Biology subjects

van Eeuwijk, F.

Publications and source records attributed to van Eeuwijk, F..

11 recordsLinked to original sources

bioRxiv2026

A hierarchical Bayesian model for dynamic phenotypes in multi-environment trials with genotype by environment interaction

New phenotyping tools produce longitudinal phenotypes that describe plant growth and development. Estimates for the parameters that describe the dynamics of such longitudinal phenotypes can be incorporated as predictors in models for static target traits like yield and biomass. Within a hierarchical Bayesian context, we present a differential equation (DE) model for log-logistic growth in longitudinal traits for sets of genotypes across multiple environments. The DE parameters coincide with a midpoint, a slope, and an asymptote at the end of growth and development, i.e., final biomass. Each DE parameter in our hierarchical model results from contributions of genotype, environment, and G x E. This formulation allows one to assess and quantify the genotype-specificity of the DE parameters. When none of the DE parameters in our model requires a G x E term, the dynamic parameters characterizing the particular trait will be useful features for inclusion in prediction models for yield or biomass. We fitted a DE model to APSIM-simulated longitudinal wheat biomass data and quantified the contribution of genotype, environment and G x E to the phenotypic variation in the DE parameters. Only the upper asymptote showed clear G x E. Estimates for genotypic and G x E parameters within the DE parameters were used to characterize genotypes. Estimates for environmental and G x E effects formed inputs for environmental characterization.

plant biology↗

bioRxiv2026

Physics-Informed Neural Network Methods for Predicting Plant Height Development

Plant growth is a dynamic process affected by genes and growing environment, with all kinds of interactions between them. These complex relationships make the prediction of plant growth challenging. We propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN). While PINNs have been widely applied to physical dynamical systems, their use in modelling the dynamics of plant growth systems is still largely unexplored. We illustrate the construction of a PINN on plant height data in wheat and compare its performance with alternative models for longitudinal plant data. All temporal prediction models only require time and temperature as input. Among a set of competing models, our PINN had the lowest average root mean squared error (RMSE) of prediction and the smallest standard deviation across multiple random initialisations. Therefore, we conclude that incorporating biological growth constraints into data-driven growth models can enhance prediction accuracy of longitudinal plant traits. HighlightsO_LIIntegrating plant growth equations into a temporal neural network improves plant height growth prediction over ordinary differential equations and machine learning models, especially when training data are limited. C_LI

plant biology↗

bioRxiv2023

A Strategy for Differential Abundance Analysis of Sparse Microbiome Data with Group-wise Structured Zeros

Comparing the abundance of microbial communities between different groups or obtained under different experimental conditions using count sequence data is a challenging task due to various issues such as inflated zero counts, overdispersion, and non-normality. Several methods and procedures based on counts, their transformation and compositionality have been proposed in the literature to detect differentially abundant species in datasets containing hundreds to thousands of microbial species. Despite efforts to address the the large numbers of zeros present in microbiome datasets, even after careful data preprocessing, the performance of existing methods is impaired by the presence of inflated zero counts and group-wise structured zeros (i.e., all zero counts in a group). We propose and validate using extensive simulations an approach combining two differential abundance testing methods, namely DESeq2-ZINBWaVE and DESeq2, to address the issues of zero-inflation and group-wise structured zeros, respectively. This combined approach was subsequently successfully applied to two plant microbiome datasets that revealed a number of taxa as interesting candidates for further experimental validation.

