Genome-Wide Association and Genomic Prediction Models of Tocochromanols in Fresh Sweet Corn Kernels

Unlocking the genetic basis of vitamin E content in sweet corn kernels: Expanding breeding targets through genome-wide association studies

Tocochromanols, collectively known as Vitamin E, serve as natural lipid-soluble antioxidants that are exclusively obtained through dietary intake in humans. Synthesized by all plants, tocochromanols play an important role in protecting polyunsaturated fatty acids in plant seeds from lipid peroxidation. While the genes involved in tocochromanol biosynthesis have been fully elucidated in Arabidopsis thaliana, Oryza sativa and Zea mays, the genetic basis of tocochromanol accumulation in sweet corn remains poorly understood. This gap is a consequence of limited natural genetic diversity and harvest at immature growth stages. In this study, we conducted comprehensive genome-wide association studies (GWAS) on a sweet corn panel of 295 individuals with a high-density molecular marker set. In total, thirteen quantitative trait loci (QTLs) for individual and derived tocochromanol traits were identified. Our analysis identified novel roles for three genes, ZmCS2, Zmshki1 and ZmB4FMV1, in the regulation of α-tocopherol accumulation in sweet corn kernels. We genetically validated the role of Zmshki1 through the generation of a knock-out line using CRISPR-Cas9 technology. Further gene-based GWAS revealed the function of the canonical tyrosine metabolic enzymes ZmCS2 and Zmhppd1 in the regulation of total tocochromanol content. This comprehensive assessment of the genetic basis for variation in vitamin E content establishes a solid foundation for enhancing vitamin E content not only in sweet corn, but also in other cereal crops.

Genome-wide association study and genotypic variation for the major tocopherol content in rice grain

Rice tocopherols, vitamin E compounds with antioxidant activity, play essential roles in human health. Even though the key genes involved in vitamin E biosynthetic pathways have been identified in plants, the genetic architecture of vitamin E content in rice grain remains unclear. A genome-wide association study (GWAS) on 179 genotypically diverse rice accessions with 34,323 SNP markers was conducted to detect QTLs that define total and α- tocopherol contents in rice grains. Total and α-tocopherol contents had a strong positive correlation and varied greatly across the accessions, ranging from 0.230-31.76 and 0.011-30.83 (μg/g), respectively. A total of 13 QTLs were identified, which were spread across five of the rice chromosomes. Among the 13 QTLs, 11 were considered major with phenotypic variation explained (PVE) greater than 10%. Twelve transcription factor (TF) genes, one microprotein (miP), and a transposon were found to be associated with the QTLs with putative roles in controlling tocopherol contents. Moreover, intracellular transport proteins, ABC transporters, nonaspanins, and SNARE, were identified as associated genes on chromosomes 1 and 8. In the vicinity of seven QTLs, protein kinases were identified as key signaling factors. Haplotype analysis revealed the QTLs qAlph1.1, qTot1.1, qAlph2.1, qAlph6.1, qTot6.1, and qTot8.3 to have significant haplogroups. Quantitative RT-PCR validated the expression direction and magnitude of WRKY39 (Os02g0265200), PIP5Ks (Os08g0450800), and MADS59 (Os06g0347700) in defining the major tocopherol contents. This study provides insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in rice and other cereals.

Multi-layer molecular analysis reveals distinctive metabolomic and transcriptomic profiles of different sweet corn varieties

In plants, sugar metabolism involves a complex interplay of genetic, molecular and environmental factors. To better understand the molecular mechanisms underlying these processes, we utilized a multi-layered approach that integrated transcriptomic and metabolomic datasets generated from multiple different varieties of sweet corn. Through this analysis, we found 2533 genes that were differentially expressed in the immature kernel tissues of sweet corn, including genes involved in transcriptional regulation, sugar metabolism, primary metabolism, and other processes associated with adaptability of sweet corn. We also detected 31 differential metabolites among the three types of sweet corn. Utilizing an integrated approach encompassing transcriptomics and eGWAS, we elucidated the transcriptional regulatory patterns governing these differential metabolites. Specifically, we delved into the transcriptional modulation of malate- and ubiquitin-associated genes across a range of sweet corn varieties, shedding new light on the molecular mechanisms underlying their regulation. This study provides a framework for future research aimed at improving the current understanding of sugar metabolism and regulatory gene networks in sweet corn, which could ultimately lead to the development of novel strategies for crop improvement.

