Biology subjects

Brown-Guedira, G.

Publications and source records attributed to Brown-Guedira, G..

14 recordsLinked to original sources

bioRxiv2026

Environmentally-mediated yield effects of vernalization and photoperiod alleles in historic winter wheat trials

In common wheat (Triticum aestivum L.), variation at the Vrn1 and Ppd1 loci changes plant phenology, resulting in differential adaptation useful to breeders. Because the effects of phenology on grain yield are conditional on environmental factors, the incorporation of markers for Vrn1 and Ppd1 alleles into breeding approaches has proven difficult. Historic phenotypic, genotypic, and environmental data can provide insight into the relationship between major phenology alleles and grain yield as mediated by environmental variables. Analyses of eight years of breeding trials (1,038 lines at 219 site-years) determined that weak vrn1 alleles and Ppd1 insensitivity alleles have variable effects. These effects change depending on an environment's winter temperature and latitude, respectively, but the environmentally-driven changes in effect of vrn1 alleles are much more variable than effects of Ppd1 alleles. Mediation analyses showed phenologically-dependent environmental variables are one mechanism through which phenological changes created by vrn1 and Ppd1 variants alter yield across multiple developmental stages. Environmentally-driven relationships between flowering time and yield were modeled to estimate yield allele effects as a function of effects on phenology and environmental variables. Weak winter alleles at vrn1 may play a stabilizing role on yield, due to the correlation of winter conditions that drive larger effects with later-season warm temperatures that penalize later maturity. A better mechanistic understanding of how these loci drive environment-specific yield effects may facilitate utilization of markers for these alleles and improve predictive modeling of yield.

genetics↗

bioRxiv2026

Genome-Wide Markers Predict Metribuzin Tolerance in Southern Soft Red Winter Wheat

Metribuzin is a versatile herbicide effective against various annual grasses and broadleaf weeds found in wheat fields. However, it can cause foliar damage to wheat, impacting plant health and yield. A clearer understanding of the genetic architecture associated with metribuzin tolerance is necessary to guide marker-based breeding strategies. This study evaluated 351 historic Gulf Atlantic Wheat Nursery (GAWN) wheat breeding lines representative of southern US soft red winter wheat (SRWW) germplasm. Field trials were conducted at Winnsboro (WN) and Baton Rouge (BR), Louisiana, in 2016 and 2017. Metribuzin was applied at specific growth stages, and tolerance was assessed based on visual foliar damage. Genomic data from 6,252 filtered single nucleotide polymorphism (SNP) markers were used to estimate narrow-sense heritability, conduct genome-wide association (GWAS), and assess genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP). Broad-sense heritability ranged from 0.54 to 0.69 within environments and reached 0.77 across environments, while narrow-sense heritability ranged from 0.35 to 0.47, indicating moderate additive genetic control. No SNP surpassed the significance threshold, but genomic prediction (GP) showed moderate to strong predictive ability (PA) across environments, with the highest accuracy (r = 0.62) observed between BR17 and WN17. These results indicate that metribuzin tolerance in SRWW is primarily controlled by multiple small-effect loci and that GS provides a more effective breeding strategy than marker-assisted selection for improving tolerance in southern wheat germplasm.

genomics↗

bioRxiv2026

Genomic Prediction Enables Same-Season Selection for Reduced Glycosidic Nitrile in Eastern U.S. Winter Barley

