Biology subjects

Ferguson, J. N.

Publications and source records attributed to Ferguson, J. N..

13 recordsLinked to original sources

bioRxiv2026

AI-enabled simultaneous phenotyping of leaf vein and stomatal traits uncovers independent genetic control in maize

BackgroundLeaves maintain hydraulic homeostasis during photosynthesis through the coordinated action of stomata, which regulate gas exchange and transpiration, and veins, which supply water to the leaf lamina. While functional links between stomatal and vascular traits are known in dicots, their potential genetic coordination in C4 crops remains poorly understood. We investigated the genetic architecture of these traits in maize using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis ResultsWe phenotyped 285 recombinant inbred lines and the MAGIC founder lines, generating 8,072 images from 2,026 leaf samples taken from seedlings grown in controlled conditions. A YOLOv8-based model automatically detected stomata, while a custom and efficient image-processing pipeline quantified vein traits and stomatal spatial distribution patterns along cell bundles. This enabled simultaneous characterization of stomatal density, size, and distribution together with vein density, thickness, and bundle-associated spatial patterning. Substantial phenotypic variation was observed among genotypes, with strong correlations between abaxial and adaxial traits but no significant correlations between stomatal and vein traits. QTL mapping identified 37 genomic regions associated with stomatal and vein traits, including loci containing known developmental regulators such as stomatal density and distribution1 and stomagen1, as well as novel loci controlling stomatal spatial patterns, divergence between leaf surfaces and veins traits. ConclusionsThese results support independent genetic control of stomata and veins and decoupled contribution to water-use efficiency, providing a novel genetic framework to independently optimize leaf hydraulic capacity and gas exchange in target environments.

plant biology↗

bioRxiv2025

KineticGP: a computational framework for genomic prediction of leaf photosynthesis traits

Crop traits are the integrated outcome of genetic factors, environment effects, and their complex interactions, rendering accurate prediction from genetic markers alone a challenging problem. Here we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C4 photosynthesis to make predictions of leaf photosynthesis traits across genotypes from a multiple parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model for photosynthesis rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP allowed surveying the genetic variability in enzyme kinetic parameters that can be used to raise targets for improvement of photosynthesis. The approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the prediction accuracy of photosynthetic traits.

systems biology↗

bioRxiv2025

Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize

Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design. Significance statementWe present a comprehensive evaluation of hyperspectral reflectance for predicting 25 physiological traits, including fluorescence kinetics, in maize across three seasons using rigorous nested cross-validation. By comparing ML models, prediction scenarios, and data aggregation strategies, our study reveals trait-specific limits of generalizability and offers a robust framework for deploying hyperspectral data in breeding applications.

plant biology↗

bioRxiv2025

Improving Multi-Trait Genomic Prediction Efficiency Through The Incorporation Of Synthetic Traits Selected Based on Co-heritability

Genomic prediction (GP) is an essential tool in the field of plant breeding to accelerate the cultivar development pipeline by predicting the performance of unphenotyped lines. The precision of prediction is constrained by the heritability of the target trait when applying a single-trait genomic prediction model. To overcome this limitation, a multi-trait genomic prediction model leveraging high-heritability secondary traits co-heritable with the target trait can boost predictive ability for the target trait. However, this is practically challenging because it requires additional phenotyping effort and prior knowledge of trait co-heritability. This study aimed to assess the efficiency of multi-trait genomic prediction models powered by secondary traits derived from high-throughput phenotyping data when predicting important leaf functional target traits, i.e., nitrogen (N) content and specific leaf area (SLA) in diverse sorghum accessions. Since these traits can be predicted from hyperspectral reflectance data, there is significant potential for other wavelengths within the existing dataset to meet the criteria needed to improve prediction accuracy using multi-trait approaches. Therefore, experiments were performed on traditional direct measures of leaf N content and SLA, plus partial least squares regression predictions of them (Leaf N-PLSR, SLA-PLSR), i.e., four target traits in total. Three secondary, "synthetic traits" (S1, S2, S3), each a ratio of two wavelengths within the hyperspectral data, were identified based on high co-heritability with a given target trait. Single-trait GBLUP (Genomic Best Linear Unbiased Predictor) was fitted as a baseline model, followed by three multi-trait GBLUP models using synthetic traits and target traits together. Model performance was assessed using k-fold (k=5) cross-validation (CV), which consisted of single-trait, CV1, and CV2 schemes. The synthetic traits high genetic correlation and heritability met the requirements for their use as secondary traits. There was a significant increase in accuracy when synthetic traits were used in the multi-trait genomic prediction model compared to a single trait alone for all four target traits. It improved prediction accuracy while using secondary traits derived from hyperspectral high-throughput phenotyping data in the multi-trait genomic prediction model, suggesting that this approach could be broadly applied in a post-hoc fashion to many datasets without any additional phenotyping effort. Our analysis highlights a practical approach to improve multi-trait genomic prediction model performance using synthetic traits with no intrinsic biological meaning selected through co-heritability estimation.

