Biology subjects

Gore, M. A.

Publications and source records attributed to Gore, M. A..

At least 19 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

An open field phenomics resource for multimodal maize yield prediction across divergent environments

Temporal drone phenotyping captures crop development, but irregular flight schedules complicate comparisons across environments. We release curated imagery from 356 flights across 19 Genomes to Fields environments containing 1,180 maize (Zea mays L.) hybrids. To evaluate its utility, we integrated functional principal components of vegetation index and weather trajectories with genomic information. Combinined genomic and phenomic kernels improved yield prediction, reaching correlations up to r = 0.501 for held-out hybrids in environments represented in training and 0.408 when environments were also withheld. Accumulated growing degree days offered no consistent predictive advantage over days after planting, and weather contributed modest, task-dependent gains. A transformer neural process learned directly from irregular observations, serving as a novel application of neural process models in agriculture. Mapping vegetation index functional principal components identified recurrent quantitative trait loci on chromosomes 3 and 7. This resource and its reproducible analyses guide the use of temporal spectral data for crop prediction and genetic discovery.

plant biology↗

bioRxiv2026

Gene-metabolite networks reveal physiological trade-offs but not transcriptional co-regulation between carotenoids and dry matter in cassava (Manihot esculenta) roots

Provitamin A biofortification of cassava (Manihot esculenta) is constrained by a negative genetic correlation between total carotenoid (TC) and dry matter (DM) contents, but its underlying biological mechanism remains unclear. We examined this relationship in 24 African, Latin American, and hybrid cassava genotypes by measuring gene expression and metabolites in inner and outer storage root layers that varied spatially in carotenoid accumulation, enabling comparisons across and within genotypes. TC, and particularly upstream cis-carotenes, were negatively associated with DM after accounting for genotype, and this relationship strengthened across developmental timepoints, consistent with a physiological component of the trade-off. Gene-metabolite co-expression networks constructed from genetic, non-genetic, and overall phenotypic trait components showed no significant topological overlap between carotenoid- and starch-related gene sets, indicating that the TC/DM relationship is not driven by transcriptional co-regulation of the main pathways, although individual associations between carotenoids and carbohydrate-related genes suggested possible indirect metabolic links. Instead, TC accumulation was most strongly associated with regulatory, stress-, redox-, and plastid-related genes, enriched for heme-binding and oxidoreductase functions. These findings suggest that the TC/DM trade-off is not a fixed constraint, and identify candidate regulators of carotenoid content for future functional validation to support cassava provitamin A biofortification. HighlightGenetic, spatial, and developmental variation in cassava storage roots implicate cis-carotenes, but not direct transcriptional regulation of the carotenoid and starch pathways, in the trade-off between carotenoids and dry matter.

plant biology↗

bioRxiv2026

Genomic sampling and population structure of farmer-maintained varieties reveal previously uncharacterized diversity of Theobroma cacao L. in Costa Rica

Understanding crop genetic diversity is essential for conservation and breeding, yet farmer-maintained germplasm remains largely underrepresented in genomic studies. Theobroma cacao L. has a complex domestication history and extensive global diversity, and cacao currently cultivated in Central America, particularly in Costa Rica, has been understudied compared to South American and Mexican cultivars despite cultural and historical importance. In this study, we investigate the genetic diversity of cacao from farmer-managed systems across Costa Rica to search for Criollo germplasm and identify and characterize any unique local genetic groups. Ninety-four trees were sampled from 17 farms across four regions of the country and sequenced using whole genome resequencing. Farmer materials were analyzed alongside 166 previously characterized reference accessions representing major cacao genetic groups. Population structure analyses, phylogenetic reconstruction, and network approaches revealed that Costa Rican cacao encompasses multiple known genetic groups, including Criollo-derived lineages, while also harboring locally distinct diversity not fully represented in current global reference collections. Analyses revealed close kinship between many accessions with no clear geographic patterns corresponding to the observed population differentiation, reflecting the effects of farmers in creating dominant patterns of gene flow through seed-saving, clonal propagation, and sharing genotypes among farms. Heterozygosity levels varied substantially among individuals, consistent with a mixture of highly inbred Criollo trees and more heterozygous, admixed genotypes. We find that farmer-managed cacao systems are reservoirs of genetic diversity, including possibly rare or historically important lineages, underscoring the value of these farming systems for effective conservation and management of genomic resources for cacao resilience and improvement.

genomics↗

bioRxiv2026

Unlocking the potential of Capsicum Germplasm Collections for Climate Resilience and Fruit Quality

