Biology subjects

Santantonio, N.

Publications and source records attributed to Santantonio, N..

10 recordsLinked to original sources

bioRxiv2026

Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials

Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.

genetics↗

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

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↗

bioRxiv2024

Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa

Multi-spectral imaging (MSI) collection by unoccupied aerial vehicles (UAV) is an important tool to measure growth of forage crops. Information from estimated growth curves can be used to infer harvest biomass and to gain insights in the relationship of growth dynamics and harvest biomass stability across cuttings and years. In this study, we used MSI to evaluate Alfalfa (Medicago sativa L. subsp. sativa) to understand the longitudinal relationship between vegetative indices (VIs) and forage/biomass, as well as evaluation of irrigation treatments and genotype by environment interactions (GEI) of different alfalfa cultivars. Alfalfa is a widely cultivated perennial forage crop grown for high yield, nutritious forage quality for feed rations, tolerance to abiotic stress, and nitrogen fixation properties in crop rotations. The direct relationship between biomass and VIs such as Normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red edge normalized difference vegetation index (NDRE), and Near infrared (NIR) provide a non-destructive and high throughput approach to measure biomass accumulation over subsequent alfalfa harvests. In this study, we aimed to estimate the genetic parameters of alfalfa VIs and utilize longitudinal modeling of VIs over growing seasons to identify potential relationships between stability in growth parameters and cultivar stability for alfalfa biomass yield across cuttings and years. We found VIs of GNDVI, NDRE, NDVI, NIR and simple ratios to be moderately heritable with median values for the field trial in Ithaca, NY to be 0.64, 0.56, 0.45, 0.45 and 0.40 respectively, Normal Irrigation (NI) trial in Leyendecker, NM to be 0.3967, 0.3813, 0.3751, 0.3239 and 0.3019 respectively, and Summer Irrigation Termination (SIT) trial in Leyendecker, NM to be of 0.11225, 0.1389, 0.1375, 0.2539 and 0.1343, respectively. Genetic correlations between NDVI and harvest biomass ranged from 0.52 - .99 in 2020 and 0.08 - .99 in 2021 in the NY trial. Genetic correlations for NI trial in NM for NDVI ranged from 0.72 - .98 in 2021 and SIT ranged from 0.34-1.0 in 2021. Genotype by genotype by interaction (GGE) biplots were used to differentiate between stable and unstable cultivars for locations NY and NM, and Random regression modeling approaches were used to estimate growth parameters for each cutting. Results showed high correspondence between stability in growth parameters and stability, or persistency, in harvest biomass across cuttings and years. In NM, the SIT trial showed more variation in growth curves due to stress conditions. The temporal growth curves derived from NDVI, NIR and Simple ratio were found to be the best phenotypic indices on studying the stability of growth parameters across different harvests. The strong correlation between VIs and biomass present opportunities for more efficient screening of cultivars, and the correlation between estimated growth parameters and harvest biomass suggest longitudinal modeling of VIs can provide insights into temporal factors influencing cultivar stability.

genetics↗

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↗

bioRxiv2022

Spatio-temporal modeling of high-throughput multi-spectral aerial images improves agronomic trait genomic prediction in hybrid maize

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) was measured by a multi-spectral MicaSense camera and ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multi-trait model, a two-stage approach that quantified NDVI local environmental effects (NLEE) was proposed. Firstly, NLEE were separated from additive genetic effects over the growing season using two-dimensional spline (2DSpl), separable autoregressive (AR1) models, or random regression models (RR). Secondly, the NLEE were leveraged within agronomic trait genomic best linear unbiased prediction (GBLUP) either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of NLEE across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields (G2F) hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to all baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2DSpl NLEE were most correlated to the soil parameters and grain yield 2DSpl effects. Simulation of field effects demonstrated improved specificity for RR models. In summary, NLEE increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

genetics↗

bioRxiv2020

A hybrid optimal contribution approach to drive short-term gains while maintaining long-term sustainability in a modern plant breeding program

