Biology subjects

Schnable, J. C.

Publications and source records attributed to Schnable, J. C..

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

Embeddings from standardized sorghum leaf images capture variation in disease response that human scoring misses

Ordinal scoring of plant disease severity by human raters compresses variation in lesion color, size, and number. Inter-rater variability further complicates comparisons and integrated analyses across environments. We developed a low-cost portable imaging chamber to rapidly image large numbers of leaves under standardized lighting, orientation, and backdrops in the field and employed this system to image more than 11,000 leaves across three states. Embeddings from vision encoders predicted human-assigned disease severity scores. No significant GWAS hits were identified using human-assigned or vegetation-index-based disease severity scores, but GWAS using embeddings identified twelve genomic hotspots controlling leaf appearance. Nine were linked to variation in disease symptom severity. Five hotspots corresponded to previously characterized sorghum genes: all three hotspots not linked to disease and two of the nine that were. Roughly one-third of tested embedding--hotspot associations replicated across at least two states, and twenty replicated across all three. eQTL, PheWAS, and large-effect variant analyses identified single candidate genes with plausible mechanistic links to disease symptom severity for six of the seven hotspots not mapping to characterized genes. These results demonstrate the power of combining scalable, standardized leaf imaging with pretrained image encoders to capture genetically controlled variation in diverse disease symptoms that human ordinal scoring misses.

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

Nitrogen adaptation and phenotypic transitions from wild to improved sorghum

Crop domestication and subsequent improvement under modern agronomic conditions have altered the nitrogen (N) regimes experienced by crops, yet how selection has reshaped the genetic architecture of phenotypic responses to N availability remains poorly understood. Here, we integrated population genomic analyses of n = 289 sorghum accessions spanning multiple stages of domestication and improvement with N-response (NR) trait-associated variants identified through genome-wide association studies (GWAS) of phenotypic data collected under two contrasting N conditions in n = 347 diverse sorghum accessions. Unlike maize and sunflower, sorghum showed a reduced deleterious burden both genome-wide and within historically balanced and recently selected regions following domestication and improvement. Additionally, we identified 470 trait-associated loci (TALs), including 273 associated with NR traits. The effects of these NR-associated loci showed no apparent bias toward derived alleles, suggesting that selection on NR traits in sorghum has not been sufficiently strong or directional to consistently overcome genetic drift. Together, these results reveal distinct patterns of selection in sorghum and provide promising genetic targets for improving N-use efficiency in sustainable agricultural systems.

genomics↗

bioRxiv2026

Haplotype-rich cis-regulation underlies transcriptomic diversity across the breeding history of maize (Zea mays)

O_LIUnderstanding how regulatory variation evolves under selection is central to linking genetic diversity with phenotypic evolution,yet the determinants of transcriptomic diversity in crops remain unclear. Maize, with its strong population structure shaped by modern breeding, provides a powerful system to investigate regulatory variation. C_LIO_LIWe analyzed RNA-seq data from a large maize diversity panel and integrated population-level expression analyses with high-resolution cis-eQTN fine-mapping. Although nucleotide diversity was reduced by nearly half in some heterotic groups, transcriptomic diversity declined by only 10-20%. C_LIO_LIFine-mapping revealed that gene expression variation is predominantly controlled by multiple small-effect cis-regulatory variants with the majority of genes where cis-eQTN were identified exhibiting three or more functionally distinct haplotypes. Expression divergence between heterotic groups scaled with allele frequency differentiation at standing regulatory variants and intensified during recent breeding, consistent with reweighting of pre-existing regulatory variation. C_LIO_LIGenes under stronger evolutionary constraint harboured regulatory variants with smaller effects, suggesting purifying selection acting on the magnitude of regulatory perturbations. Together, these results show that transcriptomic diversity in maize is less sensitive to population bottlenecks than nucleotide diversity, yet remains shaped by polygenic regulatory architectures that are constrained by selection. C_LI

