Biology subjects

Hirsch, C. N.

Publications and source records attributed to Hirsch, C. N..

At least 19 recordsLinked to original sources

bioRxiv2026

A breeder in the sky: scoring flowering with fewer flights

Flowering time is a trait of broad interest and importance to both plant breeders and plant biologists. Unlike many other traits, flowering time cannot be scored accurately from a single observation; its measurement requires repeated observations of the same experiments over time. Both classification- and object-detection-based approaches have demonstrated the potential to estimate flowering time from Unmanned aerial vehicle (UAV) imagery rather than by manual observation, but they still require data collection at many time points. Here, we train and deploy a regression-based framework for scoring flowering time. Using a dataset of more than 200,000 UAV images and associated flowering-time records collected across 27 environments, we demonstrate that this approach enables the prediction of flowering time from as few as one observation per field experiment and successfully generalizes to field experiments in environments not represented in the training dataset. These results indicate that regression-based approaches for predicting flowering time from sparse UAV observations have the potential to substantially reduce the data-collection burden in field experiments

plant biology↗

bioRxiv2026

An open field phenomics resource for multimodal maize yield prediction across divergent environments

Temporal drone phenotyping captures crop development, but irregular flight schedules complicate comparisons across environments. We release curated imagery from 356 flights across 19 Genomes to Fields environments containing 1,180 maize (Zea mays L.) hybrids. To evaluate its utility, we integrated functional principal components of vegetation index and weather trajectories with genomic information. Combinined genomic and phenomic kernels improved yield prediction, reaching correlations up to r = 0.501 for held-out hybrids in environments represented in training and 0.408 when environments were also withheld. Accumulated growing degree days offered no consistent predictive advantage over days after planting, and weather contributed modest, task-dependent gains. A transformer neural process learned directly from irregular observations, serving as a novel application of neural process models in agriculture. Mapping vegetation index functional principal components identified recurrent quantitative trait loci on chromosomes 3 and 7. This resource and its reproducible analyses guide the use of temporal spectral data for crop prediction and genetic discovery.

plant biology↗

bioRxiv2026

Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups

The early 20th-century discovery of heterosis and the establishment of heterotic groups transformed maize (Zea mays L.) into a keystone of global agriculture. However, maize breeding faces two significant challenges: the gradual decline of general combining ability (GCA) variance within heterotic groups and the impracticality of testing all possible single crosses in the early stages of a breeding program. Here, we developed genomic best linear unbiased prediction (GBLUP)-based multi-kernel models, using additive and two alternative non-additive genomic relationship matrices, to estimate the variance components associated with the GCA of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability (SCA) arising from their crosses. We further applied these models to predict the performance of untested single-cross combinations under varying levels of parental information. We showed that the SS and NSS groups retained significant GCA variance across traits in both early- and late-maturity groups. The SS group, in contrast, exhibited no detectable GCA variance in grain yield for the intermediate-flowering subset of hybrids, highlighting a limitation for future genetic improvement. Furthermore, our results showed that GBLUP-based multi-kernel models effectively identified superior hybrids when parental information was available. In the absence of this information, however, these models underperformed compared to covariance-based approaches. Both types of non-additive matrices produced similar results, affirming the robustness of the inferred genetic architecture. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multi-kernel models can improve the efficiency of hybrid breeding programs.

genetics↗

bioRxiv2026

Inter-variety competition dynamics in US inbred and hybrid maize

Variety mixtures provide a potential avenue in US cropping systems to improve yield stability and disease resistance. However, implementation of variety mixtures requires an understanding of the competitive dynamics of the crop. In this study, we examine the effects of plant competition both between and within plots through five unique experiments: 1) 5,000 diverse inbred lines in single-row plots, 2) hybrids in two-row plots developed from the above inbred lines, 3) over 4,000 hybrids measured in 141 locations in two-row plots as part of Genomes to Fields, 4) mixtures of two hybrids within a two-row plot planted across two years and five locations, and 5) mixtures of up to twenty hybrids in four-row plots in three locations. Across all experiments, we find that competitive interactions are extremely limited. Within inbred lines, height of the neighboring plot accounts for 1.2% of the variance in focal plot height. Similarly, neighbor height explains 1.7% of the variance in focal plot yield in hybrids developed from the inbred lines. The genetics of neighboring plots explains 1.55% of the variation in yield across 141 location-year environments, reinforcing the generally modest impacts of neighbor competition. In evaluating mixtures of hybrids in both two and four-row plots, we observe no yield penalty compared to conventional single hybrid plots, even with large height differentials of the hybrids included in the mixture or in mixtures of up to 20 hybrids within a plot. Finally, we observe that mixtures have more yield stability compared to conventional plots, highlighting a new avenue for increased stability in higher risk environments. The lack of yield penalty and stability benefits are promising for future investigations of mixtures that may complement each other in disease resistance or abiotic stress tolerance and increase overall yield stability in the field.

