to lower auxin level. These results elucidated the critical role of YIGE2 and the redundancy between YIGE2 and YIGE1 in maize ear development, providing a new genetic resource for maize yield improvement.

Hotspot Regions of Quantitative Trait Loci and Candidate Genes for Ear-Related Traits in Maize: A Literature Review

In this study, hotspot regions, QTL clusters, and candidate genes for eight ear-related traits of maize (ear length, ear diameter, kernel row number, kernel number per row, kernel length, kernel width, kernel thickness, and 100-kernel weight) were summarized and analyzed over the past three decades. This review aims to (1) comprehensively summarize and analyze previous studies on QTLs associated with these eight ear-related traits and identify hotspot bin regions located on maize chromosomes and key candidate genes associated with the ear-related traits and (2) compile major and stable QTLs and QTL clusters from various mapping populations and mapping methods and techniques providing valuable insights for fine mapping, gene cloning, and breeding for high-yield and high-quality maize. Previous research has demonstrated that QTLs for ear-related traits are distributed across all ten chromosomes in maize, and the phenotypic variation explained by a single QTL ranged from 0.40% to 36.76%. In total, 23 QTL hotspot bins for ear-related traits were identified across all ten chromosomes. The most prominent hotspot region is bin 4.08 on chromosome 4 with 15 QTLs related to eight ear-related traits. Additionally, this study identified 48 candidate genes associated with ear-related traits. Out of these, five have been cloned and validated, while twenty-eight candidate genes located in the QTL hotspots were defined by this study. This review offers a deeper understanding of the advancements in QTL mapping and the identification of key candidates associated with eight ear-related traits. These insights will undoubtedly assist maize breeders in formulating strategies to develop higher-yield maize varieties, contributing to global food security.

Genetic architecture of ear traits based on association mapping and co-expression networks in maize inbred lines and hybrids

Ear traits are key contributors to grain yield in maize; therefore, exploring their genetic basis facilitates the improvement of grain yield. However, the underlying molecular mechanisms of ear traits remain obscure in both inbred lines and hybrids. Here, two association panels, respectively, comprising 203 inbred lines (IP) and 246 F1 hybrids (HP) were employed to identify candidate genes for six ear traits. The IP showed higher phenotypic variation and lower phenotypic mean than the HP for all traits, except ear tip-barrenness length. By conducting a genome-wide association study (GWAS) across multiple environments, 101 and 228 significant single-nucleotide polymorphisms (SNPs) associated with six ear traits were identified in the IP and HP, respectively. Of these significant SNPs identified in the HP, most showed complete-incomplete dominance and over-dominance effects for each ear trait. Combining a gene co-expression network with GWAS results, 186 and 440 candidate genes were predicted in the IP and HP, respectively, including known ear development genes ids1 and sid1. Of these, nine candidate genes were detected in both populations and expressed in maize ear and spikelet tissues. Furthermore, two key shared genes (GRMZM2G143330 and GRMZM2G171139) in both populations were found to be significantly associated with ear traits in the maize Goodman diversity panel with high-density variations. These findings advance our knowledge of the genetic architecture of ear traits between inbred lines and hybrids and provide a valuable resource for the genetic improvement of ear traits in maize.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01426-9.

The Genetic Structures and Molecular Mechanisms Underlying Ear Traits in Maize (Zea mays L.)

Maize (Zea mays L.) is one of the world's staple food crops. In order to feed the growing world population, improving maize yield is a top priority for breeding programs. Ear traits are important determinants of maize yield, and are mostly quantitatively inherited. To date, many studies relating to the genetic and molecular dissection of ear traits have been performed; therefore, we explored the genetic loci of the ear traits that were previously discovered in the genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping studies, and refined 153 QTL and 85 quantitative trait nucleotide (QTN) clusters. Next, we shortlisted 19 common intervals (CIs) that can be detected simultaneously by both QTL mapping and GWAS, and 40 CIs that have pleiotropic effects on ear traits. Further, we predicted the best possible candidate genes from 71 QTL and 25 QTN clusters that could be valuable for maize yield improvement.

Mapping and Functional Analysis of QTL for Kernel Number per Row in Tropical and Temperate-Tropical Introgression Lines of Maize (Zea mays L.)