Climate change increasingly threatens global Capsicum (pepper) production. Accelerating the deployment of climate-resilient cultivars requires effective use of genetic diversity conserved in genebanks. We implement a "turbocharging" strategy in Capsicum by integrating genome-wide association studies and genomic prediction in a core collection (n = 423), followed by genomic prediction across the global collection (n = 10,250) using the core as a training population. We generated genomic estimated breeding values (GEBVs) for 31 high-accuracy traits (r > 0.5) encompassing hyperspectral phenotypes (heat/control), agronomic performance (heat/control) and fruit quality. To enhance accessibility and decision-making, we developed a large language model (LLM) integrated application that enables flexible, preference-based selection of candidates. By narrowing the parental decision space, this framework streamlines screening of large germplasm collections while balancing climate resilience, quality attributes and market demands. Our approach provides a scalable decision-support system to accelerate climate-resilient Capsicum breeding and maximize global genetic resources.

plant biology↗

bioRxiv2025

Investigating the role of stomatal dynamics on agronomic traits using a slac1-2 Zea mays mutant

The production of staple food crops is becoming increasingly difficult due to the amount of freshwater needed to realize food security for a growing global population. Climate projections show hotter and drier growing seasons in traditionally productive agricultural regions, which will increase the demand for limited water resources. Thus, strategies to improve water use efficiency will only become more important for sustainable agriculture. At the leaf level, stomatal conductance has a large influence on transpirational water loss and therefore water use efficiency. The anion channel SLAC1 has been shown in several plant species to function as the primary mechanism for stomatal closure, which allows stomatal aperture to change dynamically in response to environmental stimuli. Given that slac1 is a single gene that can significantly alter stomatal conductance, it has the potential to serve as a control point for improving water use efficiency. Here we fully characterize a Zea mays slac1-2 mutant in multiple field environments. Interestingly, homozygous slac1-2 hybrids did not show improved net CO2 assimilation or increased grain yield despite having greater stomatal conductance compared to a wild-type hybrid. Net CO2 assimilation and grain yield were either lower or similar in slac1-2 compared to wild-type across environments. Furthermore, the slac1-2 hybrid did not have increased nitrogen uptake. These results suggest that the C4 carbon concentrating mechanism removes any stomatal conductance limitations to CO2 assimilation, even in highly productive wild-type Z. mays hybrids. Because the slac1-2 hybrids eliminate stomatal conductance as a major variable for modulating water loss, future studies will be able to investigate alternate regulators of plant water potential to identify novel mechanisms for increasing water use efficiency.

physiology↗

bioRxiv2025

Modification of Non-photochemical Quenching Pathways in the C4 Model Plant Setaria viridis Revealed Shared and Unique Photoprotection Mechanisms as Compared to C3 Plants

Light is essential for photosynthesis; however, excess light can increase the accumulation of photoinhibitory reactive oxygen species that reduce photosynthetic efficiency. Plants have evolved photoprotective non-photochemical quenching (NPQ) pathways to dissipate excess light energy. In tobacco and soybean (C3 plants), overexpression of three NPQ genes, violaxanthin de-epoxidase (VDE), Photosystem II Subunit S (PsbS), and zeaxanthin epoxidase (ZEP), hereafter VPZ, resulted in faster NPQ induction and relaxation kinetics, and increased crop yields in field conditions. NPQ is well-studied in C3 plants; however, NPQ and the translatability of the VPZ approach in C4 plants is poorly understood. The green foxtail Setaria viridis is an excellent model to study photosynthesis and photoprotection in C4 plants. To understand the regulation of NPQ and photosynthesis in C4 plants, we performed transient overexpression in Setaria protoplasts and generated (and employed) stable transgenic Setaria plants overexpressing one of the three Arabidopsis NPQ genes or all three NPQ genes (AtVPZ lines). Overexpressing (OE) AtVDE and AtZEP in Setaria produced similar results as in C3 plants, with increased or reduced zeaxanthin (thus NPQ), respectively. However, overexpressing AtPsbS appeared to be challenging in Setaria, with largely reduced NPQ in protoplasts and under-represented homozygous AtPsbS-OE lines, potentially due to competitive and tight heterodimerization of AtPsbS and SvPsbS proteins. Furthermore, Setaria AtVPZ lines had increased zeaxanthin, faster NPQ induction, higher NPQ level, but slower NPQ relaxation. Despite this, AtVPZ lines had improved growth as compared to wildtype under several conditions, especially high temperatures, which is not related to the faster relaxation of NPQ but may be attributable to increased zeaxanthin and NPQ in C4 plants. Our results identified shared and unique characteristics of the NPQ pathway in C4 model Setaria as compared to C3 plants and provide insights to improve C4 crop yields under fluctuating environmental conditions.

