Genome-wide Association Studies in Maize: Praise and Stargaze

The genetic architecture of cell type-specific cis regulation in maize

Gene expression and complex phenotypes are determined by the activity of cis-regulatory elements. However, an understanding of how extant genetic variants affect cis regulation remains limited. Here, we investigated the consequences of cis-regulatory diversity using single-cell genomics of more than 0.7 million nuclei across 172 Zea mays (maize) inbreds. Our analyses pinpointed cis-regulatory elements distinct to domesticated maize and revealed how historical transposon activity has shaped the cis-regulatory landscape. Leveraging population genetics principles, we fine-mapped about 22,000 chromatin accessibility-associated genetic variants with widespread cell type-specific effects. Variants in TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR-binding sites were the most prevalent determinants of chromatin accessibility. Finally, integrating chromatin accessibility-associated variants, organismal trait variation, and population differentiation revealed how local adaptation has rewired regulatory networks in unique cellular contexts to alter maize flowering.

Genome-Wide Association Study and Candidate Gene Mining of Husk Number Trait in Maize

Husk number (HN) trait is an important factor affecting maize kernel dehydration rate after the physiological maturity stage. In general, a reasonable reduction in HN is a key target sought for breeding maize varieties that are suitable for mechanized harvesting. In this study, the HN of a maize natural population panel containing 232 inbred lines was analyzed, and the results showed a broad-sense heritability of 0.89, along with a wide range of phenotypic variation. With the best linear unbiased prediction (BLUP) values across the three environments, a genome-wide association study (GWAS) was conducted using 995,106 single-nucleotide polymorphism (SNP) markers. A total of 16 SNPs significantly associated with HN were identified by the mixed linear model and general linear model using the TASSEL 5.0 software program. A local linkage disequilibrium (LD) study was performed to infer the candidate interval around the lead SNPs. A total of 19 functionally annotated genes were identified. The candidate genes were divided into multiple functional types, including transcriptional regulation, signal transduction, and metabolic and cellular transport. These results provide hints for the understanding of the genetic basis of the HN trait and for the breeding of maize varieties with fewer HN and faster dehydration rate.

A genome-wide association study reveals the genetic architecture of 19 agronomic traits in broomcorn millet (Panicum miliaceum L.)

KEY MESSAGE

The genetic architecture of 19 growth and development and yield-related traits was dissected by a GWAS in multi-environment of a diverse population consisting of 301 broomcorn millet accessions. Broomcorn millet (Panicum miliaceum L.) is an ideal replant crop due to its advantages of drought resistance, short growth period, and strong adaptability. Understanding the genetic basis of agronomic traits is crucial for improving the efficiency of molecular breeding in broomcorn millet. In this study, a natural population consisting of 301 worldwide broomcorn millet accessions was genotyped using 208,169 high-quality single-nucleotide polymorphisms (SNPs). Nineteen agronomic traits, including growth period, plant architecture, and yield-related traits, were investigated across ten environments. Based on genomic and phenotypic data, a genome-wide association study (GWAS) was performed to identify significant marker-trait associations (MTAs) affecting these traits. Using the linear mixed model, 662 significant MTAs were identified for 19 traits, with 56 stable MTAs repeatedly identified over two environments. Among these stable SNPs, 40 were located in genomic regions where no relevant loci had been reported previously. Notably, superior alleles of four significant SNPs chr1_2925777, chr7_157147, chr4_3971792, and chr5_2126999 associated with panicle length (PL) and the whole growth period (GP) exhibited significantly higher phenotypic levels. Ultimately, 174 genes within the flanking regions of these four significant SNPs were annotated. Among these, six genes were identified as candidate genes associated with PL and GP. Further haplotype analysis identified seven haplotypes for longmi011379 and longmi011388. Phenotypic assessment of major haplotypes revealed significant differences between Hap1 and Hap2. These results provide a valuable resource for understanding the genetic mechanisms of growth and development and yield-related traits and developing marker-assisted selection breeding in broomcorn millet.

Review: Recent advances in unraveling the genetic architecture of kernel row number in maize

Kernel row number (KRN) is an important trait in maize that significantly impacts maize yield. The high heritability of KRN underscores its significance in maize breeding programs. In this review, we summarize recent advances in understanding the mechanisms underlying the formation, differentiation, and regulation of KRN in maize. Specifically, we have discussed gene mapping studies, functional validation of KRN-associated genes, and the application of gene editing techniques to KRN in maize. We summarized the various methods used to map and fine-map QTLs controlling KRN and provide an overview of the current status of cloned KRN-regulating genes. Despite the identification of many genes associated with KRN, the complexity of its regulation-arising from multiple loci and intricate gene interactions-remains a challenge. Balancing KRN with kernel number per row (KNR) and kernel weight is critical for optimizing yield while ensuring stability across different environments. Furthermore, we analyzed the influence of environmental factors on KRN, noting that despite its high heritability, environmental conditions can significantly affect this trait. Combining genotype-phenotype relationships with environmental data using big data and artificial intelligence could enhance maize breeding efficiency and accelerate genetic gains. This review emphasizes the importance of balancing traits, integrating environmental factors, and leveraging advanced technologies in maize breeding to achieve optimal yield and stress tolerance. Finally, we outlined future research perspectives aimed at developing high-yielding maize varieties through advances in KRN-related research.

Integration of GWAS models and GS reveals the genetic architecture of ear shank in maize

Maize is one of the most important crops for human food, animal feed, and industrial raw materials. Ear shank length (ESL) and ear shank node number (ESNN) are crucial selection criteria in maize breeding, impacting grain yield and dehydration rate during mechanical harvesting. To unravel the genetic basis of ESL and ESNN in maize, an association panel consisting of 379 multi-parent doubled-haploid (DH) lines was developed for genome-wide association studies (GWAS) and genomic selection (GS). The heritabilities of ESL and ESNN were 0.68 and 0.55, respectively, which were controlled by genetic factors and genotype-environment interaction factors. Using five different models for GWAS, 11 significant single nucleotide polymorphisms (SNPs) located on chromosomes 1, 2, and 4 were identified for ESL, with the phenotypic variation explained (PVE) value of each single SNP ranging from 4.91% to 21.35%, and 11 significant SNPs located on chromosomes 1, 2, 4, and 5 were identified for ESNN, with the PVE value of each SNP ranging from 1.22% to 18.42%. Genetic regions in bins 1.06, 2.06, and 2.08 were significantly enriched in SNPs associated with ear shank-related traits. The GS prediction accuracy using all markers by the five-fold cross-validation method for ESL and ESNN was 0.39 and 0.37, respectively, which was significantly improved by using only 500-1000 significant SNPs with the lowest P-values. The optimal training population size (TPS) and marker density (MD) for ear shank-related traits were 50%-60% and 3000, respectively. Our results provide new insights into the GS of ear shank-related traits.

Dissecting the Genetic Basis of Preharvest Sprouting in Rice Using a Genome-Wide Association Study

Preharvest sprouting (PHS) is an unfavorable trait in cereal crops that significantly reduces grain yield and quality. However, the regulatory mechanisms underlying this complex trait are still largely unknown. Here, 276 rice accessions from the 3000 Rice Genomes Project were used to perform a genome-wide association study. In total, seven PHS-associated quantitative trait loci (QTLs) were identified, including two novel QTLs and five previously reported QTLs. Among them, four QTLs were identified in 2020 and 2021, and rice accessions carrying at least two favorable alleles exhibited significantly improved PHS resistance. Within these four stable QTLs, five candidate genes were identified based on haplotype and gene expression analyses, including two genes in qPhs-1.1, one gene in qPhs-3, one gene in qPhs-6, and one gene in qPhs-7. Notably, the novel QTL qPhs-6 was found to contain the candidate gene Pi starvation-induced transcription factor 1 (OsPTF1). We discovered that OsPTF1 plays a novel role in negatively regulating PHS in rice and identified two elite haplotypes of OsPTF1 associated with low PHS. Our results provide future insight into the genetic basis of PHS and will prove useful in the future studies on the role of OsPTF1 in PHS in rice.