1Plant breeding programs must adapt genomic selection to an already complex system. Inbred or hybrid plant breeding programs must make crosses, produce inbred individuals, and phenotype inbred lines or their hybrid test-crosses to select and validate superior material for product release. These products are few, and while it is clear that population improvement is necessary for continued genetic gain, it may not be sufficient to generate superior products. Rapid-cycle recurrent truncation genomic selection has been proposed to increase genetic gain by reducing generation time. This strategy has been shown to increase short-term gains, but can quickly lead to loss of genetic variance through inbreeding as relationships drive prediction. The optimal contribution of each individual can be determined to maximize gain in the following generation while limiting inbreeding. While optimal contribution strategies can maintain genetic variance in later generations, they suffer from a lack of short-term gains in doing so. We present a hybrid approach that branches out yearly to push the genetic value of potential varietal materials while maintaining genetic variance in the recurrent population, such that a breeding program can achieve short-term success without exhausting long-term potential. Because branching increases the genetic distance between the phenotyping pipeline and the recurrent population, this method requires sacrificing some trial plots to phenotype materials directly out of the recurrent population. We envision the phenotypic pipeline not only for selection and validation, but as an information generator to build predictive models and develop new products.

genetics↗

bioRxiv2018

A low resolution epistasis mapping approach to identify chromosome arm interactions in allohexaploid wheat

1Epistasis is an important contributor to genetic variance, even in inbred populations where it is present as additive by additive interactions. Testing for epistasis presents a multiple testing problem as the search space for modest numbers of markers is large. Additionally, single markers do not necessarily track functional units of interacting chromatin as well as haplotype based methods do. To harness the power of multiple markers while drastically minimizing the number of tests conducted, we present a low resolution test for epistatic interactions across whole chromosome arms. Two additive genetic covariance matrices are constructed from markers on two different chromosome arms. The Hadamard product of these additive covariance matrices is then used to produce the additive by additive epistasis covariance matrix between the two chromosome arms. The covariance matrices are subsequently used to estimate an epistatic interaction variance parameter in a mixed model framework, while correcting for background additive and epistatic effects. We find significant epistatic interactions for 2% of interactions tested for four agronomic traits in a population of winter wheat. Interactions across homeologous chromosome arms were identified, but were less abundant than other interaction chromosome arm pairs. Of these, homeologous chromosome arm pair 4BL and 4DL showed a strong relationship between the product of their additive effects and the interaction effect that may be indicative of functional redundancy. Several chromosome arms were involved in many interactions across the genome, suggesting that they may contain important large effect regulatory factors. The differential patterns of epistasis across different traits suggests that detection of epistatic interactions is robust when correcting for background additive and epistatic effects in the population. The low resolution epistasis mapping method presented here identifies important epistatic interactions with a limited number of statistical tests at the cost of relatively lower precision.

genetics↗

bioRxiv2018

A subfunctionalization epistasis model to evaluate homeologous gene interactions in allopolyploid wheat

1Hybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq vl.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the non-additive genetic signal, but are less important than other epistatic interactions.

genetics↗

bioRxiv2018

Prediction of subgenome additive and interaction effects in allohexaploid wheat

1Whole genome duplications have played an important role in the evolution of angiosperms. These events often occur through hybridization between closely related species, resulting in an allopolyploid with multiple subgenomes. With the availability of affordable genotyping and a reference genome to locate markers, breeders of allopolyploids now have the opportunity to manipulate subgenomes independently. This also presents a unique opportunity to investigate epistatic interactions between homeologous orthologs across subgenomes. We present a statistical framework for partitioning genetic variance to the subgenomes of an allopolyploid, predicting breeding values for each subgenome, and determining the importance of inter-genomic epistasis. We demonstrate using an allohexaploid wheat breeding population evaluated in Ithaca, NY and an important wheat dataset previously shown to demonstrate non-additive genetic variance. Subgenome covariance matrices were constructed and used to calculate subgenome interaction covariance matrices across subgenomes for variance component estimation and genomic prediction. We propose a method to extract population structure from all subgenomes at once before covariances are calculated to reduce collinearity between subgenome estimates. Variance parameter estimation was shown to be reliable for additive subgenome effects, but was less reliable for subgenome interaction components. Predictive ability was equivalent to current genomic prediction methods. Including only inter-genomic interactions resulted in the same increase in accuracy as modeling all pairwise marker interactions. Thus, we provide a new tool for breeders of allopolyploid crops to characterize the genetic architecture of existing populations, determine breeding goals, and develop new strategies for selection of additive effects and fixation of inter-genomic epistasis.

genetics↗