About

Gina Brown-Guedira is a Professor in the Department of Crop and Soil Sciences at NC State University, affiliated with the College of Agriculture and Life Sciences. Her research focuses on plant genetics, with particular emphasis on wheat and other crops, leveraging multi-omics data and machine learning to improve crop yield predictions and resistance traits. She has contributed to the discovery of major QTLs for disease resistance, fine mapping of rust resistance genes, and the development of high-yielding, disease-resistant wheat cultivars adapted to the southeastern United States. Her work aims to enhance crop resilience and productivity through advanced genetic and genomic approaches.

Selected publications

• Insights into biomass accumulation and challenges in grain yield prediction of elite breeding materials using UAV‐based vegetation indices in soft red winter wheat

  The Plant Phenome Journal · 2026-03-31

  articleOpen access

  Abstract High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to understand the genetic basis of biomass accumulation in winter wheat and how this biomass is related to grain yield using unmanned aerial vehicle (UAV)‐based vegetation indices. A large panel of 596 soft red winter wheat genotypes was evaluated for agronomic performance in six environments to verify the ability of HTPs to predict grain yield using multivariate genomic prediction and random regression with Legendre polynomials to model growth through time. An additional set of 22 breeding lines was directly measured for above‐ground biomass, serving as a ground truth for the HTP‐derived biomass estimates. Cumulative vegetation indices were found to be a reliable method to infer biomass accumulation. Vegetation indices capture reliable phenotypes but exhibit low and inconsistent genetic correlation to grain yield, especially when incorporating residual covariance between traits. Predictive abilities of grain yield increased when using vegetation indices as a secondary trait in a multi‐trait genomic prediction model, but increases were highly variable across environments and growing stages, which may be confounded by micro‐environmental variation and lead to biased estimates of true genetic merit. Our results suggest that UAV‐based vegetation indices can be used to understand genetic parameters of biomass accumulation, but wheat breeders should use caution in their use as proxies for grain yield.

  PublisherDOI

• Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches

  Frontiers in Plant Science · 2026-02-16

  articleOpen accessSenior author

  Optimizing biomass partitioning is essential for achieving sustainable yield improvement in wheat, particularly under increasing environmental stress. Traits such as spike partitioning index (SPI), harvest index (HI), and fruiting efficiency (FE) are central to understanding how assimilates are allocated between vegetative and reproductive organs. However, their complex physiology and the difficulty of manual phenotyping have limited their routine use in breeding programs. This study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat. Three trials of facultative soft wheat lines (2022–2024) and an independent validation set of advanced breeding lines were used to develop genomic prediction (GP), phenomic prediction (PP), and integrated multi-omic models combining genomic, phenomic, and environmental covariates (ECs). Kernel-based best linear unbiased prediction (BLUP), and machine-learning based, random forest regression and partial least squares regression were implemented to estimate predictive ability (PA). Phenomics-driven models markedly outperformed GP across most traits, achieving PA up to 0.61 for SPI, 0.56 for FE, 0.71 for grains/m 2 (GN), and 0.66 for grain yield (GY). Hyperspectral data provided higher accuracy than vegetation indices, and multi-omic integration slightly improved prediction (PA up to 0.73 for GN). These results demonstrate that UAV-based hyperspectral phenotyping can effectively capture canopy-level physiological signals associated with biomass partitioning, offering a scalable and data-driven approach for in-season selections. This can help wheat breeding programs to optimize biomass partitioning in modern wheat cultivars for long-term yield resilience and genetic gain.