A Summary of Two Decades of QTL and Candidate Genes That Control Seed Tocopherol Contents in Maize (Zea mays L.)

Tocopherols are secondary metabolites synthesized through the shikimate biosynthetic pathway in the plastids of most plants. It is well known that α-Tocopherol (vitamin E) has many health benefits for humans and animals; therefore, it is highly used in human and animal diets. Tocopherols vary considerably in most crop (and plant) species and within cultivars of the same species depending on environmental and growth conditions; tocopherol content is a polygenic, complex traits, and its inheritance is poorly understood. The objective of this review paper was to summarize all identified quantitative trait loci (QTL) that control seed tocopherols and related contents identified in maize (Zea mays) during the past two decades (2002-2022). Candidate genes identified within these QTL regions are also discussed. The QTL described here, and candidate genes identified within these genomic regions could be used in breeding programs to develop maize cultivars with high, beneficial levels of seed tocopherol contents.

Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa)

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Genomic prediction of tocochromanols in exotic-derived maize

Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic-derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.

Assessing the Potential for Genome-Assisted Breeding in Red Perilla Using Quantitative Trait Locus Analysis and Genomic Prediction

Red perilla is an important medicinal plant used in Kampo medicine. The development of elite varieties of this species is urgently required. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on compound phenotypes (i.e., conventional selection) is expensive and time consuming. Here, we propose genomic selection (GS) and marker-assisted selection (MAS), which use marker information for selection, as suitable selection methods for medicinal plants, and we evaluate the effectiveness of these methods in perilla breeding. Three breeding populations generated from crosses between one red and three green perilla genotypes were used to elucidate the genetic mechanisms underlying the production of major medicinal compounds using quantitative trait locus analysis and evaluating the accuracy of genomic prediction (GP). We found that GP had a sufficiently high accuracy for all traits, confirming that GS is an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that using these populations in breeding may simultaneously enhance multiple target traits. This study contributes to research on the genetic mechanisms of the major medicinal compounds of red perilla, as well as the breeding efficiency of this medicinal plant.

Characterization and genetic dissection of maize ear leaf midrib acquired by 3D digital technology

The spatial morphological structure of plant leaves is an important index to evaluate crop ideotype. In this study, we characterized the three-dimensional (3D) data of the ear leaf midrib of maize at the grain-filling stage using the 3D digitization technology and obtained the phenotypic values of 15 traits covering four different dimensions of the ear leaf midrib, of which 13 phenotypic traits were firstly proposed for featuring plant leaf spatial structure. Cluster analysis results showed that the 13 traits could be divided into four groups, Group I, -II, -III and -IV. Group I contains HorizontalLength, OutwardGrowthMeasure, LeafAngle and DeviationTip; Group II contains DeviationAngle, MaxCurvature and CurvaturePos; Group III contains LeafLength and ProjectionArea; Group IV contains TipTop, VerticalHeight, UpwardGrowthMeasure, and CurvatureRatio. To investigate the genetic basis of the ear leaf midrib curve, 13 traits with high repeatability were subjected to genome-wide association study (GWAS) analysis. A total of 828 significantly related SNPs were identified and 1365 candidate genes were annotated. Among these, 29 candidate genes with the highest significant and multi-method validation were regarded as the key findings. In addition, pathway enrichment analysis was performed on the candidate genes of traits to explore the potential genetic mechanism of leaf midrib curve phenotype formation. These results not only contribute to further understanding of maize leaf spatial structure traits but also provide new genetic loci for maize leaf spatial structure to improve the plant type of maize varieties.

