Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations.

Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high-throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri. Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green-Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non-modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids). By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant-environment interactions and have implications for crop improvement strategies.

RootBot: High-throughput root stress phenotyping robot.

Premise: Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil. Methods and Results: RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data. Conclusions: The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.

GWAS analysis of maize host plant resistance to western corn rootworm (Coleoptera: Chrysomelidae) reveals candidate small effect loci for resistance breeding.

Western corn rootworm, Diabrotica virgifera virgifera (LeConte) (Coleoptera: Chrysomelidae), is the most serious economic pest of maize, Zea mays (L.) (Poales: Poaceae), in the U.S. Corn Belt and also threatens production in Europe. Traditional management options have repeatedly failed over time as western corn rootworm rapidly develops resistance to insecticides, transgenic maize and even crop rotation. Traits that improve host plant resistance and tolerance are highly sought after by plant breeders for crop protection and pest management. However, maize resistance to western corn rootworm appears to be highly complex and despite over 75 yr of breeding efforts, there are no naturally resistant hybrids available commercially. Using phenotypic data from field and greenhouse experiments on a highly diverse collection of 282 inbred lines, we screened and genetically mapped western corn rootworm-related traits to identify genetic loci which may be useful for future breeding or genetic engineering efforts. Our results confirmed that western corn rootworm resistance is complex with relatively low heritability due in part to strong genotype by environment impacts and the inherent difficulties of phenotyping below ground root traits. The results of the Genome Wide Associated Study identified 29 loci that are potentially associated with resistance to western corn rootworm. Of these loci, 16 overlap with those found in previous transcription or mapping studies indicating a higher likelihood they are truly involved in maize western corn rootworm resistance. Taken together with previous studies, these results indicate that breeding for natural western corn rootworm resistance will likely require the stacking of multiple small effect loci.

Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.

Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.

2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative.

OBJECTIVES: This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available. DATA DESCRIPTION: The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

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.

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.

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 toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 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 best linear unbiased predictor (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 data type 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 a 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 the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

Predicting phenotypes from genetic, environment, management, and historical data using CNNs.

KEY MESSAGE: Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a dataset (feature engineering) in a subjective and potentially oversimplified manner. Deep neural networks such as Multilayer Perceptrons (MPL) and Convolutional Neural Networks (CNN) can overcome this by allowing the data itself to determine which factors are most important. CNN models were developed for predicting agronomic yield from a combination of replicated trials and historical yield survey data. The results were more accurate than standard methods when tested on held-out G, E, and M data (r = 0.50 vs. r = 0.43), and performed slightly worse than standard methods when only G was held out (r = 0.74 vs. r = 0.80). Pre-training on historical data increased accuracy compared to trial data alone. Saliency map analysis indicated the CNN has "learned" to prioritize many factors of known agricultural importance.

The contributions from the progenitor genomes of the mesopolyploid Brassiceae are evolutionarily distinct but functionally compatible.

The members of the tribe Brassiceae share a whole-genome triplication (WGT), and one proposed model for its formation is a two-step pair of hybridizations producing hexaploid descendants. However, evidence for this model is incomplete, and the evolutionary and functional constraints that drove evolution after the hexaploidy are even less understood. Here, we report a new genome sequence of Crambe hispanica, a species sister to most sequenced Brassiceae. Using this new genome and three others that share the hexaploidy, we traced the history of gene loss after the WGT using the Polyploidy Orthology Inference Tool (POInT). We confirm the two-step formation model and infer that there was a significant temporal gap between those two allopolyploidizations, with about a third of the gene losses from the first two subgenomes occurring before the arrival of the third. We also, for the 90,000 individual genes in our study, make parental subgenome assignments, inferring, with measured uncertainty, from which of the progenitor genomes of the allohexaploidy each gene derives. We further show that each subgenome has a statistically distinguishable rate of homoeolog losses. There is little indication of functional distinction between the three subgenomes: the individual subgenomes show no patterns of functional enrichment, no excess of shared protein-protein or metabolic interactions between their members, and no biases in their likelihood of having experienced a recent selective sweep. We propose a "mix and match" model of allopolyploidy, in which subgenome origin drives homoeolog loss propensities but where genes from different subgenomes function together without difficulty.

Distinct C4 sub-types and C3 bundle sheath isolation in the Paniceae grasses.

