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.

Variation in Leaf Chlorophyll Concentration in Response to Nitrogen Application Across Maize Hybrids in Contrasting Environments

Leaf chlorophyll concentration was measured for 84 publicly available maize hybrids grown under three nitrogen fertilizer treatments in two contrasting environments in Nebraska. The effect of nitrogen treatment on chlorophyll response was found to be significant (p < 0.05) for both locations. In Scottsbluff, chlorophyll concentrations increased significantly with increasing nitrogen rate, while no significant difference was found between medium and high nitrogen in Lincoln. Within equivalent nitrogen treatments, chlorophyll was more abundant in Lincoln than Scottsbluff for nearly every hybrid. Hybrid response was not consistent between environments, with approximately 11% of variance explained by genotype by environment interaction.

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.

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition

OBJECTIVES

The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data.

DATA DESCRIPTION

This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

A complete telomere-to-telomere assembly of the maize genome

A complete telomere-to-telomere (T2T) finished genome has been the long pursuit of genomic research. Through generating deep coverage ultralong Oxford Nanopore Technology (ONT) and PacBio HiFi reads, we report here a complete genome assembly of maize with each chromosome entirely traversed in a single contig. The 2,178.6 Mb T2T Mo17 genome with a base accuracy of over 99.99% unveiled the structural features of all repetitive regions of the genome. There were several super-long simple-sequence-repeat arrays having consecutive thymine-adenine-guanine (TAG) tri-nucleotide repeats up to 235 kb. The assembly of the entire nucleolar organizer region of the 26.8 Mb array with 2,974 45S rDNA copies revealed the enormously complex patterns of rDNA duplications and transposon insertions. Additionally, complete assemblies of all ten centromeres enabled us to precisely dissect the repeat compositions of both CentC-rich and CentC-poor centromeres. The complete Mo17 genome represents a major step forward in understanding the complexity of the highly recalcitrant repetitive regions of higher plant genomes.

2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project

OBJECTIVES

This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions.

DATA DESCRIPTION

Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

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: (i) assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum, (ii) evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), and (iii) 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 (average R 2 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 (average R 2 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 (average R 2 0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping; whereas extra-weight spiking improves model transferability and extends its utility.

A role for heritable transcriptomic variation in maize adaptation to temperate environments

Background

Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments.

Result

We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments.

Conclusion

Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions.

Supplementary information

The online version contains supplementary material available at 10.1186/s13059-023-02891-3.

The genome of Orychophragmus violaceus provides genomic insights into the evolution of Brassicaceae polyploidization and its distinct traits

Orychophragmus violaceus, referred to as "eryuelan" (February orchid) in China, is an early-flowering ornamental plant. The high oil content and abundance of unsaturated fatty acids in O. violaceus seeds make it a potential high-quality oilseed crop. Here, we generated a whole-genome assembly for O. violaceus using Nanopore and Hi-C sequencing technologies. The assembled genome of O. violaceus was ∼1.3 Gb in size, with 12 pairs of chromosomes. Through investigation of ancestral genome evolution, we determined that the genome of O. violaceus experienced a tetraploidization event from a diploid progenitor with the translocated proto-Calepineae karyotype. Comparisons between the reconstructed subgenomes of O. violaceus identified indicators of subgenome dominance, indicating that subgenomes likely originated via allotetraploidy. O. violaceus was phylogenetically close to the Brassica genus, and tetraploidy in O. violaceus occurred approximately 8.57 million years ago, close in time to the whole-genome triplication of Brassica that likely arose via an intermediate tetraploid lineage. However, the tetraploidization in Orychophragmus was independent of the hexaploidization in Brassica, as evidenced by the results from detailed phylogenetic analyses and comparisons of the break and fusion points of ancestral genomic blocks. Moreover, identification of multi-copy genes regulating the production of high-quality oil highlighted the contributions of both tetraploidization and tandem duplication to functional innovation in O. violaceus. These findings provide novel insights into the polyploidization evolution of plant species and will promote both functional genomic studies and domestication/breeding efforts in O. violaceus.

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.

Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and BLUP models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield - those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

A Transferable Machine Learning Framework for Predicting Transcriptional Responses of Genes Across Species

Leveraging existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in other, less well-studied, species with assembled genomes. However, orthology is not a reliable predictor for the transcriptional responses of genes to stress. Machine learning methods can quantitatively estimate expression patterns and gene functions using known annotations and collections of features describing each gene. In this chapter, we describe a supervised machine learning framework to predict stress-responsive genes across species using only features derived from nucleotide sequences, using the example of cold stress-responsive genes in different Panicoid grass species.

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.

Genome of Paspalum vaginatum and the role of trehalose mediated autophagy in increasing maize biomass

A number of crop wild relatives can tolerate extreme stress to a degree outside the range observed in their domesticated relatives. However, it is unclear whether or how the molecular mechanisms employed by these species can be translated to domesticated crops. Paspalum (Paspalum vaginatum) is a self-incompatible and multiply stress-tolerant wild relative of maize and sorghum. Here, we describe the sequencing and pseudomolecule level assembly of a vegetatively propagated accession of P. vaginatum. Phylogenetic analysis based on 6,151 single-copy syntenic orthologues conserved in 6 related grass species places paspalum as an outgroup of the maize-sorghum clade. In parallel metabolic experiments, paspalum, but neither maize nor sorghum, exhibits a significant increase in trehalose when grown under nutrient-deficit conditions. Inducing trehalose accumulation in maize, imitating the metabolic phenotype of paspalum, results in autophagy dependent increases in biomass accumulation.

Genetic analysis of seed traits in Sorghum bicolor that affect the human gut microbiome

Prebiotic fibers, polyphenols and other molecular components of food crops significantly affect the composition and function of the human gut microbiome and human health. The abundance of these, frequently uncharacterized, microbiome-active components vary within individual crop species. Here, we employ high throughput in vitro fermentations of pre-digested grain using a human microbiome to identify segregating genetic loci in a food crop, sorghum, that alter the composition and function of human gut microbes. Evaluating grain produced by 294 sorghum recombinant inbreds identifies 10 loci in the sorghum genome associated with variation in the abundance of microbial taxa and/or microbial metabolites. Two loci co-localize with sorghum genes regulating the biosynthesis of condensed tannins. We validate that condensed tannins stimulate the growth of microbes associated with these two loci. Our work illustrates the potential for genetic analysis to systematically discover and characterize molecular components of food crops that influence the human gut microbiome.

Diet affects the human gut microbiome, but studies linking crop genetics to seed traits that influence the human gut microbiome are lacking. Here, the authors develop an in vitro microbiome screening method and reveal the association between sorghum genes regulating condensed tannin biosynthesis and human gut microbiome.

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.

SNP discovery in proso millet (Panicum miliaceum L.) using low-pass genome sequencing

Domesticated ~10,000 years ago in northern China, Proso millet (Panicum miliaceum L.) is a climate-resilient and human health-promoting cereal crop. The genome size of this self-pollinated allotetraploid is 923 Mb. Proso millet seeds are an important part of the human diet in many countries. In the USA, its use is restricted to the birdseed and pet food market. Proso millet is witnessing gradual demand in the global human health and wellness food market owing to its health-promoting properties such as low glycemic index and gluten-free. The breeding efforts for developing improved proso millet cultivars are hindered by the dearth of genomic resources available to researchers. The publication of the reference genome and availability of cost-effective NGS methodologies could lead to the identification of high-quality genetic variants, which can be incorporated into breeding pipelines. Here, we report the identification of single-nucleotide polymorphisms (SNPs) by low-pass (1×) genome sequencing of 85 diverse proso millet accessions from 23 different countries. The 2 × 150 bp Illumina paired-end reads generated after sequencing were aligned to the proso millet reference genome. The resulting sequence alignment information was used to call SNPs. We obtained 972,863 bi-allelic SNPs after quality filtering of the raw SNPs. These SNPs were used to assess the population structure and phylogenetic relationships among the accessions. Most of the accessions were found to be highly inbred with heterozygosity ranging between .05 and .20. Principal component analysis (PCA) showed that PC1 (principal component) and PC2 explained 19% of the variability in the population. PCA also clustered all the genotypes into three groups. A neighbor-joining tree clustered the genotypes into four distinct groups exhibiting diverse representation within the population. The SNPs identified in our study could be used for molecular breeding and genetics research (e.g., genetic and association mapping, and population genetics) in proso millet after proper validation.