plant biology↗

bioRxiv2025

Gene-by-Environment Interaction Significantly Drives Bacterial Endophyte Communities in Maize Stalks

1.BackgroundMany microbes are known to boost the performance of crop plants, and their use as crop treatments ("biologicals") is an active area of research and commercialization. Although many identified microbes can boost plant production under laboratory settings, most fail to translate to field settings, and even the ones that are commercialized are often unreliable. While many factors likely underlie these issues, one that has been underexplored is the idea that interactions between plants and microbes can vary across both different host genetics and different environments, and that specific combinations of host and environment can result in very different microbial outcomes. ResultsTo quantify how a plant microbiome can change across host genotype and environment and to determine the relative importance of these factors, we sampled maize stalks from 20 specific hybrid varieties grown at 15 locations in the United States. Using 16S rRNA gene sequencing, we find that the stalk microbiome is highly variable, with a handful of bacterial taxa conserved at broader taxonomic levels (Phylum, Class) and almost none at finer levels (Genus, Species). Local environment and gene-by-environment interactions both had consistently large and significant effects on the microbial community (measured by alpha-and beta-diversity summary statistics, individual taxa, and inferred functional capacity), whereas host genotype had little to no consistent effect across environments. We also identified specific soil factors (pH and potassium) as the most significant environmental drivers of the community, even though these communities were living inside the stalks. ConclusionsTaken together, our results indicate that while host genetics can significantly affect the stalk microbial community, these effects are almost entirely predicated on interactions with the environment. These results imply that reliable deployment of microbes for crop production will require significant investment in trials across many locations and genetic backgrounds, either to estimate the GxE effects directly or to screen for microbes whose effects are less dependent on these two factors.

microbiology↗

bioRxiv2025

Genome-wide selection on transposable elements in maize

While most evolutionary research has focused on single nucleotide polymorphisms (SNPs), transposable elements (TEs) represent a major but understudied source of mutations that can influence organismal fitness. Previous studies on TEs often overlook the mechanisms and rates of transposition, rely on short-read sequencing that limits TE detection, or focus on small genomes such as Arabidopsis or Drosophila. In this study, we leveraged high-quality, long-read genome assemblies from 26 maize inbreds to investigate natural selection on TEs. We developed a novel and interpretable method, {Phi}SFS, which incorporates TE age and improves resolution for detecting selection. Using this approach, we identified key factors influencing selection on TEs: (1) the distance to the nearest gene, (2) the pre-insertion DNA methylation level at the insertion site, and (3) intrinsic TE characteristics, including copy number and expression level. This work represents the first application of long-read genome assemblies to study TE selection in a major crop species with a typical plant genome size. Our {Phi}SFSmethod offers a broadly applicable framework for detecting selection on TEs, and the factors uncovered provide new insights into the evolutionary dynamics and trade-offs between TEs and host genes.