plant biology↗

bioRxiv2024

Overexpression of Xanthophyll Cycle Genes Leads to Faster NPQ Acclimation in the C4 Monocot Setaria viridis

Slow relaxation and induction of Non-Photochemical Quenching (NPQ) limits plant productivity under fluctuating light in C3 plants but it is unknown if this trait extends to C4 plants. Three genes have been implicated in determining NPQ induction and relaxation rates under intermittent leaf shading; VDE and ZEP interconvert the xanthophyll pigments violaxanthin and zeaxanthin, and PsbS regulates the overall level of NPQ. Here we report the overexpression of the Arabidopsis thaliana VDE, ZEP, and PsbS in the NADP-Malic Enzyme C4 monocot Setaria viridis. We demonstrate these transgenic plants have faster NPQ induction and relaxation under fluctuating light but observe no impact on Photosystem II photochemistry yield. Under illumination, faster NPQ induction resulted in higher transient NPQ levels and a lower overall chlorophyll fluorescence yield. Together this introduces the potential for lower productivity due to over-quenching of chlorophyll during light fluctuation. Although we observed faster NPQ acclimation rates in our transgenic lines, hyperspectral imaging and pigment analysis suggest that there is little photosynthetic advantage to increasing NPQ acclimation over wildtype due to naturally fast C4 acclimation rates. These results indicate that further work is necessary in evaluating the role of NPQ acclimation in C4 growth dynamics towards the goal of improving crop productivity.

plant biology↗

bioRxiv2024

Climate resilience conserved in global germplasm repositories: Picking the most promising parents for agile plant breeding

Crop diversity is an essential resource for national and international breeding programs aimed at preparing global agriculture for a changing climate to ensure global food security. To do this there are related risks that need to be evaluated (1) does the genetic diversity needed for climate adaptation exist somewhere? And (2) is such genetic diversity accessible? To evaluate these risks, we consider the test case of publicly available genotyped and georeferenced sorghum landraces (n = 1,937) to ask if diversity is sufficient to support breeding for climate change adaptation. Answering these questions allows for characterization of the best potential parents and the geographies that harbor the most potentially promising genetypes for crop improvement. We subset this data into national, regional, and global geographic regions, and complete/mini core collections to understand the potential for climate adaptation in regional germplasm. Study accessions were given a future climate resilience score based on future climatic projections and a genomic adaptive capacity score using genomic estimated adaptive values (GEAVs) generated from environmental genomic selection - EGS) to ask whether this accessible diversity stored in germplasm repositories is potentially sufficient to meet forecasted changes in growing environments under climate change. We find that genomic resilience capacity is highly variable among countries and regions. High geographical variability was also found for climate resilience. To equitably adapt agriculture to future climate conditions, increased accessibility to plant genetic resources is essential.

genomics↗

bioRxiv2024

Integrative multi-omic analysis identifies genes associated with cuticular wax biogenesis in adult maize leaves

Studying the genetic basis of leaf wax composition and its correlation with leaf cuticular conductance (gc) is crucial for improving crop water-use efficiency. The leaf cuticle, which comprises a cutin matrix and various waxes, functions as an extracellular hydrophobic layer, protecting against water loss upon stomatal closure. To address the limited understanding of genes associated with the natural variation of leaf cuticular waxes and their connection to gc, we conducted statistical genetic analyses using leaf transcriptomic, metabolomic, and physiological data sets collected from a maize (Zea mays L.) panel of [~]300 inbred lines. Through a random forest analysis with 60 cuticular wax traits, it was shown that high molecular weight wax esters play an important role in predicting gc. Integrating results from genome-wide and transcriptome-wide studies (GWAS and TWAS) via a Fishers combined test revealed 231 candidate genes detected by all three association tests. Among these, 11 genes exhibit known or predicted roles in cuticle-related processes. Throughout the genome, multiple hotspots consisting of GWAS signals for several traits from one or more wax classes were discovered, identifying four additional plausible candidate genes and providing insights into the genetic basis of correlated wax traits. Establishing a partially shared genetic architecture, we identified 35 genes for both gc and at least one wax trait, with four considered plausible candidates. Our study uncovered the genetic control of maize leaf waxes, establishing a link between wax composition and gc, with implications for potentially breeding more water-use efficient maize. SIGNIFICANCE STATEMENTWe exploited natural variation in the abundance of maize leaf cuticular waxes to identify genetic determinants of wax composition and its relationship to cuticle function as a barrier against water loss. We identified a set of strongly supported candidate genes with plausible functions in cuticular wax biosynthesis or deposition and added to the evidence for wax esters as the most important wax for water barrier function, offering new tools for modification of cuticle-dependent traits.

