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Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.

A comprehensive understanding of inflorescence development is crucial for crop genetic improvement, as inflorescence meristems give rise to reproductive organs and determine grain yield. However, dissecting inflorescence development at the cellular level has been challenging owing to a lack of specific marker genes to distinguish among cell types, particularly in different types of meristems that are vital for organ formation. In this study, we used spatial enhanced resolution omics-sequencing (Stereo-seq) to construct a precise spatial transcriptome map of the developing maize ear primordium, identifying 12 cell types, including 4 newly defined cell types found mainly in the inflorescence meristem. By extracting the meristem components for detailed clustering, we identified three subtypes of meristem and validated two MADS-box genes that were specifically expressed at the apex of determinate meristems and involved in stem cell determinacy. Furthermore, by integrating single-cell RNA transcriptomes, we identified a series of spatially specific networks and hub genes that may provide new insights into the formation of different tissues. In summary, this study provides a valuable resource for research on cereal inflorescence development, offering new clues for yield improvement.

Tocopherol is an important lipid-soluble antioxidant beneficial for both human health and plant growth. Here, we fine mapped a major QTL-qVE1affecting γ-tocopherol content in maize kernel, positionally cloned and confirmed the underlying gene ZmPORB1(por1), as a protochlorophyllide oxidoreductase. A 13.7 kb insertion reduced the tocopherol and chlorophyll content, and the photosynthetic activity by repressing ZmPORB1expression in embryos of NIL-K22, but did not affect the levels of the tocopherol precursors HGA (homogentisic acid) and PMP (phytyl monophosphate). Furthermore, ZmPORB1is inducible by low oxygen and light, thereby involved in the hypoxia response in developing embryos. Concurrent with natural hypoxia in embryos, the redox state has been changed with NO increasing and H2O2decreasing, which lowered γ-tocopherol content via scavenging reactive nitrogen species. In conclusion, we proposed that the lower light-harvesting chlorophyll content weakened embryo photosynthesis, leading to fewer oxygen supplies and consequently diverse hypoxic responses including an elevated γ-tocopherol consumption. Our findings shed light on the mechanism for fine-tuning endogenous oxygen concentration in the maize embryo through a novel feedback pathway involving the light and low oxygen regulation of ZmPORB1expression and chlorophyll content.

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.

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.

Appropriate root system architecture (RSA) can improve maize yields in densely planted fields, but little is known about its genetic basis in maize. Here we performed root phenotyping of 14,301 field-grown plants from an association mapping panel to study the genetic architecture of maize RSA. A genome-wide association study identified 81 high-confidence RSA-associated candidate genes and revealed that 28 (24.3%) of known root-related genes were selected during maize domestication and improvement. We found that modern maize breeding has selected for a steeply angled root system. Favourable alleles related to steep root system angle have continuously accumulated over the course of modern breeding, and our data pinpoint the root-related genes that have been selected in different breeding eras. We confirm that two auxin-related genes, ZmRSA3.1and ZmRSA3.2, contribute to the regulation of root angle and depth in maize. Our genome-wide identification of RSA-associated genes provides new strategies and genetic resources for breeding maize suitable for high-density planting.

1The genetic architecture of crude fat content in maize kernels were studied by GWAS using six models.2A total of 744 significant QTNs, and 64 novel QTNs were found for the crude fat content in maize kernels.3Four known genes were identified and eight promising candidate genes were successfully screened.

How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades, especially under an unpredicted climate change. Crop breeding, initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding, has played a critical role in securing the global food supply. However, regarding the changing environment and ever-increasing human population, can we breed outstanding crop varieties fast enough to achieve high productivity, good quality, and widespread adaptability? This review outlines the recent achievements in understanding cereal crop breeding, including the current knowledge about crop agronomic traits, newly developed techniques, crop big biological data research, and the possibility of integrating them for intelligence-driven breeding by design, which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops. This review focuses on the major cereal crops, including rice, maize, and wheat, to explain how intelligence-driven breeding by design is becoming a reality.

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7and the tillering-related gene ZmTb1demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/or http://124.220.197.196/.