evolutionary biology↗

bioRxiv2025

Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery

Genomic and phenomic analyses suggest additional heritable phenomic features can improve modeling of important end traits like senescence or yield. Field phenotyping generally uses trait values averaged across individual experimental units (plants or numerous plants within plots), ignoring the full distributional pattern of collected measures. Images of plants or plots, as captured by drones (unoccupied aerial vehicles / UAVs / drones), can be viewed as individual distribution functions that capture biological information. This study introduces and validates distributional data analysis in two crops and experiment types - cotton (Gossypium hirsutum L.) single plant vegetation index (VI) analysis and maize (Zea mays L.) plot-level yield predictions. In both crops, the concept of within-day variance decomposition was demonstrated. In cotton, genotypes exerted significant influences on temporal quantile functions of VIs. Maize yield prediction using distributional data with elastic-net regression indicated improvements in yield prediction between 12.7%-21.6% with quantiles outside the conventionally used median responsible for added predictive power. A novel data visualization method for per-pixel heritability allowed distributional features to be explainable and interpretable. These results have implications for future plant phenomic studies, indicating that distributional data analysis applied across temporal imagery captures novel, heritable, and interpretable biological signal that is lost when working with conventional measures of central tendency such as mean or median summary values of experimental units. SignificanceRepeated aerial imaging of agricultural experiments produces image data sets that capture plant development in high spatial and temporal resolutions. Frequently, images are summarized by measures of central tendency, such as mean or median values. Here, functional data distributional methods were applied to cotton (Gossypium hirsutum L.) and maize (Zea mays L.) image data, capturing more information than standard approaches. Cotton genotypes significantly impacted distributional spectral data while in maize, distributional data enabled more accurate predictions of grain yield versus models trained with median data alone. Distributional data were more explainable by genetics, with novel data visualization techniques able to shine light on specific parts of plant imagery with high and low genetic variance.

plant biology↗

bioRxiv2025

Optimizing population simulations to accurately parallel empirical data for digital breeding

The use of computational and data-driven approaches to accelerate and optimize breeding programs is becoming common practice among plant breeders. Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. However, accurately simulating traits that match empirical trait data remains a challenge. Here we tested if incorporating information about the genetic architecture from genome-wide association studies (GWAS) of maize agronomic traits with varying heritabilities into simulations can improve the concordance between simulated and empirical data in a population of hybrids developed from crosses of 333 maize recombinant inbred lines grown in four to eleven environments. Using at least 200 non-redundant top GWAS hits as causative variants, regardless of statistical significance, resulted in mean correlations between simulated and empirical trait data of 0.397 to 0.616 within environments and 0.610 to 0.915 across environments. Reducing the GWAS estimated marker effect sizes in the simulations further improved concordance with empirical data. This study provides valuable insights into methods for simulating more realistic phenotypes for digital breeding to parallel empirical trait distributions, and that these simulated traits are highly concordant with observed variance partitioning (i.e. genotype, environment, etc.), and genomic prediction performance. PLAIN LANGUAGE SUMMARYPlant breeders need to evaluate many possible changes to breeding schemes and resource allocation. One method of optimizing these changes is through computer-based simulations, which can be time- and cost-efficient. The genetic architecture of randomly simulated traits rarely parallels the genetic architecture of real-world traits, which can mislead the interpretation of such simulations. In this study, the genetic architecture of real-world traits was used to inform the simulation of digital traits to develop an optimized simulation pipeline for creating and assessing simulated breeding programs. The utility of these informed simulations is demonstrated through genomic prediction assessment, in which we show that the rank order of individuals and the distribution of traits is similar between simulated and real-world data. CORE IDEAS[bullet] Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. [bullet]Incorporating the genetic architecture of traits in simulations can improve the concordance with empirical data. [bullet]Reducing GWAS marker effect size improves correlation and distribution concordance of simulated and empirical data. [bullet]Increasing the number of causative variants beyond GWAS significance thresholds improves simulation performance. [bullet]Simulated data used in genomic prediction produces results similar to empirical genomic predicted data.

plant biology↗

bioRxiv2025

Genomic insights and breeding strategies for nixtamalization moisture content in hybrid maize