Maize2035: A decadal vision for intelligent maize breeding

Maize, a cornerstone of global food security, has undergone remarkable transformations through breeding, yet further increase in global maize production faces mounting challenges in a changing world. In this Perspective paper, we overview the historical successes of maize breeding that laid the foundation for present opportunities. We examine both the specific and shared breeding goals related to diverse geographies and end-use demands. Achieving these coordinated breeding objectives requires a holistic approach to trait improvement for sustainable agriculture. We discuss cutting-edge solutions, including multi-omics approaches from single-cell analysis to holobionts, smart breeding with advanced technologies and algorithms, and the transformative potential of rational design with synthetic biology approaches. A transition toward a data-driven future is currently underway, with large-scale precision agriculture and autonomous systems poised to revolutionize farming practice. Realizing these futuristic opportunities hinges on collaborative efforts spanning scientific discoveries, technology translations, and socioeconomic considerations in maximizing human and environmental well-being.

Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize

BACKGROUND

Drought is a major abiotic stress in sub-Saharan Africa, impacting maize growth and development leading to severe yield loss. Drought tolerance is a complex trait regulated by multiple genes, making direct grain yield selection ineffective. To dissect the genetic architecture of grain yield and flowering traits under drought stress, a genome-wide association study (GWAS) was conducted on a panel of 236 maize lines testcrossed and evaluated under managed drought and optimal growing conditions in multiple environments using seven multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, ISIS EM-BLASSO, and FARMCPU) from mrMLM and GAPIT R packages. Genomic prediction with RR-BLUP model was applied on BLUEs across locations under optimum and drought conditions.

RESULTS

A total of 172 stable and reliable quantitative trait nucleotides (QTNs) were identified, of which 77 are associated with GY, AD, SD, ASI, PH, EH, EPO and EPP under drought and 95 are linked to GY, AD, SD, ASI, PH, EH, EPO and EPP under optimal conditions. Among these QTNs, 17 QTNs explained over 10% of the phenotypic variation (R2 ≥ 10%). Furthermore, 43 candidate genes were discovered and annotated. Two major candidate genes, Zm00001eb041070 closely associated with grain yield near peak QTN, qGY_DS1.1 (S1_216149215) and Zm00001eb364110 closely related to anthesis-silking interval near peak QTN, qASI_DS8.2 (S8_167256316) were identified, encoding AP2-EREBP transcription factor 60 and TCP-transcription factor 20, respectively under drought stress. Haplo-pheno analysis identified superior haplotypes for qGY_DS1.1 (S1_216149215) associated with the higher grain yield under drought stress. Genomic prediction revealed moderate to high prediction accuracies under optimum and drought conditions.

CONCLUSION

The lines carrying superior haplotypes can be used as potential donors in improving grain yield under drought stress. Integration of genomic selection with GWAS results leads not only to an increase in the prediction accuracy but also to validate the function of the identified candidate genes as well increase in the accumulation of favorable alleles with minor and major effects in elite breeding lines. This study provides valuable insight into the genetic architecture of grain yield and secondary traits under drought stress.

Co-Location of QTL for Vigor and Resistance to Three Diseases in Juglans microcarpa × J. regia Rootstocks

A QTL on chromosome 4D of the Juglans microcarpa × J. regia genome that co-located resistance against Agrobacterium tumefaciens, Phytophthora pini, and Phytophthora cinnamomi disease scores was investigated for additional traits. Phenotypic data for Pratylenchus vulnus counts and tree height were analyzed in this study for the same hybrids previously used to identify this QTL. Using the same GBS genotype data, the same co-located QTL for A. tumefaciens and Phytophthora spp. disease scores were reproduced and the QTL for P. vulnus counts and tree height were co-located with resistance to A. tumefaciens and Phytophthora spp. Moreover, we found GBS genotype data to harbor additional genetic variation unrelated to any of the traits analyzed. Marker-assisted and genomic selection models were created and assessed for their performance in selection. The ability to predict traits using SNP data was strongest with two-year tree height, followed by A. tumefaciens disease score, three-year tree height, Phytophthora spp. disease score, and P. vulnus counts. These results suggest a shared mechanism of action that links disease to tree height. Moreover, deploying these selection models would assist efforts in walnut improvement for rootstock genotypes.

Crop root system architecture in drought response

Drought is a natural disaster that profoundly impacts on global agricultural production, significantly reduces crop yields, and thereby poses a severe threat to worldwide food security. Addressing the challenge of effectively improving crop drought resistance (DR) to mitigate yield loss under drought conditions is a global issue. An optimal root system architecture (RSA) plays a pivotal role in enhancing the capacity of crops to efficiently uptake water and nutrients, which consequently strengthens their resilience against environmental stresses. In this review, we discuss the compositions and roles of crop RSA and summarize the most recent developments in augmenting drought tolerance in crops by manipulating RSA-related genes. Based on the current research, we propose the potential optimal RSA configuration that could be helpful in enhancing crop DR. Lastly, we discuss the existing challenges and future directions for breeding crops with enhanced DR capabilities through genetic improvements targeting RSA.

Identification of novel candidate genes for Ascochyta blight resistance in chickpea

Ascochyta blight, caused by the necrotrophic fungus Ascochyta rabiei, is a major threat to chickpea production worldwide. Resistance genes with broad-spectrum protection against virulent A. rabiei strains are required to secure chickpea yield in the US Northern Great Plains. Here, we performed a genome-wide association (GWA) study to discover novel sources of genetic variation for Ascochyta blight resistance using a worldwide germplasm collection of 219 chickpea lines. Ascochyta blight resistance was evaluated at 3, 9, 11, 13, and 14 days post-inoculation. Multiple GWA models revealed eight quantitative trait nucleotides (QTNs) across timepoints mapped to chromosomes 1, 3, 4, 6, and 7. Of these eight QTNs, only CM001767.1_28299946 on Chr 4 had previously been reported. QTN CM001766.1_36967269 on Chr 3 explained up to 33% of the variation in disease severity and was mapped to an exonic region of the pentatricopeptide repeat-containing protein At4g02750-like gene (LOC101506608). This QTN was confirmed across all models and timepoints. A total of 153 candidate genes, including genes with roles in pathogen recognition and signaling, cell wall biosynthesis, oxidative burst, and regulation of DNA transcription, were observed surrounding QTN-targeted regions. Further gene expression analysis on the QTNs identified in this study will provide insights into defense-related genes that can be further incorporated into breeding of new chickpea cultivars to minimize fungicide applications required for successful chickpea production in the US Northern Great Plains.

Discovery of candidate genes enhancing kernel protein content in tropical maize introgression lines

BACKGROUND

In this study, tropical maize germplasms were used to construct a multiparent population (MPP) to identify novel genes associated with kernel protein content. The kernel protein content was quantified using near-infrared spectroscopy (NIRS) in the MPP, which was evaluated in three different environments.

RESULTS

QTL mapping revealed 23 significant QTLs tightly linked to kernel protein content, with phenotypic variation ranging from 8.88 to 28.86%. Genome-wide association analysis (GWAS) identified 21 SNPs that were significantly associated with kernel protein content, with phenotypic variation explained (PVE) ranging from 4.09 to 16.15%. Through combined QTL mapping and GWAS, co-localized loci were identified, as well as two novel genes (Zm00001d034933 and Zm00001d029999) that had not been previously reported.

