Two teosintes made modern maize.

The origins of maize were the topic of vigorous debate for nearly a century, but neither the current genetic model nor earlier archaeological models account for the totality of available data, and recent work has highlighted the potential contribution of a wild relative, Zea mays ssp. mexicana. Our population genetic analysis reveals that the origin of modern maize can be traced to an admixture between ancient maize and Zea mays ssp. mexicana in the highlands of Mexico some 4000 years after domestication began. We show that variation in admixture is a key component of maize diversity, both at individual loci and for additive genetic variation underlying agronomic traits. Our results clarify the origin of modern maize and raise new questions about the anthropogenic mechanisms underlying dispersal throughout the Americas.

Complex genetic architecture underlying the plasticity of maize agronomic traits.

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.

A multi-omics integrative network map of maize.

Networks are powerful tools to uncover functional roles of genes in phenotypic variation at a system-wide scale. Here, we constructed a maize network map that contains the genomic, transcriptomic, translatomic and proteomic networks across maize development. This map comprises over 2.8 million edges in more than 1,400 functional subnetworks, demonstrating an extensive network divergence of duplicated genes. We applied this map to identify factors regulating flowering time and identified 2,651 genes enriched in eight subnetworks. We validated the functions of 20 genes, including 18 with previously unknown connections to flowering time in maize. Furthermore, we uncovered a flowering pathway involving histone modification. The multi-omics integrative network map illustrates the principles of how molecular networks connect different types of genes and potential pathways to map a genome-wide functional landscape in maize, which should be applicable in a wide range of species.

Genome sequencing reveals evidence of adaptive variation in the genus Zea.

Maize is a globally valuable commodity and one of the most extensively studied genetic model organisms. However, we know surprisingly little about the extent and potential utility of the genetic variation found in wild relatives of maize. Here, we characterize a high-density genomic variation map from 744 genomes encompassing maize and all wild taxa of the genus Zea, identifying over 70 million single-nucleotide polymorphisms. The variation map reveals evidence of selection within taxa displaying novel adaptations. We focus on adaptive alleles in highland teosinte and temperate maize, highlighting the key role of flowering-time-related pathways in their adaptation. To show the utility of variants in these data, we generate mutant alleles for two flowering-time candidate genes. This work provides an extensive sampling of the genetic diversity of Zea, resolving questions on evolution and identifying adaptive variants for direct use in modern breeding.

High-Throughput CRISPR/Cas9 Mutagenesis Streamlines Trait Gene Identification in Maize.

Maize (Zea mays) is one of the most important crops in the world. However, few agronomically important maize genes have been cloned and used for trait improvement, due to its complex genome and genetic architecture. Here, we integrated multiplexed CRISPR/Cas9-based high-throughput targeted mutagenesis with genetic mapping and genomic approaches to successfully target 743 candidate genes corresponding to traits relevant for agronomy and nutrition. After low-cost barcode-based deep sequencing, 412 edited sequences covering 118 genes were precisely identified from individuals showing clear phenotypic changes. The profiles of the associated gene-editing events were similar to those identified in human cell lines and consequently are predictable using an existing algorithm originally designed for human studies. We observed unexpected but frequent homology-directed repair through endogenous templates that was likely caused by spatial contact between distinct chromosomes. Based on the characterization and interpretation of gene function from several examples, we demonstrate that the integration of forward and reverse genetics via a targeted mutagenesis library promises rapid validation of important agronomic genes for crops with complex genomes. Beyond specific findings, this study also guides further optimization of high-throughput CRISPR experiments in plants.

ZmCOL3, a CCT gene represses flowering in maize by interfering with the circadian clock and activating expression of ZmCCT.

Flowering time is a trait vital to the adaptation of flowering plants to different environments. Here, we report that CCT domain genes play an important role in flowering in maize (Zea mays L.). Among the 53 CCT family genes we identified in maize, 28 were located in flowering time quantitative trait locus regions and 15 were significantly associated with flowering time, based on candidate-gene association mapping analysis. Furthermore, a CCT gene named ZmCOL3 was shown to be a repressor of flowering. Overexpressing ZmCOL3 delayed flowering time by approximately 4 d, in either long-day or short-day conditions. The absence of one cytosine in the ZmCOL3 3'UTR and the presence of a 551 bp fragment in the promoter region are likely the causal polymorphisms contributing to the maize adaptation from tropical to temperate regions. We propose a modified model of the maize photoperiod pathway, wherein ZmCOL3 acts as an inhibitor of flowering either by transactivating transcription of ZmCCT, one of the key genes regulating maize flowering, or by interfering with the circadian clock.

The Conserved and Unique Genetic Architecture of Kernel Size and Weight in Maize and Rice.

