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Background: Maize (Zea mays L. ssp. mays) was domesticated from teosinte (Zea mays ssp. parviglumis) about 9000 years ago in southwestern Mexico and adapted to a range of environments worldwide. Researchers have depicted the maize domestication and adaptation processes over the past two decades, but efforts have been limited either in sample size or genetic diversity. To better understand these processes, we conducted a genome-wide survey of 982 maize inbred lines and 190 teosinte accessions using over 40,000 single-nucleotide polymorphism markers., Results: Population structure, principal component analysis, and phylogenetic trees all confirmed the evolutionary relationship between maize and teosinte, and determined the evolutionary lineage of all species within teosinte. Shared haplotype analysis showed similar levels of ancestral alleles from Zea mays ssp. parviglumis and Zea mays ssp. mexicana in maize. Scans for selection signatures identified 394 domestication sweeps by comparing wild and cultivated maize and 360 adaptation sweeps by comparing tropical and temperate maize. Permutation tests revealed that the public association signals for flowering time were highly enriched in the domestication and adaptation sweeps. Genome-wide association study identified 125 loci significantly associated with flowering-time traits, ten of which identified candidate genes that have undergone selection during maize adaptation., Conclusions: In this study, we characterized the history of maize domestication and adaptation at the population genomic level and identified hundreds of domestication and adaptation sweeps. This study extends the molecular mechanism of maize domestication and adaptation, and provides resources for basic research and genetic improvement in maize., (© 2022. The Author(s).)

Metabolites are the intermediate and final products of metabolism, which play essential roles in plant growth, evolution and adaptation to changing climates. However, it is unclear how evolution contributes to metabolic variation in plants. Here, we investigated the metabolomics data from leaf and seed tissues in maize and rice. Using principal components analysis based on leaf metabolites but not seed metabolites, metabolomics data could be clearly separated for rice Indica and Japonica accessions, while two maize subgroups, temperate and tropical, showed more visible admixture. Rice and maize seed exhibited significant interspecific differences in metabolic variation, while within rice, leaf and seed displayed similar metabolic variations. Among 10 metabolic categories, flavonoids had higher variation in maize than rice, indicating flavonoids are a key constituent of interspecific metabolic divergence. Interestingly, metabolic regulation was also found to be reshaped dramatically from positive to negative correlations, indicative of the differential evolutionary processes in maize and rice. Moreover, perhaps due to this divergence significantly more metabolic interactions were identified in rice than maize. Furthermore, in rice, the leaf was found to harbor much more intense metabolic interactions than the seed. Our result suggests that metabolomes are valuable for tracking evolutionary history, thereby complementing and extending genomic insights concerning which features are responsible for interspecific differentiation in maize and rice., (© 2020 Society for Experimental Biology and John Wiley & Sons Ltd.)

It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC 2 F 7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling-stage leaf, grouting-stage leaf, young kernel and mature kernel) with clear tissue-specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi-tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs., (© 2019 The Authors The Plant Journal © 2019 John Wiley & Sons Ltd.)

Primary metabolism plays a pivotal role in normal plant growth, development and reproduction. As maize is a major crop worldwide, the primary metabolites produced by maize plants are of immense importance from both calorific and nutritional perspectives. Here a genome-wide association study (GWAS) of 61 primary metabolites using a maize association panel containing 513 inbred lines identified 153 significant loci associated with the level of these metabolites in four independent tissues. The genome-wide expression level of 760 genes was also linked with metabolite levels within the same tissue. On average, the genetic variants at each locus or transcriptional variance of each gene identified here were estimated to have a minor effect (4.4-7.8%) on primary metabolic variation. Thirty-six loci or genes were prioritized as being worthy of future investigation, either with regard to functional characterization or for their utility for genetic improvement. This target list includes the well-known opaque 2 (O2) and lkr/sdh genes as well as many less well-characterized genes. During our investigation of these 36 loci, we analyzed the genetic components and variations underlying the trehalose, aspartate and aromatic amino acid pathways, thereby functionally characterizing four genes involved in primary metabolism in maize., (© 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd.)

Maize is the cereal crop with the highest production worldwide, and its oil is a key energy resource. Improving the quantity and quality of maize oil requires a better understanding of lipid metabolism. To predict the function of maize genes involved in lipid biosynthesis, we assembled transcriptomic and lipidomic data sets from leaves of B73 and the high-oil line By804 in two distinct time-series experiments. The integrative analysis based on high-dimensional regularized regression yielded lipid-transcript associations indirectly validated by Gene Ontology and promoter motif enrichment analyses. The co-localization of lipid-transcript associations using the genetic mapping of lipid traits in leaves and seedlings of a B73 × By804 recombinant inbred line population uncovered 323 genes involved in the metabolism of phospholipids, galactolipids, sulfolipids and glycerolipids. The resulting association network further supported the involvement of 50 gene candidates in modulating levels of representatives from multiple acyl-lipid classes. Therefore, the proposed approach provides high-confidence candidates for experimental testing in maize and model plant species., (© 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd.)