bioinformatics↗

bioRxiv2021

Genetic mapping of genotype-by-ploidy effects in Arabidopsis thaliana

Plants can express different phenotypic responses following polyploidization, but ploidy-dependent phenotypic variation has so far not been assigned to specific genetic factors. To map such effects, segregating populations at different ploidy levels are required. The availability of an efficient haploid-inducer line in Arabidopsis thaliana allows for the rapid development of large populations of segregating haploid offspring. Because Arabidopsis haploids can be self-fertilised to give rise to homozygous doubled haploids, the same genotypes can be phenotyped at both the haploid and diploid ploidy level. Here, we compared the phenotypes of recombinant haploid and diploid offspring derived from a cross between two late flowering accessions to map genotype x ploidy (GxP) interactions. Ploidy-specific quantitative trait loci (QTLs) were detected at both ploidy levels. This implies that mapping power will increase when phenotypic measurements of monoploids are included in QTL analyses. A multi-trait analysis further revealed pleiotropic effects for a number of the ploidy specific QTLs as well as opposite effects at different ploidy levels for general QTLs. Taken together, we provide evidence of genetic variation between different Arabidopsis accessions being causal for dissimilarities in phenotypic responses to altered ploidy levels, revealing a GxP effect. Additionally, by investigating a population derived from late flowering accessions we revealed a major vernalisation specific QTL for variation in flowering time, countering the historical bias of research in early flowering accessions.

genetics↗

bioRxiv2021

Local adaptation shapes metabolic diversity in the global population of Arabidopsis thaliana

The biosynthesis, structure and accumulation of secondary metabolites in plants are largely controlled by genetic factors, which can vary substantially among genotypes within a species. Here we studied a global population of Arabidopsis thaliana accessions for qualitative and quantitative variation in volatile and non-volatile secondary metabolites using essentially untargeted metabolomics. Genome-wide association (GWA) mapping revealed that metabolic variation mainly traces back to genetic variation in dedicated biosynthesis genes. Effect sizes of genetic variants, estimated by a Bayesian procedure, indicate that most of the genetic variation in the accumulation of secondary metabolites is explained by large-effect genes and defined by multiple polymorphisms. The various genetic variants resulted from independent mutation events and combined into distinctive haplotypes, which are representative for specific geographical regions. A strong relationship between the effect-size of regulatory loci, their allele frequencies and fixation index indicates that selection forces discriminate between haplotypes, resulting in different phytochemical profiles. Finally, we demonstrate that haplotype frequencies deviate from neutral theory predictions, suggesting that metabolic profiles are shaped by local adaptation and co-evolution of independent loci.

genetics↗

bioRxiv2021

Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points

Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput field phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variation in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless step-wise procedure that allows for carry on of estimated means and variances from stage to stage. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at defined time points or periods, and (3) dose-response curves. In a first stage, these intermediate traits are extracted from low-level traits time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as final height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the first two above-mentioned categories. Results indicate that, for the first stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if estimated means and variances are carried forward from one processing stage to the next. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at defined time points from HTFP data. HighlightsO_LIGeneral plot-level model for repeated high-throughput field phenotyping measurements C_LIO_LIExtraction of three main intermediate trait categories for dynamic modeling C_LIO_LISeamless processing approach that integrates temporal and spatial modeling C_LIO_LIPhenomics data processing cheatsheet C_LI

plant biology↗

bioRxiv2020

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea mays L.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr-1, which is higher than rates (0.7% yr-1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models. HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

plant biology↗

bioRxiv2020

Intercontinental prediction of soybean phenology via hybrid ensemble of knowledge-based and data-driven models

The timing of crop development has significant impacts on management decisions and subsequent yield formation. A large intercontinental dataset recording the timing of soybean developmental stages was used to establish ensembling approaches that leverage both discrete-time dynamical system models of soybean phenology and data-driven, machine-learned models to achieve accurate and interpretable predictions. We demonstrate that the knowledge-based, dynamical models can improve machine learning by generating expert-engineered features. Combining the predictions of the diverse component models via super learning resulted in a mean absolute error of 4.12 and 4.55 days to flowering (R1) and physiological maturity (R7), providing an improvement relative to the best benchmark model error of 6.90 and 15.47 days, respectively. The hybrid intercontinental model applies to a much wider range of management and temperature conditions than previous mechanistic models, enabling improved decision support as alternative cropping systems arise, farm sizes increase, and changes in the global climate continue to accelerate.

plant biology↗

bioRxiv2020

The influence of QTL allelic diversity on QTL detection in multi-parent populations: a simulation study in sugar beet