Maize (Zea mays) is a major staple crop. Maize kernel size and weight are important contributors to its yield. Here, we measured kernel length, kernel width, kernel thickness, hundred kernel weight, and kernel test weight in 10 recombinant inbred line populations and dissected their genetic architecture using three statistical models. In total, 729 quantitative trait loci (QTLs) were identified, many of which were identified in all three models, including 22 major QTLs that each can explain more than 10% of phenotypic variation. To provide candidate genes for these QTLs, we identified 30 maize genes that are orthologs of 18 rice (Oryza sativa) genes reported to affect rice seed size or weight. Interestingly, 24 of these 30 genes are located in the identified QTLs or within 1 Mb of the significant single-nucleotide polymorphisms. We further confirmed the effects of five genes on maize kernel size/weight in an independent association mapping panel with 540 lines by candidate gene association analysis. Lastly, the function of ZmINCW1, a homolog of rice GRAIN INCOMPLETE FILLING1 that affects seed size and weight, was characterized in detail. ZmINCW1 is close to QTL peaks for kernel size/weight (less than 1 Mb) and contains significant single-nucleotide polymorphisms affecting kernel size/weight in the association panel. Overexpression of this gene can rescue the reduced weight of the Arabidopsis (Arabidopsis thaliana) homozygous mutant line in the AtcwINV2 gene (Arabidopsis ortholog of ZmINCW1). These results indicate that the molecular mechanisms affecting seed development are conserved in maize, rice, and possibly Arabidopsis.

The genetic architecture of amino acids dissection by association and linkage analysis in maize.

Amino acids are both constituents of proteins, providing the essential nutrition for humans and animals, and signalling molecules regulating the growth and development of plants. Most cultivars of maize are deficient in essential amino acids such as lysine and tryptophan. Here, we measured the levels of 17 different total amino acids, and created 48 derived traits in mature kernels from a maize diversity inbred collection and three recombinant inbred line (RIL) populations. By GWAS, 247 and 281 significant loci were identified in two different environments, 5.1 and 4.4 loci for each trait, explaining 7.44% and 7.90% phenotypic variation for each locus in average, respectively. By linkage mapping, 89, 150 and 165 QTLs were identified in B73/By804, Kui3/B77 and Zong3/Yu87-1 RIL populations, 2.0, 2.7 and 2.8 QTLs for each trait, explaining 13.6%, 16.4% and 21.4% phenotypic variation for each QTL in average, respectively. It implies that the genetic architecture of amino acids is relative simple and controlled by limited loci. About 43.2% of the loci identified by GWAS were verified by expression QTL, and 17 loci overlapped with mapped QTLs in the three RIL populations. GRMZM2G015534, GRMZM2G143008 and one QTL were further validated using molecular approaches. The amino acid biosynthetic and catabolic pathways were reconstructed on the basis of candidate genes proposed in this study. Our results provide insights into the genetic basis of amino acid biosynthesis in maize kernels and may facilitate marker-based breeding for quality protein maize.

Distant eQTLs and Non-coding Sequences Play Critical Roles in Regulating Gene Expression and Quantitative Trait Variation in Maize.

A detailed understanding of genetic architecture of mRNA expression by millions of genetic variants is important for studying quantitative trait variation. In this study, we identified 1.25M SNPs with a minor allele frequency greater than 0.05 by combining reduced genome sequencing (GBS), high-density array technologies (600K), and previous deep RNA-sequencing data from 368 diverse inbred lines of maize. The balanced allelic frequencies and distributions in a relatively large and diverse natural panel helped to identify expression quantitative trait loci (eQTLs) associated with more than 18 000 genes (63.4% of tested genes). We found that distant eQTLs were more frequent (∼75% of all eQTLs) across the whole genome. Thirteen novel associated loci affecting maize kernel oil concentration were identified using the new dataset, among which one intergenic locus affected the kernel oil variation by controlling expression of three other known oil-related genes. Altogether, this study provides resources for expanding our understanding of cellular regulatory mechanisms of transcriptome variation and the landscape of functional variants within the maize genome, thereby enhancing the understanding of quantitative variations.

Maize pan-transcriptome provides novel insights into genome complexity and quantitative trait variation.

Gene expression variation largely contributes to phenotypic diversity and constructing pan-transcriptome is considered necessary for species with complex genomes. However, the regulation mechanisms and functional consequences of pan-transcriptome is unexplored systematically. By analyzing RNA-seq data from 368 maize diverse inbred lines, we identified almost one-third nuclear genes under expression presence and absence variation, which tend to play regulatory roles and are likely regulated by distant eQTLs. The ePAV was directly used as "genotype" to perform GWAS for 15 agronomic phenotypes and 526 metabolic traits to efficiently explore the associations between transcriptomic and phenomic variations. Through a modified assembly strategy, 2,355 high-confidence novel sequences with total 1.9 Mb lengths were found absent within reference genome. Ten randomly selected novel sequences were fully validated with genomic PCR, including another two NBS_LRR candidates potentially affect flavonoids and disease-resistance. A simulation analysis suggested that the pan-transcriptome of the maize whole kernel is approaching a maximum value of 63,000 genes, and through developing two test-cross populations and surveying several most important yield traits, the dispensable genes were shown to contribute to heterosis. Novel perspectives and resources to discover maize quantitative trait variations were provided to better understand the kernel regulation networks and to enhance maize breeding.

Genome-wide dissection of the maize ear genetic architecture using multiple populations.

Improvement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear. We used 10 different recombination inbred line (RIL) populations genotyped with high-density markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits. Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor- or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation. The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.

Genomic, Transcriptomic, and Phenomic Variation Reveals the Complex Adaptation of Modern Maize Breeding.

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.