In C4 plants, the enzymatic machinery underpinning photosynthesis can vary, with, for example, three distinct C4 acid decarboxylases being used to release CO2 in the vicinity of RuBisCO. For decades, these decarboxylases have been used to classify C4 species into three biochemical sub-types. However, more recently, the notion that C4 species mix and match C4 acid decarboxylases has increased in popularity, and as a consequence, the validity of specific biochemical sub-types has been questioned. Using five species from the grass tribe Paniceae, we show that, although in some species transcripts and enzymes involved in multiple C4 acid decarboxylases accumulate, in others, transcript abundance and enzyme activity is almost entirely from one decarboxylase. In addition, the development of a bundle sheath isolation procedure for a close C3 species in the Paniceae enables the preliminary exploration of C4 sub-type evolution.

Phylogeny and multiple independent whole-genome duplication events in the Brassicales.

PREMISE: Whole-genome duplications (WGDs) are prevalent throughout the evolutionary history of plants. For example, dozens of WGDs have been phylogenetically localized across the order Brassicales, specifically, within the family Brassicaceae. A WGD event has also been identified in the Cleomaceae, the sister family to Brassicaceae, yet its placement, as well as that of WGDs in other families in the order, remains unclear. METHODS: Phylo-transcriptomic data were generated and used to infer a nuclear phylogeny for 74 Brassicales taxa. Genome survey sequencing was also performed on 66 of those taxa to infer a chloroplast phylogeny. These phylogenies were used to assess and confirm relationships among the major families of the Brassicales and within Brassicaceae. Multiple WGD inference methods were then used to assess the placement of WGDs on the nuclear phylogeny. RESULTS: Well-supported chloroplast and nuclear phylogenies for the Brassicales and the putative placement of the Cleomaceae-specific WGD event Th-ɑ are presented. This work also provides evidence for previously hypothesized WGDs, including a well-supported event shared by at least two members of the Resedaceae family, and a possible event within the Capparaceae. CONCLUSIONS: Phylogenetics and the placement of WGDs within highly polyploid lineages continues to be a major challenge. This study adds to the conversation on WGD inference difficulties by demonstrating that sampling is especially important for WGD identification and phylogenetic placement. Given its economic importance and genomic resources, the Brassicales continues to be an ideal group for assessing WGD inference methods.

Progressive heterosis in genetically defined tetraploid maize.

Progressive heterosis, i.e., the additional hybrid vigor in double-cross tetraploid hybrids not found in their single-cross tetraploid parents, has been documented in a number of species including alfalfa, potato, and maize. In this study, four artificially induced maize tetraploids, directly derived from standard inbred lines, were crossed in pairs to create two single-cross hybrids. These hybrids were then crossed to create double-cross hybrids containing genetic material from all four original lines. Replicated field-based phenotyping of the materials over four years indicated a strong progressive heterosis phenotype in tetraploids but not in their diploid counterparts. In particular, the above ground dry weight phenotype of double-cross tetraploid hybrids was on average 34% and 56% heavier than that of the single-cross tetraploid hybrids and the double-cross diploid counterparts, respectively. Additionally, whole-genome resequencing of the original inbred lines and further analysis of these data did not show the expected spectrum of alleles to explain tetraploid progressive heterosis under the complementation of complete recessive model. These results underscore the reality of the progressive heterosis phenotype, its potential utility for increasing crop biomass production, and the need for exploring alternative hypothesis to explain it at a molecular level.

Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: (i) gene-family-guided splitting and (ii) ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction and have the area under the ROC curve (auROC) values ranging from 0.75 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the hypothesis that the 3' UTR is more important for fine-tuning mRNA abundance levels while the 5' UTR is more important for large-scale changes.

Author Correction: Genome-Guided Phylo-Transcriptomic Methods and the Nuclear Phylogenetic Tree of the Paniceae Grasses.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Patterns of Population Variation in Two Paleopolyploid Eudicot Lineages Suggest That Dosage-Based Selection on Homeologs Is Long-Lived.