genetics↗

bioRxiv2025

CSR Calculator: An R package and Shiny application for assigning plant ecological strategies using trait data

O_LIThe competitor, stress-tolerator, ruderal (CSR) theory, first proposed by John Philip Grime, is a useful framework for understanding plant ecological strategies and predicting responses to environmental changes and pressures. However, current tools for assigning CSR strategies are limited to an Excel sheet format and have yet to be integrated into modern computational platforms that enable reproducible research. C_LIO_LIWe present CSRcalculator (https://github.com/TeddyGaskin/CSRcalculator), an open-source R package and shiny application that calculates CSR scores and assigns strategies based on user-uploaded trait data. CSRcalculator supports CSR assignments according to the three most prominent models: The original soft approach, the global StrateFy, and a morpho-physiological model. C_LIO_LIThe R package outputs a table including CSR scores, assigned strategies and intermediate traits for the selected model. The shiny application produces this same table alongside an interactive ternary plot to visualise the strategy distribution and an optional summary table describing the overall CSR strategy distribution, using metrics such as, the modal strategy, axis means, standard deviations, and ranges. Group-level analyses calculate the same statistics across user-defined categories. C_LIO_LIWe provide a worked example using our tool to assign CSR strategies to plants for an example dataset. C_LI

plant biology↗

bioRxiv2025

High throughput screen of NPQ in sorghum shows highly polygenic architecture of photoprotection

O_LINatural genetic variation in photosynthesis and photoprotection within crop germplasm represents an untapped resource for crop improvement. Sorghum bicolor (sorghum) is one of the worlds most widely grown crops, yet the genetic basis of photoprotection in sorghum is not well understood. C_LIO_LIThis study examined genetic variation in non-photochemical quenching traits by screening a field-grown panel of 861 genetically diverse natural sorghum accessions across two years. C_LIO_LIBroad-sense heritability ranged between 0.3 to 0.65 across different chlorophyll fluorescence parameters. A combination of genome- and transcriptome-wide (GWAS and TWAS) identification of genetic correlates with the observed trait variation uncovered a complex genetic architecture of many significant small-effect loci. An ensemble approach based on GWAS and TWAS results and the covariance between different fluorescence parameters was used to identify 110 unique candidate genes. C_LIO_LIThe resulting high-confidence candidates reveal novel genetic associations with photoprotection and offer resources for further genetic studies and crop genomic improvement efforts. C_LI

plant biology↗

bioRxiv2025

Extensive photophysiological variation in wild barley is linked to environmental origin

O_LIIntraspecific variation between crop wild relatives (CWRs) represents a source of untapped genetic diversity for crop improvement. At the same time, improving photosynthesis in crops has the potential to enhance yield. Thus, exploring variation for photophysiology within CWRs is an important, yet underexplored, research area. C_LIO_LIWe describe a common garden experiment where 320 wild barley accessions were grown across two seasons. A photophysiology phenotyping pipeline was employed to quantify >30 traits within this diversity panel. Population genetics, genome-wide association analyses (GWAS) and deep phenotyping were performed to address local adaptation hypotheses. C_LIO_LIHeritable variation was detected across this photophysiological spectrum, with genotype-by-environment (GxE) interactions being prevalent. Evidence for local adaptation was observed in the form of subpopulation differences, signals of selection, and allele frequency variation associated to markers identified via GWAS. Phenotyping of representative accessions across distinct water availabilities highlighted a role for stomatal conductance (gs) in adaptation to dry environments. C_LIO_LIWe identified substantial variation in key photosynthesis-associated traits in a CWR closely related to barley, an economically important crop species. Our results demonstrate that this variation is partially due to local adaptation, where plasticity in gs appears important for maintaining photosynthesis and biomass accumulation in water restricted conditions. C_LI

plant biology↗

bioRxiv2025

MYB59 is linked to natural variation of water use associated with warmer temperatures in Arabidopsis thaliana