CONCLUSIONS

These genes encode pentatricopeptide repeat-containing proteins (PPR proteins), which regulate kernel endosperm development. The significant SNPs associated with these genes accounted for 23.59% of the PVE, whereas the QTLs accounted for 46.02% of the phenotypic variation. Since kernel protein synthesis and storage occur in the endosperm, this study suggests that Zm00001d034933 and Zm00001d029999 may potentially regulate kernel protein content in maize.

QTL mapping and genome-wide association analysis reveal genetic loci and candidate gene for resistance to gray leaf spot in tropical and subtropical maize germplasm

KEY MESSAGE

Using QTL mapping and GWAS, two candidate genes (Zm00001d051039 and Zm00001d051147) were consistently identified across the three different environments and BLUP values. GWAS analysis identified the candidate gene, Zm00001d044845. These genes were subsequently validated to exhibit a significant association with maize gray leaf spot (GLS) resistance. Gray leaf spot (GLS) is a major foliar disease of maize (Zea mays L.) that causes significant yield losses worldwide. Understanding the genetic mechanisms underlying gray leaf spot resistance is crucial for breeding high-yielding and disease-resistant varieties. In this study, eight tropical and subtropical germplasms were crossed with the temperate germplasm Ye107 to develop a nested association mapping (NAM) population comprising 1,653 F2:8 RILs, consisting of eight recombinant inbred line (RIL) subpopulations, using the single-seed descent method. The NAM population was evaluated for GLS resistance in three different environments, and genotyping by sequencing of the NAM population generated 593,719 high-quality single-nucleotide polymorphisms (SNPs). Linkage analysis and genome-wide association studies (GWASs) were conducted to identify candidate genes regulating GLS resistance in maize. Both analyses identified 25 QTLs and 149 SNPs that were significantly associated with GLS resistance. Candidate genes were screened 20 Kb upstream and downstream of the significant SNPs, and three novel candidate genes (Zm00001d051039, Zm00001d051147, and Zm00001d044845) were identified. Zm00001d051039 and Zm00001d051147 were located on chromosome 4 and co-localized in both linkage (qGLS4-1 and qGLS4-2) and GWAS analyses. SNP-138,153,206 was located 0.499 kb downstream of the candidate gene Zm00001d051039, which encodes the protein IN2-1 homolog B, a homolog of glutathione S-transferase (GST). GSTs and protein IN2-1 homolog B scavenge reactive oxygen species under various stress conditions, and GSTs are believed to protect plants from a wide range of biotic and abiotic stresses by detoxifying reactive electrophilic compounds. Zm00001d051147 encodes a probable beta-1,4-xylosyltransferase involved in the biosynthesis of xylan in the cell wall, enhancing resistance. SNP-145,813,215 was located 2.69 kb downstream of the candidate gene. SNP-5,043,412 was consistently identified in three different environments and BLUP values and was located 8.788 kb downstream of the candidate gene Zm00001d044845 on chromosome 9. Zm00001d044845 encodes the U-box domain-containing protein 4 (PUB4), which is involved in regulating plant immunity. qRT-PCR analysis showed that the relative expression levels of the three candidate genes were significantly upregulated in the leaves of the TML139 (resistant) parent, indicating that these three candidate genes could be associated with resistance to GLS. The findings of this study are significant for marker-assisted breeding aimed at enhancing resistance to GLS in maize and lay the foundation for further elucidation of the genetic mechanisms underlying resistance to gray leaf spot in maize and breeding of new disease-resistant varieties.

Phenotype identification and genome-wide association study of ear-internode vascular bundles in maize (Zea mays)

The vascular bundle in the ear-internode of maize is a key conduit for transporting photosynthetic materials between "source" and "sink", making it critically important to examine its micro-phenotypes and genetic architecture to identify advantageous characteristics and cultivate high-yielding and high-quality varieties. Unfortunately, the limited observation methods and scope of study precludes any comprehensive and systematic investigations into the microscopic phenotypes and genetic mechanisms of vascular bundle in maize ear-internode. In this study, 47 phenotypic traits were extracted in 495 maize inbred lines using micro computed tomography (Micro-CT) scanning technology and a deep learning-based phenotype acquisition method for stem vascular bundle, which included stem slice-related, epidermis zone-related, periphery zone-related, inner zone-related and vascular bundles-related traits. Phenotypic analysis indicated that there was extensive phenotypic variation of vascular bundle traits in ear-internode, especially that in the inner zone. Of these, 30 phenotypic traits with heritability greater than 0.70 were conducted for GWAS, and a total of 4,225 significant SNPs and 416 candidate genes with detailed functional annotations were identified. Furthermore, 20 genes were highly expressed in stem-related tissues, especially in maize internodes. Functional analysis of candidate genes indicated that the pathways obtained for candidate genes of different trait groups were distinct, mainly involved in vitamin synthesis and metabolism, transport of substances, carbohydrate derivative catabolic process, protein transport and localization, and anatomical structure development. The results of this study will help to further understand the phenotypic traits of stem vascular bundles and provide a reference for revealing the genetic mechanism of maize ear-internode vascular bundles.

Increasing oil content in Brassica oilseed species

Brassica oilseed species are the third most important in the world, providing approximately 15 % of the total vegetable oils. Three species (Brassica rapa, B. juncea, B. napus) dominate with B. napus being the most common in Canada, China and Europe. Originally, B. napus was a crop producing seed with high erucic acid content, which still persists today, to some extent, and is used for industrial purposes. In contrast, cultivars which produce seed used for food and feed are low erucic acid cultivars which also have reduced glucosinolate content. Because of the limit to agricultural land, recent efforts have been made to increase productivity of oil crops, including Brassica oilseed species. In this article, we have detailed research in this regard. We have covered modern genetic, genomic and metabolic control analysis approaches to identifying potential targets for the manipulation of seed oil content. Details of work on the use of quantitative trait loci, genome-wide association and comparative functional genomics to highlight factors influencing seed oil accumulation are given and functional proteins which can affect this process are discussed. In summary, a wide variety of inputs are proving useful for the improvement of Brassica oilseed species, as major sources of global vegetable oil.

Mining Candidate Genes for Maize Tassel Spindle Length Based on a Genome-Wide Association Analysis

Maize tassel spindle length is closely related to the number of pollen grains and the duration of the flowering stage, ultimately affecting maize yield and adaptations to stress conditions. In this study, 182 maize inbred lines were included in an association population. A genome-wide association study was conducted on maize tassel spindle length using the Q + K model. With p ≤ 1.0 × 10-4 applied as the significance threshold, 240 SNPs significantly associated with tassel spindle length were detected, which were associated with 99 quantitative trait loci (QTLs), with 21 QTLs detected in two or more environments. Moreover, 51 candidate genes were detected in 21 co-localized QTLs. A KEGG enrichment analysis and candidate gene expression analysis indicated that Zm00001d042312 affects plant hormone signal transduction and is highly expressed in maize tassels. A haplotype analysis of Zm00001d042312 revealed three main haplotypes, with significant differences between Hap1 and Hap2. In conclusion, we propose that Zm00001d042312 is a gene that regulates maize tassel spindle length. This study has further elucidated the genetic basis of maize tassel spindle length, while also providing excellent genetic targets and germplasm resources for the genetic improvement of maize tassel spindle length and yield.

Regulatory balance between ear rot resistance and grain yield and their breeding applications in maize and other crops

BACKGROUND

Fungi are prevalent pathogens that cause substantial yield losses of major crops. Ear rot (ER), which is primarily induced by Fusarium or Aspergillus species, poses a significant challenge to maize production worldwide. ER resistance is regulated by several small effect quantitative trait loci (QTLs). To date, only a few ER-related genes have been identified that impede molecular breeding efforts to breed ER-resistant maize varieties.

