Biology subjects

Robbins, K. R.

Publications and source records attributed to Robbins, K. R..

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

Inter-variety competition dynamics in US inbred and hybrid maize

Variety mixtures provide a potential avenue in US cropping systems to improve yield stability and disease resistance. However, implementation of variety mixtures requires an understanding of the competitive dynamics of the crop. In this study, we examine the effects of plant competition both between and within plots through five unique experiments: 1) 5,000 diverse inbred lines in single-row plots, 2) hybrids in two-row plots developed from the above inbred lines, 3) over 4,000 hybrids measured in 141 locations in two-row plots as part of Genomes to Fields, 4) mixtures of two hybrids within a two-row plot planted across two years and five locations, and 5) mixtures of up to twenty hybrids in four-row plots in three locations. Across all experiments, we find that competitive interactions are extremely limited. Within inbred lines, height of the neighboring plot accounts for 1.2% of the variance in focal plot height. Similarly, neighbor height explains 1.7% of the variance in focal plot yield in hybrids developed from the inbred lines. The genetics of neighboring plots explains 1.55% of the variation in yield across 141 location-year environments, reinforcing the generally modest impacts of neighbor competition. In evaluating mixtures of hybrids in both two and four-row plots, we observe no yield penalty compared to conventional single hybrid plots, even with large height differentials of the hybrids included in the mixture or in mixtures of up to 20 hybrids within a plot. Finally, we observe that mixtures have more yield stability compared to conventional plots, highlighting a new avenue for increased stability in higher risk environments. The lack of yield penalty and stability benefits are promising for future investigations of mixtures that may complement each other in disease resistance or abiotic stress tolerance and increase overall yield stability in the field.

plant biology↗

bioRxiv2025

CaMPARI2 Enables Stimulus-Locked Whole-Brain Activity Mapping at Cellular Resolution in Unrestrained Larval Zebrafish

Visualizing active neurons and circuits in vivo is critical for investigating the neural activity that underlies behavior. While several established methodologies are available to achieve this end in larval zebrafish, they are limited by the scale of tissue visualization, temporal resolution, need to restrain larvae, and/or accessibility of necessary instruments. Here, we establish a pipeline for the visualization and quantification of spatiotemporally precise whole-brain neural activity in larval zebrafish using CaMPARI2, a genetically encoded calcium indicator. Using temporally specific photoconverting UV light exposures, we capture whole-brain "snapshots" of neural activity time-locked to stimuli during unrestrained larval behavior. We optimize experimental conditions for establishing sub-second neuronal activity changes across acoustically-evoked behavioral paradigms spanning minutes to hours. We then leverage this system to pinpoint brain-wide neural activity changes during nonassociative habituation learning, observing distinct activity signatures in the subpallium, preoptic area, and habenulae that are altered through pharmacological disruption of habituation learning. This approach effectively complements the temporal precision achievable through post hoc activity detection methods and expands the accessibility of large-scale behavioral circuit dissection beyond highly specialized real-time volumetric imaging equipment.

neuroscience↗

bioRxiv2025

Genomic prediction for general combining ability in hybrid canola (Brassica napa L.)

Hybrid breeding is a method of selecting parental lines and determining crosses that are likely to yield the best hybrids. Genomic prediction (GP), a tool that uses genome wide markers, can be employed to predict the performance of untested hybrids based on general combining ability (GCA) of their parents. We investigated the potential of GP for GCA prediction in a commercial canola breeding program. We used female tester data, where many female lines are crossed with few male lines, to predict economically important traits in canola. Multi-year and location data for grain yield, oil, protein, days to flowering, days to maturity, total glucosinate and saturated fat were available for prediction. Three different cross-validation strategies were implemented to determine the predictive ability (PA) of each trait. In the first cross-validation scheme (CV1), a prediction model was validated using five-fold cross validation strategy. In the second cross-validation scheme (CV2), an unseen year was predicted using the previous years data as a training set. In the third cross-validation scheme (CV3), Inbred pe se performance was added to the training set to exploit the covariance between traits of inbred and hybrid trials. The highest PA was observed for CV1 while the lowest PA was seen for CV2. In CV1, PA ranged from 0.34 to 0.62. The highest PA was observed for protein (0.62) while the lowest PA was observed for days to maturity (0.34). For CV2, PA ranged from 0.16 to 0.46, while for CV3 PA ranged from 0.27 to 0.71. The highest PA for CV2 (0.46) and CV3 (0.71) was observed for total glucosinate while the lowest PA (0.16 CV2 and 0.27 -CV3) was for days to maturity. The current study demonstrates the potential of using marker information to select parents with the highest GCA to create the best hybrid combinations.