Characterization of crtRB1- and vte4-based biofortified sweet corn inbreds for seed vigour and physico-biochemical traits

Sweet corn possessing recessive shrunken2 (sh2) gene is popular worldwide. Traditional sweet corn is poor in vitamin A and vitamin E. Plant breeders during the selection of sweet corn genotypes mainly emphasize on plant architecture and yield. Seed germination and seedling vigour play important role for early establishment in field, thereby increasing yield and income. Here, we analysed a set of 15 sh2-based biofortified sweet corn inbreds with crtRB1 (β-carotene hydroxylase1) and vte4 (γ-tocopherol methyltransferase) genes and three traditional sh2-based sweet corn inbreds for nutritional quality, seed vigour and various physico-biochemical traits. The newly developed inbreds possessed significantly higher provitamin A (proA: 15.60 µg/g) and vitamin E [α-tocopherol (α-T): 20.42 µg/g] than the traditional sweet corn inbreds (proA: 2.51 µg/g, α-T: 11.16 µg/g). The biofortified sweet corn inbreds showed wide variation for germination (80.67-87.33%), vigour index-I (2097.17-2925.28 cm), vigour index-II (134.27-216.19 mg) and electrical conductivity (10.19-13.21 μS cm^-1 g^-1). Wide variation was also observed for dehydrogenase (1.29-1.59 OD g^-1 ml^-1), super oxide dismutase (4.01-9.82 g^-1), peroxidase (11.66-16.47 μM min^-1 g^-1), esterase (22.98-34.76 nM min^-1 g^-1) and α-amylase (5.91-8.15 OD g^-1 ml^-1). Enrichment of proA and vitamin E in sweet corn did not affect seed vigour and physico-biochemical traits. Correlation analysis revealed that electrical conductivity and α-amylase activity was the reliable indicator for assessing seed germination and vigour. The study identified superior biofortified sweet corn genotypes that would contribute to better vigour and establishment in field. This is the first report of analysis of biofortified sweet corn genotypes for seed vigour and physico-biochemical traits.

Marker-assisted pyramiding of γ-tocopherol methyltransferase and glutamate formiminotransferase genes for development of biofortified sweet corn hybrids

Micronutrients, including vitamins, minerals, and other bioactive compounds, have tremendous impacts on human health. Much progress has been made in improving the micronutrient content of inbred lines in various crops through biofortified breeding. However, biofortified breeding still falls short for the rapid generation of high-yielding hybrids rich in multiple micronutrients. Here, we bred multi-biofortified sweet corn hybrids efficiently through marker-assisted selection. Screening by molecular markers for vitamin E and folic acid, we obtained 15 inbred lines carrying favorable alleles (six for vitamin E, nine for folic acid, and three for both). Multiple biofortified corn hybrids were developed through crossing and genetic diversity analysis.

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of ∼1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including 5 that had not been previously associated with maize grain tocochromanols: 4 biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for colocalized eQTL and GWAS association signals. Our multiomics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.

Transcriptome-wide association and prediction for carotenoids and tocochromanols in fresh sweet corn kernels

Sweet corn (Zea mays L.) is consistently one of the most highly consumed vegetables in the United States, providing a valuable opportunity to increase nutrient intake through biofortification. Significant variation for carotenoid (provitamin A, lutein, zeaxanthin) and tocochromanol (vitamin E, antioxidants) levels is present in temperate sweet corn germplasm, yet previous genome-wide association studies (GWAS) of these traits have been limited by low statistical power and mapping resolution. Here, we employed a high-quality transcriptomic dataset collected from fresh sweet corn kernels to conduct transcriptome-wide association studies (TWAS) and transcriptome prediction studies for 39 carotenoid and tocochromanol traits. In agreement with previous GWAS findings, TWAS detected significant associations for four causal genes, β-carotene hydroxylase (crtRB1), lycopene epsilon cyclase (lcyE), γ-tocopherol methyltransferase (vte4), and homogentisate geranylgeranyltransferase (hggt1) on a transcriptome-wide level. Pathway-level analysis revealed additional associations for deoxy-xylulose synthase2 (dxs2), diphosphocytidyl methyl erythritol synthase2 (dmes2), cytidine methyl kinase1 (cmk1), and geranylgeranyl hydrogenase1 (ggh1), of which, dmes2, cmk1, and ggh1 have not previously been identified through maize association studies. Evaluation of prediction models incorporating genome-wide markers and transcriptome-wide abundances revealed a trait-dependent benefit to the inclusion of both genomic and transcriptomic data over solely genomic data, but both transcriptome- and genome-wide datasets outperformed a priori candidate gene-targeted prediction models for most traits. Altogether, this study represents an important step toward understanding the role of regulatory variation in the accumulation of vitamins in fresh sweet corn kernels.