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

A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants

Only a small fraction of annotated plant genes possesses experimentally validated associations with specific phenotypes. Phenotype associated genes have distinct structural, molecular, and evolutionary characteristics compared to non-validated gene models. Here, we developed a simple classifier that uses sequence and evolutionary features which can be generated for any species with an annotated reference genome assembly, to accurately distinguish phenotype associated genes from both the overall population of annotated gene models and a specific set of genes identified as being tolerant of loss of function mutations. A model trained solely on genes from maize (Zea mays) identified and prioritized rice (Oryza sativa) and arabidopsis (Arabidopsis thaliana) genes that were highly enriched in genes with experimentally validated links to phenotypes in both of these evolutionarily distant species. Gene models predicted to have a higher probability of being linked to phenotypes displayed patterns consistent with known biological properties of phenotype associated genes. Notably, the sets of genes predicted to have a high probability of being linked to phenotype variation did not consist exclusively of well-characterized gene families, but included many uncharacterized gene families carrying domains of unknown function. The quantitative scores generated by this model offer a valuable resource for prioritizing and exploring the vast number of uncharacterized gene models in plants, reducing the risk of failure in future reverse genetics efforts and potentially accelerating gene discovery and functional annotation in crops. Significance StatementAnnotation of an organisms genome produces tens of thousands of predicted genes, termed gene models. However, only a few of these have been associated with phenotypes. We built a simple algorithm that uses DNA sequences and evolutionary conservation-based information to predict which genes are most likely to change a plants phenotype when disrupted. In maize, it separates known phenotype-associated genes from other gene models; it also works in rice and arabidopsis. High-scoring genes show signals of biological importance and include many with no known function. This algorithm provides a shortlist of candidate for gene characterization and crop improvement.

plant biology↗

bioRxiv2025

Assessing the impact of yield plasticity on hybrid performance in maize

Improving crop resilience in the face of increasingly extreme and unpredictable weather and reduced access to agricultural inputs such as nitrogen fertilizer and water will require an improved understanding of phenotypic plasticity in crops. To understand the roles of different component traits in determining overall plasticity for grain yield, we generated data from a panel of 122 maize (Zea mays) hybrids grown in replicated field trials in 34 environments spanning 700 miles (1126 km) of the U.S. Corn Belt. We observed that the levels of genetic versus environmental control and the relationships between mean parent release year, overall performance, and linear plasticity were trait-dependent across the 18 agronomic and yield components studied. Importantly and unexpectedly, we observed no clear tradeoff between linear plasticity and mean performance and found only rare examples where genotype-by-environment interactions would alter selection decisions based on the environments tested in our dataset. Furthermore, we showed that overall plasticity was repeatable and appears to be under considerable genetic control but that plasticity in response to nitrogen fertilization was not, which may help explain the limited success in breeding for nitrogen use efficiency. Together, these findings improve our understanding of phenotypic plasticity, with implications for maize breeding.

plant biology↗

bioRxiv2024

Nitrogen response and growth trajectory of sorghum CRISPR-Cas9 mutants using high-throughput phenotyping

Inorganic nitrogen (N) fertilizer has emerged as one of the key factors driving increased crop yields in the past several decades; however, the overuse of chemical N fertilizer has led to severe ecological and environmental burdens. Understanding how crops respond to N fertilizer has become a central topic in plant science and plant genetics, with the ultimate goal of enhancing N use efficiency (NUE) in crop production. As one of the most essential macronutrients, N significantly influences crop performance across different developmental stages of plant, phenotypic traits result from the accumulative effects of genetic factors, prevailing environmental conditions (specifically N availability), and their complex interactions. To characterize the targeting N-responsiveness and growth trajectory, we employed CRISPR-Cas9 technique to generate sorghum mutants using CRISPR technology. Using a LemnaTec plant imaging system, we obtained time series imagery data from 29 to 130 days after sowing (DAS) for these CRISPR-edited mutants under high N and low N greenhouse conditions. After imagery data analysis, we extracted a number of morphological and greenness index traits as a proxy of plant growth and N responses. Subsequently, we employed two different methods to model the temporal N-responsive traits, allowing us to estimate seven key parameters from the growth curve. Our findings revealed that the wildtype and the edited sorghum lines exhibited differences in N responses for several of the key growth-related parameters. The high-throughput N phenotyping pipeline paves the way for a better understanding of the N responses of edited lines in a dynamic manner and sheds light on further improvements in crop NUE.

plant biology↗

bioRxiv2024

Genes and pathways determining flowering time variation in temperate adapted sorghum

The timing of the transition from vegetative to reproductive growth is determined by a complex genetic architecture integrating signals from a diverse set of external and internal stimuli and plays a key role in determining plant fitness and adaptation. However, significant divergence in the identities and functions of many flowering time pathway components has been reported among plant species. Here we employ a combination of genome and transcriptome wide association studies to identify genetic determinants of variation in flowering time across multiple environments in a large panel of primarily photoperiod-insensitive sorghum (Sorghum bicolor), a major crop that has, to date, been the subject of substantially less genetic investigation than its relatives. Gene families that form core components of the flowering time pathway in other species, FT-like and SOC1-like genes, appear to play similar roles in sorghum, but the genes identified are not orthologous to the primary FT-like or SOC1-like genes which play similar roles in related species. The aging pathway appears to play a significant role in determining non-photoperiod determined variation in flowering time in sorghum. Two components of this pathway were identified in a transcriptome wide association study, while a third was identified via genome wide association. Our results demonstrate that, while the functions of larger gene families are conserved, functional data from even closely related species is not a reliable guide to which gene copies will play roles in determining natural variation in flowering time.