Genes that are inherently subject to strong selective constraints tend to be overretained in duplicate after polyploidy. They also continue to experience similar, but somewhat relaxed, constraints after that polyploidy event. We sought to assess for how long the influence of polyploidy is felt on these genes' selective pressures. We analyzed two nested polyploidy events in Brassicaceae: the At-α genome duplication that is the most recent polyploidy in the model plant Arabidopsis thaliana and a more recent hexaploidy shared by the genus Brassica and its relatives. By comparing the strength and direction of the natural selection acting at the population and at the species level, we find evidence for continued intensified purifying selection acting on retained duplicates from both polyploidies even down to the present. The constraint observed in preferentially retained genes is not a result of the polyploidy event: the orthologs of such genes experience even stronger constraint in nonpolyploid outgroup genomes. In both the Arabidopsis and Brassica lineages, we further find evidence for segregating mildly deleterious variants, confirming that the population-level data uncover patterns not visible with between-species comparisons. Using the A. thaliana metabolic network, we also explored whether network position was correlated with the measured selective constraint. At both the population and species level, nodes/genes tended to show similar constraints to their neighbors. Our results paint a picture of the long-lived effects of polyploidy on plant genomes, suggesting that even yesterday's polyploids still have distinct evolutionary trajectories.

Location of low copy genes in chromosomes of Brachiaria spp.

Repetitive DNA sequences have been widely used in cytogenetic analyses. The use of gene sequences with a low-copy-number, however, is little explored especially in plants. To date, the karyotype details in Brachiaria spp. are limited to the location of rDNA sites. The challenge lies in developing new probes based on incomplete sequencing data for the genus or complete sequencing of related species, since there are no model species with a sequenced genome in Brachiaria spp. The present study aimed at the physical location of conserved genes in chromosomes of Brachiaria ruziziensis, Brachiaria brizantha, and Brachiaria decumbens using RNAseq data, as well as sequences of Setaria italica and Sorghum bicolor through the fluorescent in situ hybridization technique. Five out of approximately 90 selected sequences generated clusters in the chromosomes of the species of Brachiaria studied. We identified genes in synteny with 5S and 45S rDNA sites, which contributed to the identification of chromosome pairs carrying these genes. In some cases, the species of Brachiaria evaluated had syntenic segments conserved across the chromosomes. The use of genomic sequencing data is essential for the enhancement of cytogenetic analyses.

Genome-Guided Phylo-Transcriptomic Methods and the Nuclear Phylogentic Tree of the Paniceae Grasses.

The past few years have witnessed a paradigm shift in molecular systematics from phylogenetic methods (using one or a few genes) to those that can be described as phylogenomics (phylogenetic inference with entire genomes). One approach that has recently emerged is phylo-transcriptomics (transcriptome-based phylogenetic inference). As in any phylogenetics experiment, accurate orthology inference is critical to phylo-transcriptomics. To date, most analyses have inferred orthology based either on pure sequence similarity or using gene-tree approaches. The use of conserved genome synteny in orthology detection has been relatively under-employed in phylogenetics, mainly due to the cost of sequencing genomes. While current trends focus on the quantity of genes included in an analysis, the use of synteny is likely to improve the quality of ortholog inference. In this study, we combine de novo transcriptome data and sequenced genomes from an economically important group of grass species, the tribe Paniceae, to make phylogenomic inferences. This method, which we call "genome-guided phylo-transcriptomics", is compared to other recently published orthology inference pipelines, and benchmarked using a set of sequenced genomes from across the grasses. These comparisons provide a framework for future researchers to evaluate the costs and benefits of adding sequenced genomes to transcriptome data sets.

Subgenome Dominance in an Interspecific Hybrid, Synthetic Allopolyploid, and a 140-Year-Old Naturally Established Neo-Allopolyploid Monkeyflower.

Recent studies have shown that one of the parental subgenomes in ancient polyploids is generally more dominant, having retained more genes and being more highly expressed, a phenomenon termed subgenome dominance. The genomic features that determine how quickly and which subgenome dominates within a newly formed polyploid remain poorly understood. To investigate the rate of emergence of subgenome dominance, we examined gene expression, gene methylation, and transposable element (TE) methylation in a natural, <140-year-old allopolyploid (Mimulus peregrinus), a resynthesized interspecies triploid hybrid (M. robertsii), a resynthesized allopolyploid (M. peregrinus), and progenitor species (M. guttatus and M. luteus). We show that subgenome expression dominance occurs instantly following the hybridization of divergent genomes and significantly increases over generations. Additionally, CHH methylation levels are reduced in regions near genes and within TEs in the first-generation hybrid, intermediate in the resynthesized allopolyploid, and are repatterned differently between the dominant and recessive subgenomes in the natural allopolyploid. Subgenome differences in levels of TE methylation mirror the increase in expression bias observed over the generations following hybridization. These findings provide important insights into genomic and epigenomic shock that occurs following hybridization and polyploid events and may also contribute to uncovering the mechanistic basis of heterosis and subgenome dominance.