Biology subjects

Flint-Garcia, S.

Publications and source records attributed to Flint-Garcia, S..

14 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

Cis-regulatory variation and transcription factor binding contribute to allelic genotype-by-environment interactions for gene expression in maize

Genotype-by-environment interactions (GxE), or differences in how genotypes perform across varying environments, are a pervasive source of phenotypic variation and underlie differences in local adaptation. Though GxE is well characterized across kingdoms of life, less is known about what causes GxE interactions, particularly at the molecular level. In this study, we use allele-specific gene expression estimates in a maize (Zea mays L.) B73 x Mo17 hybrid to isolate cis-regulatory effects on gene expression for each of the two parental alleles. The hybrid was grown in two environments, and expression differences between the parental alleles were used to characterize allele-by-environment (AxE) interactions and study the influence of gene-proximal sequence variation on transcript abundance AxE. We tested the hypothesis that gene-proximal sequence variation can cause GxE in gene expression by modifying transcription factor binding. Our results show that sequence variation in gene promoter regions has a small but consistent enrichment in genes that show transcriptional AxE. Further, we demonstrate that differential transcription factor binding potential caused by sequence variation is also enriched in AxE genes. Predictive models trained on sequence and transcription factor binding variation show that while these features contain some information about whether a gene will show transcriptional AxE, they alone are not sufficient to reliably distinguish AxE genes. These findings support the hypothesis that gene expression GxE can be caused by sequence variation that modifies transcription factor binding, while also reinforcing the complex and context-specific nature of GxE interactions.

genomics↗

bioRxiv2026

From Kernel to Kitchen: Asparagine Synthetase 3 is Involved in Free Asparagine Accumulation in Maize Kernels

Acrylamide is a probable dietary carcinogen formed through high-temperature cooking processes from one of its precursors, free asparagine (Asn). To reduce acrylamide formation potential in maize-based food products, the genetic basis for the accumulation of free Asn in maize kernels must be elucidated. In this study we integrated complementary quantitative genetic approaches including quantitative trait locus (QTL) mapping of a biparental population and genome-wide association studies (GWAS) in a diverse inbred panel. We ultimately identified one major QTL for free Asn which was not reflected in the GWAS results. Subsequent sequence analysis of the QTL mapping population parents revealed a 921bp deletion in As-paragine Synthetase 3 (ASN3) in the low Asn parent. Validation is underway with rtPCR, genotyping the association panel for the deletion, and evaluation of a CRISPR-Cas9 knockout of ASN3. This work provides a foundation for breeding and genome-editing strategies to reduce acrylamide-forming potential in maize-based foods.

Plant Biology↗

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

The origins and adaptive consequences of polyploidy in a dominant prairie grass

Polyploidy is ubiquitous across North American prairies, which provide essential ecosystem services and rich soil for agriculture. Yet the mechanism driving polyploid abundance is unclear. Multiple hypotheses have been proposed including polyploid abundance is proportional to the opportunity for whole genome duplication (WGD), and WGD alters phenotypes that may increase fitness. We tested these two hypotheses together in the mixed-ploidy species Andropogon gerardi, a dominant grass species in endangered North American tallgrass prairies. Leveraging a novel, phased allopolyploid reference genome, we found the A. gerardi hexaploid arose after the C4 grassland expansion in the early Pleistocene, when glacial cycles likely increased secondary contact between the diploid progenitors. We sequenced A. gerardi from 25 popula-tions and examined cytotype performance and morphology in a controlled environment to investigate the consequences of the contemporary mixed-ploidy populations. We found the 9x A. gerardi cytotype is a neopolyploid and a result of recurrent WGD events. Further, we demonstrate the 9x neopolyploids have greater growth and a decreased stomatal pore index, which is adaptive in xeric climates where the 9x cy-totype is most common. Together, our results support both hypotheses for polyploid abundance in North America: WGD is a product of opportunity and can have immediate fitness consequences. Although the changes to fitness may provide an advantage to 9x A. gerardi, the establishment of 9x may lower overall population fitness due to the lower reproductive viability of 9x individuals. Significance statementPolyploid species are abundant in North American prairies and make up many of the dominant species in the ecosystem. This prominence could be a result of whole genome duplication conferring an advantage that increases the frequency of polyploids or could simply indicate that the opportunity for whole genome duplication is higher in this ecosystem, or both. Through examining three polyploidization events in A. gerardi, the dominant species in endangered tallgrass North American prairies, we found whole genome duplication is both surprisingly common and confers traits that are beneficial in some environments.

plant biology↗

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

Allele-specific expression reveals multiple paths to highland adaptation in maize

Maize is a staple food of smallholder farmers living in highland regions up to 4,000 meters above sea level worldwide. Mexican and South American highlands are two major highland maize growing regions, and population genetic data suggests the maizes adaptation to these regions occurred largely independently, providing a case study for parallel evolution. To better understand the mechanistic basis of highland adaptation, we crossed maize landraces from 108 highland and lowland sites of Mexico and South America with the inbred line B73 to produce F1 hybrids and grew them in both highland and lowland sites in Mexico. We identified thousands of genes with divergent expression between highland and lowland populations. Hundreds of these genes show patterns of convergent evolution between Mexico and South America. To dissect the genetic architecture of the divergent gene expression, we developed a novel allele-specific expression analysis pipeline to detect genes with divergent functional cis-regulatory variation between highland and lowland populations. We identified hundreds of genes with divergent cis-regulation between highland and lowland landrace alleles, with 20 in common between regions, further suggesting convergence in the genes underlying highland adaptation. Further analyses suggest multiple mechanisms contribute to this convergence. Our findings reveal a complex genetic architecture of cis-regulatory alleles underlying adaptation to highlands in maize. Although the vast majority of evolutionary changes associated with highland adaptation were region-specific, our findings highlight an important role for convergence at the gene expression and gene regulation levels as well.

