Population-level gene expression can repeatedly link genes to functions in maize.

Transcriptome-wide association studies (TWAS) can provide single gene resolution for candidate genes in plants, complementing genome-wide association studies (GWAS) but efforts in plants have been met with, at best, mixed success. We generated expression data from 693 maize genotypes, measured in a common field experiment, sampled over a 2-h period to minimize diurnal and environmental effects, using full-length RNA-seq to maximize the accurate estimation of transcript abundance. TWAS could identify roughly 10 times as many genes likely to play a role in flowering time regulation as GWAS conducted data from the same experiment. TWAS using mature leaf tissue identified known true-positive flowering time genes known to act in the shoot apical meristem, and trait data from a new environment enabled the identification of additional flowering time genes without the need for new expression data. eQTL analysis of TWAS-tagged genes identified at least one additional known maize flowering time gene through trans-eQTL interactions. Collectively these results suggest the gene expression resource described here can link genes to functions across different plant phenotypes expressed in a range of tissues and scored in different experiments.

A common resequencing-based genetic marker data set for global maize diversity.

Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole-genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high-confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering-time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.

Genome-wide association study reveals marker-trait associations for major agronomic traits in proso millet (Panicum miliaceum L.).

MAIN CONCLUSION: The pilot-scale genome-wide association study in the US proso millet identified twenty marker-trait associations for five morpho-agronomic traits identifying genomic regions for future studies (e.g. molecular breeding and map-based cloning). Proso millet (Panicum miliaceum L.) is an ancient grain recognized for its excellent water-use efficiency and short growing season. It is an indispensable part of the winter wheat-based dryland cropping system in the High Plains of the USA. Its grains are endowed with high nutritional and health-promoting properties, making it increasingly popular in the global market for healthy grains. There is a dearth of genomic resources in proso millet for developing molecular tools to complement conventional breeding for developing high-yielding varieties. Genome-wide association study (GWAS) is a widely used method to dissect the genetics of complex traits. In this pilot study of the first-ever GWAS in the US proso millet, 71 globally diverse genotypes of 109 the US proso millet core collection were evaluated for five major morpho-agronomic traits at two locations in western Nebraska, and GWAS was conducted to identify single nucleotide polymorphisms (SNPs) associated with these traits. Analysis of variance showed that there was a significant difference among the genotypes, and all five traits were also found to be highly correlated with each other. Sequence reads from genotyping-by-sequencing (GBS) were used to identify 11,147 high-quality bi-allelic SNPs. Population structure analysis with those SNPs showed stratification within the core collection. The GWAS identified twenty marker-trait associations (MTAs) for the five traits. Twenty-nine putative candidate genes associated with the five traits were also identified. These genomic regions can be used to develop genetic markers for marker-assisted selection in proso millet breeding.

Rhythmic lipid and gene expression responses to chilling in panicoid grasses.

Chilling stress threatens plant growth and development, particularly affecting membrane fluidity and cellular integrity. Understanding plant membrane responses to chilling stress is important for unraveling the molecular mechanisms of stress tolerance. Whereas core transcriptional responses to chilling stress and stress tolerance are conserved across species, the associated changes in membrane lipids appear to be less conserved, as which lipids are affected by chilling stress varies by species. Here, we investigated changes in gene expression and membrane lipids in response to chilling stress during one 24 hour cycle in chilling-tolerant foxtail millet (Setaria italica), and chilling-sensitive sorghum (Sorghum bicolor), and Urochloa (browntop signal grass, Urochloa fusca, lipids only), leveraging their evolutionary relatedness and differing levels of chilling-stress tolerance. We show that most chilling-induced lipid changes are conserved across the three species, while we observed distinct, time-specific responses in chilling-tolerant foxtail millet, indicating the presence of a finely orchestrated adaptive mechanism. We detected rhythmicity in lipid responses to chilling stress in the three grasses, which were also present in Arabidopsis (Arabidopsis thaliana), suggesting the conservation of rhythmic patterns across species and highlighting the importance of accounting for time of day. When integrating lipid datasets with gene expression profiles, we identified potential candidate genes that showed corresponding transcriptional changes in response to chilling stress, providing insights into the differences in regulatory mechanisms between chilling-sensitive sorghum and chilling-tolerant foxtail millet.

Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping.

The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited "open" versus. "closed" branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.

A co-opted steroid synthesis gene, maintained in sorghum but not maize, is associated with a divergence in leaf wax chemistry.

Virtually all land plants are coated in a cuticle, a waxy polyester that prevents nonstomatal water loss and is important for heat and drought tolerance. Here, we describe a likely genetic basis for a divergence in cuticular wax chemistry between Sorghum bicolor, a drought tolerant crop widely cultivated in hot climates, and its close relative Zea mays (maize). Combining chemical analyses, heterologous expression, and comparative genomics, we reveal that: 1) sorghum and maize leaf waxes are similar at the juvenile stage but, after the juvenile-to-adult transition, sorghum leaf waxes are rich in triterpenoids that are absent from maize; 2) biosynthesis of the majority of sorghum leaf triterpenoids is mediated by a gene that maize and sorghum both inherited from a common ancestor but that is only functionally maintained in sorghum; and 3) sorghum leaf triterpenoids accumulate in a spatial pattern that was previously shown to strengthen the cuticle and decrease water loss at high temperatures. These findings uncover the possibility for resurrection of a cuticular triterpenoid-synthesizing gene in maize that could create a more heat-tolerant water barrier on the plant's leaf surfaces. They also provide a fundamental understanding of sorghum leaf waxes that will inform efforts to divert surface carbon to intracellular storage for bioenergy and bioproduct innovations.

Predicting transcriptional responses to cold stress across plant species.

Although genome-sequence assemblies are available for a growing number of plant species, gene-expression responses to stimuli have been cataloged for only a subset of these species. Many genes show altered transcription patterns in response to abiotic stresses. However, orthologous genes in related species often exhibit different responses to a given stress. Accordingly, data on the regulation of gene expression in one species are not reliable predictors of orthologous gene responses in a related species. Here, we trained a supervised classification model to identify genes that transcriptionally respond to cold stress. A model trained with only features calculated directly from genome assemblies exhibited only modest decreases in performance relative to models trained by using genomic, chromatin, and evolution/diversity features. Models trained with data from one species successfully predicted which genes would respond to cold stress in other related species. Cross-species predictions remained accurate when training was performed in cold-sensitive species and predictions were performed in cold-tolerant species and vice versa. Models trained with data on gene expression in multiple species provided at least equivalent performance to models trained and tested in a single species and outperformed single-species models in cross-species prediction. These results suggest that classifiers trained on stress data from well-studied species may suffice for predicting gene-expression patterns in related, less-studied species with sequenced genomes.

Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms.

Unmanned aerial vehicle (UAV)-based imagery has become widely used to collect time-series agronomic data, which are then incorporated into plant breeding programs to enhance crop improvements. To make efficient analysis possible, in this study, by leveraging an aerial photography dataset for a field trial of 233 different inbred lines from the maize diversity panel, we developed machine learning methods for obtaining automated tassel counts at the plot level. We employed both an object-based counting-by-detection (CBD) approach and a density-based counting-by-regression (CBR) approach. Using an image segmentation method that removes most of the pixels not associated with the plant tassels, the results showed a dramatic improvement in the accuracy of object-based (CBD) detection, with the cross-validation prediction accuracy (r2) peaking at 0.7033 on a detector trained with images with a filter threshold of 90. The CBR approach showed the greatest accuracy when using unfiltered images, with a mean absolute error (MAE) of 7.99. However, when using bootstrapping, images filtered at a threshold of 90 showed a slightly better MAE (8.65) than the unfiltered images (8.90). These methods will allow for accurate estimates of flowering-related traits and help to make breeding decisions for crop improvement.

