Biology subjects

Lyerly, J.

Publications and source records attributed to Lyerly, J..

5 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

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↗

bioRxiv2023

HaploCatcher: An R Package for Prediction of Haplotypes

Wheat (Triticum aestivum L.) is crucial to global food security, but is often threatened by diseases, pests, and environmental stresses. Wheat stem sawfly (Cephus cinctus Norton) poeses a major threat to food security in the United States, and solid-stem varieties, which carry the stem-solidness locus (Sst1), are the main source of genetic resistance against sawfly. Marker-assisted selection uses molecular markers to identify lines possessing beneficial haplotypes, like that of the Sst1 locus. In this study, an R package titled "HaploCatcher" was developed to predict specific haplotypes of interest in genome-wide genotyped lines. A training population of 1,056 lines genotyped for the Sst1 locus, known to confer stem solidness, and genome-wide markers was curated to make predictions of the Sst1 haplotypes for 292 lines from the Colorado State University wheat breeding program. Predicted Sst1 haplotypes were compared to marker derived haplotypes. Our results indicated that the training set was substantially predictive, with kappa scores of 0.83 for k-nearest neighbors and 0.88 for random forest models. Forward validation on newly developed breeding lines demonstrated that a random forest model, trained on the total available training data, had comparable accuracy between forward and cross-validation. Estimated group means of lines classified by haplotypes from PCR-derived markers and predictive modeling did not significantly differ. The HaploCatcher package is freely available and may be utilized by breeding programs, using their own training populations, to predict haplotypes for whole genome sequenced early generation material. CORE IDEASO_LIIdentification, introgression, and frequency increase of large effect loci are important for cultivar development. C_LIO_LIThe Sst1 locus has a significant effect on cutting score in fields exposed to sawfly infestation. C_LIO_LIHistorical genetic information can be utilized to predict haplotypes for lines which have genome-wide genetic data. C_LIO_LIAn R package, HaploCatcher, has been developed to facilitate this analysis in other programs. C_LI

plant biology↗

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↗

bioRxiv2023

UTILIZATION OF A PUBLICLY AVAILABLE DIVERSITY PANEL IN GENOMIC PREDICTION OF FUSARIUM HEAD BLIGHT RESISTANCE TRAITS IN WHEAT

Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker assisted selection (MAS) coupled with genomic selection (GS) has been suggested when breeding for FHB resistance. An historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011-2021 was partitioned and used in genomic prediction. Two traits were curated from 2011-2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and Deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and K-means clustering was performed across environments to assign environments into clusters. Two clusters were identified for FDK and three for DON. Cross-validation on SUWWSN data from 2011-2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracy r {approx} 0.58 and r {approx} 0.53, respectively. Forward validation for DON indicated a predictive accuracy of r {approx} 0.57 and r {approx} 0.45, respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of r {approx} 0.65 and r {approx} 0.60, respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of r {approx} 0.67 and r {approx} 0.60, respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model to create a public resource for genomic prediction of FHB resistance traits across public wheat breeding programs. CORE IDEASO_LIThe data from the Southern Uniform Winter Wheat Nursery may be used for genomic prediction. C_LIO_LICreating training populations based on like-check performance improves forward genomic predictive accuracies. C_LIO_LIFiltering out locations with low genomic, per-plot, narrow-sense heritability may improve predictive accuracies. C_LI

plant biology↗