Kernel number per row (KNR) is an essential component of maize (Zea mays L.) grain yield (GY), and understanding its genetic mechanism is crucial to improve GY. In this study, two F₇ recombinant inbred line (RIL) populations were created using a temperate-tropical introgression line TML418 and a tropical inbred line CML312 as female parents and a backbone maize inbred line Ye107 as the common male parent. Bi-parental quantitative trait locus (QTL) mapping and genome-wide association analysis (GWAS) were then performed on 399 lines of the two maize RIL populations for KNR in two different environments using 4118 validated single nucleotide polymorphism (SNP) markers. This study aimed to: (1) detect molecular markers and/or the genomic regions associated with KNR; (2) identify the candidate genes controlling KNR; and (3) analyze whether the candidate genes are useful in improving GY. The authors reported a total of 7 QTLs tightly linked to KNR through bi-parental QTL mapping and identified 21 SNPs significantly associated with KNR through GWAS. Among these, a highly confident locus qKNR7-1 was detected at two locations, Dehong and Baoshan, with both mapping approaches. At this locus, three novel candidate genes (Zm00001d022202, Zm00001d022168, Zm00001d022169) were identified to be associated with KNR. These candidate genes were primarily involved in the processes related to compound metabolism, biosynthesis, protein modification, degradation, and denaturation, all of which were related to the inflorescence development affecting KNR. These three candidate genes were not reported previously and are considered new candidate genes for KNR. The progeny of the hybrid Ye107 × TML418 exhibited strong heterosis for KNR, which the authors believe might be related to qKNR7-1. This study provides a theoretical foundation for future research on the genetic mechanism underlying KNR in maize and the use of heterotic patterns to develop high-yielding hybrids.

Designed and validated novel allele-specific primer to differentiate Kernel Row Number (KRN) in tropical field corn

BACKGROUND

Kernel row number (KRN) is an important yield component trait with a direct impact on the productivity of maize. The variability in KRN is influenced by the inflorescence meristem size, which is determined by the CLAVATA-WUSCHEL pathway. A CLAVATA receptor-like protein, encoded by the FASCIATED EAR2 (fea2gene), enhances the growth of inflorescence meristem and is thus involved in the determination of KRN. The amplicon sequencing-based method was employed to dissect the allelic variation of the fea2 gene in tropical field corn.

METHODOLOGY/PRINCIPAL FINDING

Amplicon-based sequencing of AI 535 (Low KRN) and AI 536 (High KRN) was undertaken for the gene fea 2 gene that codes for KRN in maize. Upon multiple sequence alignment of both sequences, A to T transversion at the 1311 position was noticed between Low KRN and High KRN genotypes resulting in different allelic forms of a fea2 gene in tropical maize. An allele-specific primer 1311 fea2.1 was designed and validated that can differentiate High and Low KRN genotypes.

CONCLUSION/SIGNIFICANCE

Maize has high variability for KRN and is exemplified by the wide values ranging from 8-26 KRN in the maize germpalsm. The sequence-based approach of SNP detection through the use of a specific primer facilitated the detection of variation present in the target trait. This makes it possible to capture these variations in the early generation. In the study, the PCR-based differentiation method described for the identification of desirable high KRN genotypes would augment the breeding programs for improving the productivity of field corn.

Integrated QTL Mapping, Meta-Analysis, and RNA-Sequencing Reveal Candidate Genes for Maize Deep-Sowing Tolerance

Synergetic elongation of mesocotyl and coleoptile are crucial in governing maize seedlings emergence, especially for the maize sown in deep soil. Studying the genomic regions controlling maize deep-sowing tolerance would aid the development of new varieties that are resistant to harsh conditions, such as drought and low temperature during seed germination. Using 346 F2:3 maize population families from W64A × K12 cross at three sowing depths, we identified 33 quantitative trait loci (QTLs) for the emergence rate, mesocotyl, coleoptile, and seedling lengths via composite interval mapping (CIM). These loci explained 2.89% to 14.17% of phenotypic variation in a single environment, while 12 of 13 major QTLs were identified at two or more sowing environments. Among those, four major QTLs in Bin 1.09, Bin 4.08, Bin 6.01, and Bin 7.02 supported pleiotropy for multiple deep-sowing tolerant traits. Meta-analysis identified 17 meta-QTLs (MQTLs) based on 130 original QTLs from present and previous studies. RNA-Sequencing of mesocotyl and coleoptile in both parents (W64A and K12) at 3 cm and 20 cm sowing environments identified 50 candidate genes expressed differentially in all major QTLs and MQTLs regions: six involved in the circadian clock, 27 associated with phytohormones biosynthesis and signal transduction, seven controlled lignin biosynthesis, five regulated cell wall organization formation and stabilization, three were responsible for sucrose and starch metabolism, and two in the antioxidant enzyme system. These genes with highly interconnected networks may form a complex molecular mechanism of maize deep-sowing tolerance. Findings of this study will facilitate the construction of molecular modules for deep-sowing tolerance in maize. The major QTLs and MQTLs identified could be used in marker-assisted breeding to develop elite maize varieties.