Rhizoctonia solaniis a widely distributed phytopathogen that causes banded leaf and sheath blight in maize and sheath blight in rice. Here, we identified an F-box protein (ZmFBL41) that confers resistance to banded leaf and sheath blight through a genome-wide association study in maize. Rice overexpressing ZmFBL41showed elevated susceptibility to R. solani. Two amino acid substitutions in this allele prevent its interaction with ZmCAD, which encodes the final enzyme in the monolignol biosynthetic pathway, resulting in the inhibition of ZmCAD degradation and, consequently, the accumulation of lignin and restriction of lesion expansion. Knocking out the ZmCAD-homologous gene OsCAD8Bin rice enhanced susceptibility to R. solani. The results reveal a susceptibility mechanism in which R. solanitargets the host proteasome to modify the secondary metabolism of the plant cell wall for its invasion. More importantly, it provides an opportunity to generate R. solani–resistant varieties of different plant species.

Maize is one of the most important crops globally, and it shows remarkable genetic diversity. Knowledge of this diversity could help in crop improvement; however, gold-standard genomes have been elucidated only for modern temperate varieties. Here, we present a high-quality reference genome (contig N50 of 15.78 megabases) of the maize small-kernel inbred line, which is derived from a tropical landrace. Using haplotype maps derived from B73, Mo17 and SK, we identified 80,614 polymorphic structural variants across 521 diverse lines. Approximately 22% of these variants could not be detected by traditional single-nucleotide-polymorphism-based approaches, and some of them could affect gene expression and trait performance. To illustrate the utility of the diverse SK line, we used it to perform map-based cloning of a major effect quantitative trait locus controlling kernel weight—a key trait selected during maize improvement. The underlying candidate gene ZmBARELY ANY MERISTEM1dprovides a target for increasing crop yields.

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 ZmSPL6from 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.

Background: Maize (Zea maysL.) is at the vanguard facing the upcoming breeding challenges. However, both a super pan-genome for the Zeagenus and a comprehensive genetic variation map for maize breeding are still lacking. Results: Here, we construct an approximately 6.71-Gb pan-Zeagenome that contains around 4.57-Gb non-B73 reference sequences from fragmented de novo assemblies of 721 pan-Zeaindividuals. We annotate a total of 58,944 pan-Zeagenes and find around 44.34% of them are dispensable in the pan-Zeapopulation. Moreover, 255,821 common structural variations are identified and genotyped in a maize association mapping panel. Further analyses reveal gene presence/absence variants and their potential roles during domestication of maize. Combining genetic analyses with multi-omics data, we demonstrate how structural variants are associated with complex agronomic traits. Conclusions: Our results highlight the underexplored role of the pan-Zeagenome and structural variations to further understand domestication of maize and explore their potential utilization in crop improvement.

Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits. We propose an integrative multi-trait breeding strategy via machine learning algorithm, target-oriented prioritization (TOP). Using a large hybrid maize population, we demonstrate that the accuracy for identifying a candidate that is phenotypically closest to an ideotype, or target variety, achieves up to 91%. The strength of TOP is enhanced when omics level traits are included. We show that TOP enables selection of inbreds or hybrids that outperform existing commercial varieties. It improves multiple traits and accurately identifies improved candidates for new varieties, which will greatly influence breeding.

Maize production is threatened by drought stress worldwide. Identification of the genetic components underlying drought tolerance in maize is of great importance. Here we report a genome-wide association study (GWAS) of maize drought tolerance at the seedling stage that identified 83 genetic variants, which were resolved to 42 candidate genes. The peak GWAS signal showed that the natural variation in ZmVPP1, encoding a vacuolar-type H+pyrophosphatase, contributes most significantly to the trait. Further analysis showed that a 366-bp insertion in the promoter, containing three MYB cis elements, confers drought-inducible expression of ZmVPP1 in drought-tolerant genotypes. Transgenic maize with enhanced ZmVPP1 expression exhibits improved drought tolerance that is most likely due to enhanced photosynthetic efficiency and root development. Taken together, this information provides important genetic insights into the natural variation of maize drought tolerance. The identified loci or genes can serve as direct targets for both genetic engineering and selection for maize trait improvement.