Background: Flavonoids constitute a diverse class of secondary metabolites which exhibit potent bioactivities for human health and have been indicated to play an important role in plant development and defense. However, accumulation and variation of flavonoid content in diverse maize lines and the genes responsible for their biosynthesis in this important crop remain largely unknown. In this study, we combine genetic mapping, metabolite profiling and gene regulatory network analysis to further enhance understanding of the maize flavonoid pathway., Results: We repeatedly detected 25 QTL corresponding to 23 distinct flavonoids across different environments or populations. In addition, a total of 39 genes were revealed both by an expression based network analysis and genetic mapping. Finally, the function of three candidate genes, including two UDP-glycosyltransferases (UGT) and an oxygenase which belongs to the flavone synthase super family, was revealed via preliminary molecular functional characterization., Conclusion: We explored the genetic influences on the flavonoid biosynthesis based on integrating the genomic, transcriptomic and metabolomic information which provided a rich source of potential candidate genes. The integrated genomics based genetic mapping strategy is highly efficient for defining the complexity of functional genetic variants and their respective regulatory networks as well as in helping to select candidate genes and allelic variance before embarking on laborious transgenic validations.

MODEM is a comprehensive database of maize multidimensional omics data, including genomic, transcriptomic, metabolic and phenotypic information from the cellular to individual plant level. This initial release contains approximately 1.06 M high quality SNPs for 508 diverse inbred lines obtained by combining variations from RNA sequencing on whole kernels (15 days after pollination) of 368 lines and a 50 K array for all 508 individuals. As all of these data were derived from the same diverse panel of lines, the database also allows various types of genetic mapping (including characterization of phenotypic QTLs, pQTLs; expression QTLs, eQTLs and metabolic QTLs, mQTLs). MODEM is thus designed to promote a better understanding of maize genetic architecture and deep functional annotation of the complex maize genome (and potentially those of other crop plants) and to explore the genotype-phenotype relationships and regulation of maize kernel development at multiple scales, which is also comprehensive for developing novel methods. MODEM is additionally designed to link with other databases to make full use of current resources, and it provides visualization tools for easy browsing. All of the original data and the related mapping results are freely available for easy query and download. This platform also provides helpful tools for general analyses and will be continually updated with additional materials, features and public data related to maize genetics or regulation as they become available.Database URL: (http://modem.hzau.edu.cn)., (© The Author(s) 2016. Published by Oxford University Press.)

The adoption of recombinant inbred line and introgression line populations, as well as the study of association mapping panels, has greatly accelerated our ability to identify the genes underlying plant phenotypic variance. In tandem, the development of metabolomics approaches has greatly enhanced our ability to comprehensively define cellular chemical composition. As a consequence, breeding for chemical composition is being extended beyond our traditional targets of oil and protein to include components such as essential amino acids, vitamins, and antioxidant secondary metabolites with considerable purported consequences for human health. Here, we review the above-mentioned developments paying particular attention to the genetic architecture of metabolic traits as well as updating the perspective for utilizing metabolomics in maize improvement., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Metabolic quantitative trait locus (QTL) studies have allowed us to better understand the genetic architecture underlying naturally occurring plant metabolic variance. Here, we use two recombinant inbred line (RIL) populations to dissect the genetic architecture of natural variation of 155 metabolites measured in the mature maize (Zea mays) kernel. Overall, linkage mapping identified 882 metabolic QTLs in both RIL populations across two environments, with an average of 2.1 QTLs per metabolite. A large number of metabolic QTLs (more than 65%) were identified with moderate effects (r(2) = 2.1%-10%), while a small portion (less than 35%) showed major effects (r(2) > 10%). Epistatic interactions between these identified loci were detected for more than 30% of metabolites (with the proportion of phenotypic variance ranging from 1.6% to 37.8%), implying that genetic epistasis is not negligible in determining metabolic variation. In total, 57 QTLs were validated by our previous genome-wide association study on the same metabolites that provided clues for exploring the underlying genes. A gene regulatory network associated with the flavonoid metabolic pathway was constructed based on the transcriptional variations of 28,769 genes in kernels (15 d after pollination) of 368 maize inbred lines. A large number of genes (34 of 58) in this network overlapped with previously defined genes controlled by maize PERICARP COLOR1, while three of them were identified here within QTL intervals for multiple flavonoids. The deeply characterized RIL populations, elucidation of metabolic phenotypes, and identification of candidate genes lay the foundation for maize quality improvement., (© 2016 American Society of Plant Biologists. All Rights Reserved.)