CORE IDEASO_LINixtamalization moisture content can be selected early in breeding programs using NIR spectroscopy. C_LIO_LIYield does not significantly correlate with nixtamalization moisture content in diverse or elite populations. C_LIO_LIAdditive and dominance gene action impact nixtamalization moisture content in hybrid maize. C_LIO_LIGenomic prediction can be used to assess nixtamalization moisture content early in hybrid maize breeding. C_LI Nixtamalization moisture content, a measure of the quantity of water absorbed during the nixtamalization of a grain such as maize, has a large impact on the end-quality of masa-based products. An application to predict nixtamalization moisture content from raw inbred and hybrid maize grain was recently developed, but its utility in a breeding context has not been assessed. Important breeding considerations for nixtamalization moisture content were assessed in diverse maize hybrids, modern commercial hybrids, and historically high-acreage hybrids grown in up to three environments across two years. This study demonstrated that nixtamalization moisture content is heavily influenced by growing conditions, but sufficient genetic variance is present to allow breeders to make gains from selection. Contrary to prior theory, there was no substantial correlation between nixtamalization moisture content and yield suggesting breeders can select for both traits without negatively impacting either trait. Both additive and dominant genetic action was observed and genomic prediction was able to predict nixtamalization moisture content in hybrids with an average Spearmans rank correlation coefficient greater than 0.441 and a root mean square error below 0.006. The findings here suggest that nixtamalization moisture content can be selected for early in breeding cycles, allowing breeders to develop improved food-grade maize germplasm without negatively impacting important traits such as yield. PLAIN LANGUAGE SUMMARYPlant breeders need to understand the biological mechanisms underlying a trait of interest to maximize the efficiency of their efforts. Nixtamalization moisture content is a highly complex trait that is determined by both genetic and environmental factors. In this study, nixtamalization moisture content was assessed in a diverse set of hybrid and inbred maize to understand the biological mechanisms underlying nixtamalization moisture content. The relationship between nixtamalization moisture content and yield was assessed, the genetic architecture and mode of gene action underlying nixtamalization moisture content were evaluated, and the efficacy of genomic prediction in assessing nixtamalization moisture content was determined. The findings of this study will allow breeders to create optimized breeding strategies for nixtamalization moisture content, thus improving the raw materials that are used to produce globally consumed products such as tortillas and tortilla chips.

plant biology↗

bioRxiv2024

Transcriptome profiling of maize transcription factor mutants to probe gene regulatory network predictions

Transcription factors (TFs) play important roles in regulation of gene expression and phenotype. A variety of approaches have been utilized to develop gene-regulatory networks (GRNs) to predict the regulatory targets for each TF, such as yeast-one-hybrid (Y1H) screens and gene co-expression network (GCN) analysis. Here we identified potential TF targets and used a reverse genetics approach to test the predictions of several GRNs in maize. Loss-of-function mutant alleles were isolated for 22 maize TFs. These mutants did not exhibit obvious morphological phenotypes. However, transcriptomic profiling identified differentially expressed genes in each of the mutant genotypes, and targeted metabolic profiling indicated variable phenolic accumulation in some mutants. An analysis of expression levels for predicted target genes based on Y1H screens identified a small subset of predicted targets that exhibit altered expression levels. The analysis of predicted targets from GCN-based methods found significant enrichments for prediction sets of some TFs, but most predicted targets did not exhibit altered expression. This could result from false-positive GCN predictions, a TF with a secondary regulatory role resulting in minor effects on gene regulation, or redundant gene regulation by other TFs. Collectively, these findings suggest that loss-of-function for single uncharacterized TFs might have limited phenotypic impacts but can reveal subsets of GRN predicted targets with altered expression.

plant biology↗

bioRxiv2024

Dissecting the temporal phenomics and genomics of maize canopy cover using UAV mediated image capture

Canopy cover is an important agronomic trait influencing photosynthesis, weed suppression, biomass accumulation, and yield. Conventional methods to quantify canopy cover are time and labor-intensive. As such, little is known about how canopy cover develops over time, the stability of canopy cover across environments, or the genetic architecture of canopy cover. We used unoccupied aerial vehicle-mediated image capture to quantify plot-level canopy coverage in maize throughout the growing season. Images of 501 diverse inbred lines were acquired between 300 and 1300 growing degree days in the 2018-2021 growing seasons. We observed that the maize canopy developed following a logistic curve. Phenotypic variation in percent canopy coverage and canopy growth rate was explained by genetic and environmental factors and genotype-by-environment interactions, however the percent of variance explained by each factor varied throughout the growing season. Environmental factors explained the largest portion of trait variance during the adult vegetative growth stage and had a larger impact on canopy growth rates than percent canopy coverage. We conducted multiple genome wide association studies and found that canopy cover is a complex, polygenic trait with a diverse range of marker trait associations throughout development. The change in associations indicated that single time point phenotyping was insufficient to capture the full phenomic and genetic diversity of canopy cover in maize.