Glycosidic nitriles (GN) in barley are precursors to carcinogens formed during distillation, making GN reduction a critical breeding objective for malting and distilling industries. Measurement of GN is time-consuming. Grain must first be malted before GN can be quantified, and generally cannot be completed before selections must be made in a winter barley breeding program. Here, feasibility of same-season genomic selection against GN content was evaluated in elite Virginia Tech winter barley germplasm. In 2023, all 176 elite breeding lines screened for presence of GN were shown to be GN producers. A subset of 95 lines was then quantitatively measured for GN concentration to determine the genetic variability for the trait. Efficacy of genomic selection for GN was first assessed using a divergent selection approach on the remaining 81 predicted lines. The highest 16 and lowest 16 of the predicted lines were chosen for GN quantification. A significant phenotypic difference was found between the predicted high and low group means (0.8 ppm; P = 0.003). An additional 120 lines were quantified the following year to determine repeatability. GN exhibited moderate narrow-sense heritability (h2 = 0.42) and a high genetic correlation (r = 0.79) across years. Moderate predictive ability as was observed in cross-validation (range 0.38 - 0.61), and forward prediction using 2023 to predict 2024 (r = 0.39). A genome-wide scan did not identify any major-effect loci, suggesting GN content is polygenic, thus enabling same-season genomic selection to reduce GN content in this germplasm.

plant biology↗

bioRxiv2026

Reaction Norm Modeling of High-Dimensional Genomic and Environmental Data Improves Prediction Accuracy in Winter Wheat

Genomic prediction models that account genotype-by-environment (GxE) have the potential to accelerate the rate of genetic gain for yield and agronomic performance, yet relatively few studies have applied GxE prediction in public soft red winter wheat (Triticum aestivum) breeding programs. In this study, we extended a reaction norm-based genomic prediction framework by integrating weather-based environmental covariates to more effectively capture genotype- environment interactions. Key agronomic traits, including seed yield, plant height, test weight, and heading date, were evaluated across 33 environments (location-year) using over 3,200 breeding lines from the North Carolina State University small grains breeding program. Multiple genomic prediction models were compared using several cross-validation (CV) schemes representing common breeding scenarios. Across traits, the reaction norm M5 model, which incorporates both GxE and genotype-by-environmental covariate interactions (GxO), achieved the highest prediction accuracy (PA) in CV2 (predicting incomplete field trials) and CV1 for yield and test weight (predicting new lines). The highest PA was observed for test weight under CV2 (0.54) and for yield under CV1 (0.41). Under CV0 (predicting new environments), the M3 model incorporating GxE produced highest PA across traits, with the greatest accuracy for plant height (0.45), although differences among M2, M3, and M4 were small. Prediction under CV00 (predicting new lines in new environments) remained more challenging, with PA values 0.10 - 0.20 across traits. Overall, our results demonstrate that integrating environmental covariates into genomic prediction models can improve predictive performance across diverse wheat-growing environments in North Carolina, supporting their utility for applied breeding efforts. CORE IDEASO_LIIntegrating genotype-by-environment (GxE) interactions with environmental covariates improves prediction accuracy across environments. C_LIO_LIModel performance varies by prediction scenario, with different approaches performing best for new lines, incomplete trials, or new environments. C_LIO_LIPrediction of new lines in new environments remains challenging. C_LI PLAIN LANGUAGE SUMMARYThis study explores how adding environmental information to genomic prediction models can improve prediction accuracy in a public winter wheat breeding program. Using data from multi-environment trials conducted across diverse conditions in North Carolina, we evaluated statistical models that capture how different wheat lines respond to changing environments. By incorporating weather data, we improved the ability to predict performance across locations and years. These findings provide practical insights for refining selection strategies and accelerating genetic gain in wheat breeding.

genetics↗

bioRxiv2025

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

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. Abstract 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 < 0.05) isolate {xi} genotype interactions were identified, confirming that the phenotypic variation was caused by isolate-specific resistance genes. Overall, 12 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, 3B, 4A, 5B, 6A and 7A. MTAs on chromosomes 5B, 4A, 6A, and 1D likely corresponded with the known genes Stb1, Stb7, Stb15 and Stb19, respectively. MTAs on chromosomes 1A, 3B, 4A, 6A and 7A were not associated with previously known Stb genes and may be novel. Candidate genes near the marker locations have been identified for further investigations. The GWAS showed that Indiana soft winter wheat germplasm segregates for novel and known Stb genes and constitutes a valuable breeding resource for Z. tritici resistance.

plant biology↗

bioRxiv2025

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

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.