  PublisherOA PDFDOI

• Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi‐kernel genomic prediction models integrating secondary traits and environmental covariates

  The Plant Genome · 2025-06-01 · 2 citations

  articleOpen accessSenior author

  Abstract Achieving significant genetic gains in grain yield (GY) in wheat ( Triticum aestivum L.) requires optimization of the key biomass partitioning traits such as spike partitioning index (SPI) and fruiting efficiency (FE). However, traditional manual phenotyping of these traits is labor‐intensive and destructive, making it unsuitable for evaluating large germplasm panels. This study developed genomic prediction models to estimate these traits using diverse statistical methods while enhancing predictive ability (PA) by integrating environmental covariates (ECs) and secondary traits. A panel of 341 soft wheat elite lines was evaluated for biomass partitioning and yield‐related traits from 2022 to 2024 in Citra, FL. Genomic best linear unbiased predictor (GBLUP) and Bayesian methods performed similarly or better than machine learning models for SPI, harvest index (HI), and GY. On the other hand, random forest models performed better in predicting effective tillers m −2 (ET), 1000‐grain weight (TGW), and grain numbers per m 2 (GN). Multi‐kernel models incorporating ECs and secondary traits, such as plant height (PH) and aboveground biomass, substantially improved PA compared to genomics‐only approaches. For 1000‐grain weight, PA increased from 18% to 78%, with similar enhancements varying across other traits. Validations performed on separate breeding trial confirmed the reliability of the multi‐kernel models, even though they showed a slightly lower PA compared to within‐panel validations. These findings highlight the potential of integrating diverse data types or omics to enhance the prediction of biomass partitioning traits, speeding up genetic advancements, and the development of high‐yield wheat varieties to address future food security challenges.

  PublisherOA PDFDOI

• Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials

  Agronomy · 2025-05-28 · 5 citations

  articleOpen accessSenior author

  Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. Genomic (G) data were combined with hyperspectral (H) and multispectral + thermal (M) imaging across the 2022 and 2023 growing seasons at the Plant Science Research and Education Unit, Citra, Florida. A panel of 312 wheat genotypes was analyzed using GBLUP-based models, integrating G + H and G + M data from SP to predict BP yield. SP models demonstrated promising predictive ability, with G + H models achieving moderate within-year (0.43 to 0.51) and across-year (0.43) prediction accuracies, while G + M models reached 0.53 to 0.58 and 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy of 0.47 when M data from the 2022 SP, combined with G, was used to predict BP yield in 2023. Additionally, the top 25% specificity (coincide index) was evaluated, with models showing up to 47–51% within a year and 43–45% between years overlap in the highest predicted-yielding lines between SP and BP trials, further emphasizing the potential of SP data for early selection. These findings suggest that SP trials can provide meaningful predictions for BP yields, enabling earlier selection and faster breeding cycles.

  PublisherDOI

• Genetic mapping of resistance to <i>Fusarium</i> head blight in soft red winter wheat line NC13‐20076

  Crop Science · 2025-03-01

  articleOpen accessSenior author

  Abstract Fusarium head blight (FHB) infection causes yield loss, quality degradation, and the production of damaging mycotoxins in common wheat ( Triticum aestivum L). Marker analysis suggests that NC13‐20076 does not possess previously identified FHB resistance quantitative trait loci (QTL) screened for in eastern winter wheat germplasm. A doubled haploid population of 168 lines from the cross of GA06493‐13LE6 and NC13‐20076 was phenotyped in inoculated nurseries in six environments. Heading date, plant height, and visual ratings of Fusarium damage on heads were recorded in the field; percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) accumulation were recorded post‐harvest. Interval and multiple QTL mapping were performed on each environment‐by‐trait combination. Plant height and heading date QTL were identified on chromosomes 4A, 5A, 6A, and 7B, and peak markers were used as covariates in mapping of disease response traits. Disease response QTL were identified on chromosomes 1A, 2A, 2B, 3A, 3B, 4A, 5A, 7A, and 7D. The largest percent variance (PV) QTL identified for FHB visual ratings (10.8%) and DON accumulation (10.1%) were found on chromosome 5A ( QFvr.nc‐5A , QDon.nc‐5A ). The largest PV (10.3%) QTL identified for FDK were found on 1A ( QFdk.nc‐1A ). Disease response QTL for multi‐environment scans of visual ratings, FDK, and DON accumulation accounted for 4.0%–10.8%, 4.1%–10.3%, and 4.9%–10.1% of the total variance, respectively. The present results indicate that NC13‐20076 contains several FHB response QTL, which overlap with previously identified QTL and demonstrate the importance of NC13‐20076 as a readily accessible source of FHB resistance.