plant biology↗

bioRxiv2023

Mutator transposon insertions within maize genes often provide a novel outward reading promoter

The highly active family of Mutator (Mu) DNA transposons has been widely used for forward and reverse genetics in maize. There are examples of Mu-suppressible alleles which result in conditional phenotypic effects based on the activity of Mu. Phenotypes from these Mu- suppressible mutations are observed in Mu-active genetic backgrounds, but absent when Mu activity is lost. For some Mu-suppressible alleles, phenotypic suppression likely results from an outward-reading promoter within Mu that is only active when the autonomous Mu element is silenced or lost. We isolated 35 Mu alleles from the UniformMu population that represent insertions in 24 different genes. Most of these mutant alleles are due to insertions within gene coding sequences, but several 5 UTR and intron insertions were included. RNA-seq and de novo transcript assembly were utilized to document the transcripts produced from 33 of these Mu insertion alleles. For 20 of the 33 alleles, there was evidence of transcripts initiating within the Mu sequence reading through the gene. This outward-reading promoter activity was detected in multiple types of Mu elements and doesnt depend on the orientation of Mu. Expression analyses of Mu-initiated transcripts revealed the Mu promoter often provides gene expression levels and patterns that are similar to the wild-type gene. These results suggest the Mu promoter may represent a minimal promoter that can respond to gene cis-regulatory elements. Findings from this study have implications for maize researchers using the UniformMu population, and more broadly highlights a strategy for transposons to co-exist with their host. Article SummaryMutator (Mu) transposable elements are a widely used tool for insertional mutagenesis in maize and often insert in the 5 regions of genes. The characterization of transcripts for Mu insertion alleles reveals complex transcripts. These often result in one transcript that covers the first portion of the gene terminating in Mu and a second transcript initiating within Mu covering the latter portion of the gene. This may reflect a strategy for Mu to minimize the consequences of insertions within genes.

plant biology↗

bioRxiv2023

Combined analysis of transposable elements and structural variation in maize genomes reveals genome contraction outpaces expansion

BackgroundStructural differences between genomes are a major source of genetic variation that contributes to phenotypic differences. Transposable elements, mobile genetic sequences capable of increasing their copy number and propagating themselves within genomes, can generate structural variation. However, their repetitive nature makes it difficult to characterize fine-scale differences in their presence at specific positions, limiting our understanding of their impact on genome variation. Domesticated maize is a particularly good system for exploring the impact of transposable element proliferation as over 70% of the genome is annotated as transposable elements. High-quality transposable element annotations were recently generated for de-novo genome assemblies of 26 diverse inbred maize lines. ResultsWe generated base-pair resolved pairwise alignments between the B73 maize reference genome and the remaining 25 inbred maize line assemblies. From this data, we classified transposable elements as either shared or polymorphic in a given pairwise comparison. Our analysis uncovered substantial structural variation between lines, representing both putative insertion and deletion events. Putative insertions in SNP depleted regions, which represent recently diverged identity by state blocks, suggest some TE families may still be active. However, our analysis reveals that, genome-wide, deletions of transposable elements account for more structural variation than insertions. These deletions are often large structural variants containing multiple transposable elements. ConclusionsCombined, our results highlight how transposable elements contribute to structural variation and demonstrate that deletion events are a major contributor to genomic differences.

genomics↗

bioRxiv2023

Linking genetic and environmental factors through marker effect networks to understand trait plasticity

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene co-expression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of [~]10,000 non-redundant markers from 400 maize hybrids across nine environments. We demonstrate that networks can be generated using this approach, and that the markers that are co-varying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity, and specific environmental factors that modulate the genome.

plant biology↗

bioRxiv2022

Differences in activity and stability drive transposable element variation in tropical and temperate maize.