Association mapping across a multitude of traits collected in diverse environments in maize

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data-18M markers-from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction.

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 F_st 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.

Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize

The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and identified n = 622 plant loci that are linked to the abundance of n = 104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments towards harnessing the full potential of root-associated microbial symbionts in maize production.

The Unique Seed Protein Composition of Quality Protein Popcorn Promotes Growth of Beneficial Bacteria From the Human Gut Microbiome

The effects of fiber, complex carbohydrates, lipids, and small molecules from food matrices on the human gut microbiome have been increasingly studied. Much less is known about how dietary protein can influence the composition and function of the gut microbial community. Here, we used near-isogenic maize lines of conventional popcorn and quality-protein popcorn (QPP) to study the effects of the opaque-2 mutation and associated quality-protein modifiers on the human gut microbiome. Opaque-2 blocks the synthesis of major maize seed proteins (α-zeins), resulting in a compensatory synthesis of new seed proteins that are nutritionally beneficial with substantially higher levels of the essential amino acids lysine and tryptophan. We show that QPP lines stimulate greater amounts of butyrate production by human gut microbiomes in in vitro fermentation of popped and digested corn from parental and QPP hybrids. In human gut microbiomes derived from diverse individuals, bacterial taxa belonging to the butyrate-producing family Lachnospiraceae, including the genera Coprococcus and Roseburia were consistently increased when fermenting QPP vs. parental popcorn lines. We conducted molecular complementation to further demonstrate that lysine-enriched seed protein can stimulate growth and butyrate production by microbes through distinct pathways. Our data show that organisms such as Coprococcus can utilize lysine and that other gut microbes, such as Roseburia spp., instead, utilize fructoselysine produced during thermal processing (popping) of popcorn. Thus, the combination of seed composition in QPP and interaction of protein adducts with carbohydrates during thermal processing can stimulate the growth of health-promoting, butyrate-producing organisms in the human gut microbiome through multiple pathways.

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.

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

Experimental Design for Controlled Environment High-Throughput Plant Phenotyping

It is essential that the scientific community develop and deploy accurate and high-throughput techniques to capture factors that influence plant phenotypes if we are to meet the projected demands for food and energy. In recognition of this fact, multiple research institutions have invested in automated high-throughput plant phenotyping (HTPP) systems designed for use in controlled environments. These systems can generate large amounts of data in relatively short periods of time, potentially allowing researchers to gain insights about phenotypic responses to environmental, biological, and management factors. Reliable inferences about these factors depends on the use of proper experimental design when planning phenotypic studies in order to avoid issues such as lack of power and confounding. In this chapter, the topic of experimental design will be discussed, from basic principles to examples specific to controlled environment plant phenotyping. Examples will be provided based on the package agricolae in the R statistical language.

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.

Tandem duplicate expression patterns are conserved between maize haplotypes of the α-zein gene family

Tandem duplication gives rise to copy number variation and subsequent functional novelty among genes as well as diversity between individuals in a species. Functional novelty can result from either divergence in coding sequence or divergence in patterns of gene transcriptional regulation. Here, we investigate conservation and divergence of both gene sequence and gene regulation between the copies of the α-zein gene family in maize inbreds B73 and W22. We used RNA-seq data generated from developing, self-pollinated kernels at three developmental stages timed to coincide with early and peak zein expression. The reference genome annotations for B73 and W22 were modified to ensure accurate inclusion of their respective α-zein gene models to accurately assess copy-specific expression. Expression analysis indicated that although the total expression of α-zeins is higher in W22, the pattern of expression in both lines is conserved. Additional analysis of publicly available RNA-seq data from a diverse population of maize inbreds also demonstrates variation in absolute expression, but conservation of expression patterns across a wide range of maize genotypes and α-zein haplotypes.