Meta-QTL analysis and candidate genes identification for various abiotic stresses in maize (Zea mays L.) and their implications in breeding programs

Global climate change leads to the concurrence of a number of abiotic stresses including moisture stress (drought, waterlogging), temperature stress (heat, cold), and salinity stress, which are the major factors affecting maize production. To develop abiotic stress tolerance in maize, many quantitative trait loci (QTL) have been identified, but very few of them have been utilized successfully in breeding programs. In this context, the meta-QTL analysis of the reported QTL will enable the identification of stable/real QTL which will pave a reliable way to introgress these QTL into elite cultivars through marker-assisted selection. In this study, a total of 542 QTL were summarized from 33 published studies for tolerance to different abiotic stresses in maize to conduct meta-QTL analysis using BiomercatorV4.2.3. Among those, only 244 major QTL with more than 10% phenotypic variance were preferably utilised to carry out meta-QTL analysis. In total, 32 meta-QTL possessing 1907 candidate genes were detected for different abiotic stresses over diverse genetic and environmental backgrounds. The MQTL2.1, 5.1, 5.2, 5.6, 7.1, 9.1, and 9.2 control different stress-related traits for combined abiotic stress tolerance. The candidate genes for important transcription factor families such as ERF, MYB, bZIP, bHLH, NAC, LRR, ZF, MAPK, HSP, peroxidase, and WRKY have been detected for different stress tolerances. The identified meta-QTL are valuable for future climate-resilient maize breeding programs and functional validation of candidate genes studies, which will help to deepen our understanding of the complexity of these abiotic stresses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01294-9.

Genetic variation in YIGE1 contributes to ear length and grain yield in maize

Ear length (EL), which is controlled by quantitative trait loci (QTLs), is an important component of grain yield and as such is a key target trait in maize breeding. However, very few EL QTLs have been cloned, and their molecular mechanisms are largely unknown. Here, using a genome wide association study (GWAS), we identified a QTL, YIGE1, which encodes an unknown protein that regulates EL by affecting pistillate floret number. Overexpression of YIGE1 increased female inflorescence meristem (IM) size, increased EL and kernel number per row (KNPR), and thus enhanced grain yield. By contrast, CRISPR/Cas9 knockout and Mutator insertion mutant lines of YIGE1 displayed decreased IM size and EL. A single-nucleotide polymorphism (SNP) located in the regulatory region of YIGE1 had a large effect on its promoter strength, which positively affected EL by increasing gene expression. Further analysis shows that YIGE1 may be involved in sugar and auxin signal pathways to regulate maize ear development, thus affecting IM activity and floret production in maize inflorescence morphogenesis. These findings provide new insights into ear development and will ultimately facilitate maize molecular breeding.

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize

Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quantitative trait loci (QTLs) associated with grain yield in sorghum and maize. We placed great emphasis on discussing 22 fine-mapped QTLs and 30 functionally characterized genes, which greatly hinders our deep understanding at the molecular mechanism level. This review provides a general overview of the comprehensive findings on grain yield QTLs and discusses the emerging trend in molecular marker-assisted breeding with these QTLs.

Transcriptomic analysis of the maize inbred line Chang7-2 and a large-grain mutant tc19

Backgrounds

Grain size is a key factor in crop yield that gradually develops after pollination. However, few studies have reported gene expression patterns in maize grain development using large-grain mutants. To investigate the developmental mechanisms of grain size, we analyzed a large-grain mutant, named tc19, at the morphological and transcriptome level at five stages corresponding to days after pollination (DAP).

