Biology subjects

Sabadin, F.

Publications and source records attributed to Sabadin, F..

5 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

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

Genotyping-By-Sequencing and DNA array for genomic prediction in soybean oil composition

Soybean oil is intended for various purposes, such as cooking oil and biodiesel. The oil composition changes the shelf life, palatability, and how healthy this oil is for the human diet. Genomic selection jointly uses these traits, phenotypes, and markers from one of the available genotyping platforms to increase genetic gain over time. This study aims to evaluate the impact of different genotyping platforms, DNA arrays, and Genotyping-by-Sequencing (GBS) on genomic selection in relation to the composition of fatty acids in soybean oil and total oil content. We used different quality control parameters, such as heterozygote rate, minor allele frequency, and missing data rate in ten combinations, and two prediction models, BayesB and BRR. To compare the impact of the genotyping approaches, we calculated the principal components analysis from the kinship matrices, the SNP density, and the traits prediction accuracies for each approach. Principal component analysis showed that the DNA array explained better the population genetic architecture. On the other hand, prediction accuracies varied between the different genotyping platforms and only GBS was affected under different quality control parameters. Although the DNA array has important and well-studied polymorphisms for soybeans and is stable, it also has ascertainment bias. GBS, although not stable and requires more robust quality control, can discover alleles specific to the population under study. As soybean oil is used for different functions and the fatty acid profiles are different for each objective, the work constitutes a critical study and direction for improving the composition of soybean oil.

genetics↗

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↗

bioRxiv2021

Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

The selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using "static" training sets and populations, which are adequate only to predict a breeding programs initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequili brium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.

genetics↗