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.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Maize Tassel Detection From UAV Imagery Using Deep Learning

The timing of flowering plays a critical role in determining the productivity of agricultural crops. If the crops flower too early, the crop would mature before the end of the growing season, losing the opportunity to capture and use large amounts of light energy. If the crops flower too late, the crop may be killed by the change of seasons before it is ready to harvest. Maize flowering is one of the most important periods where even small amounts of stress can significantly alter yield. In this work, we developed and compared two methods for automatic tassel detection based on the imagery collected from an unmanned aerial vehicle, using deep learning models. The first approach was a customized framework for tassel detection based on convolutional neural network (TD-CNN). The other method was a state-of-the-art object detection technique of the faster region-based CNN (Faster R-CNN), serving as baseline detection accuracy. The evaluation criteria for tassel detection were customized to correctly reflect the needs of tassel detection in an agricultural setting. Although detecting thin tassels in the aerial imagery is challenging, our results showed promising accuracy: the TD-CNN had an F1 score of 95.9% and the Faster R-CNN had 97.9% F1 score. More CNN-based model structures can be investigated in the future for improved accuracy, speed, and generalizability on aerial-based tassel detection.

Meta-Analysis Identifies Pleiotropic Loci Controlling Phenotypic Trade-offs in Sorghum

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome wide association studies conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

Rhizosphere Microbiomes in a Historical Maize-Soybean Rotation System Respond to Host Species and Nitrogen Fertilization at the Genus and Subgenus Levels

Root-associated microbes are key players in plant health, disease resistance, and nitrogen (N) use efficiency. It remains largely unclear how the interplay of biological and environmental factors affects rhizobiome dynamics in agricultural systems. In this study, we quantified the composition of rhizosphere and bulk soil microbial communities associated with maize (Zea mays L.) and soybean (Glycine max L.) in a long-term crop rotation study under conventional fertilization and low-N regimes. Over two growing seasons, we evaluated the effects of environmental conditions and several treatment factors on the abundance of rhizosphere- and soil-colonizing microbial taxa. Time of sampling, host plant species, and N fertilization had major effects on microbiomes, while no effect of crop rotation was observed. Using variance partitioning as well as 16S sequence information, we further defined a set of 82 microbial genera and functional taxonomic groups at the subgenus level that show distinct responses to treatment factors. We identified taxa that are highly specific to either maize or soybean rhizospheres, as well as taxa that are sensitive to N fertilization in plant rhizospheres and bulk soil. This study provides insights to harness the full potential of soil microbes in maize and soybean agricultural systems through plant breeding and field management. IMPORTANCE Plant roots are colonized by large numbers of microbes, some of which may help the plant acquire nutrients and fight diseases. Our study contributes to a better understanding of root-colonizing microbes in the widespread and economically important maize-soybean crop rotation system. The long-term goal of this research is to optimize crop plant varieties and field management to create the best possible conditions for beneficial plant-microbe interactions to occur. These beneficial microbes may be harnessed to sustainably reduce dependency on pesticides and industrial fertilizer. We identify groups of microbes specific to the maize or to the soybean host and microbes that are sensitive to nitrogen fertilization. These microbes represent candidates that may be influenced through plant breeding or field management, and future research will be directed toward elucidating their roles in plant health and nitrogen usage.

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

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 (K_HCO3 ), which can be rate limiting when atmospheric CO₂ availability is restricted due to low stomatal conductance. We hypothesize that natural variation in K_HCO3 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 K_HCO3 from 20 C₄ grasses to compare kinetic properties with specific amino acid substitutions. There was nearly a twofold range in K_HCO3 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 K_HCO3 . 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.