Sorghum Association Panel whole-genome sequencing establishes cornerstone resource for dissecting genomic diversity.

Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole-genome sequencing (WGS) of 400 sorghum (Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25-72×), enabling the development of a high-density genomic marker set of 43 983 694 variants including single-nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower-density marker sets. WGS identified and scored variants in 5-kb bins where available genotyping-by-sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six Fst peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.

Genetic control of photoprotection and photosystem II operating efficiency in plants.

Photoprotection against excess light via nonphotochemical quenching (NPQ) is indispensable for plant survival. However, slow NPQ relaxation under low light conditions can decrease yield of field-grown crops up to 40%. Using semi-high-throughput assay, we quantified the kinetics of NPQ and photosystem II operating efficiency (ΦPSII) in a replicated field trial of more than 700 maize (Zea mays) genotypes across 2 yr. Parametrized kinetics data were used to conduct genome-wide association studies. For six candidate genes involved in NPQ and ΦPSII kinetics in maize the loss of function alleles of orthologous genes in Arabidopsis (Arabidopsis thaliana) were characterized: two thioredoxin genes, and genes encoding a transporter in the chloroplast envelope, an initiator of chloroplast movement, a putative regulator of cell elongation and stomatal patterning, and a protein involved in plant energy homeostasis. Since maize and Arabidopsis are distantly related, we propose that genes involved in photoprotection and PSII function are conserved across vascular plants. The genes and naturally occurring functional alleles identified here considerably expand the toolbox to achieving a sustainable increase in crop productivity.

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

Oligogalactolipid production during cold challenge is conserved in early diverging lineages.

Severe cold, defined as a damaging cold beyond acclimation temperatures, has unique responses, but the signaling and evolution of these responses are not well understood. Production of oligogalactolipids, which is triggered by cytosolic acidification in Arabidopsis (Arabidopsis thaliana), contributes to survival in severe cold. Here, we investigated oligogalactolipid production in species from bryophytes to angiosperms. Production of oligogalactolipids differed within each clade, suggesting multiple evolutionary origins of severe cold tolerance. We also observed greater oligogalactolipid production in control samples than in temperature-challenged samples of some species. Further examination of representative species revealed a tight association between temperature, damage, and oligogalactolipid production that scaled with the cold tolerance of each species. Based on oligogalactolipid production and transcript changes, multiple angiosperm species share a signal of oligogalactolipid production initially described in Arabidopsis, namely cytosolic acidification. Together, these data suggest that oligogalactolipid production is a severe cold response that originated from an ancestral damage response that remains in many land plant lineages and that cytosolic acidification may be a common signaling mechanism for its activation.

Genome-Wide Characterization of DNase I-Hypersensitive Sites and Cold Response Regulatory Landscapes in Grasses.

Deep sequencing of DNase-I treated chromatin (DNase-seq) can be used to identify DNase I-hypersensitive sites (DHSs) and facilitates genome-scale mining of de novo cis-regulatory DNA elements. Here, we adapted DNase-seq to generate genome-wide maps of DHSs using control and cold-treated leaf, stem, and root tissues of three widely studied grass species: Brachypodium distachyon, foxtail millet (Setaria italica), and sorghum (Sorghum bicolor). Functional validation demonstrated that 12 of 15 DHSs drove reporter gene expression in transiently transgenic B. distachyon protoplasts. DHSs under both normal and cold treatment substantially differed among tissues and species. Intriguingly, the putative DHS-derived transcription factors (TFs) are largely colocated among tissues and species and include 17 ubiquitous motifs covering all grass taxa and all tissues examined in this study. This feature allowed us to reconstruct a regulatory network that responds to cold stress. Ethylene-responsive TFs SHINE3, ERF2, and ERF9 occurred frequently in cold feedback loops in the tissues examined, pointing to their possible roles in the regulatory network. Overall, we provide experimental annotation of 322,713 DHSs and 93 derived cold-response TF binding motifs in multiple grasses, which could serve as a valuable resource for elucidating the transcriptional networks that function in the cold-stress response and other physiological processes.