AIM OF REVIEW

Our aim here is to explore the research progress and mine genic resources related to ER resistance, and to propose a regulatory model elucidating the ER-resistant mechanism in maize as well as a trade-off model illustrating how crops balance fungal resistance and grain yield. Key Scientific Concepts of Review: This review presents a comprehensive bibliometric analysis of the research history and current trends in the genetic and molecular regulation underlying ER resistance in maize. Moreover, we analyzed and discovered the genic resources by identifying 162 environmentally stable loci (ESLs) from various independent forward genetics studies as well as 1391 conservatively differentially expressed genes (DEGs) that respond to Fusarium or Aspergillus infection through multi-omics data analysis. Additionally, this review discusses the syntenies found among maize ER, wheat Fusariumhead blight (FHB), and rice Bakanaedisease (RBD) resistance-related loci, along with the significant overlap between fungal resistance loci and reported yield-related loci, thus providing valuable insights into the regulatory mechanisms underlying the trade-offs between yield and defense in crops.

GWAS and Meta-QTL Analysis of Kernel Quality-Related Traits in Maize

The quality of corn kernels is crucial for their nutritional value, making the enhancement of kernel quality a primary objective of contemporary corn breeding efforts. This study utilized 260 corn inbred lines as research materials and assessed three traits associated with grain quality. A genome-wide association study (GWAS) was conducted using the best linear unbiased estimator (BLUE) for quality traits, resulting in the identification of 23 significant single nucleotide polymorphisms (SNPs). Additionally, nine genes associated with grain quality traits were identified through gene function annotation and prediction. Furthermore, a total of 697 quantitative trait loci (QTL) related to quality traits were compiled from 27 documents, followed by a meta-QTL analysis that revealed 40 meta-QTL associated with these traits. Among these, 19 functional genes and reported candidate genes related to quality traits were detected. Three significant SNPs identified by GWAS were located within the intervals of these QTL, while the remaining eight significant SNPs were situated within 2 Mb of the QTL. In summary, the findings of this study provide a theoretical framework for analyzing the genetic basis of corn grain quality-related traits and for enhancing corn quality.

Genetic architecture of kernel-related traits in sweet and waxy maize revealed by genome-wide association analysis

Introduction

Maize (Zea mays L.) is one of the most important crops worldwide, the kernel size-related traits are the major components of maize grain yield.

Methods

To 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.

Results

In 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.

Discussion

These 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.

Deciphering the Genetic Basis of Kernel Composition in a Maize Association Panel

The primary objective in contemporary maize breeding is to pursue high quality alongside high yield. Deciphering the genetic basis of natural variation in starch, protein, oil, and fiber contents is essential for manipulating kernel composition, thereby enhancing the kernel quality and meeting growing demands. Here, we identified 12 to 88 statistically significant loci associated with kernel composition traits through a genome-wide association study (GWAS) using a panel of 212 diverse inbred lines. A regional association study pinpointed numerous causal candidate genes at these loci. Coexpression and protein-protein interaction network analyses of candidate genes revealed several causal genes directly or indirectly involved in the metabolic processes related to kernel composition traits. Subsequent mutant experiment revealed that nonsense mutations in ZmTIFY12 affect starch, protein, and fiber content, whereas nonsense mutations in ZmTT12 affect starch, protein, and oil content. These findings provide valuable guidance for improving kernel quality in maize breeding efforts.

Strategies and bibliometric analysis of legumes biofortification to address malnutrition

MAIN CONCLUSION

Biofortification of legumes using diverse techniques such as plant breeding, agronomic practices, genetic modification, and nano-technological approaches presents a sustainable strategy to address micronutrient deficiencies of underprivileged populations. The widespread issue of chronic malnutrition, commonly referred to as "hidden hunger," arises from the consumption of poor-quality food, leading to various health and cognitive impairments. Biofortified food crops have been a sustainable solution to address micronutrient deficiencies. This review highlights multiple biofortification techniques, such as plant breeding, agronomic practices, genetic modification, and nano-technological approaches, aimed at enhancing the nutrient content of commonly consumed crops. Emphasizing the biofortification of legumes, this review employs bibliometric analysis to examine research trends from 2000 to 2023. It identifies key authors, influential journals, contributing countries, publication trends, and prevalent keywords in this field. The review highlights the progress in developing biofortified crops and their potential to improve global nutrition and help underprivileged populations.

Identification of anther thermotolerance genes by the integration of linkage and association analysis in maize

Maize is one of the world's most important staple crops, yet its production is increasingly threatened by the rising frequency of high-temperature stress (HTS). To investigate the genetic basis of anther thermotolerance under field conditions, we performed linkage and association analysis to identify HTS response quantitative trait loci (QTL) using three recombinant inbred line (RIL) populations and an association panel containing 375 diverse maize inbred lines. These analyses resulted in the identification of 16 co-located large QTL intervals. Among the 37 candidate genes identified in these QTL intervals, five have rice or Arabidopsis homologs known to influence pollen and filament development. Notably, one of the candidate genes, ZmDUP707, has been subject to selection pressure during breeding. Its expression is suppressed by HTS, leading to pollen abortion and barren seeds. We also identified several additional candidate genes potentially underly QTL previously reported by other researchers. Taken together, our results provide a pool of valuable candidate genes that could be employed by future breeding programs aiming at enhancing maize HTS tolerance.

Prioritizing Maize Metabolic Gene Regulators through Multi-Omic Network Integration

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

GRAPHICAL ABSTRACT

Leveraging Multiomics Insights and Exploiting Wild Relatives' Potential for Drought and Heat Tolerance in Maize

Climate change, particularly drought and heat stress, may slash agricultural productivity by 25.7% by 2080, with maize being the hardest hit. Therefore, unraveling the molecular nature of plant responses to these stressors is vital for the development of climate-smart maize. This manuscript's primary objective was to examine how maize plants respond to these stresses, both individually and in combination. Additionally, the paper delved into harnessing the potential of maize wild relatives as a valuable genetic resource and leveraging AI-based technologies to boost maize resilience. The role of multiomics approaches particularly genomics and transcriptomics in dissecting the genetic basis of stress tolerance was also highlighted. The way forward was proposed to utilize a bunch of information obtained through omics technologies by an interdisciplinary state-of-the-art forward-looking big-data, cyberagriculture system, and AI-based approach to orchestrate the development of climate resilient maize genotypes.

Understanding grain development in the Poaceae family by comparing conserved and distinctive pathways through omics studies in wheat and maize

The Poaceae family, commonly known as the grass family, encompasses a diverse group of crops that play an essential role in providing food, fodder, biofuels, environmental conservation, and cultural value for both human and environmental well-being. Crops in Poaceae family are deeply intertwined with human societies, economies, and ecosystems, making it one of the most significant plant families in the world. As the major reservoirs of essential nutrients, seed grain of these crops has garnered substantial attention from researchers. Understanding the molecular and genetic processes that controls seed formation, development and maturation can provide insights for improving crop yield, nutritional quality, and stress tolerance. The diversity in photosynthetic pathways between C3 and C4 plants introduces intriguing variations in their physiological and biochemical processes, potentially affecting seed development. In this review, we explore recent studies performed with omics technologies, such as genomics, transcriptomics, proteomics and metabolomics that shed light on the mechanisms underlying seed development in wheat and maize, as representatives of C3 and C4 plants respectively, providing insights into their unique adaptations and strategies for reproductive success.