Water-use contributes significantly to plant growth and productivity, yet the extent to which variation in water-use is a function of adaptive differentiation is unknown. Here, we studied natural variation of water use in Arabidopsis thaliana to understand how climatic history impacts water use strategies. We performed a survey of vegetative water use (VWU) and life-history traits across A. thaliana ecotypes in controlled and outdoor settings. Performance of select ecotypes in controlled experiments was reflective of performance in outdoor conditions. Through trait-climate and genome-wide association (GWAS) analyses, we tested for signals of environmental adaption in water use. Ecotypes from warmer environments were noted in displaying enhanced water use, independent of precipitation. GWAS identified MYB59 as a determiner of VWU. Functionally significant MYB59 SNPs showed associations with temperature, but not precipitation, and myb59 mutants demonstrated reduced water use under high temperatures. Our study suggests intraspecific variation in water-use can be explained in part by climatic history, where temperature is the most significant driver. MYB59 appears involved in this association and holds promise for study in crops.

plant biology↗

bioRxiv2024

Reducing stomatal density by expression of a synthetic EPF increases leaf intrinsic water use efficiency and reduces plant water use in a C4 crop

Enhancing crop water use efficiency (WUE) is a key target trait for climatic resilience and expanding cultivation on marginal lands. Reducing stomatal conductance (gs) through manipulating stomatal density has been observed to translate to improved WUE in multiple C3 crop species. However, reducing gs in C3 species often reduces photosynthetic carbon gain. A different response is expected in C4 plants because they possess specialized anatomy and biochemistry which concentrates CO2 at the site of fixation. This modifies the photosynthesis (AN) relationship with intracellular CO2 concentration (ci) so that photosynthesis is CO2-saturated and reductions in gs are unlikely to impair AN. To test this hypothesis, genetic strategies were investigated to reduce stomatal density in the C4 crop sorghum. Constitutive expression of a synthetic epidermal patterning factor (EPF) transgenic allele in sorghum, lead to reduced stomatal densities. A moderate reduction in stomatal density did not strengthen stomatal limitation to AN, improved WUE, reduced water use, and avoided loss of carbon fixation during a period of water deprivation. However, these positive outcomes were associated with negative pleiotropic effects on reproductive development and photosynthetic capacity. Avoiding pleiotropy by targeting expression of the transgene to specific tissues provides a potential pathway to optimal agronomic outcomes.

plant biology↗

bioRxiv2023

The genetic basis of dynamic non-photochemical quenching and photosystem II efficiency in fluctuating light reveals novel molecular targets for maize (Zea mays) improvement

Maize (Zea mays L.) is a major global crop species which uses C4 photosynthesis. Although C4 is typically considered to be more efficient than C3 photosynthesis, especially under warmer and drier conditions, there is substantial evidence that its efficiency can still be further improved, which may benefit crop performance. Improving photosynthetic efficiency via targeted manipulation of non-photochemical quenching has focused on a limited set of genes that are known to be important determinants of the NPQ response in C3 plants. The C4 pathway may alter NPQ responses but only relatively few studies have explored genetic variation in NPQ kinetics in species that perform C4 photosynthesis. In addition, studies of NPQ responses in field-grown plants of either C3 or C4 species are especially limited. Here we apply high-definition phenotyping of NPQ responses and photosynthetic efficiency and quantitative trait locus (QTL) mapping using a field-grown maize Multi-parent Advanced Generation Inter-Cross (MAGIC) population, which combines the allelic diversity of eight contrasting inbred lines. We find substantial and consistent variation for dynamic NPQ and PSII efficiency for two subsequent field seasons. Further exploration of candidate genes within three major QTL regions identified a strong impact of allelic variation in expression of the minor PSII antenna protein CP24 (LHCB6) on a major QTL for NPQ and efficiency of PSII photochemistry on chromosome 10.

plant biology↗

bioRxiv2023

Leaf excision has minimal impact on photosynthetic parameters across crop functional types