plant biology↗

bioRxiv2023

Development of a Next Generation SNP Genotyping Array for Wheat

High throughput genotyping arrays have provided a cost effective, reliable and interoperable system for genotyping hexaploid wheat and its related germplasm pool. Existing, highly cited arrays including our 35K Axiom Wheat Breeders genotyping array and the Illumina 90K iSelect array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in sequencing wheat varieties and landraces has given us access to a vast pool of SNP diversity, whilst technological improvements in array design has allowed us to fit significantly more probes onto a 384-well format Axiom array than was previously possible. Here we describe a novel High Density Axiom genotyping array, the Triticum aestivum Next Generation array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins "Core Collection". We use a novel "minimal marker" optimisation approach to select up to six SNPs in each 1.5 MB region of the wheat genome with the highest combined varietal discrimination potential. A design iteration step allowed us to test and replace skim-sequence derived SNPs which failed to convert to reliable Axiom markers, resulting in a final design, designated TaNG1.1 with 43,372 SNPs derived from a haplotype-optimised combination of novel SNPs, DArTAG-derived and legacy wheat Axiom markers. We show that this design has an even distribution of SNPs across chromosomes and sub-genomes compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous Axiom array. We also demonstrate the improved performance of TaNG1.1 for Genome Wide Association Studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available, and the marker annotations, initial genotyping results and software used to generate the optimised marker sets are freely available.

genomics↗

bioRxiv2023

Sequencing 4.3 million mutations in wheat promoters to understand and modify gene expression

Wheat is an important contributor to global food security, and further improvements are required to feed a growing human population. New functional genetics and genomics tools can help us to understand the function of different genes and to engineer beneficial changes. In this study, we used a promoter capture assay to sequence 2-kb regions upstream of all high-confidence annotated genes from 1,513 mutagenized plants from the tetraploid wheat variety Kronos. We identified 4.3 million induced mutations with an accuracy of 99.8%, resulting in a mutation density of 41.9 mutations per kb. We also remapped Kronos exome capture reads to Chinese Spring RefSeq v1.1, identified 4.7 million mutations, and predicted their effects on annotated genes. Using these predictions, we identified 59% more non-synonymous substitutions and 49% more truncation mutations than in the original study. To show the biological value of the new promoter dataset, we selected two mutations within the promoter of the VRN-A1 vernalization gene. Both mutations, located within transcription factor binding sites, significantly altered VRN-A1 expression, and one reduced the number of spikelets per spike. These publicly available sequenced mutant datasets provide rapid and inexpensive access to induced variation in the promoters and coding regions of most wheat genes. These mutations can be used to understand and modulate gene expression and phenotypes for both basic and commercial applications, where limited governmental regulations can facilitate deployment. These mutant collections, together with gene editing, provide valuable tools to accelerate functional genetic studies in this economically important crop. Significance StatementWe sequenced 4.3 million induced mutations in the promoters and 4.7 million in the coding regions of most wheat genes. We also show how this public resource can be used to understand gene function, modulate gene expression, and generate changes in valuable wheat agronomic traits.

genomics↗

bioRxiv2023

Bearded or Smooth? Awns Improve Yield when Wheat Experiences Heat Stress During Grain Fill

The presence or absence of awns - whether wheat heads are "bearded" or "smooth"- is the most visible phenotype distinguishing wheat cultivars. Previous studies suggest that awns may improve yields in heat or water-stressed environments, but the exact contribution of awns to yield differences remains unclear. Here we leverage historical phenotypic, genotypic, and climate data to estimate the yield effects of awns under different environmental conditions over a 12-year period in the Southeast US. Lines were classified as awned or awnless based on sequence data, and observed heading dates were used to associate grain fill periods of each line in each environment with climatic data and grain yield. In most environments, awn suppression was associated with higher yields, but awns were associated with better performance in heat-stressed environments more common at southern locations. Wheat breeders in environments where awns are only beneficial in some years may consider selection for awned lines to reduce year-to-year yield variability, and with an eye towards future climates.