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↗

bioRxiv2023

Genomic resources for Macadamia tetraphylla and an examination of its historic use as a crop resource in Hawaii

Macadamia tetraphylla is a wild relative of the economically valuable crop Macadamia integrifolia. Genomic knowledge of crop wild relatives is central to determining their possible role in breeding programs to mitigate biotic and abiotic stress in the future. The goal of this project was to develop a genomic resource for macadamia agriculture in Hawai i through constructing a transcriptome of M. tetraphylla and testing for hybridity in University of Hawai i at M[a]noa breeding material. The transcriptome assembly of M. tetraphylla revealed large differences in gene expression attributable to tissue type. Advanced breeding lines (HI862 and HI879) appear to be hybridized with the crop wild relative M. tetraphylla. Additionally, a putative M. tetraphylla tree sampled from a remnant orchard planting at the Waim[a]nalo research station on Oahu did not match anecdotal accounts of the orchard as it appeared to be of hybrid ancestry.

genomics↗

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

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize grain is low. Several large-effect genes with cis-acting variants affecting mRNA expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0 to 13.6% when compared to GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes. Core IdeasO_LIWith varying levels of vitamin E activity, tocochromanols found in maize grain are essential for human health C_LIO_LISelecting for higher vitamin E content in maize grain can be enhanced with genomic prediction C_LIO_LIPrediction models leveraging existing biological knowledge were evaluated in two panels of maize inbred lines C_LIO_LIMultikernel prediction models based on previously identified QTL improved predictive ability C_LIO_LIA multi-trait prediction model that had transcript abundances of a few large-effect causal genes performed the best C_LI

genetics↗

bioRxiv2022

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of [~]1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including five that had not been previously associated with maize grain tocochromanols: four biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for co-localized eQTL and GWAS association signals. Our multi-omics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.

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

Generalizable approaches for genomic prediction of metabolites in plants

Plant metabolites are important for plant breeders to improve nutrition and agronomic performance, yet integrating selection for metabolomic traits is limited by phenotyping expense and limited genetic characterization, especially of uncommon metabolites. As such, developing biologically-based and generalizable genomic selection methods for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for more than 600 metabolites measured by GC-MS and LC-MS in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite GWAS (mGWAS) and selected loci to use in multi-kernel models that encompassed metabolome-wide mGWAS results, or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel, consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We tested if similar metabolites had consistent model ranks and found that, while different metrics of similarity had different results, using annotation-free methods to group metabolites led to consistent within-group model rankings. Overall, testing biological rationales for developing kernels for genomic prediction across populations, contributes to developing frameworks for plant breeding for metabolite traits.

genomics↗

bioRxiv2021

Integration of GWAS and TWAS to elucidate the genetic architecture of natural variation for leaf cuticular conductance in maize

The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed. Dissecting the genetic architecture of natural variation for maize leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we performed an integrated genome- and transcriptome-wide association study (GWAS/TWAS) to identify candidate genes putatively regulating variation in leaf gc. Of the 22 plausible candidate genes identified, five were predicted to be involved in cuticle precursor biosynthesis and export, two in cell wall modification, nine in intracellular membrane trafficking, and seven in the regulation of cuticle development. A gene encoding an INCREASED SALT TOLERANCE1-LIKE1 (ISTL1) protein putatively involved in intracellular protein and membrane trafficking was identified in GWAS and TWAS as the strongest candidate causal gene. A set of maize nested near-isogenic lines that harbor the ISTL1 genomic region from eight donor parents were evaluated for gc, confirming the association between gc and ISTL1 in a haplotype-based association analysis. The findings of this study provide novel insights into the role of regulatory variants in the development of the maize leaf cuticle, and will ultimately assist breeders to develop drought-tolerant maize for target environments. Sentence summaryWe performed an integrated GWAS/TWAS and identified 22 candidate genes putatively regulating variation in maize leaf gc. The association between gc and the strongest candidate causal gene, ISTL1, was validated with maize nested near-isogenic lines.

genetics↗