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Applications of Genomic Tools in Plant Breeding: Crop Biofortification

Crop breeding has mainly been focused on increasing productivity, either directly or by decreasing the losses caused by biotic and abiotic stresses (that is, incorporating resistance to diseases and enhancing tolerance to adverse conditions, respectively). Quite the opposite, little attention has been paid to improve the nutritional value of crops. It has not been until recently that crop biofortification has become an objective within breeding programs, through either conventional methods or genetic engineering. There are many steps along this long path, from the initial evaluation of germplasm for the content of nutrients and health-promoting compounds to the development of biofortified varieties, with the available and future genomic tools assisting scientists and breeders in reaching their objectives as well as speeding up the process. This review offers a compendium of the genomic technologies used to explore and create biodiversity, to associate the traits of interest to the genome, and to transfer the genomic regions responsible for the desirable characteristics into potential new varieties. Finally, a glimpse of future perspectives and challenges in this emerging area is offered by taking the present scenario and the slow progress of the regulatory framework as the starting point.

Genome-wide association study suggests an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels

Despite being one of the most consumed vegetables in the United States, the elemental profile of sweet corn (Zea mays L.) is limited in its dietary contributions. To address this through genetic improvement, a genome-wide association study was conducted for the concentrations of 15 elements in fresh kernels of a sweet corn association panel. In concordance with mapping results from mature maize kernels, we detected a probable pleiotropic association of zinc and iron concentrations with nicotianamine synthase5 (nas5), which purportedly encodes an enzyme involved in synthesis of the metal chelator nicotianamine. In addition, a pervasive association signal was identified for cadmium concentration within a recombination suppressed region on chromosome 2. The likely causal gene underlying this signal was heavy metal ATPase3 (hma3), whose counterpart in rice, OsHMA3, mediates vacuolar sequestration of cadmium and zinc in roots, whereby regulating zinc homeostasis and cadmium accumulation in grains. In our association panel, hma3 associated with cadmium but not zinc accumulation in fresh kernels. This finding implies that selection for low cadmium will not affect zinc levels in fresh kernels. Although less resolved association signals were detected for boron, nickel, and calcium, all 15 elements were shown to have moderate predictive abilities via whole-genome prediction. Collectively, these results help enhance our genomics-assisted breeding efforts centered on improving the elemental profile of fresh sweet corn kernels.

The utility of metabolomics as a tool to inform maize biology

With the rise of high-throughput omics tools and the importance of maize and its products as food and bioethanol, maize metabolism has been extensively explored. Modern maize is still rich in genetic and phenotypic variation, yielding a wide range of structurally and functionally diverse metabolites. The maize metabolome is also incredibly dynamic in terms of topology and subcellular compartmentalization. In this review, we examine a broad range of studies that cover recent developments in maize metabolism. Particular attention is given to current methodologies and to the use of metabolomics as a tool to define biosynthetic pathways and address biological questions. We also touch upon the use of metabolomics to understand maize natural variation and evolution, with a special focus on research that has used metabolite-based genome-wide association studies (mGWASs).

Identification of Gene Associated with Sweetness in Corn (Zea mays L.) by Genome-Wide Association Study (GWAS) and Development of a Functional SNP Marker for Predicting Sweet Corn

Sweetness is an economically important eating quality trait for sweet-corn breeding. To investigate the genetic control of the sweetness trait, we conducted a genome-wide association study (GWAS) in an association panel consisting of 250 sweet corn and waxy corn inbred and recombinant inbred lines (RILs), together with the genotypes obtained from the high-density 600K maize genotyping single-nucleotide polymorphism (SNP) array. GWAS results identified 12 significantly associated SNPs on chromosomes 3, 4, 5, and 7. The most associated SNP, AX_91849634, was found on chromosome 3 with a highly significant p-value of ≤1.53 × 10-14. The candidate gene identified within the linkage disequilibrium (LD) of this marker was shrunken2 (Zm00001d044129; sh2), which encodes ADP-glucose pyrophosphorylase (AGPase), a 60 kDa subunit enzyme that affects starch metabolism in the maize endosperm. Several SNP markers specific to variants in sh2 were developed and validated. According to the validation in a set of 81 inbred, RIL, and popular corn varieties, marker Sh2_rs844805326, which was developed on the basis of the SNP at the position 154 of exon 1, was highly efficient in classifying sh2-based sweet corn from other types of corn. This functional marker is extremely useful for marker-assisted breeding in sh2-sweet corn improvement and marketable seed production.

Sweet Corn Research around the World 2015–2020

Modern sweet corn is distinguished from other vegetable corns by the presence of one or more recessive alleles within the maize endosperm starch synthesis pathway. This results in reduced starch content and increased sugar concentration when consumed fresh. Fresh sweet corn originated in the USA and has since been introduced in countries around the World with increasing popularity as a favored vegetable choice. Several reviews have been published recently on endosperm genetics, breeding, and physiology that focus on the basic biology and uses in the US. However, new questions concerning sustainability, environmental care, and climate change, along with the introduction of sweet corn in other countries have produced a variety of new uses and research activities. This review is a summary of the sweet corn research published during the five years preceding 2021.

Location and Variety but Not Phosphate Starter Fertilization Influence the Profiles of Fatty Acids, Carotenoids, and Tocochromanols in Kernels of Modern Corn (Zea mays L.) Hybrids Cultivated in Germany

Phosphate is a limiting plant nutrient and essential for corn growth and development. Thus, the impact of phosphate fertilization, location, and the variety of modern corn (Zea mays L.) hybrids on the profiles of fatty acids, carotenoids, and tocochromanols (vitamin E) was assessed in corn grains. Eight different corn hybrids were grown with (52.9 kg of phosphorus per ha) or without starter fertilizer at three experimental sites in Germany. Location (p < 0.05) and genetics (p < 0.001) but not phosphate fertilization significantly altered the concentrations of individual saturated and unsaturated fatty acids, carotenoids, and tocochromanols. Significant (p < 0.05) interaction effects on the concentrations were mainly observed between the variety and the location. In conclusion, the choice of the corn variety had a more significant impact on the biosynthesis of fatty acids, carotenoids, and tocochromanols than the location or phosphate application on phosphate-sufficient soils.

Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn

Sweet corn is one of the most important vegetables in the United States and Canada. Here, we present a de novo assembly of a sweet corn inbred line Ia453 with the mutated shrunken2-reference allele (Ia453-sh2). This mutation accumulates more sugar and is present in most commercial hybrids developed for the processing and fresh markets. The ten pseudochromosomes cover 92% of the total assembly and 99% of the estimated genome size, with a scaffold N50 of 222.2 Mb. This reference genome completely assembles the large structural variation that created the mutant sh2-R allele. Furthermore, comparative genomics analysis with six field corn genomes highlights differences in single-nucleotide polymorphisms, structural variations, and transposon composition. Phylogenetic analysis of 5,381 diverse maize and teosinte accessions reveals genetic relationships between sweet corn and other types of maize. Our results show evidence for a common origin in northern Mexico for modern sweet corn in the U.S. Finally, population genomic analysis identifies regions of the genome under selection and candidate genes associated with sweet corn traits, such as early flowering, endosperm composition, plant and tassel architecture, and kernel row number. Our study provides a high-quality reference-genome sequence to facilitate comparative genomics, functional studies, and genomic-assisted breeding for sweet corn.

Validated MAGIC and GWAS population mapping reveals the link between vitamin E content and natural variation in chorismate metabolism in tomato

Tocochromanols constitute the different forms of vitamin E (VTE), essential components of the human diet, and display a high membrane protectant activity. By combining interval mapping and genome-wide association studies (GWAS), we unveiled the genetic determinants of tocochromanol accumulation in tomato (Solanum lycopersicum) fruits. To enhance the nutritional value of this highly consumed vegetable, we dissected the natural intraspecific variability of tocochromanols in tomato fruits and genetically engineered their biosynthetic pathway. These analyses allowed the identification of a total of 25 quantitative trait loci interspersed across the genome pinpointing the chorismate-tyrosine pathway as a regulatory hub controlling the supply of the aromatic head group for tocochromanol biosynthesis. To validate the link between the chorismate-tyrosine pathway and VTE, we engineered tomato plants to bypass the pathway at the arogenate branch point. Transgenic tomatoes showed moderate increments in tocopherols (up to approximately 20%) and a massive accumulation of tocotrienols (up to approximately 3400%). Gene expression analyses of these plants reveal a trade-off between VTE and natural variation in chorismate metabolism explained by transcriptional reprogramming of specific structural genes of the pathway. By restoring the accumulation of alpha-tocotrienols (α-t3) in fruits, the plants produced here are of high pharmacological and nutritional interest.

Genome-wide association analysis of natural variation in seed tocochromanols of barley

Tocochromanols (tocols for short), commonly called Vitamin E, are lipid-soluble plant antioxidants vital for regulating lipid peroxidation in chloroplasts and seeds. Barley (Hordeum vulgare L.) seeds contain all eight different isoforms of tocols; however, the extent of natural variation in their composition and their underlying genetic basis is not known. Tocol levels in barley seeds were quantified in diverse H. vulgare panels comprising 297 wild lines from a diversity panel and 160 cultivated spring-type accessions from the mini-core panel representing the genetic diversity of the USDA barley germplasm collection. Significant differences were observed in the concentration of tocols between the two panels. To identify the genes associated with tocols, genome-wide association analysis was conducted with single nucleotide polymorphisms (SNPs) from Illumina arrays for the mini-core panel and genotyping-by-sequencing for the wild barley panel. Forty unique SNPs in the wild barley and 27 SNPs in the mini-core panel were significantly associated with various tocols. Marker-trait associations (MTAs) were identified on chromosomes 1, 6, and 7 for key genes in the tocol biosynthesis pathway, which have also been reported in other studies. Several novel MTAs were identified on chromosomes 2, 3, 4 and 5 and were found to be in proximity to genes involved in the generation of precursor metabolites required for tocol biosynthesis. This study provides a valuable resource for barley breeding programs targeting specific isoforms of seed tocols and for investigating the physiological roles of these metabolites in seed longevity, dormancy, and germination.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Genome-Wide Association Study for Maize Leaf Cuticular Conductance Identifies Candidate Genes Involved in the Regulation of Cuticle Development

The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed at night and under water-limited conditions. Elucidating the genetic architecture of natural variation for leaf cuticular conductance (g_c) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we conducted a genome-wide association study of g_c of adult leaves in a maize inbred association panel that was evaluated in four environments (Maricopa, AZ, and San Diego, CA, in 2016 and 2017). Five genomic regions significantly associated with g_c were resolved to seven plausible candidate genes (ISTL1, two SEC14 homologs, cyclase-associated protein, a CER7 homolog, GDSL lipase, and β-D-XYLOSIDASE 4). These candidates are potentially involved in cuticle biosynthesis, trafficking and deposition of cuticle lipids, cutin polymerization, and cell wall modification. Laser microdissection RNA sequencing revealed that all these candidate genes, with the exception of the CER7 homolog, were expressed in the zone of the expanding adult maize leaf where cuticle maturation occurs. With direct application to genetic improvement, moderately high average predictive abilities were observed for whole-genome prediction of g_c in locations (0.46 and 0.45) and across all environments (0.52). The findings of this study provide novel insights into the genetic control of g_c and have the potential to help breeders more effectively develop drought-tolerant maize for target environments.

We aren't good at picking candidate genes, and it's slowing us down

In order to gain a molecular understanding of the genetic basis for plant traits, we need to be able to identify the underlying gene and the causal allele for genetic loci. This process usually involves a step where a researcher selects likely candidate genes from a list. The process of picking candidate genes is inherently prone to distortion due to human bias, and this is slowing down our research enterprise.

Natural variation for carotenoids in fresh kernels is controlled by uncommon variants in sweet corn

Sweet corn (Zea mays L.) is highly consumed in the United States, but does not make major contributions to the daily intake of carotenoids (provitamin A carotenoids, lutein and zeaxanthin) that would help in the prevention of health complications. A genome-wide association study of seven kernel carotenoids and twelve derivative traits was conducted in a sweet corn inbred line association panel ranging from light to dark yellow in endosperm color to elucidate the genetic basis of carotenoid levels in fresh kernels. In agreement with earlier studies of maize kernels at maturity, we detected an association of β-carotene hydroxylase (crtRB1) with β-carotene concentration and lycopene epsilon cyclase (lcyE) with the ratio of flux between the α- and β-carotene branches in the carotenoid biosynthetic pathway. Additionally, we found that 5% or less of the evaluated inbred lines possessing the shrunken2 (sh2) endosperm mutation had the most favorable lycE allele or crtRB1 haplotype for elevating β-branch carotenoids (β-carotene and zeaxanthin) or β-carotene, respectively. Genomic prediction models with genome-wide markers obtained moderately high predictive abilities for the carotenoid traits, especially lutein, and outperformed models with less markers that targeted candidate genes implicated in the synthesis, retention, and/or genetic control of kernel carotenoids. Taken together, our results constitute an important step toward increasing carotenoids in fresh sweet corn kernels.

High throughput profiling of tocochromanols in leaves and seeds of Arabidopsis and Maize

BACKGROUND

Tocochromanols are a group of lipid-soluble antioxidants produced by all plants and include the tocopherols, which are ubiquitous in the plant kingdom, and the biosynthetically-related compounds tocotrienols, which along with tocopherols commonly occur in seed of monocots. Most tocochromanols have some level of vitamin E activity, with α-tocopherol being the highest, and as such are essential nutrients in the human diet. Tocochromanols are particularly abundant in seeds and are critical for maintaining seed longevity and proper germination and as seed oils are a major component of the human diet, they are an important source of dietary vitamin E. In vegetative tissues, tocochromanols are important components in plant responses to stressful environments and can accumulate to high levels in response to various stresses including high light, heat, and dark.

RESULTS

We report a robust, high throughput extraction and HPLC analysis method to quantify the levels of tocopherols and tocotrienols in leaves and seeds of plants, using Arabidopsis and maize tissues as examples.

CONCLUSION

The described method provides a rapid, high-throughput, cost-effective approach to quantifying the composition and content of tocopherols, and if needed simultaneously tocotrienols, in vegetative tissues and seeds. Optimized extraction methods are described for the two tissue types and have been used to study tocochromanol (vitamin E) natural variation in seed of large Arabidopsis and maize diversity panels, to assess gene function in T-DNA and Mu-tagged populations of Arabidopsis and maize, respectfully, and study the impact of environmental stresses, including high light stress, heat stress, and dark on tocopherols content and composition of vegetative tissue.

Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize

Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and is presumed to function during leaf rolling, thereby reducing water loss during temperature extremes and drought. In this study, epidermal leaf impressions were collected from a genetically and anatomically diverse population of maize inbred lines. Subsequently, convolutional neural networks were employed to measure microscopic, bulliform cell-patterning phenotypes in high-throughput. A genome-wide association study, combined with RNAseq analyses of the bulliform cell ontogenic zone, identified candidate regulatory genes affecting bulliform cell column number and cell width. This study is the first to combine machine learning approaches, transcriptomics, and genomics to study bulliform cell patterning, and the first to utilize natural variation to investigate the genetic architecture of this microscopic trait. In addition, this study provides insight toward the improvement of macroscopic traits such as drought resistance and plant architecture in an agronomically important crop plant.