Doubled haploid technology and synthetic apomixis: Recent advances and applications in future crop breeding

Doubled haploid (DH) technology and synthetic apomixis approaches can considerably shorten breeding cycles and enhance breeding efficiency. Compared with traditional breeding methods, DH technology offers the advantage of rapidly generating inbred lines, while synthetic apomixis can effectively fix hybrid vigor. In this review, we focus on (i) recent advances in identifying and characterizing genes responsible for haploid induction (HI), (ii) the molecular mechanisms of HI, (iii) spontaneous haploid genome doubling, and (iv) crop synthetic apomixis. We also discuss the challenges and potential solutions for future crop breeding programs utilizing DH technology and synthetic apomixis. Finally, we provide our perspectives about how to integrate DH and synthetic apomixis for precision breeding and de novo domestication.

Genome-wide association studies dissect low-phosphorus stress response genes underling field and seedling traits in maize

Phosphorus (P) is an essential element for plant growth, and its deficiency can cause decreased crop yield. This study systematically evaluated the low-phosphate (Pi) response traits in a large population at maturity and seedling stages, and explored candidate genes and their interrelationships with specific traits. The results revealed a greater sensitivity of seedling maize to low-Pi stress compared to that at maturity stage. The phenotypic response patterns to low-Pi stress at different stages were independent. Chlorophyll content was found to be a potential indicator for screening low-Pi-tolerant materials in the field. A total of 2900 and 1446 significantly associated genes at the maturity and seedling stages were identified, respectively. Among these genes, 972 were uniquely associated with maturity traits, while 330 were specifically detected at the seedling stage under low-Pi stress. Moreover, 768 and 733 genes were specifically associated with index values (low-Pi trait/normal-Pi trait) at maturity and seedling stage, respectively. Genetic network diagrams showed that the low-Pi response gene Zm00001d022226 was specifically associated with multiple primary P-related traits under low-Pi conditions. A total of 963 out of 2966 genes specifically associated with traits under low-Pi conditions or index values were found to be induced by low-Pi stress. Notably, ZmSPX4.1 and ZmSPX2 were sharply up-regulated in response to low-Pi stress across different lines or tissues. These findings advance our understanding of maize's response to low-Pi stress at different developmental stages, shedding light on the genes and pathways implicated in this response.

Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to Sclerotinia sclerotiorum

Sclerotinia sclerotiorum (Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor genes. Genome-wide identification of genes involved in QDR to Ss is yet to be conducted. In this study, we integrated several assays including genome-wide association study (GWAS), multi-omics co-localization, and machine learning prediction to identify, on a genome-wide scale, genes involved in the oilseed rape QDR to Ss. Employing GWAS and multi-omics co-localization, we identified seven resistance-associated loci (RALs) associated with oilseed rape resistance to Ss. Furthermore, we developed a machine learning algorithm and named it Integrative Multi-Omics Analysis and Machine Learning for Target Gene Prediction (iMAP), which integrates multi-omics data to rapidly predict disease resistance-related genes within a broad chromosomal region. Through iMAP based on the identified RALs, we revealed multiple calcium signaling genes related to the QDR to Ss. Population-level analysis of selective sweeps and haplotypes of variants confirmed the positive selection of the predicted calcium signaling genes during evolution. Overall, this study has developed an algorithm that integrates multi-omics data and machine learning methods, providing a powerful tool for predicting target genes associated with specific traits. Furthermore, it makes a basis for further understanding the role and mechanisms of calcium signaling genes in the QDR to Ss.

Modern Breeding Strategies and Tools for Durable Late Blight Resistance in Potato

Cultivated potato (Solanum tuberosum) is a major crop worldwide. It occupies the second place after cereals (corn, rice, and wheat). This important crop is threatened by the Oomycete Phytophthora infestans, the agent of late blight disease. This pathogen was first encountered during the Irish famine during the 1840s and is a reemerging threat to potatoes. It is mainly controlled chemically by using fungicides, but due to health and environmental concerns, the best alternative is resistance. When there is no disease, no treatment is required. In this study, we present a summary of the ongoing efforts concerning resistance breeding of potato against this devastating pathogen, P. infestans. This work begins with the search for and selection of resistance genes, whether they are from within or from outside the species. The genetic methods developed to date for gene mining, such as effectoromics and GWAS, provide researchers with the ability to identify genes of interest more efficiently. Once identified, these genes are cloned using molecular markers (MAS or QRL) and can then be introduced into different cultivars using somatic hybridization or recombinant DNA technology. More innovative technologies have been developed lately, such as gene editing using the CRISPR system or gene silencing, by exploiting iRNA strategies that have emerged as promising tools for managing Phytophthora infestans, which can be employed. Also, gene pyramiding or gene stacking, which involves the accumulation of two or more R genes on the same individual plant, is an innovative method that has yielded many promising results. All these advances related to the development of molecular techniques for obtaining new potato cultivars resistant to P. infestans can contribute not only to reducing losses in agriculture but especially to ensuring food security and safety.

Metabolite-based genome-wide association studies enable the dissection of the genetic bases of flavonoids, betaine and spermidine in wolfberry (Lycium)

Wolfberry is a plant with medicinal and food values. However, its bioactive ingredients and the corresponding genetic bases have not been determined. Here, we de novo generated a chromosome-level genome assembly for wolfberry, yielding a genome sequence of ~1.77 Gb with contig N50 of 50.55 Mb and 39 224 predicted gene models. A variation map, using 307 re-sequenced accessions, was called based on this genome assembly. Furthermore, the fruit metabolome of these accessions was profiled using 563 annotated metabolites, which separated Lycium barbarum L. and non-L. barbarum L. The flavonoids, coumarins, alkaloids and nicotinic acid contents were higher in the former than in the latter. A metabolite-based genome-wide association study mapped 156 164 significant single nucleotide polymorphisms corresponding to 340 metabolites. This included 19 219 unique lead single nucleotide polymorphisms in 1517 significant association loci, of which three metabolites, flavonoids, betaine and spermidine, were highlighted. Two candidate genes, LbUGT (evm.TU.chr07.2692) and LbCHS (evm.TU.chr07.2738), with non-synonymous mutations, were associated with the flavonoids content. LbCHS is a structural gene that interacts with a nearby MYB transcription factor (evm.TU.chr07.2726) both in L. barbarum and L. ruthenicum. Thus, these three genes might be involved in the biosynthesis/metabolism of flavonoids. LbSSADH (evm.TU.chr09.627) was identified as possibly participating in betaine biosynthesis/metabolism. Four lycibarbarspermidines (E-G and O) were identified, and only the lycibarbarspermidines O content was higher in L. barbarum varieties than in non-L. barbarum varieties. The evm.TU.chr07.2680 gene associated with lycibarbarspermidines O was annotated as an acetyl-CoA-benzylalcohol acetyltransferase, suggesting that it is a candidate gene for spermidine biosynthesis. These results provide novel insights into the specific metabolite profile of non-L. barbarum L. and the genetic bases of flavonoids, betaine and spermidine biosynthesis/metabolism.

Transcriptome-wide association mapping provides insights into the genetic basis and candidate genes governing flowering, maturity and seed weight in rice bean (Vigna umbellata)

BACKGROUND

Rice bean (Vigna umbellata), an underrated legume, adapts to diverse climatic conditions with the potential to support food and nutritional security worldwide. It is used as a vegetable, minor food crop and a fodder crop, being a rich source of proteins, minerals, and essential fatty acids. However, little effort has been made to decipher the genetic and molecular basis of various useful traits in this crop. Therefore, we considered three economically important traits i.e., flowering, maturity and seed weight of rice bean and identified the associated candidate genes employing an associative transcriptomics approach on 100 diverse genotypes out of 1800 evaluated rice bean accessions from the Indian National Genebank.