Multiple highly expressed phosphoenolpyruvate carboxylase genes have divergent enzyme kinetic properties in two C4 grasses.

BACKGROUND AND AIMS: Phosphoenolpyruvate (PEP) carboxylase (PEPC) catalyses the irreversible carboxylation of PEP with bicarbonate to produce oxaloacetate. This reaction powers the carbon-concentrating mechanism (CCM) in plants that perform C4 photosynthesis. This CCM is generally driven by a single PEPC gene product that is highly expressed in the cytosol of mesophyll cells. We found two C4 grasses, Panicum miliaceum and Echinochloa colona, that each have two highly expressed PEPC genes. We characterized the kinetic properties of the two most abundant PEPCs in E. colona and P. miliaceum to better understand how the enzyme's amino acid structure influences its function. METHODS: Coding sequences of the two most abundant PEPC proteins in E. colona and P. miliaceum were synthesized by GenScript and were inserted into bacteria expression plasmids. Point mutations resulting in substitutions at conserved amino acid residues (e.g. N-terminal serine and residue 890) were created via site-directed PCR mutagenesis. The kinetic properties of semi-purified plant PEPCs from Escherichia coli were analysed using membrane-inlet mass spectrometry and a spectrophotometric enzyme-coupled reaction. KEY RESULTS: The two most abundant P. miliaceum PEPCs (PmPPC1 and PmPPC2) have similar sequence identities (>95 %), and as a result had similar kinetic properties. The two most abundant E. colona PEPCs (EcPPC1 and EcPPC2) had identities of ~78 % and had significantly different kinetic properties. The PmPPCs and EcPPCs had different responses to allosteric inhibitors and activators, and substitutions at the conserved N-terminal serine and residue 890 resulted in significantly altered responses to allosteric regulators. CONCLUSIONS: The two, significantly expressed C4Ppc genes in P. miliaceum were probably the result of genomes combining from two closely related C4Panicum species. We found natural variation in PEPC's sensitivity to allosteric inhibition that seems to bypass the conserved 890 residue, suggesting alternative evolutionary pathways for increased malate tolerance and other kinetic properties.

Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean.

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.

Kinetic variation in grass phosphoenolpyruvate carboxylases provides opportunity to enhance C4 photosynthetic efficiency.

The high rates of photosynthesis and the carbon-concentrating mechanism (CCM) in C4 plants are initiated by the enzyme phosphoenolpyruvate (PEP) carboxylase (PEPC). The flow of inorganic carbon into the CCM of C4 plants is driven by PEPC's affinity for bicarbonate (KHCO3 ), which can be rate limiting when atmospheric CO2 availability is restricted due to low stomatal conductance. We hypothesize that natural variation in KHCO3 across C4 plants is driven by specific amino acid substitutions to impact rates of C4 photosynthesis under environments such as drought that restrict stomatal conductance. To test this hypothesis, we measured KHCO3 from 20 C4 grasses to compare kinetic properties with specific amino acid substitutions. There was nearly a twofold range in KHCO3 across these C4 grasses (24.3 ± 1.5 to 46.3 ± 2.4 µm), which significantly impacts modeled rates of C4 photosynthesis. Additionally, molecular engineering of a low-HCO3- affinity PEPC identified key domains that confer variation in KHCO3 . This study advances our understanding of PEPC kinetics and builds the foundation for engineering increased-HCO3- affinity and C4 photosynthetic efficiency in important C4 crops.

Variation in morpho-physiological and metabolic responses to low nitrogen stress across the sorghum association panel.