plant biology↗

bioRxiv2024

Bridging Genomic Gaps: The Pan-Grass Syntenic Gene Set in Grass Crop Evolution

Understanding orthologous and homeologous relationships among genes can inform our understanding of how evolution and artificial selection have shaped the genomes of related species to adapt to different niches and produce different phenotypes. High-confidence information on orthologous relationships can also increase the impact of the functional characterization in one species by ensuring this information is available to researchers studying orthologs in other species. The grasses are ecologically and economically critical plants, and many genes are conserved in syntenic blocks between species. Here we leverage that conserved gene order to create and describe the Pan-Grass Syntenic Gene Set (PGSGS), a curated database of orthologous and homeologous relationships among the genes of 17 grass species, including both major and orphan crops as well as wild grasses. As a demonstration of the utility of the PGSGS, the dynamics of chromosome structure and gene content changes after polypoidy in several grass lineages are analyzed. This dataset serves as a seminal foundation for improving our understanding of genome evolution subsequent to polyploidy, as well as encouraging and enabling integration of functional genetic and functional genomic information across the grasses, the original promise of the "grasses as a single genetic system" model. SIGNIFICANCE STATEMENTThe Pan-Grass Syntenic Gene Set (PGSGS) leverages our understanding of the genomic background of major grass species, including rice, maize, sorghum, wheat, barley, millet, and sugarcane. It represents a milestone for deciphering the genetic foundations of essential traits such as yield, disease resistance, and environmental adaptability - key factors contributing to agriculture and food security. Scrutinizing pan-syntenic genes unveils the intricate interplay of diverse gene acquisition and loss mechanisms, potentially speeding molecular breeding knowledge transference. The PGSGS emerges as a valuable resource for gene annotation, with implications for improving grass crops.

genomics↗

bioRxiv2024

3D Reconstruction Enables High-Throughput Phenotyping and Quantitative Genetic Analysis of Phyllotaxy

Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180{degrees} angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.

plant biology↗

bioRxiv2024

Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions

Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcripts impacts on plant metabolism has employed data from controlled environments. Here we generate and employ data on variation in the abundance of twenty-six metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies) and transcripts (through both transcriptome-wide association studies and an explainable AI approach based on the random forest) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation.

genomics↗

bioRxiv2024

Off-the-shelf image analysis models outperform human visual assessment in identifying genes controlling seed color variation in sorghum

Seed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. Today seed color is often quantified via either qualitative human assessment or biochemical assays for specific colored metabolites. Imaging-based approaches have the potential to be more quantitative than human scoring while lower cost than biochemical assays. We assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor ) using a dataset of > 1,500 images. Quantitative measurements of seed color from images were substantially more consistent across biological replicates than human assessment. Genome-wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co-localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes - colored metabolites in sorghum seeds - and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. The cross-species transferability of image analysis tools, without the retraining, may aid efforts to develop higher value and health-promoting crop varieties in sorghum and other specialty and orphan grain crops.

plant biology↗

bioRxiv2024

Models trained to predict differential expression across plant organs identify distal and proximal regulatory regions

AO_SCPLOWBSTRACTC_SCPLOWA large proportion of standing phenotypic variation is explained by genetic variation in noncoding regulatory regions. However, tools for the automated identification and characterization of noncoding regulatory sequences in genomes have lagged far behind those employed to annotate and predict the functions of protein coding sequences. We developed a modified transformer model and trained it to predict relative patterns of expression across a diverse set of tissues given a large sequence window for each gene of interest in the maize (Zea mays) genome. Nucleotides in the input DNA sequence with high saliency in gene expression pattern prediction overlapped with regions identified via comparative genomic or chromatin-based approaches as potential regulatory sequences. High saliency regions identified in a second species, sorghum (Sorghum bicolor), without species-specific training were also associated with potential regulatory sequences in noncoding regions upstream and downstream of each gene of interest. The potential impact of a scaleable and transferable approach to identifying regulatory sequences using saliency calculated from large context window models spans multiple applications. Specific use cases could include genome annotation, interpretation of natural genetic variation, and targeted editing in noncoding regions to alter patterns of levels of gene expression.