RESULTS

The transcriptomics-based genotyping of one-hundred diverse rice bean cultivars followed by pre-processing of genotypic data resulted in 49,271 filtered markers. The STRUCTURE, PCA and Neighbor-Joining clustering of 100 genotypes revealed three putative sub-populations. The marker-trait association analysis involving various genome-wide association study (GWAS) models revealed significant association of 82 markers on 48 transcripts for flowering, 26 markers on 22 transcripts for maturity and 22 markers on 21 transcripts for seed weight. The transcript annotation provided information on the putative candidate genes for the considered traits. The candidate genes identified for flowering include HSC80, P-II PsbX, phospholipid-transporting-ATPase-9, pectin-acetylesterase-8 and E3-ubiquitin-protein-ligase-RHG1A. Further, the WRKY1 and DEAD-box-RH27 were found to be associated with seed weight. Furthermore, the associations of PIF3 and pentatricopeptide-repeat-containing-gene with maturity and seed weight, and aldo-keto-reductase with flowering and maturity were revealed.

CONCLUSION

This study offers insights into the genetic basis of key agronomic traits in rice bean, including flowering, maturity, and seed weight. The identified markers and associated candidate genes provide valuable resources for future exploration and targeted breeding, aiming to enhance the agronomic performance of rice bean cultivars. Notably, this research represents the first transcriptome-wide association study in pulse crop, uncovering the candidate genes for agronomically useful traits.

Genome-wide identification and analyses of ZmAPY genes reveal their roles involved in maize development and abiotic stress responses

Apyrase is a class of enzyme that catalyzes the hydrolysis of nucleoside triphosphates/diphosphates (NTP/NDP), which widely involved in regulation of plant growth and stress responses. However, apyrase family genes in maize have not been identified, and their characteristics and functions are largely unknown. In this study, we identified 16 apyrases (named as ZmAPY1-ZmAPY16) in maize genome, and analyzed their phylogenetic relationships, gene structures, chromosomal distribution, upstream regulatory transcription factors and expression patterns. Analysis of the transcriptome database unveiled tissue-specific and abiotic stress-responsive expression of ZmAPY genes in maize. qPCR analysis further confirmed their responsiveness to drought, heat, and cold stresses. Association analyses indicated that variations of ZmAPY5 and ZmAPY16 may regulate maize agronomic traits and drought responses. Our findings shed light on the molecular characteristics and evolutionary history of maize apyrase genes, highlighting their roles in various biological processes and stress responses. This study forms a basis for further exploration of apyrase functions in maize.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01474-9.

Development of genomic and genetic resources facilitating molecular genetic studies on untapped Myanmar rice germplasms

To counteract the growing population and climate changes, resilient varieties adapted to regional environmental changes are required. Landraces are valuable genetic resources for achieving this goal. Recent advances in sequencing technology have enabled national seed/gene banks to share genomic and genetic information from their collections including landraces, promoting the more efficient utilization of germplasms. In this study, we developed genomic and genetic resources for Myanmar rice germplasms. First, we assembled a diversity panel consisting of 250 accessions representing the genetic diversity of Myanmar indica varieties, including an elite lowland variety, Inn Ma Yebaw (IMY). Our population genetic analyses illustrated that the diversity panel represented Myanmar indica varieties well without any apparent population structure. Second, de novo genome assembly of IMY was conducted. The IMY assembly was constructed by anchoring 2888 contigs, which were assembled from 30× coverage of long reads, into 12 chromosomes. Although many gaps existed in the IMY genome assembly, our quality assessments indicated high completeness in the gene-coding regions, identical to other near-gap-free assemblies. Together with dense variant information, the diversity panel and IMY genome assembly will facilitate deeper genetic research and breeding projects that utilize the untapped Myanmar rice germplasms.

A genome-wide association study identifies genes associated with cuticular wax metabolism in maize

The plant cuticle is essential in plant defense against biotic and abiotic stresses. To systematically elucidate the genetic architecture of maize (Zea mays L.) cuticular wax metabolism, 2 cuticular wax-related traits, the chlorophyll extraction rate (CER) and water loss rate (WLR) of 389 maize inbred lines, were investigated and a genome-wide association study (GWAS) was performed using 1.25 million single nucleotide polymorphisms (SNPs). In total, 57 nonredundant quantitative trait loci (QTL) explaining 5.57% to 15.07% of the phenotypic variation for each QTL were identified. These QTLs contained 183 genes, among which 21 strong candidates were identified based on functional annotations and previous publications. Remarkably, 3 candidate genes that express differentially during cuticle development encode β-ketoacyl-CoA synthase (KCS). While ZmKCS19 was known to be involved in cuticle wax metabolism, ZmKCS12 and ZmKCS3 functions were not reported. The association between ZmKCS12 and WLR was confirmed by resequencing 106 inbred lines, and the variation of WLR was significant between different haplotypes of ZmKCS12. In this study, the loss-of-function mutant of ZmKCS12 exhibited wrinkled leaf morphology, altered wax crystal morphology, and decreased C32 wax monomer levels, causing an increased WLR and sensitivity to drought. These results confirm that ZmKCS12 plays a vital role in maize C32 wax monomer synthesis and is critical for drought tolerance. In sum, through GWAS of 2 cuticular wax-associated traits, this study reveals comprehensively the genetic architecture in maize cuticular wax metabolism and provides a valuable reference for the genetic improvement of stress tolerance in maize.

Dissection of quantitative trait nucleotides and candidate genes associated with agronomic and yield-related traits under drought stress in rapeseed varieties: integration of genome-wide association study and transcriptomic analysis

Introduction

An important strategy to combat yield loss challenge is the development of varieties with increased tolerance to drought to maintain production. Improvement of crop yield under drought stress is critical to global food security.

Methods

In this study, we performed multiomics analysis in a collection of 119 diverse rapeseed (Brassica napus L.) varieties to dissect the genetic control of agronomic traits in two watering regimes [well-watered (WW) and drought stress (DS)] for 3 years. In the DS treatment, irrigation continued till the 50% pod development stage, whereas in the WW condition, it was performed throughout the whole growing season.

Results

The results of the genome-wide association study (GWAS) using 52,157 single-nucleotide polymorphisms (SNPs) revealed 1,281 SNPs associated with traits. Six stable SNPs showed sequence variation for flowering time between the two irrigation conditions across years. Three novel SNPs on chromosome C04 for plant weight were located within drought tolerance-related gene ABCG16, and their pleiotropically effects on seed weight per plant and seed yield were characterized. We identified the C02 peak as a novel signal for flowering time, harboring 52.77% of the associated SNPs. The 288-kbps LD decay distance analysis revealed 2,232 candidate genes (CGs) associated with traits. The CGs BIG1-D, CAND1, DRG3, PUP10, and PUP21 were involved in phytohormone signaling and pollen development with significant effects on seed number, seed weight, and grain yield in drought conditions. By integrating GWAS and RNA-seq, 215 promising CGs were associated with developmental process, reproductive processes, cell wall organization, and response to stress. GWAS and differentially expressed genes (DEGs) of leaf and seed in the yield contrasting accessions identified BIG1-D, CAND1, and DRG3 genes for yield variation.

Discussion

The results of our study provide insights into the genetic control of drought tolerance and the improvement of marker-assisted selection (MAS) for breeding high-yield and drought-tolerant varieties.

Natural variation in Brachypodium distachyon responses to combined abiotic stresses

The demand for agricultural production is becoming more challenging as climate change increases global temperature and the frequency of extreme weather events. This study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of stresses. Heat alone causes the largest amounts of tissue damage while the combination of stresses causes the largest decrease in biomass compared to other treatments. Notably, Bd21-0, the reference line for B. distachyon, did not have robust growth under stress conditions, especially the heat and combined drought and heat treatments. The climate of origin was significantly associated with B. distachyon responses to the assessed stress conditions. Additionally, a GWAS found loci associated with changes in plant height and the amount of damaged tissue under stress. Some of these SNPs were closely located to genes known to be involved in responses to abiotic stresses and point to potential causative loci in plant stress response. However, SNPs found to be significantly associated with a response to heat or drought individually are not also significantly associated with the combination of stresses. This, with the phenotypic data, suggests that the effects of these abiotic stresses are not simply additive, and the responses to the combined stresses differ from drought and heat alone.