Deciphering the influence of genetics on primary metabolism in plants will provide insights useful for genetic improvement and enhance our fundamental understanding of plant growth and development. Although maize (Zea mays) is a major crop for food and feed worldwide, the genetic architecture of its primary metabolism is largely unknown. Here, we use high-density linkage mapping to dissect large-scale metabolic traits measured in three different tissues (leaf at seedling stage, leaf at reproductive stage, and kernel at 15 d after pollination [DAP]) of a maize recombinant inbred line population. We identify 297 quantitative trait loci (QTLs) with moderate (86.2% of the mapped QTL, R(2) = 2.4 to 15%) to major effects (13.8% of the mapped QTL, R(2) >15%) for 79 primary metabolites across three tissues. Pairwise epistatic interactions between these identified loci are detected for more than 25.9% metabolites explaining 6.6% of the phenotypic variance on average (ranging between 1.7 and 16.6%), which implies that epistasis may play an important role for some metabolites. Key candidate genes are highlighted and mapped to carbohydrate metabolism, the tricarboxylic acid cycle, and several important amino acid biosynthetic and catabolic pathways, with two of them being further validated using candidate gene association and expression profiling analysis. Our results reveal a metabolite-metabolite-agronomic trait network that, together with the genetic determinants of maize primary metabolism identified herein, promotes efficient utilization of metabolites in maize improvement., (© 2015 American Society of Plant Biologists. All rights reserved.)

The temperate-tropical division of early maize germplasms to different agricultural environments was arguably the greatest adaptation process associated with the success and near ubiquitous importance of global maize production. Deciphering this history is challenging, but new insight has been gained from examining 558 529 single nucleotide polymorphisms, expression data of 28 769 genes, and 662 traits collected from 368 diverse temperate and tropical maize inbred lines in this study. This is a new attempt to systematically exploit the mechanisms of the adaptation process in maize. Our results indicate that divergence between tropical and temperate lines apparently occurred 3400-6700 years ago. Seven hundred and one genomic selection signals and transcriptomic variants including 2700 differentially expressed individual genes and 389 rewired co-expression network genes were identified. These candidate signals were found to be functionally related to stress responses, and most were associated with directionally selected traits, which may have been an advantage under widely varying environmental conditions faced by maize as it was migrated away from its domestication center. Our study also clearly indicates that such stress adaptation could involve evolution of protein-coding sequences as well as transcriptome-level regulatory changes. The latter process may be a more flexible and dynamic way for maize to adapt to environmental changes along its short evolutionary history., (Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.)

Association mapping is a powerful approach for dissecting the genetic architecture of complex quantitative traits using high-density SNP markers in maize. Here, we expanded our association panel size from 368 to 513 inbred lines with 0.5 million high quality SNPs using a two-step data-imputation method which combines identity by descent (IBD) based projection and k-nearest neighbor (KNN) algorithm. Genome-wide association studies (GWAS) were carried out for 17 agronomic traits with a panel of 513 inbred lines applying both mixed linear model (MLM) and a new method, the Anderson-Darling (A-D) test. Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold -log10 (P) >5.74 (α=1). Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold -log10 (P) >7.05 (α=0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test, a few of which were also detected in independent linkage analysis. This study indicates that combining IBD based projection and KNN algorithm is an efficient imputation method for inferring large missing genotype segments. In addition, we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power. The candidate SNPs and associated genes also provide a rich resource for maize genetics and breeding.

Plants produce a variety of metabolites that have a critical role in growth and development. Here we present a comprehensive study of maize metabolism, combining genetic, metabolite and expression profiling methodologies to dissect the genetic basis of metabolic diversity in maize kernels. We quantify 983 metabolite features in 702 maize genotypes planted at multiple locations. We identify 1,459 significant locus-trait associations (P≤1.8 × 10(-6)) across three environments through metabolite-based genome-wide association mapping. Most (58.5%) of the identified loci are supported by expression QTLs, and some (14.7%) are validated through linkage mapping. Re-sequencing and candidate gene association analysis identifies potential causal variants for five candidate genes involved in metabolic traits. Two of these genes were further validated by mutant and transgenic analysis. Metabolite features associated with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.

The tropical maize race Tuxpeño is a well-known race of Mexican dent germplasm which has greatly contributed to the development of tropical and subtropical maize gene pools. In order to investigate how it could be exploited in future maize improvement, a panel of maize germplasm accessions was assembled and characterized using genome-wide Single Nucleotide Polymorphism (SNP) markers. This panel included 321 core accessions of Tuxpeño race from the International Maize and Wheat Improvement Center (CIMMYT) germplasm bank collection, 94 CIMMYT maize lines (CMLs) and 54 U.S. Germplasm Enhancement of Maize (GEM) lines. The panel also included other diverse sources of reference germplasm: 14 U.S. maize landrace accessions, 4 temperate inbred lines from the U.S. and China, and 11 CIMMYT populations (a total of 498 entries with 795 plants). Clustering analyses (CA) based on Modified Rogers Distance (MRD) clearly partitioned all 498 entries into their corresponding groups. No sub clusters were observed within the Tuxpeño core set. Various breeding strategies for using the Tuxpeño core set, based on grouping of the studied germplasm and genetic distance among them, were discussed. In order to facilitate sampling diversity within the Tuxpeño core, a minicore subset of 64 Tuxpeño accessions (20% of its usual size) representing the diversity of the core set was developed, using an approach combining phenotypic and molecular data. Untapped diversity represents further use of the Tuxpeño landrace for maize improvement through the core and/or minicore subset available to the maize community.