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↗

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↗

bioRxiv2022

Genomic selection in organisms with biphasic lifecycles: a Saccharina latissima (sugar kelp) case study

Sugar kelp (Saccharina latissima) has a biphasic life cycle, allowing selection on both the diploid sporophytes (SPs) and haploid gametophytes (GPs). We trained a genomic selection (GS) model from farm-tested SP phenotypic data and used a mixed-ploidy additive relationship matrix to predict GP breeding values. Top-ranked GPs were used to make crosses for further farm evaluation. The relationship matrix included 866 individuals: a) founder SPs sampled from the wild; b) progeny GPs from founders; c) Farm-tested SPs crossed from b); and d) progeny GPs from farm-tested SPs. The complete pedigree-based relationship matrix was estimated for all individuals. A subset of founder SPs (n = 58) and GPs (n = 276) were genotyped with Diversity Array Technology and whole genome sequencing, respectively. We evaluated GS prediction accuracy via cross validation on farm-tested SPs in two years using a basic GBLUP model. We also estimated the general combining ability (GCA) and specific combining ability (SCA) variances of parental GPs. A total of 11 yield-related and morphology traits were evaluated. The cross validation accuracies for dry weight per meter (r ranged from 0.16 to 0.35) and wet weight per meter (r ranged 0.19 to 0.35) were comparable to GS accuracy for yield traits in terrestrial crops. For morphology traits, cross validation accuracy exceeded 0.18 in all scenarios except for blade thickness in the second year. Accuracy in a third validation year for dry weight per meter over a confirmation set of 87 individuals was 0.31.

genetics↗

bioRxiv2021

Quantitative genetic analysis of interactions in the pepper-Phytophthora capsici pathosystem

The development of pepper cultivars with durable resistance to the oomycete Phytophthora capsici has been challenging due to differential interactions between the species that allow certain pathogen isolates to cause disease on otherwise resistant host genotypes. Currently, little is known about the pathogen genes that are involved in these interactions. To investigate the genetic basis of P. capsici virulence on individual pepper genotypes, we inoculated sixteen pepper accessions - representing commercial varieties, sources of resistance, and host differentials - with 117 isolates of P. capsici, for a total of 1,864 host-pathogen combinations. Analysis of disease outcomes revealed a significant effect of inter-species genotype-by-genotype interactions, although these interactions were quantitative rather than qualitative in scale. Isolates were classified into five pathogen subpopulations, as determined by their genotypes at over 60,000 single-nucleotide polymorphisms (SNPs). While absolute virulence levels on certain pepper accessions significantly differed between subpopulations, a multivariate phenotype reflecting relative virulence levels on certain pepper genotypes compared to others showed the strongest association with pathogen subpopulation. A genome-wide association study (GWAS) identified four pathogen loci significantly associated with virulence, two of which colocalized with putative RXLR effector genes and another with a polygalacturonase gene cluster. All four loci appeared to represent broad-spectrum virulence genes, as significant SNPs demonstrated consistent effects regardless of the host genotype tested. Host genotype-specific virulence variants in P. capsici may be difficult to map via GWAS, perhaps controlled by many genes of small effect or by multiple alleles that have arisen independently at the same loci.

plant biology↗

bioRxiv2021

Genome-wide Imputation Using the Practical Haplotype Graph in the Heterozygous Crop Cassava

Genomic applications such as genomic selection and genome-wide association have become increasingly common since the advent of genome sequencing. Genotype imputation makes it possible to infer whole genome information from limited input data, making large sampling for genomic applications more feasible, especially in non-model species where resources are less abundant. Imputation becomes increasingly difficult in heterozygous species where haplotypes must be phased. The Practical Haplotype Graph is a recently developed tool that can accurately impute genotypes, using a reference panel of haplotypes. The Practical Haplotype Graph is a haplotype database that implements a trellis graph to predict haplotypes using minimal input data. Genotyping information is aligned to the database and missing haplotypes are predicted from the most likely path through the graph. We showcase the ability of the Practical Haplotype Graph to impute genomic information in the highly heterozygous crop cassava (Manihot esculenta). Accurately phased haplotypes were sampled from runs of homozygosity across a diverse panel of individuals to populate the graph, which proved more accurate than relying on computational phasing methods. At 1X input sequence coverage, the Practical Haplotype Graph achieves a high concordance between predicted and true genotypes (R=0.84), as compared to the standard imputation tool Beagle (R=0.69). This improved accuracy was especially visible in the prediction of rare and heterozygous alleles. We validate the Practical Haplotype Graph as an accurate imputation tool in the heterozygous crop cassava, showing its potential for application in heterozygous species.

genomics↗

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↗