A diverse collection of 359 advanced maize (Zea maysL.) inbred lines from the International Maize and Wheat Improvement Center (CIMMYT) and International Institute for Tropical Agriculture (IITA) breeding programs for drought, low N, soil acidity (SA), and pest and disease resistance was genotyped using 1260 single nucleotide polymorphism (SNP) markers. Model‐based population partition, neighbor‐joining (NJ) clustering, and principal component analysis (PCA) based on the genotypic data were employed to classify the lines into subgroups. A subgroup largely consisting of lines developed from La Posta Sequía (LPS) consistently separated from other lines when using different methods based on both SNP and SNP haplotype data. Lines related by pedigree tended to cluster together. Nine main subsets of lines were determined based on pedigree information, environmental adaptation, and breeding scheme. Analysis of molecular variance (AMOVA) revealed that variation within these subsets was much higher than that among subsets. Genetic diversity and linkage disequilibrium (LD) level were tested in the whole panel and within each subset. The potential of the panel for association mapping was tested using 999 SNP markers with minor allelic frequency (MAF) ≥ 0.05 and phenotypic data (grain yield [GY], ears per plant [EPP], and anthesis to silking interval [ASI]). Results show the panel is ideal for association mapping where type I error can be controlled using a mixed linear model (Q+ K). Use of pedigree, heterotic group, and ecological adaptation information together with molecular characterization of this panel presents a valuable genetic resource for stress tolerance breeding in maize.

Association mapping through linkage disequilibrium (LD) analysis is a powerful tool for the dissection of complex agronomic traits and for the identification of alleles that can contribute to the enhancement of a target trait. With the developments of high throughput genotyping techniques and advanced statistical approaches as well as the assembling and characterization of multiple association mapping panels, maize has become the model crop for association analysis. In this paper, we summarize progress in maize association mapping and the impacts of genetic diversity, rate of LD decay, population size, and population structure. We also review the use of candidate genes and gene‐based markers in maize association mapping studies that has generated particularly promising results. In addition, we examine recent developments in genome‐wide genotyping techniques that promise to improve the power of association mapping and significantly refine our understanding of the genetic architecture of complex quantitative traits. The new challenges and opportunities associated with genome‐wide analysis studies are discussed. In conclusion, we review the current and future impacts of association mapping on maize improvement along with the potential benefits for poor people in developing countries who are dependent on this crop for their food security and livelihoods.

Lipids play an important role in plants due to their abundance and their extensive participation in many metabolic processes. Genes involved in lipid metabolism have been extensively studied in Arabidopsisand other plant species. In this study, a total of 1003 maize lipid-related genes were cloned and annotated, including 42 genes with experimental validation, 732 genes with full-length cDNA and protein sequences in public databases and 229 newly cloned genes. Ninety-seven maize lipid-related genes with tissue-preferential expression were discovered by in silicogene expression profiling based on 1984483 maize Expressed Sequence Tags collected from 182 cDNA libraries. Meanwhile, 70 QTL clusters for maize kernel oil were identified, covering 34.5% of the maize genome. Fifty-nine (84%) QTL clusters co-located with at least one lipid-related gene, and the total number of these genes amounted to 147. Interestingly, thirteen genes with kernel-preferential expression profiles fell within QTL clusters for maize kernel oil content. All the maize lipid-related genes identified here may provide good targets for maize kernel oil QTL cloning and thus help us to better understand the molecular mechanism of maize kernel oil accumulation.

The growing demands for food, feed, and bioenergy worldwide will require a 2% per annum increase in global maize (Zea maysL.) production. Maize is one of the most important staple food crops across the developing world as well as being an important feed crop for global livestock production and the emerging biofuel industry. Maize genotypes can range from 0.5 to 5 m standing height at flowering, mature in 60 to 330 d from planting, produce 1 to 4 ears per plant, 10 to 1800 kernels per ear and yield from 0.5 to 23.5 Mg of grain per hectare. Even greater genetic diversity is present in related species yet surprisingly little of the maize‐related biodiversity is present in the current elite breeding pools. Improved methods and tools for germplasm conservation, characterization, and data sharing, as well as for population improvement, gene pool enhancement, and genomics‐aided breeding are urgently needed if increases in maize productivity, particularly in the developing world, are to keep pace with predicted increases in demand. Progress in the private sector, particularly with the development of temperate maize cultivars, is far beyond that of the public sector, particularly regarding tropical maize cultivar development. This article provides an overview of progress at the International Maize and Wheat Improvement Center (CIMMYT) with national partners across the developing world. Particular emphasis is given to issues related to the continued development of elite maize breeding material as global public goods, especially regarding the introgression of new variations from genetic resources and the legal and phytosanitary issues related to international exchange of maize germplasm.