Much of the profound interspecific variation in genome content has been attributed to transposable elements (TEs). To explore the extent of TE variation within species, we developed an optimized open-source algorithm, panEDTA, to de novo annotate TEs in a pan-genome context. We then generated a unified TE annotation for a maize pan-genome derived from 26 reference-quality genomes, which revealed an excess of 35.1 Mb of TE sequences per genome in tropical maize relative to temperate maize. A small number (n = 216) of TE families, mainly LTR retrotransposons, drive these differences. Evidence from the methylome, transcriptome, LTR age distribution, and LTR insertional polymorphisms revealed that 64.7% of the variability was contributed by LTR families that were young, less methylated, and more expressed in tropical maize, while 18.5% was driven by LTR families with removal or loss in temperate maize. This study demonstrates the use of a comprehensive pan-TE annotation to reveal the driving role of TEs in within-species genomic variation via their ongoing amplification and purging.

genomics↗

bioRxiv2022

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 linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing 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 models 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.

plant biology↗

bioRxiv2022

Limited consequences for loss of RNA-directed DNA methylation in Setaria viridis domains rearranged methyltransferase (DRM) mutants

The Domains Rearranged Methyltransferases (DRMs) are crucial for RNA-directed DNA methylation (RdDM) in plant species. Setaria viridis is a model monocot species with a relatively compact genome that has limited transposable element content. CRISPR-based genome editing approaches were used to create loss-of-function alleles for the two putative functional DRM genes in S. viridis to probe the role of RdDM. The analysis of drm1ab double mutant plants revealed limited morphological consequences for the loss of RdDM. Whole-genome methylation profiling provided evidence for wide-spread loss of methylation in CHH sequence contexts, particularly in regions with high CHH methylation in wild-type plants. Evidence was also found for locus-specific loss of CG and CHG methylation, even in some regions that lack CHH methylation. Transcriptome profiling identified a limited number of genes with altered expression in the drm1ab mutants. The majority of genes with elevated CHH methylation directly surrounding the transcription start site or in nearby promoter regions do not have altered expression in the drm1ab mutant even when this methylation is lost, suggesting limited regulation of gene expression by RdDM. Detailed analysis of the expression of transposable elements identified several transposons that are transcriptionally activated in drm1ab mutants. These transposons likely require active RdDM for maintenance of transcriptional repression. Significance statementMethylation profiling of Setaria viridis plants that lack functional Domains Rearranged Methyltransferase genes reveal widespread loss of DNA methylation in the CHH sequence context. Transcriptome analysis reveals a small set of genes and transposons that are silenced by RNA-directed DNA methylation.

plant biology↗

bioRxiv2021

Stability of DNA methylation and chromatin accessibility in structurally diverse maize genomes

Accessible chromatin and unmethylated DNA are associated with many genes and cis-regulatory elements. Attempts to understand natural variation for accessible chromatin regions (ACRs) and unmethylated regions (UMRs) often rely upon alignments to a single reference genome. This limits the ability to assess regions that are absent in the reference genome assembly and monitor how nearby structural variants influence variation in chromatin state. In this study, de novo genome assemblies for four maize inbreds (B73, Mo17, Oh43 and W22) are utilized to assess chromatin accessibility and DNA methylation patterns in a pan-genome context. The number of UMRs and ACRs that can be identified is more accurate when chromatin data is aligned to the matched genome rather than a single reference genome. While there are UMRs and ACRs present within genomic regions that are not shared between genotypes, these features are substantially enriched within shared regions, as determined by chromosomal alignments. Characterization of UMRs present within shared genomic regions reveals that most UMRs maintain the unmethylated state in other genotypes with only a small number being polymorphic between genotypes. However, the majority of UMRs between genotypes only exhibit partial overlaps suggesting that the boundaries between methylated and unmethylated DNA are dynamic. This instability is not solely due to sequence variation as these partially overlapping UMRs are frequently found within genomic regions that lack sequence variation. The ability to compare chromatin properties among individuals with structural variation enables pan-epigenome analyses to study the sources of variation for accessible chromatin and unmethylated DNA. Article summaryRegions of the genome that have accessible chromatin or unmethylated DNA are often associated with cis-regulatory elements. We assessed chromatin accessibility and DNA methylation in four structurally diverse maize genomes. There are accessible or unmethylated regions within the non-shared portions of the genomes but these features are depleted within these regions. Evaluating the dynamics of methylation and accessibility between genotypes reveals conservation of features, albeit with variable boundaries suggesting some instability of the precise edges of unmethylated regions.

plant biology↗