Multi-parent populations (MPPs) are important resources for studying plant genetic architecture and detecting quantitative trait loci (QTLs). In MPPs, the QTL effects can show various levels of allelic diversity, which is an important factor influencing the detection of QTLs. In MPPs, the allelic effects can be more or less specific. They can depend on an ancestor, a parent or the combination of parents in a cross. In this paper, we evaluated the effect of QTL allelic diversity on the QTL detection power in MPPs. We simulated: a) cross-specific QTLs; b) parental and ancestral QTLs; and c) bi-allelic QTLs. Inspired by a real application, we tested different MPP designs (diallel, chessboard, factorial, and NAM) derived from five or nine parents to explore the ability to sample genetic diversity and detect QTLs. Using a fixed total population size, the QTL detection power was larger in MPPs with fewer but larger crosses derived from a reduced number of parents. The use of a larger set of parents was useful to detect rare alleles with a large phenotypic effect. The benefit of using a larger set of parents was however conditioned on an increase of the total population size. We also determined empirical confidence intervals for QTL location to compare the resolution of different designs. For QTLs representing 6% of the phenotypic variation, using 1600 offspring individuals, we found 95% empirical confidence intervals of 50 and 26 cM for cross-specific and bi-allelic QTLs, respectively. MPPs derived from less parents with few but large crosses generally increased the QTL detection power. Using a larger set of parents to cover a wider genetic diversity can be useful to detect QTLs with a reduced minor allele frequency when the QTL effect is large and when the total population size is increased.

genetics↗

bioRxiv2020

The usefulness of multi-parent multi-environment QTL analysis: an illustration in different NAM populations

Commonly QTL detection in multi-parent population (MPPs) data measured in multiple environments (ME) is done by a single environment analysis on phenotypic values averaged across environments. This method can be useful to detect QTLs with a consistent effect across environments but it does not allow to estimate environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyze MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariances. Using data from the EU-NAM and the US-NAM populations, we show that these methods allow to estimate the QTLxE effects and that they give a more precise description of the trait genetic architecture than separate within environment analyses. The MPP-ME models we propose can also be extended to integrate environmental indices (e.g. temperature, precipitation, etc.) to understand better the mechanisms behind the QTLxE effects. Therefore, our methodology allows to exploit the full potential of MPP-ME data: to estimate QTL effect variations a) within the MPP between sub-populations due to different genetic backgrounds; and b) between environments.

genetics↗

bioRxiv2018

Reciprocal cybrids reveal how organellar genomes affect plant phenotypes

Assessing the impact of variation in chloroplast and mitochondrial DNA (collectively termed the plasmotype) on plant phenotypes is challenging due to the difficulty in separating their effect from nuclear derived variation (the nucleotype). Haploid inducer lines can be used as efficient plasmotype donors to generate new plasmotype-nucleotype combinations (cybrids)(Ravi et al., 2014). We generated a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively phenotyped these lines for 1859 phenotypes under stable and fluctuating conditions. We show that natural variation in the plasmotype results in additive as well as epistatic effects across all phenotypic categories. Plasmotypes which induce more additive phenotypic changes also cause more significant epistatic effects, suggesting a possible common basis for both additive and epistatic effects. On average epistatic interactions explained twice as much of the variance in phenotypes as additive plasmotype effects. The impact of plasmotypic variation was also more pronounced under fluctuating and stressful environmental conditions. Thus, the phenotypic impact of variation in plasmotypes is the outcome of multilevel Nucleotype X Plasmotype X Environment interactions and, as such, the plasmotype is likely to serve as a reservoir of variation which is only exposed under certain conditions. The production of cybrids using haploid inducers is a quick and precise method for assessing the phenotypic effects of natural variation in organellar genomes. It will facilitate efficient screening of unique nucleotype-plasmotype combinations to both improve our understanding of natural variation in nucleotype plasmotype interactions and identify favourable combinations to improve plant performance.

genetics↗