DCT4-A New Member of the Dicarboxylate Transporter Family in C4 Grasses

Malate transport shuttles atmospheric carbon into the Calvin–Benson cycle during NADP-ME C₄ photosynthesis. Previous characterizations of several plant dicarboxylate transporters (DCT) showed that they efficiently exchange malate across membranes. Here, we identify and characterize a previously unknown member of the DCT family, DCT4, in Sorghum bicolor. We show that SbDCT4 exchanges malate across membranes and its expression pattern is consistent with a role in malate transport during C₄ photosynthesis. SbDCT4 is not syntenic to the characterized photosynthetic gene ZmDCT2, and an ortholog is not detectable in the maize reference genome. We found that the expression patterns of DCT family genes in the leaves of Zea mays, and S. bicolor varied by cell type. Our results suggest that subfunctionalization, of members of the DCT family, for the transport of malate into the bundle sheath plastids, occurred during the process of independent recurrent evolution of C₄ photosynthesis in grasses of the PACMAD clade. We also show that this subfunctionalization is lineage independent. Our results challenge the dogma that key C₄ genes must be orthologues of one another among C₄ species, and shed new light on the evolution of C₄ photosynthesis.

Genomic resources in plant breeding for sustainable agriculture

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

72-h diurnal RNA-seq analysis of fully expanded third leaves from maize, sorghum, and foxtail millet at 3-h resolution

OBJECTIVES

The purpose of this data set is to capture the complete diurnal (i.e., daily) transcriptome of fully expanded third leaves from the C4 panacoid grasses sorghum (Sorghum bicolor), maize (Zea mays), and foxtail millet (Setaria italica) with RNA-seq transcriptome profiling. These data are the cornerstone of a larger project that examined the conservation and divergence of gene expression networks within these crop plants. This data set focuses on temporal changes in gene expression to identify the network architecture responsible for daily regulation of plant growth and metabolic activities. The power of this data set is fine temporal resolution combined with continuous sampling over multiple days.

DATA DESCRIPTION

The data set is 72 individual RNA-seq samples representing 24 time course samples each for sorghum, maize, and foxtail millet plants cultivated in a growth chamber under equal intervals of light and darkness. The 24 samples are separated by 3-h intervals so that the data set is a fine scale 72-h analysis of gene expression in the leaves of each plant type. FASTQ files from Illumina sequencing are available at the National Center for Biotechnology Information Sequence Read Archive.

Genome-Wide DNA Polymorphism Analysis and Molecular Marker Development for the Setaria italica Variety "SSR41" and Positional Cloning of the Setaria White Leaf Sheath Gene SiWLS1

Genome-wide DNA polymorphism analysis and molecular marker development are important for forward genetics research and DNA marker-assisted breeding. As an ideal model system for Panicoideae grasses and an important minor crop in East Asia, foxtail millet (Setaria italica) has a high-quality reference genome as well as large mutant libraries based on the "Yugu1" variety. However, there is still a lack of genetic and mutation mapping tools available for forward genetics research on S. italica. Here, we screened another S. italica genotype, "SSR41", which is morphologically similar to, and readily cross-pollinates with, "Yugu1". High-throughput resequencing of "SSR41" identified 1,102,064 reliable single nucleotide polymorphisms (SNPs) and 196,782 insertions/deletions (InDels) between the two genotypes, indicating that these two genotypes have high genetic diversity. Of the 8,361 high-quality InDels longer than 20 bp that were developed as molecular markers, 180 were validated with 91.5% accuracy. We used "SSR41" and these developed molecular markers to map the white leaf sheath gene SiWLS1. Further analyses showed that SiWLS1 encodes a chloroplast-localized protein that is involved in the regulation of chloroplast development in bundle sheath cells in the leaf sheath in S. italica and is related to sensitivity to heavy metals. Our study provides the methodology and an important resource for forward genetics research on Setaria.

Voxel carving-based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency

Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and light interception efficiency of agricultural crop canopies will be essential to meeting the growing food needs. Canopy architecture is inherently three-dimensional (3D), but many approaches to measuring canopy architecture component traits treat the canopy as a two-dimensional (2D) structure to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos. Our approach builds on the voxel carving algorithm to allow for fully automatic reconstruction of hundreds of plants. It was employed to generate 3D reconstructions of individual plants within a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of known and previously unreported loci controlling light interception efficiency in sorghum. The approach is generalizable and scalable, and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. We also propose a set of best practices to increase 3D reconstructions' accuracy.