  PublisherOA PDFDOI

• Enhancing genomic‐based forward prediction accuracy in wheat by integrating UAV‐derived hyperspectral and environmental data with machine learning under heat‐stressed environments

  The Plant Genome · 2025-01-08 · 10 citations

  articleOpen accessSenior author

  Integrating genomic, hyperspectral imaging (HSI), and environmental data enhances wheat yield predictions, with HSI providing detailed spectral insights for predicting complex grain yield (GY) traits. Incorporating HSI data with single nucleotide polymorphic markers (SNPs) resulted in a substantial improvement in predictive ability compared to the conventional genomic prediction models. Over the course of several years, the prediction ability varied due to diverse weather conditions. The most comprehensive parametric model tested, which included SNPs, HSI, and environmental covariates data, consistently achieved the best results, closely followed by machine learning (ML) approaches when considering the same omics data. For example, the most comprehensive model (M9), under the forward prediction cross-validation scheme, predicted the GY of the 2023 growing season using data from 2021 and 2022 for a correlation between predicted and observed values of 0.53. This model demonstrated superior performance compared to less complex models, emphasizing the advantage of integrating numerous data sources and their interactive effects. Furthermore, when comparing the top 25% of the predicted lines versus the corresponding observed lines with the highest GY, the M9 model returned a coincide index (CI) of 55% (i.e., in both sets, 55% of the top 25% values were common), whereas for the highest performing ML model (gradient boosting regression), the CI was of 46%. This study highlights the potential of multi-data source approaches to accelerate the selection of heat-tolerant wheat genotypes.

  PublisherOA PDFDOI

• TaWUS2D regulates the number of grains per spikelet by enhancing the number of fertile ovaries in Multi-Ovary Wheat

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-08

  preprintOpen access

  Innovative genetic improvements in the staple crop Triticum aestivum (bread wheat) are urgently needed to address the growing global food security crisis. Here, we report the map-based cloning of TaWUS2D, the gene responsible for the dominant multi-ovary phenotype in wheat. Multi-ovary lines are characterized by the development of three fertile ovaries per floret that results in three grains, as opposed to wildtype single ovary wheat. We used HiFi long-reads to assemble a 14.48 Gbp genome scaffold assembly in the background of mutli-ovary wheat line MOV. Using high-resolution genetic mapping, combined with additional genomic resources, we defined the Mov-1 locus to a 135 Kbp region containing two genes. Using five independent deletion mutants and eight TILLING mutants, we demonstrate that a functional WUSCHEL-like protein, TaWUS2D, is required for the multi-ovary phenotype. TaWUS2D is upregulated in the MOV genetic background. This research lays the groundwork for developing new approaches to improve wheat production potential and sustainability in the face of current and future global food challenges.

  PublisherOA PDFDOI

• Genome-Wide Association study in a US soft winter wheat population reveals novel and known sources of resistance to the Septoria tritici blotch pathogen <i>Zymoseptoria tritici</i>