genetics↗

bioRxiv2021

Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 93% imputation accuracy with 0.01x sequence coverage, which was only slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.9%). Compared to Beagle, on average, PHG imputation was ~4% (p-value = 0.00027) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. The reduced accuracy of imputation with GBS data (90.4%) is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in our reference panel. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

genomics↗

bioRxiv2020

Genetic variation for plant growth traits in a common wheat population is dominated by known variants and novel QTL

Genetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL. We find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models. In a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs.

genetics↗

bioRxiv2019

Natural genetic variation underlying tiller development in barley (Hordeum vulgare L)

In barley (Hordeum vulgare L.), lateral branches called tillers contribute to grain yield and define shoot architecture, but genetic control of tiller number and developmental rate are not well characterized. The primary objectives of this work were to examine relationships between tiller number and other agronomic and morphological traits and identify natural genetic variation associated with tiller number and rate, and related traits. We grew 768 lines from the USDA National Small Grain Core Collection in the field and collected data over two years for tiller number and rate, and agronomic and morphological traits. Our results confirmed that spike row-type and days to heading are correlated with tiller number, and as much as 28% of tiller number variance is attributed to these traits. In addition, negative correlations between tiller number and leaf width and stem diameter were observed, indicating trade-offs between tiller development and other vegetative growth. Thirty-three quantitative trait loci (QTL) were associated with tiller number or rate. Of these, 40% overlapped QTL associated with days to heading and 22% overlapped QTL associated with spike row-type, further supporting that tiller development is influenced by these traits. Despite this, some QTL associated with tiller number or rate, including the major QTL on chromosome 3H, were not associated with any other traits, suggesting that tiller number can be modified independently of other important agronomic traits. These results enhance our knowledge of the genetic control of tiller development in barley, which is important for optimizing tiller number and rate for yield improvement.

genetics↗

bioRxiv2019

Sequence based mapping identifies AWNS1, a candidate transcription repressor underlying awn suppression at the B1 locus in wheat

Awns are stiff, hair-like structures that grow from the spikelets of wheat (Triticum aestivum L.) and other grasses. In wild wheat relatives, awns play a role in seed dispersal. Awn suppression in domesticated wheat, where awns are shortened or even eliminated entirely, is variably adaptive with both awned and awnless types under widespread cultivation. Although the B1 locus on the long arm of chromosome 5A is a major determinant of awn suppression, no underlying gene or mechanism of action has been identified. Using association mapping, we identified a SNP marker located on the distal end of 5AL (5A28417) predictive of awn status in a panel of 640 U.S. winter wheat breeding lines, indicating that B1 was the determinant of awn suppression in this germplasm. Analysis of historical data available for the panel determined 5A28417 was also significantly associated with grain test weight. Evaluation of spike morphology and kernel traits was undertaken in a 341 RIL population developed from a cross between awned soft winter wheat cultivar LA95135 and awnless cultivar SS-MPV57. Awn suppression in the population co-segregated with 5A28417 and was co-located with QTL for number of spikelets per spike, kernel weight and kernel length. Fine-mapping located B1 to a region containing only two predicted genes, including a C2H2 zinc finger transcription factor 219 bp from 5A28417 that we named AWNS-A1. Deletions encompassing both genes were present in awned mutants of the awnless cultivar Brundage. Polymorphisms in the AWNS-A1 coding region were not observed in diverse wheat germplasm. However, sequencing of wheat lines representing different marker haplotypes in the surrounding region identified a deletion 3 kb downstream of AWNS-A1. A marker for this deletion was highly predictive of awn suppression in a collection of diverse wheat accessions, and indicates that AWNS-A1 is likely the major determinant of awn suppression in global wheat germplasm. AWNS-A1 is more highly expressed in developing spikes of awnless individuals, suggesting a mechanism for awn suppression.

genetics↗

bioRxiv2018

Genome-wide association studies for yield-related traits in soft red winter wheat grown in Virginia

Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 324 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate promising avenues for increasing grain yield by exploiting variation in traits relating to the number of grains per unit area, as well as phenological traits influencing grain-filling duration of genotypes.

plant biology↗