Background: Drought is a major environmental disaster that causes crop yield loss worldwide. Metabolites are involved in various environmental stress responses of plants. However, the genetic control of metabolomes underlying crop environmental stress adaptation remains elusive. Results: Here, we perform non-targeted metabolic profiling of leaves for 385 maize natural inbred lines grown under well-watered as well as drought-stressed conditions. A total of 3890 metabolites are identified and 1035 of these are differentially produced between well-watered and drought-stressed conditions, representing effective indicators of maize drought response and tolerance. Genetic dissections reveal the associations between these metabolites and thousands of single-nucleotide polymorphisms (SNPs), which represented 3415 metabolite quantitative trait loci (mQTLs) and 2589 candidate genes. 78.6% of mQTLs (2684/3415) are novel drought-responsive QTLs. The regulatory variants that control the expression of the candidate genes are revealed by expression QTL (eQTL) analysis of the transcriptomes of leaves from 197 maize natural inbred lines. Integrated metabolic and transcriptomic assays identify dozens of environment-specific hub genes and their gene-metabolite regulatory networks. Comprehensive genetic and molecular studies reveal the roles and mechanisms of two hub genes, Bx12and ZmGLK44, in regulating maize metabolite biosynthesis and drought tolerance. Conclusion: Our studies reveal the first population-level metabolomes in crop drought response and uncover the natural variations and genetic control of these metabolomes underlying crop drought adaptation, demonstrating that multi-omics is a powerful strategy to dissect the genetic mechanisms of crop complex traits.

Background: Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown. Results: Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding. Conclusion: Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performance in terms of prediction precision, model stability, and computing efficiency through a series of benchmark tests. We also assess the factors that are essential to ensure the best performance of genomic selection prediction by taking complex scenarios in crop hybrid breeding into account. LightGBM has been implemented as a toolbox, CropGBM, encompassing multiple novel functions and analytical modules to facilitate genomically designed breeding in crops.

Background: In maize hybrid breeding, complementary pools of parental lines with reshuffled genetic variants are established for superior hybrid performance. To comprehensively decipher the genetics of heterosis, we present a new design of multiple linked F1 populations with 42,840 F1 maize hybrids, generated by crossing a synthetic population of 1428 maternal lines with 30 elite testers from diverse genetic backgrounds and phenotyped for agronomic traits. Results: We show that, although yield heterosis is correlated with the widespread, minor-effect epistatic QTLs, it may be resulted from a few major-effect additive and dominant QTLs in early developmental stages. Floral transition is probably one critical stage for heterosis formation, in which epistatic QTLs are activated by paternal contributions of alleles that counteract the recessive, deleterious maternal alleles. These deleterious alleles, while rare, epistatically repress other favorable QTLs. We demonstrate this with one example, showing that Brachytic2represses the Ubiquitin3locus in the maternal lines; in hybrids, the paternal allele alleviates this repression, which in turn recovers the height of the plant and enhances the weight of the ear. Finally, we propose a molecular design breeding by manipulating key genes underlying the transition from vegetative-to-reproductive growth. Conclusion: The new population design is used to dissect the genetic basis of heterosis which accelerates maize molecular design breeding by diminishing deleterious epistatic interactions.

R2R3-MYBtranscription factors play important role in transcriptional controls during higher plant metabolism and development. In this study, an R2R3-MYBgene was isolated from maize according to an EST, which expressed differentially between a hybrid and the two parents on a cDNA chip. The full-length cDNA, designated by ZmMYBL1(GenBank accession no. AY365033) consists of 1417 nucleotides and contains an open reading frame of 828 bp. The deduced amino acid sequence contained two conserved MYB domains near its N-terminus, a conserved E1 motif and an acidic Ser/Thr rich region toward its C-terminus. Southern blot analysis revealed ZmMYBL1could be a single copy gene belonging to a multi-gene family in the maize genome. Expression analysis showed ZmMYBL1transcripts accumulated in various tissues examined, with strong level in tassel and weak level in leaf. Also it was under-expressed in root, stem, and leaf of hybrid as compared with that of the two parents. ZmMYBL1was mapped on maize chromosome bin7.03 between two SSR markers, bn1g339and umc1865using Yuyu22 recombinant inbred line population. A QTL for root average diameter in maize seedlings was also localized on the corresponding region of chromosome 7 within the interval ZmMYBLI-bnIg1805. A possible role of ZmMYBL1and its relation to maize heterosis were discussed based on these results.R2R3-MYBtranscription factors play important role in transcriptional controls during higher plant metabolism and development. In this study, an R2R3-MYBgene was isolated from maize according to an EST, which expressed differentially between a hybrid and the two parents on a cDNA chip. The full-length cDNA, designated by ZmMYBL1(GenBank accession no. AY365033) consists of 1417 nucleotides and contains an open reading frame of 828 bp. The deduced amino acid sequence contained two conserved MYB domains near its N-terminus, a conserved E1 motif and an acidic Ser/Thr rich region toward its C-terminus. Southern blot analysis revealed ZmMYBL1could be a single copy gene belonging to a multi-gene family in the maize genome. Expression analysis showed ZmMYBL1transcripts accumulated in various tissues examined, with strong level in tassel and weak level in leaf. Also it was under-expressed in root, stem, and leaf of hybrid as compared with that of the two parents. ZmMYBL1was mapped on maize chromosome bin7.03 between two SSR markers, bn1g339and umc1865using Yuyu22 recombinant inbred line population. A QTL for root average diameter in maize seedlings was also localized on the corresponding region of chromosome 7 within the interval ZmMYBLI-bnIg1805. A possible role of ZmMYBL1and its relation to maize heterosis were discussed based on these results.