plant biology↗

bioRxiv2024

Unveiling shared genetic regulators for plant architectural and biomass yield traits in sorghum

Sorghum is emerging as an ideal genetic model for designing high-biomass bioenergy crops. Biomass yield, a complex trait influenced by various plant architectural features, is typically regulated by numerous genes. This study aims to dissect the genetic mechanisms underlying fourteen plant architectural and ten biomass yield traits in a sorghum association panel (SAP) across two growing seasons. We identified 321 associated loci via genome-wide association studies involving 234,264 single nucleotide polymorphisms (SNPs). These loci encompass both genes with a priori links to biomass traits, such as maturity, dwarfing (Dw), leafbladeless1, cryptochrome, and several loci not previously linked to roles in determining these traits. We identified 22 pleiotropic loci associated with variation in multiple phenotypes. Three of these loci, located on chromosomes 3 (S03_15463061), 6 (S06_42790178; Dw2), and 9 (S09_57005346; Dw1), exert significant and consistent effects on multiple traits. Additionally, we identified three genomic hotspots on chromosomes 6, 7, and 9, containing multiple SNPs associated with variation in plant architecture and biomass yield traits. Positive correlations were observed among linked SNPs close to or within the same genomic regions. Thirteen haplotypes were identified from these positively correlated SNPs on chr 6, with haplotypes 8 and 11 emerging as optimal combinations, exhibiting pronounced effects on the traits. Lastly, network analysis revealed that loci associated with flowering, plant heights, leaf characteristics, plant number, and tiller number per plant were highly interconnected with other genetic loci linked to plant architecture and biomass yield traits. The pyramiding of favorable alleles related to these traits holds promise for enhancing the future development of bioenergy sorghum crops.

plant biology↗

bioRxiv2023

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Hyperspectral reflectance data can be collected from large plant populations in a high-throughput manner in both controlled and field environments. The efficacy of using hyperspectral leaf reflectance as a proxy for traits that typically require significant labor and time to collect has been evaluated in a number of studies. Commonly, estimating plant traits using hyperspectral reflectance involves collecting substantial amounts of ground truth data from plant populations, which may not be feasible for many researchers. In this study, we explore the potential of data-driven approaches to analyze hyperspectral reflectance data with little to no ground truth phenotypic measurements. Evaluations were performed using data on the reflectance of 2,151 individual wavelengths of light from the leaves of maize plants harvested from 1,658 field plots of a replicated trial including representatives of 752 maize genotypes from the Wisconsin Diversity Panel. We reduced the dimensionality of this dataset using an autoencoder neural network and principal component analyses, producing 10 latent variables and principal components, respectively. A subset of these principal components and latent variables demonstrated significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by genetic factors. Moreover, correlations were observed between variables derived from the autoencoder network and principal components with molecular traits. Notably, the most relevant latent variable (LV8) showed a much stronger correlation with chlorophyll content (R2 = 0.59) compared to the most correlated principal component (PC2; R2 = 0.31). Furthermore, one latent variable exhibited modestly better performance than a partial least squares regression model in estimating leaf chlorophyll content (PLSR; R2 = 0.58, LV8; R2 = 0.59). A number of genetic markers in the maize genome were significantly correlated with variation in different latent variables in genome wide association studies. In a number of cases, significant signals in genome wide association studies were adjacent to genes with plausible links to traits expected to influence leaf hyperspectral reflectance patterns.

plant biology↗

bioRxiv2023

Imitating the 'breeder's eye': predicting grain yield from measurements of non-yield traits

Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency and rate of genetic improvement in future breeding cycles. However, field trials conducted in one environment provide limited insight into how well crop varieties will perform in other environments. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties indicates that plant breeders will need to make decisions about new crop varieties without knowing the future environments those crop varieties will encounter in farmers fields. Therefore, significant improvements in cross-environment prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the twenty-first century. To address this challenge, we conducted linked yield trials of 752 public maize genotypes in two distinct environments: Lincoln, Nebraska, and East Lansing, Michigan. Our findings confirmed that genomic predictions of yield can outperform direct yield measurements used to train the genomic prediction model in predicting yield in a second environment. Additionally, we developed and trained another trait-based yield prediction model, which we refer to as the Silicon Breeders Eye (SBE). Our results demonstrate that SBE prediction has comparable predictive power to genomic prediction models. SBE prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations of individuals, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

genetics↗