BACKGROUND: Access to biologically available nitrogen is a key constraint on plant growth in both natural and agricultural settings. Variation in tolerance to nitrogen deficit stress and productivity in nitrogen limited conditions exists both within and between plant species. However, our understanding of changes in different phenotypes under long term low nitrogen stress and their impact on important agronomic traits, such as yield, is still limited. RESULTS: Here we quantified variation in the metabolic, physiological, and morphological responses of a sorghum association panel assembled to represent global genetic diversity to long term, nitrogen deficit stress and the relationship of these responses to grain yield under both conditions. Grain yield exhibits substantial genotype by environment interaction while many other morphological and physiological traits exhibited consistent responses to nitrogen stress across the population. Large scale nontargeted metabolic profiling for a subset of lines in both conditions identified a range of metabolic responses to long term nitrogen deficit stress. Several metabolites were associated with yield under high and low nitrogen conditions. CONCLUSION: Our results highlight that grain yield in sorghum, unlike many morpho-physiological traits, exhibits substantial variability of genotype specific responses to long term low severity nitrogen deficit stress. Metabolic response to long term nitrogen stress shown higher proportion of variability explained by genotype specific responses than did morpho-pysiological traits and several metabolites were correlated with yield. This suggest, that it might be possible to build predictive models using metabolite abundance to estimate which sorghum genotypes will exhibit greater or lesser decreases in yield in response to nitrogen deficit, however further research needs to be done to evaluate such model.

qTeller: a tool for comparative multi-genomic gene expression analysis.

MOTIVATION: Over the last decade, RNA-Seq whole-genome sequencing has become a widely used method for measuring and understanding transcriptome-level changes in gene expression. Since RNA-Seq is relatively inexpensive, it can be used on multiple genomes to evaluate gene expression across many different conditions, tissues and cell types. Although many tools exist to map and compare RNA-Seq at the genomics level, few web-based tools are dedicated to making data generated for individual genomic analysis accessible and reusable at a gene-level scale for comparative analysis between genes, across different genomes and meta-analyses. RESULTS: To address this challenge, we revamped the comparative gene expression tool qTeller to take advantage of the growing number of public RNA-Seq datasets. qTeller allows users to evaluate gene expression data in a defined genomic interval and also perform two-gene comparisons across multiple user-chosen tissues. Though previously unpublished, qTeller has been cited extensively in the scientific literature, demonstrating its importance to researchers. Our new version of qTeller now supports multiple genomes for intergenomic comparisons, and includes capabilities for both mRNA and protein abundance datasets. Other new features include support for additional data formats, modernized interface and back-end database and an optimized framework for adoption by other organisms' databases. AVAILABILITY AND IMPLEMENTATION: The source code for qTeller is open-source and available through GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/qTeller). A maize instance of qTeller is available at the Maize Genetics and Genomics database (MaizeGDB) (https://qteller.maizegdb.org/), where we have mapped over 200 unique datasets from GenBank across 27 maize genomes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quantitative Resistance Loci to Southern Rust Mapped in a Temperate Maize Diversity Panel.

Southern rust is a severe foliar disease of maize (Zea mays) resulting from infection with the obligate biotrophic fungus Puccinia polysora. This disease reduces photosynthetic productivity, which in turn reduces yields, with the greatest yield losses (up to 50%) associated with earlier onset infections. P. polysora urediniospores overwinter only in tropical and subtropical regions but cause outbreaks when environmental conditions favor initial infection. Increased temperatures and humidity during the growing season combined with an increased frequency of moderate winters are likely to increase the frequency of severe southern rust outbreaks in the U.S. Corn Belt. In summer 2020, a severe outbreak of southern rust was observed in eastern Nebraska, United States. We scored a replicated maize association panel planted in Lincoln, NE for disease severity and found that disease incidence and severity showed significant variation among maize genotypes. Genome-wide association studies identified four loci associated with significant quantitative variation in disease severity. These loci were associated with candidate genes with plausible links to quantitative disease resistance. A transcriptome-wide association study identified additional genes associated with disease severity. Together, these results indicate that substantial diversity in resistance to southern rust exists among current temperate-adapted maize germplasm, including several candidate loci that may explain the observed variation in resistance to southern rust.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement.

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.