Results

After maturation, the grain length, width, and thickness in tc19 were greater than that in Chang7-2 (control) and increased by 3.57, 8.80, and 3.88%, respectively. Further analysis showed that grain width and 100-kernel weight in tc19 was lower than in Chang7-2 at 14 and 21 DAP, but greater than that in Chang7-2 at 28 DAP, indicating that 21 to 28 DAP was the critical stage for kernel width and weight development. For all five stages, the concentrations of auxin and brassinosteroids were significantly higher in tc19 than in Chang7-2. Gibberellin was higher at 7, 14, and 21 DAP, and cytokinin was higher at 21 and 35 DAP, in tc19 than in Chang7-2. Through transcriptome analysis at 14, 21, and 28 DAP, we identified 2987, 2647 and 3209 differentially expressed genes (DEGs) between tc19 and Chang7-2. By using KEGG analysis, 556, 500 and 633 DEGs at 14, 21 and 28 DAP were pathway annotated, respectively, 77 of them are related to plant hormone signal transduction pathway. ARF3, AO2, DWF4 and XTH are higher expressed in tc19 than that in Chang7-2.

Conclusions

We found some DEGs in maize grain development by using Chang7-2 and a large-grain mutant tc19. These DEGs have potential application value in improving maize performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08230-9.

Genetic diversity and selection signatures in maize landraces compared across 50 years of in situ and ex situ conservation

Genomics-based, longitudinal comparisons between ex situ and in situ agrobiodiversity conservation strategies can contribute to a better understanding of their underlying effects. However, landrace designations, ambiguous common names, and gaps in sampling information complicate the identification of matching ex situ and in situ seed lots. Here we report a 50-year longitudinal comparison of the genetic diversity of a set of 13 accessions from the state of Morelos, Mexico, conserved ex situ since 1967 and retrieved in situ from the same donor families in 2017. We interviewed farmer families who donated in situ landraces to understand their germplasm selection criteria. Samples were genotyped by sequencing, producing 74,739 SNPs. Comparing the two sample groups, we show that ex situ and in situ genome-wide diversity was similar. In situ samples had 3.1% fewer SNPs and lower pairwise genetic distances (F_st 0.008-0.113) than ex situ samples (F_st 0.031-0.128), but displayed the same heterozygosity. Despite genome-wide similarities across samples, we could identify several loci under selection when comparing in situ and ex situ seed lots, suggesting ongoing evolution in farmer fields. Eight loci in chromosomes 3, 5, 6, and 10 showed evidence of selection in situ that could be related with farmers' selection criteria surveyed with focus groups and interviews at the sampling site in 2017, including wider kernels and larger ear size. Our results have implications for ex situ collection resampling strategies and the in situ conservation of threatened landraces.

Genetic mapping of green curd gene Gr in cauliflower

Gr5.1 is the major locus for cauliflower green curd color and mapped to an interval of 236 Kbp with four most likely candidate genes. Cauliflower with colored curd enhances not only the visual appeal but also the nutritional value of the crop. Green cauliflower results from ectopic development of chloroplasts in the normal white curd. However, the underlying genetic basis is unknown. In this study, we employed QTL-seq analysis to identify the loci that were associated with green curd phenotype in cauliflower. A F2 population was generated following a cross between a white curd (Stovepipe) and a green curd (ACX800) cauliflower plants. By whole-genome resequencing and SNP analysis of green and white F2 bulks, two QTLs were detected on chromosomes 5 (Gr5.1) and 7 (Gr7.1). Validation by traditional genetic mapping with CAPS markers suggested that Gr5.1 represented a major QTL, whereas Gr7.1 had a minor effect. Subsequent high-resolution mapping of Gr5.1 in the second large F2 population with additional CAPS markers narrowed down the target region to a genetic and physical distance of 0.3 cM and 236 Kbp, respectively. This region contained 35 genes with four of them representing the best candidates for the green curd phenotype in cauliflower. They are LOC106295953, LOC106343833, LOC106345143, and LOC106295954, which encode UMP kinase, DEAD-box RNA helicase 51-like, glutathione S-transferase T3-like, and protein MKS1, respectively. These findings lay a solid foundation for the isolation of the Gr gene and provide a potential for marker-assisted selection of the green curd trait in cauliflower breeding. The eventual isolation of Gr will also facilitate better understanding of chloroplast biogenesis and development in plants.