Genes and gene models, an important distinction

Genome sequencing has fundamentally changed how plant biologists think about genes. All or nearly all genes can ultimately be associated with a gene model. However, many gene models appear to play little or no role in the traits of an organism. A range of structural, molecular, population and evolutionary features all show a separation between genes with known phenotypes and the overall set of annotated gene models. These different features could be combined to develop models to distinguish the genes that determine the traits of plants from the subset gene other annotated gene models which are unlikely to play a role in doing so. Efforts to identify the subset of annotated gene models likely involved in specifying the characteristics of plants would help aid a wide range of researchers.

Increased Power and Accuracy of Causal Locus Identification in Time Series Genome-wide Association in Sorghum

The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track these phenotypic changes. Identifying the genetic loci regulating differences in the pattern of phenotypic change remains challenging. This study used functional principal component analysis (FPCA) to achieve this aim. Time series phenotype data were collected from a sorghum (Sorghum bicolor) diversity panel using a number of technologies including conventional color photography and hyperspectral imaging. This imaging lasted for 37 d and centered on reproductive transition. A new higher density marker set was generated for the same population. Several genes known to control trait variation in sorghum have been previously cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotypes. Partitioning was consistent with the known molecular function of the individual cloned genes. These data demonstrate that FPCA-based genome-wide association studies can enable robust time series mapping analyses in a wide range of contexts. Moreover, time series analysis can increase the accuracy and power of quantitative genetic analyses.

Leaf Angle eXtractor: A high-throughput image processing framework for leaf angle measurements in maize and sorghum

PREMISE

Maize yields have significantly increased over the past half-century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment.

METHODS

High-throughput time series image data from water-deprived maize (Zea mays subsp. mays) and sorghum (Sorghum bicolor) were obtained using battery-powered time-lapse cameras. A MATLAB-based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions.

RESULTS

Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period.

DISCUSSION

Automating leaf angle measurements using LAX makes it feasible to perform large-scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations.

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.

A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis

High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package "implant" in R for both robust feature extraction and functional data analysis. For image processing, the "implant" package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random-forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated "gold standard" GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

Interspecific analysis of diurnal gene regulation in panicoid grasses identifies known and novel regulatory motifs

Background

The circadian clock drives endogenous 24-h rhythms that allow organisms to adapt and prepare for predictable and repeated changes in their environment throughout the day-night (diurnal) cycle. Many components of the circadian clock in Arabidopsis thaliana have been functionally characterized, but comparatively little is known about circadian clocks in grass species including major crops like maize and sorghum.

Results

Comparative research based on protein homology and diurnal gene expression patterns suggests the function of some predicted clock components in grasses is conserved with their Arabidopsis counterparts, while others have diverged in function. Our analysis of diurnal gene expression in three panicoid grasses sorghum, maize, and foxtail millet revealed conserved and divergent evolution of expression for core circadian clock genes and for the overall transcriptome. We find that several classes of core circadian clock genes in these grasses differ in copy number compared to Arabidopsis, but mostly exhibit conservation of both protein sequence and diurnal expression pattern with the notable exception of maize paralogous genes. We predict conserved cis-regulatory motifs shared between maize, sorghum, and foxtail millet through identification of diurnal co-expression clusters for a subset of 27,196 orthologous syntenic genes. In this analysis, a Cochran–Mantel–Haenszel based method to control for background variation identified significant enrichment for both expected and novel 6–8 nucleotide motifs in the promoter regions of genes with shared diurnal regulation predicted to function in common physiological activities.

Conclusions

This study illustrates the divergence and conservation of circadian clocks and diurnal regulatory networks across syntenic orthologous genes in panacoid grass species. Further, conserved local regulatory sequences contribute to the architecture of these diurnal regulatory networks that produce conserved patterns of diurnal gene expression.

Genome-Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature

Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes. Variations of one or several traits are often assessed separately. High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals. Here, we test the association between markers within a gene and many traits simultaneously. This genome-phenome wide association study (GPWAS) is both a multi-marker and multi-trait test. Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation. Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles. Genes linked to phenomic variation in maize using GPWAS shared molecular, population genetic, and evolutionary features with classical mutants in maize. Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes. GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes. The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.

Towards a multiscale crop modelling framework for climate change adaptation assessment

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.