Photosynthesis is increasingly becoming a recognised target for crop improvement. Phenotyping photosynthesis-related traits on field-grown material is a key bottleneck to progress here due to logistical barriers and short measurement days. Many studies attempt to overcome these challenges by phenotyping excised leaf material in the laboratory. To date there are no demonstrated examples of the representative nature of photosynthesis measurements performed on excised leaves relative to intact leaves in crops. Here, we tested whether standardised leaf excision on the day prior to phenotyping impacted a range of common photosynthesis-related traits across crop functional types using tomato (C3-dicot), barley (C3-monocot), and maize (C4-monocot). Potentially constraining aspects of leaf physiology that could be forecasted to impair photosynthesis in excised leaves, namely leaf water potential and abscisic acid accumulation, were not different between intact and excised leaves. We also observed non-significant differences in spectral reflectance and chlorophyll fluorescence traits between the treatments across the three species. However, we did observe some significant differences between gas exchange and photosynthetic capacity associated traits across all three species. This study represents a useful reference for those who perform measurements of this nature and the differences reported should be considered in associated experimental design and statistical analyses. HighlightAcross the main photosynthesis functional types (C3-dicot, C3-monot, and C4-monot) of major crops (tomato, barley, and maize), measurements of photosynthetic parameters demonstrate few, but important, differences when measured on excised relative to intact leaves.

plant biology↗

bioRxiv2018

Non-functional and weak alleles of FRIGIDA and FLOWERING LOCUS C reduce lifetime water-use independent of leaf-level water-use-efficiency traits in Arabidopsis thaliana

Natural selection driven by water availability has resulted in considerable variation for traits associated with drought tolerance and leaf level water-use efficiency (WUE). In Arabidopsis, little is known about the variation of whole-plant water use (PWU) and whole-plant WUE (TE). To investigate the genetic basis of PWU, we developed a novel proxy trait by combining flowering time and rosette water use to estimate lifetime PWU. We validated its usefulness for large scale screening of mapping populations in a subset of ecotypes. This parameter subsequently facilitated the screening of water-use but also drought tolerance traits in a recombinant inbred line population derived from two Arabidopsis accessions with distinct water use strategies, namely C24 (low PWU) and Col-0 (high PWU). Subsequent quantitative trait loci (QTL) mapping and validation through near-isogenic lines identified two causal QTLs, which showed that a combination of weak and non-functional alleles of the FRIGIDA (FRI) and FLOWERING LOCUS C (FLC) genes substantially reduced plant water-use without penalising reproductive performance. Drought tolerance traits, stomatal conductance, intrinsic water use efficiency ({delta}13C) and rosette water-use were independent of allelic variation at FRI and FLC, suggesting that flowering is critical in determining life-time plant water use, but not leaf-level traits.

plant biology↗

bioRxiv2018

Leveraging mutational burden for complex trait prediction in sorghum

Sorghum (Sorghum bicolor (L.) Moench) is a major staple food cereal for millions of people worldwide. The sorghum genome, like other species, accumulates deleterious mutations, likely impacting its fitness. Though selection keeps deleterious mutations rare, their complete removal from the genome is impeded due to lack of recombination, drift, and their coupling with favorable loci. To study how deleterious mutations impact agronomic phenotypes, we identified putative deleterious mutations among ~5.5M segregating variants of 229 diverse sorghum lines. We provide the whole-genome estimate of the deleterious burden in sorghum, showing that about 33% of nonsynonymous substitutions are putatively deleterious. The pattern of mutation burden varies appreciably among racial groups; the caudatum shows higher mutation burden while the guinea has lower burden. Across racial groups, the mutation burden correlated negatively with biomass, plant height, Specific Leaf Area (SLA), and tissue starch content, suggesting deleterious burden decreases trait fitness. Putatively deleterious variants explain roughly half of the genetic variance. However, there is only moderate improvement in total heritable variance explained for biomass (7.6%) and plant height (5.2%). There is no advantage in total heritable variance for SLA and starch. The contribution of putatively deleterious variants to phenotypic diversity therefore appears to be dependent on the genetic architecture of traits. Overall, our results suggest that including putatively deleterious variants in models do not significantly improve breeding accuracy because of extensive linkage. However, knowledge of deleterious variants could be leveraged for sorghum breeding through genome editing.

genetics↗