Genetic basis of maize stalk strength decoded via linkage and association mapping

Stalk lodging is a severe problem that limits maize production worldwide, although little attention has been given to its genetic basis. Here we measured rind penetrometer resistance (RPR), an effective index for stalk lodging, in a multi-parent population of 1948 recombinant inbred lines (RILs) and an association population of 508 inbred lines (AMP508). Linkage and association mapping identified 53 and 29 single quantitative trait loci (QTLs) and 50 and 19 pairs of epistatic interactions for RPR in the multi-parent population and AMP508 population, respectively. Phenotypic variation explained by all identified epistatic QTLs (up to ~5%) was much less than that explained by all single additive QTLs (up to ~33% in the multi-parent population and ~ 60% in the AMP508 population). Among all detected QTLs, only eight single QTLs explained >10% of phenotypic variation in single RIL populations. Alleles that increased RPR were enriched in tropical/subtropical (TST) groups from the AMP508 population. Based on genome-wide association studies in both populations, we identified 137 candidate genes affecting RPR, which were assigned to multiple biological processes, such as the biosynthesis of cell wall components. Sixty-six candidate genes were cross-validated by multiple methods or populations. Most importantly, 23 candidate genes were upregulated or downregulated in high-RPR lines relative to low-RPR lines, supporting the associations between candidate genes and RPR. These findings reveal the complex nature of the genetic basis underlying RPR and provide loci or candidate genes for developing elite varieties that are resistant to stalk lodging via molecular breeding.

Identification of Novel QTL for Mercury Accumulation in Maize Using an Enlarged SNP Panel

Mercury (Hg) pollution not only poses a threat to the environment but also adversely affects the growth and development of plants, with potential repercussions for animals and humans through bioaccumulation in the food chain. Maize, a crucial source of food, industrial materials, and livestock feed, requires special attention in understanding the genetic factors influencing mercury accumulation. Developing maize varieties with low mercury accumulation is vital for both maize production and human health. In this study, a comprehensive genome-wide association study (GWAS) was conducted using an enlarged SNP panel comprising 1.25 million single nucleotide polymorphisms (SNPs) in 230 maize inbred lines across three environments. The analysis identified 111 significant SNPs within 78 quantitative trait loci (QTL), involving 169 candidate genes under the Q model. Compared to the previous study, the increased marker density and optimized statistical model led to the discovery of 74 additional QTL, demonstrating improved statistical power. Gene ontology (GO) enrichment analysis revealed that most genes participate in arsenate reduction and stress responses. Notably, GRMZM2G440968, which has been reported in previous studies, is associated with the significant SNP chr6.S_155668107 in axis tissue. It encodes a cysteine proteinase inhibitor, implying its potential role in mitigating mercury toxicity by inhibiting cysteine. Haplotype analyses provided further insights, indicating that lines carrying hap3 exhibited the lowest mercury content compared to other haplotypes. In summary, our study significantly enhances the statistical power of GWAS, identifying additional genes related to mercury accumulation and metabolism. These findings offer valuable insights into unraveling the genetic basis of mercury content in maize and contribute to the development of maize varieties with low mercury accumulation.

Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize

Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype-phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.

From concept to reality: Transforming agriculture through innovative rhizosphere engineering for plant health and productivity

The plant rhizosphere is regarded as a microbial hotspot due to a wide array of root exudates. These root exudates comprise diverse organic compounds such as phenolic, polysaccharides, flavonoids, fatty acids, and amino acids that showed chemotactic responses towards microbial communities and mediate significant roles in root colonization. The rhizospheric microbiome is a crucial driver of plant growth and productivity, contributing directly or indirectly by facilitating nutrient acquisition, phytohormone modulation, and phosphate solubilization under normal and stressful conditions. Moreover, these microbial candidates protect plants from pathogen invasion by secreting antimicrobial and volatile organic compounds. To enhance plant fitness and yield, rhizospheric microbes are frequently employed as microbial inoculants. However, recent developments have shifted towards targeted rhizosphere engineering or microbial recruitments as a practical approach to constructing desired plant rhizospheres for specific outcomes. The rhizosphere, composed of plants, microbes, and soil, can be modified in several ways to improve inoculant efficiency. Rhizosphere engineering is achieved through three essential mechanisms: a) plant-mediated modifications involving genetic engineering, transgenics, and gene editing of plants; b) microbe-mediated modifications involving genetic alterations of microbes through upstream or downstream methodologies; and c) soil amendments. These mechanisms shape the rhizospheric microbiome, making plants more productive and resilient under different stress conditions. This review paper comprehensively summarizes the various aspects of rhizosphere engineering and their potential applications in maintaining plant health and achieving optimum agricultural productivity.

Multi-omics-driven advances in the understanding of triacylglycerol biosynthesis in oil seeds

Vegetable oils are rich sources of polyunsaturated fatty acids and energy as well as valuable sources of human food, animal feed, and bioenergy. Triacylglycerols, which are comprised of three fatty acids attached to a glycerol backbone, are the main component of vegetable oils. Here, we review the development and application of multiple-level omics in major oilseeds and emphasize the progress in the analysis of the biological roles of key genes underlying seed oil content and quality in major oilseeds. Finally, we discuss future research directions in functional genomics research based on current omics and oil metabolic engineering strategies that aim to enhance seed oil content and quality, and specific fatty acids components according to either human health needs or industrial requirements.

Physiological Mechanisms Underlying Tassel Symptom Formation in Maize Infected with Sporisorium reilianum

Head smut is a soil-borne fungal disease caused by Sporisorium reilianum that infects maize tassels and ears. This disease poses a tremendous threat to global maize production. A previous study found markedly different and stably heritable tassel symptoms in some maize inbred lines with Sipingtou blood after infection with S. reilianum. In the present study, 55 maize inbred lines with Sipingtou blood were inoculated with S. reilianum and classified into three tassel symptom types (A, B, and C). Three maize inbred lines representing these classes (Huangzao4, Jing7, and Chang7-2, respectively) were used as test materials to investigate the physiological mechanisms of tassel formation in infected plants. Changes in enzyme activity, hormone content, and protein expression were analyzed in all three lines after infection and in control plants. The activities of peroxidase (POD), superoxide dismutase (SOD), and phenylalanine-ammonia-lyase (PAL) were increased in the three typical inbred lines after inoculation. POD and SOD activities showed similar trends between lines, with the increase percentage peaking at the V12 stage (POD: 57.06%, 63.19%, and 70.28% increases in Huangzao4, Jing7, and Chang7-2, respectively; SOD: 27.01%, 29.62%, and 47.07% in Huangzao4, Jing7, and Chang7-2, respectively. These were all higher than in the disease-resistant inbred line Mo17 at the same growth stage); this stage was found to be key in tassel symptom formation. Levels of gibberellic acid (GA3), indole-3-acetic acid (IAA), and abscisic acid (ABA) were also altered in the three typical maize inbred lines after inoculation, with changes in GA3 and IAA contents tightly correlated with tassel symptoms after S. reilianum infection. The differentially expressed proteins A5H8G4, P09233, and Q8VXG7 were associated with changes in enzyme activity, whereas P49353, P13689, and P10979 were associated with changes in hormone contents. Fungal infection caused reactive oxygen species (ROS) and nitric oxide (NO) bursts in the three typical inbred lines. This ROS accumulation caused biofilm disruption and altered host signaling pathways, whereas NO signaling triggered strong secondary metabolic responses in the host and altered the activities of defense-related enzymes. These factors together resulted in the formation of varying tassel symptoms. Thus, interactions between S. reilianum and susceptible maize materials were influenced by a variety of signals, enzymes, hormones, and metabolic cycles, encompassing a very complex regulatory network. This study preliminarily identified the physiological mechanisms leading to differences in tassel symptoms, deepening our understanding of S. reilianum-maize interactions.