evolutionary biology↗

bioRxiv2021

A B73 x Palomero Toluqueo mapping population reveals local adaptation in Mexican highland maize

Generations of farmer selection have produced a unique collection of traditional maize varieties adapted to the environmental challenges of the central Mexican highlands. In addition to agronomic and cultural value, Mexican highland maize represents a good system for the study of local adaptation and acquisition of adaptive phenotypes under cultivation. In this study, we characterized a recombinant inbred line population derived from the cross of the B73 reference line and the Mexican highland maize variety Palomero Toluqueno. Evaluation over multiple years in lowland and highland field sites in Mexico identified genomic regions linked to yield components and putatively adaptive morphological traits. A region on chromosome 7 associated with ear weight showed antagonistic allelic effects in lowland and highland fields, suggesting a trade-off consistent with local adaptation. We identified several alleles of highland origin associated with characteristic highland traits, including reduced tassel branching, increased stem pigmentation and the presence of stem macrohairs. The oligogenic architecture of characteristic morphological traits supports their role in adaptation, suggesting they have arisen from consistent directional selection acting at distinct points across the genome. We discuss these results in the context of the origin of phenotypic novelty during selection, commenting on the role of de novo mutation and the acquisition of adaptive variation by gene flow from endemic wild relatives.

plant biology↗

bioRxiv2021

Predicting Moisture Content During Maize Nixtamalization Using Machine Learning with NIR Spectroscopy

Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearmans rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait. Key MessageMoisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.

plant biology↗

bioRxiv2021

Genetic Architecture of Kernel Compositional Variation in a Maize Diversity Panel

Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which overtime has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants. However, much of the underlying genetic architecture controlling these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g. carbohydrates, protein, starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g. dent, sweetcorn, popcorn) were explored and significant differences and correlations were detected. In total, 22.9-71.1% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 70 statistically significant loci for 12 compositional traits. This study provides valuable insights in the phenotypic variation and genetic architecture underlying compositional traits that can be used in breeding programs for improving maize grain quality. Core IdeasO_LIUnderstanding kernel compositional variation is important for food grade corn improvement. C_LIO_LIGenetic and environmental factors account for most of the variation in compositional traits. C_LIO_LIA broad range in trait heritabilities was observed across compositional traits. C_LIO_LICompositional trade-offs will be important to consider when conducting multitrait breeding. C_LIO_LICompositional traits are mostly controlled by a large number of small effect loci. C_LI

plant biology↗

bioRxiv2019

The relevance of dominance and functional annotations to predict agronomic traits in hybrid maize

Heterosis has been key to the development of maize breeding but describing its genetic basis has been challenging. Previous studies of heterosis have shown the contribution of within-locus complementation effects (dominance) and their differential importance across genomic regions. However, they have generally considered panels of limited genetic diversity and have shown little benefit to including dominance effects for predicting genotypic value in breeding populations. This study examined within-locus complementation and enrichment of genetic effects by functional classes in maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the United States (1,106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1,640 hybrids). We assayed three agronomic traits: days to silking (DTS), plant height (PH) and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results point to mechanisms by which heterosis arises through local complementation in proximal gene regions and suggest the relevance of dominance and gene annotations for genomic prediction in maize.

genomics↗

bioRxiv2019

Hybrid decay: a transgenerational epigenetic decline in vigor and viability triggered in backcross populations of teosinte with maize

In the course of generating populations of maize with teosinte chromosomal introgressions, an unusual sickly plant phenotype was noted in individuals from crosses with two teosinte accessions collected near Valle de Bravo, Mexico. The plants of these Bravo teosinte accessions appear phenotypically normal themselves and the F1 plants appear similar to typical maize x teosinte F1s. However, upon backcrossing to maize, the BC1 and subsequent generations display a number of detrimental characteristics including shorter stature, reduced seed set and abnormal floral structures. This phenomenon is observed in all BC individuals and there is no chromosomal segment linked to the sickly plant phenotype in advanced backcross generations. Once the sickly phenotype appears in a lineage, normal plants are never again recovered by continued backcrossing to the normal maize parent. Whole-genome shotgun sequencing reveals a small number of genomic sequences, some with homology to transposable elements, that have increased in copy number in the backcross populations. Transcriptome analysis of seedlings, which do not have striking phenotypic abnormalities, identified segments of 18 maize genes that exhibit increased expression in sickly plants. A de novo assembly of transcripts present in plants exhibiting the sickly phenotype identified a set of 59 up-regulated novel transcripts. These transcripts include some examples with sequence similarity to transposable elements and other sequences present in the recurrent maize parent (W22) genome as well as novel sequences not present in the W22 genome. Genome-wide profiles of gene expression, DNA methylation and sRNAs are similar between sickly plants and normal controls, although a few up-regulated transcripts and transposable elements are associated with altered sRNA or methylation profiles. This study documents hybrid incompatibility and genome instability triggered by the backcrossing of Bravo teosinte with maize. We name this phenomenon \"hybrid decay\" and present ideas on the mechanism that may underlie it.

genetics↗