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-02

  preprintOpen access

  Abstract Key message GWAS of 337 soft winter wheat genotypes from a breeding program in Indiana, USA identified marker-trait associations that likely correspond with existing and novel resistances to Septoria tritici blotch disease. Septoria tritici blotch (STB), caused by the ascomycete fungus Zymoseptoria tritici , is a major disease of wheat worldwide. To find additional sources of resistance, Genome-Wide Association (GWA) was used to analyze 337 soft winter wheat genotypes from Indiana, USA, by inoculating seedlings with two isolates of Z. tritici in a complete randomized design. Necrosis and pycnidia development were assessed at 14, 18 and 22 days post inoculation, enabling the calculation of area under the disease progress curve (AUDPC) for each parameter. Adjusted necrosis and pycnidia AUDPC scores were compared with 14,341 high-quality SNPs in a GWA analysis using the FarmCPU and GAPIT CMLM models to identify markers associated with the resistance. Significant (p &lt; 0.05) isolate ξ genotype interactions were identified, confirming that the phenotypic variation was caused by isolate-specific resistance genes. Overall, 9 marker-trait associations (MTAs) were identified with Z. tritici necrosis and pycnidia resistance. All mapped MTAs were isolate and necrosis/pycnidia specific. Distinct MTAs were mapped for necrosis and pycnidia on chromosome 6A, and for pycnidia on chromosomes 1A, 1D, 4A, 7A, 3B and 5B. The MTAs on chromosomes 4A, 6A, 5B and 1D likely corresponded with the known genes Stb7 , Stb15 , Stb1 and Stb19 , respectively. Those on chromosomes 1A, 7A and 3B were not associated with previously known genes and may be novel. Candidate genes near the marker locations have been identified for further investigations. Indiana soft winter wheat germplasm segregates for novel and known Stb genes and constitutes a valuable breeding resource for Z. tritici resistance.

  PublisherOA PDFDOI

• Utilizing Multivariate Genomic Prediction to Predict Wheat Spike Characteristics in Soft Red Winter Wheat ( <scp> <i>Triticum aestivum</i> </scp> L.)

  Plant Breeding · 2025-09-11 · 1 citations

  articleOpen access

  ABSTRACT Genomic prediction (GP) can increase genetic gain by allowing for the selection of traits earlier in the breeding cycle. Spike morphology traits are of interest because of their relationship with grain yield. To better understand the prediction capabilities of agronomic traits and spike architecture traits, a population of 594 soft red winter wheat inbred lines was evaluated for traits including heading date, grain weight spike −1 , thousand kernel weight, and kernel spike −1 and spike architecture traits spikelets spike −1 (SPS), spike length (SL), and spike width (SW). Univariate GP models were utilized for each spike trait, and multivariate GP models were used to predict spike architecture traits using each combination of all seven traits for a total of 63 multivariate models per spike trait. Significantly higher cross‐validation mean prediction accuracies ( p ( Z ) &lt; 0.05) were observed for 52 models for SPS, 0 models for SW, and 13 models for SL compared with the univariate models of each trait. Methods in this study could be used for GP of spike architecture traits with further analysis of forward validation accuracy.

  PublisherOA PDFDOI

• Temporal genetic relationships between growth, development, and malting quality in winter barley ( <i>Hordeum vulgare</i> ) using aerial imagery

  The Plant Phenome Journal · 2025-12-29 · 1 citations

  articleOpen access

  Abstract Grain characteristics are the cumulative product of growth and development throughout the growing season. In barley ( Hordeum vulgare ), these traits determine the grain's value for malting purposes. The ability to accurately predict the genetic merit for malting quality is of great interest for barley breeding programs. Same‐season selection on malting quality traits is nearly impossible due to the costly, destructive, time‐intensive, malting, and testing procedures. This study examined the temporal relationships of growth and development with grain quality measures through genetic correlations of vegetative indices and end‐use traits. Normalized difference vegetation index (NDVI) calculated from aerial imagery is rapid to collect and models photosynthesis and vegetative growth. Malting quality, agronomic traits, and NDVI were measured in 385 lines of two‐row winter barley breeding germplasm across 2 years. Genetic and residual correlations between malt quality and agronomic traits from bivariate genomic prediction models point to environmental variation due to field heterogeneity and batch malting effects. The reliability of NDVI was high during fall growth, reduced during spring vegetative growth, and increased again after flowering. Heading date was positively correlated (mean ) with NDVI during heading, but genetic correlations between NDVI and other traits were not consistent across environments. Multi‐trait models using NDVI as the secondary trait were fit for each end‐use trait. Compared to single‐trait genomic prediction models, multi‐trait genomic prediction models increased predictive abilities by an average of 0.04 across multiple traits and environments. However, NDVI was not found to be effective as an economic secondary trait for selection of malting quality traits.

  PublisherOA PDFDOI