Background: Gene expression is a key determinant of cellular response. Natural variation in gene expression bridges genetic variation to phenotypic alteration. Identification of the regulatory variants controlling the gene expression in response to drought, a major environmental threat of crop production worldwide, is of great value for drought-tolerant gene identification. Results: A total of 627 RNA-seq analyses are performed for 224 maize accessions which represent a wide genetic diversity under three water regimes; 73,573 eQTLs are detected for about 30,000 expressing genes with high-density genome-wide single nucleotide polymorphisms, reflecting a comprehensive and dynamic genetic architecture of gene expression in response to drought. The regulatory variants controlling the gene expression constitutively or drought-dynamically are unraveled. Focusing on dynamic regulatory variants resolved to genes encoding transcription factors, a drought-responsive network reflecting a hierarchy of transcription factors and their target genes is built. Moreover, 97 genes are prioritized to associate with drought tolerance due to their expression variations through the Mendelian randomization analysis. One of the candidate genes, Abscisic acid 8′-hydroxylase, is verified to play a negative role in plant drought tolerance. Conclusions: This study unravels the effects of genetic variants on gene expression dynamics in drought response which allows us to better understand the role of distal and proximal genetic effects on gene expression and phenotypic plasticity. The prioritized drought-associated genes may serve as direct targets for functional investigation or allelic mining.

Background: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. Results: Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. Conclusions: Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.

After being domesticated from teosinte, cultivated maize (Zea maysssp. mays) spread worldwide and now is one of the most important staple crops. Due to its tremendous phenotypic and genotypic diversity, maize also becomes to be one of the most widely used model plant species for fundamental research, with many important discoveries reported by maize researchers. Here, we provide an overview of the history of maize domestication and key genes controlling major domestication-related traits, review the currently available resources for functional genomics studies in maize, and discuss the functions of most of the maize genes that have been positionally cloned and can be used for crop improvement. Finally, we provide some perspectives on future directions regarding functional genomics research and the breeding of maize and other crops.

Background: DNA methylation can provide a source of heritable information that is sometimes entirely uncoupled from genetic variation. However, the extent of this uncoupling and the roles of DNA methylation in shaping diversity of both gene expression and phenotypes are hotly debated. Here, we investigate the genetic basis and biological functions of DNA methylation at a population scale in maize. Results: We perform targeted DNA methylation profiling for a diverse panel of 263 maize inbred genotypes. All genotypes show similar levels of DNA methylation globally, highlighting the importance of DNA methylation in maize development. Nevertheless, we identify more than 16,000 differentially methylated regions (DMRs) that are distributed across the 10 maize chromosomes. Genome-wide association analysis with high-density genetic markers reveals that over 60% of the DMRs are not tagged by SNPs, suggesting the presence of unique information in DMRs. Strong associations between DMRs and the expression of many genes are identified in both the leaf and kernel tissues, pointing to the biological significance of methylation variation. Association analysis with 986 metabolic traits suggests that DNA methylation is associated with phenotypic variation of 156 traits. There are some traits that only show significant associations with DMRs and not with SNPs. Conclusions: These results suggest that DNA methylation can provide unique information to explain phenotypic variation in maize.