Integration of genome-wide association study, linkage analysis, and population transcriptome analysis to reveal the TaFMO1-5B modulating seminal root growth in bread wheat

Bread wheat, one of the keystone crops for global food security, is challenged by climate change and resource shortage. The root system plays a vital role in water and nutrient absorption, making it essential for meeting the growing global demand. Here, using an association-mapping population composed of 406 accessions, we identified QTrl.Rs-5B modulating seminal root development with a genome-wide association study and validated its genetic effects with two F5 segregation populations. Transcriptome-wide association study prioritized TaFMO1-5B, a gene encoding the flavin-containing monooxygenases, as the causal gene for QTrl.Rs-5B, whose expression levels correlate negatively with the phenotyping variations among our population. The lines silenced for TaFMO1-5B consistently showed significantly larger seminal roots in different genetic backgrounds. Additionally, the agriculture traits measured in multiple environments showed that QTrl.Rs-5B also affects yield component traits and plant architecture-related traits, and its favorable haplotype modulates these traits toward that of modern cultivars, suggesting the application potential of QTrl.Rs-5B for wheat breeding. Consistently, the frequency of the favorable haplotype of QTrl.Rs-5B increased with habitat expansion and breeding improvement of bread wheat. In conclusion, our findings identified and demonstrated the effects of QTrl.Rs-5B on seminal root development and illustrated that it is a valuable genetic locus for wheat root improvement.

OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis

The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.

Identification of Multiple Genetic Loci Related to Low-Temperature Tolerance during Germination in Maize (Zea maize L.) through a Genome-Wide Association Study

Low-temperature stress during the germination stage is an important abiotic stress that affects the growth and development of northern spring maize and seriously restricts maize yield and quality. Although some quantitative trait locis (QTLs) related to low-temperature tolerance in maize have been detected, only a few can be commonly detected, and the QTL intervals are large, indicating that low-temperature tolerance is a complex trait that requires more in-depth research. In this study, 296 excellent inbred lines from domestic and foreign origins (America and Europe) were used as the study materials, and a low-coverage resequencing method was employed for genome sequencing. Five phenotypic traits related to low-temperature tolerance were used to assess the genetic diversity of maize through a genome-wide association study (GWAS). A total of 14 SNPs significantly associated with low-temperature tolerance were detected (-log10(P) > 4), and an SNP consistently linked to low-temperature tolerance in the field and indoors during germination was utilized as a marker. This SNP, 14,070, was located on chromosome 5 at position 2,205,723, which explained 4.84-9.68% of the phenotypic variation. The aim of this study was to enrich the genetic theory of low-temperature tolerance in maize and provide support for the innovation of low-temperature tolerance resources and the breeding of new varieties.

GWAS and Meta-QTL Analysis of Yield-Related Ear Traits in Maize

Maize ear traits are an important component of yield, and the genetic basis of ear traits facilitates further yield improvement. In this study, a panel of 580 maize inbred lines were used as the study material, eight ear-related traits were measured through three years of planting, and whole genome sequencing was performed using the maize 40 K breeding chip based on genotyping by targeted sequencing (GBTS) technology. Five models were used to conduct a genome-wide association study (GWAS) on best linear unbiased estimate (BLUE) of ear traits to find the best model. The FarmCPU (Fixed and random model Circulating Probability Unification) model was the best model for this study; a total of 104 significant single nucleotide polymorphisms (SNPs) were detected, and 10 co-location SNPs were detected simultaneously in more than two environments. Through gene function annotation and prediction, a total of nine genes were identified as potentially associated with ear traits. Moreover, a total of 760 quantitative trait loci (QTL) associated with yield-related traits reported in 37 different articles were collected. Using the collected 760 QTL for meta-QTL analysis, a total of 41 MQTL (meta-QTL) associated with yield-related traits were identified, and 19 MQTL detected yield-related ear trait functional genes and candidate genes that have been reported in maize. Five significant SNPs detected by GWAS were located within these MQTL intervals, and another three significant SNPs were close to MQTL (less than 1 Mb). The results provide a theoretical reference for the analysis of the genetic basis of ear-related traits and the improvement of maize yield.

Combination of linkage and association mapping with genomic prediction to infer QTL regions associated with gray leaf spot and northern corn leaf blight resistance in tropical maize

Among the diseases threatening maize production in Africa are gray leaf spot (GLS) caused by Cercospora zeina and northern corn leaf blight (NCLB) caused by Exserohilum turcicum. The two pathogens, which have high genetic diversity, reduce the photosynthesizing ability of susceptible genotypes and, hence, reduce the grain yield. To identify population-based quantitative trait loci (QTLs) for GLS and NCLB resistance, a biparental population of 230 lines derived from the tropical maize parents CML511 and CML546 and an association mapping panel of 239 tropical and sub-tropical inbred lines were phenotyped across multi-environments in western Kenya. Based on 1,264 high-quality polymorphic single-nucleotide polymorphisms (SNPs) in the biparental population, we identified 10 and 18 QTLs, which explained 64.2% and 64.9% of the total phenotypic variance for GLS and NCLB resistance, respectively. A major QTL for GLS, qGLS1_186 accounted for 15.2% of the phenotypic variance, while qNCLB3_50 explained the most phenotypic variance at 8.8% for NCLB resistance. Association mapping with 230,743 markers revealed 11 and 16 SNPs significantly associated with GLS and NCLB resistance, respectively. Several of the SNPs detected in the association panel were co-localized with QTLs identified in the biparental population, suggesting some consistent genomic regions across genetic backgrounds. These would be more relevant to use in field breeding to improve resistance to both diseases. Genomic prediction models trained on the biparental population data yielded average prediction accuracies of 0.66-0.75 for the disease traits when validated in the same population. Applying these prediction models to the association panel produced accuracies of 0.49 and 0.75 for GLS and NCLB, respectively. This research conducted in maize fields relevant to farmers in western Kenya has combined linkage and association mapping to identify new QTLs and confirm previous QTLs for GLS and NCLB resistance. Overall, our findings imply that genetic gain can be improved in maize breeding for resistance to multiple diseases including GLS and NCLB by using genomic selection.

Genome-Wide Association Studies of Biluochun Tea Plant Populations in Dongting Mountain and Comprehensive Identification of Candidate Genes Associated with Core Agronomic Traits by Four Analysis Models

The elite germplasm resources are key to the beautiful appearance and pleasant flavor of Biluochun tea. We collected and measured the agronomic traits of 95 tea plants to reveal the trait diversity and breeding value of Biluochun tea plant populations. The results revealed that the agronomic traits of Biluochun tea plant populations were diverse and had high breeding value. Additionally, we resequenced these tea plant populations to reveal genetic diversity, population structure, and selection pressure. The Biluochun tea plant populations contained two groups and were least affected by natural selection based on the results of population structure and selection pressure. More importantly, four non-synonymous single nucleotide polymorphisms (nsSNPs) and candidate genes associated with (-)-gallocatechin gallate (GCG), (-)-gallocatechin (GC), and caffeine (CAF) were detected using at least two GWAS models. The results will promote the development and application of molecular markers and the utilization of elite germplasm from Biluochun populations.

Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels

KEY MESSAGE

An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance. Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.

Genomic selection to improve husk tightness based on genomic molecular markers in maize

Introduction

The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models.

Methods

An association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods.

Results

The findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness.

Discussion

The determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits.