Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

Cross-species predictive modeling reveals conserved drought responses between maize and sorghum.

Drought tolerance is a highly complex trait controlled by numerous interconnected pathways with substantial variation within and across plant species. This complexity makes it difficult to distill individual genetic loci underlying tolerance, and to identify core or conserved drought-responsive pathways. Here, we collected drought physiology and gene expression datasets across diverse genotypes of the C4 cereals sorghum and maize and searched for signatures defining water-deficit responses. Differential gene expression identified few overlapping drought-associated genes across sorghum genotypes, but using a predictive modeling approach, we found a shared core drought response across development, genotype, and stress severity. Our model had similar robustness when applied to datasets in maize, reflecting a conserved drought response between sorghum and maize. The top predictors are enriched in functions associated with various abiotic stress-responsive pathways as well as core cellular functions. These conserved drought response genes were less likely to contain deleterious mutations than other gene sets, suggesting that core drought-responsive genes are under evolutionary and functional constraints. Our findings support a broad evolutionary conservation of drought responses in C4 grasses regardless of innate stress tolerance, which could have important implications for developing climate resilient cereals.

Identification of miRNA-eQTLs in maize mature leaf by GWAS.

BACKGROUND: MiRNAs play essential roles in plant development and response to biotic and abiotic stresses through interaction with their target genes. The expression level of miRNAs shows great variations among different plant accessions, developmental stages, and tissues. Little is known about the content within the plant genome contributing to the variations in plants. This study aims to identify miRNA expression-related quantitative trait loci (miR-QTLs) in the maize genome. RESULTS: The miRNA expression level from next generation sequencing (NGS) small RNA libraries derived from mature leaf samples of the maize panel (200 maize lines) was estimated as phenotypes, and maize Hapmap v3.2.1 was chosen as the genotype for the genome-wide association study (GWAS). A total of four significant miR-eQTLs were identified contributing to miR156k-5p, miR159a-3p, miR390a-5p and miR396e-5p, and all of them are trans-eQTLs. In addition, a strong positive coexpression of miRNA was found among five miRNA families. Investigation of the effects of these miRNAs on the expression levels and target genes provided evidence that miRNAs control the expression of their targets by suppression and enhancement. CONCLUSIONS: These identified significant miR-eQTLs contribute to the diversity of miRNA expression in the maize penal at the developmental stages of mature leaves in maize, and the positive and negative regulation between miRNA and its target genes has also been uncovered.

Dysregulation of expression correlates with rare-allele burden and fitness loss in maize.

Here we report a multi-tissue gene expression resource that represents the genotypic and phenotypic diversity of modern inbred maize, and includes transcriptomes in an average of 255 lines in seven tissues. We mapped expression quantitative trait loci and characterized the contribution of rare genetic variants to extremes in gene expression. Some of the new mutations that arise in the maize genome can be deleterious; although selection acts to keep deleterious variants rare, their complete removal is impeded by genetic linkage to favourable loci and by finite population size. Modern maize breeders have systematically reduced the effects of this constant mutational pressure through artificial selection and self-fertilization, which have exposed rare recessive variants in elite inbred lines. However, the ongoing effect of these rare alleles on modern inbred maize is unknown. By analysing this gene expression resource and exploiting the extreme diversity and rapid linkage disequilibrium decay of maize, we characterize the effect of rare alleles and evolutionary history on the regulation of expression. Rare alleles are associated with the dysregulation of expression, and we correlate this dysregulation to seed-weight fitness. We find enrichment of ancestral rare variants among expression quantitative trait loci mapped in modern inbred lines, which suggests that historic bottlenecks have shaped regulation. Our results suggest that one path for further genetic improvement in agricultural species lies in purging the rare deleterious variants that have been associated with crop fitness.

Genomic estimation of complex traits reveals ancient maize adaptation to temperate North America.

By 4000 years ago, people had introduced maize to the southwestern United States; full agriculture was established quickly in the lowland deserts but delayed in the temperate highlands for 2000 years. We test if the earliest upland maize was adapted for early flowering, a characteristic of modern temperate maize. We sequenced fifteen 1900-year-old maize cobs from Turkey Pen Shelter in the temperate Southwest. Indirectly validated genomic models predicted that Turkey Pen maize was marginally adapted with respect to flowering, as well as short, tillering, and segregating for yellow kernel color. Temperate adaptation drove modern population differentiation and was selected in situ from ancient standing variation. Validated prediction of polygenic traits improves our understanding of ancient phenotypes and the dynamics of environmental adaptation.

Expanding Maize Genetic Resources with Predomestication Alleles: Maize-Teosinte Introgression Populations.

Teosinte ( subsp. H. H. Iltis & Doebley) has greater genetic diversity than maize inbreds and landraces ( subsp. ). There are, however, limited genetic resources to efficiently evaluate and tap this diversity. To broaden resources for genetic diversity studies in maize, we developed and evaluated 928 near-isogenic introgression lines (NILs) from 10 teosinte accessions in the B73 background. Joint linkage analysis of the 10 introgression populations identified several large-effect quantitative trait loci (QTL) for days to anthesis (DTA), kernel row number (KRN), and 50-kernel weight (Wt50k). Our results confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects enabling future research into the genetic basis of these traits. Additionally, we used a targeted set of NILs to validate the effects of a KRN QTL located on chromosome 2. These introgression populations offer novel tools for QTL discovery and validation as well as a platform for initiating fine mapping.

Genome-wide association of carbon and nitrogen metabolism in the maize nested association mapping population.

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C4 metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.

Fine quantitative trait loci mapping of carbon and nitrogen metabolism enzyme activities and seedling biomass in the maize IBM mapping population.

Understanding the genetic basis of nitrogen and carbon metabolism will accelerate the development of plant varieties with high yield and improved nitrogen use efficiency. A robotized platform was used to measure the activities of 10 enzymes from carbon and nitrogen metabolism in the maize (Zea mays) intermated B73 × Mo17 mapping population, which provides almost a 4-fold increase in genetic map distance compared with conventional mapping populations. Seedling/juvenile biomass was included to identify its genetic factors and relationships with enzyme activities. All 10 enzymes showed heritable variation in activity. There were strong positive correlations between activities of different enzymes, indicating that they are coregulated. Negative correlations were detected between biomass and the activity of six enzymes. In total, 73 significant quantitative trait loci (QTL) were found that influence the activity of these 10 enzymes and eight QTL that influence biomass. While some QTL were shared by different enzymes or biomass, we critically evaluated the probability that this may be fortuitous. All enzyme activity QTL were in trans to the known genomic locations of structural genes, except for single cis-QTL for nitrate reductase, Glu dehydrogenase, and shikimate dehydrogenase; the low frequency and low additive magnitude compared with trans-QTL indicate that cis-regulation is relatively unimportant versus trans-regulation. Two-gene epistatic interactions were identified for eight enzymes and for biomass, with three epistatic QTL being shared by two other traits; however, epistasis explained on average only 2.8% of the genetic variance. Overall, this study identifies more QTL at a higher resolution than previous studies of genetic variation in metabolism.

The genetic architecture of maize flowering time.

Flowering time is a complex trait that controls adaptation of plants to their local environment in the outcrossing species Zea mays (maize). We dissected variation for flowering time with a set of 5000 recombinant inbred lines (maize Nested Association Mapping population, NAM). Nearly a million plants were assayed in eight environments but showed no evidence for any single large-effect quantitative trait loci (QTLs). Instead, we identified evidence for numerous small-effect QTLs shared among families; however, allelic effects differ across founder lines. We identified no individual QTLs at which allelic effects are determined by geographic origin or large effects for epistasis or environmental interactions. Thus, a simple additive model accurately predicts flowering time for maize, in contrast to the genetic architecture observed in the selfing plant species rice and Arabidopsis.