Fine analysis of a genomic region involved in resistance to Mediterranean corn borer

BACKGROUND

Sesamia nonagrioides Lefebvere (Mediterranean corn borer, MCB) is the main pest of maize in the Mediterranean area. QTL for MCB stalk tunneling and grain yield under high MCB infestation had been located at bin 8.03-8.05 (4-21 cM and 10-30 cM respectively) in a previous analysis of the EP42 x EP39 RILs mapping population. The objective of the present work was to study with higher resolution those QTL, and validating and estimating with higher precision their locations and effects. To achieve this objective, we developed a set of 38 heterogeneous inbred families (HIFs) which were near-homozygous in the genome, except in the region under study. The HIFs were evaluated in multiple environments under artificial infestation with MCB and genotyped with SNPs.

RESULTS

The QTL for grain yield under high infestation was confirmed with higher precision and improved reliability at 112.6-116.9 Mb. On the contrary, the location of the QTL for stalk tunneling was not validated probably due to the fixation of some genomic regions during the development of the HIFs. Our study confirmed that the co-localization of the QTL for stalk tunneling and grain yield in the previous study was due to linked genes, not to pleiotropic effects. So, the QTL for grain yield can be used for improving grain yield without undesirable effect on stalk tunneling.

CONCLUSIONS

The HIF analysis is useful for validating QTL and for conducting deeper studies in traits related to corn borer resistance.

Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (Brassica napus L.) Lines

Genomic prediction is becoming a popular plant breeding method to predict the genetic merit of lines. While some genomic prediction results have been reported in canola, none have been evaluated for blackleg disease. Here, we report genomic prediction for seedling emergence, survival rate, and internal infection), using 532 Spring and Winter canola lines. These lines were phenotyped in two replicated blackleg disease nurseries grown at Wickliffe and Green Lake, Victoria, Australia. A transcriptome genotyping-by-sequencing approach revealed 98,054 single nucleotide polymorphisms (SNPs) after quality control. We assessed various genomic prediction scenarios based on Genomic Best Linear Unbiased Prediction (GBLUP), BayesR and BayesRC, which can make use of prior quantitative trait loci information, via cross-validation. Clustering based on genomic relationships showed that Winter and Spring lines were genetically distinct, indicating limited gene flow between sets. Genetic correlations within traits between Spring and Winter lines ranged from 0.68 and 0.90 (mean = 0.76). Based on GBLUP in the whole population, moderate to high genomic prediction accuracies were achieved within environments (0.35-0.74) and were reduced across environments (0.28-0.58). Prediction accuracy within the Spring set ranged from 0.30-0.69, and from 0.19-0.71 within the Winter set. The BayesR model resulted in slightly lower accuracy to GBLUP. The proportion of genetic variance explained by known blackleg quantitative trait loci (QTL) was < 30%, indicating that there is a large reservoir of genetic variation in blackleg traits that remains to be discovered, but can be captured with genomic prediction. However, providing prior information of known QTL in the BayesRC method resulted in an increased prediction accuracy for survival and internal infection, particularly with Spring lines. Overall, these promising results indicate that genomic prediction will be a valuable tool to make use of all genetic variation to improve blackleg resistance in canola.

Genetic and Molecular Mechanisms of Quantitative Trait Loci Controlling Maize Inflorescence Architecture

The establishment of inflorescence architecture is critical for the reproduction of flowering plant species. The maize plant generates two types of inflorescences, the tassel and the ear, and their architectures have a large effect on grain yield and yield-related traits that are genetically controlled by quantitative trait loci (QTLs). Since ear and tassel architecture are deeply affected by the activity of inflorescence meristems, key QTLs and genes regulating meristematic activity have important impacts on inflorescence development and show great potential for optimizing grain yield. Isolation of yield trait-related QTLs is challenging, but these QTLs have direct application in maize breeding. Additionally, characterization and functional dissection of QTLs can provide genetic and molecular knowledge of quantitative variation in inflorescence architecture. In this review, we summarize currently identified QTLs responsible for the establishment of ear and tassel architecture and discuss the potential genetic control of four ear-related and four tassel-related traits. In recent years, several inflorescence architecture-related QTLs have been characterized at the gene level. We review the mechanisms of these characterized QTLs.