Biology subjects

van Eeuwijk, F. A.

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

10 recordsLinked to original sources

bioRxiv2025

Genotype imputation and error estimation in connected multiparental populations

Multiparental populations have been produced for quantitative trait loci (QTL) mapping in many crops, where next-generation sequencing has become a cost-effective tool for genotyping. Previously, we have developed a hidden Markov framework denoted by MagicImpute_mma for genotype imputation in a multiparental population, which was implemented in Mathematica. However, its computational time increases quickly with the number of parents. In this work, we extend MagicImpute_mma into MagicImpute for increasing computational efficiency and robustness to various types of errors. Particularly, it has the following novel features: (1) allowing for multiple multiparental populations that may be connected by sharing parents, (2) allowing for many missing parents that are not available for sequencing, (3) accounting for allelic bias and overdispersion in next generation sequencing data, (4) inferring marker-specific error rates and filtering for markers with low error rates, and (5) being implemented in the high performance Julia language. Besides extensive simulation studies, we evaluate MagicImpute by three real datasets: the rice F2 population with sequence depth being low, the apple F1 population with parents being outbred, and the sorghum multi-parent advanced generation inter-cross (MAGIC) population with 10 male sterile lines (out of 29 parents) being missing. The results have shown that MagicImpute is accurate for genotype imputation in connected bi- or multiparental populations with various types of sequence errors and it opens up new opportunities for QTL mapping after imputing many missing parents.

bioinformatics↗

bioRxiv2025

Structured Multimodal Deep Learning improves Genomic Prediction in Future Environments

The development of prediction models for phenotypes as functions of genetics and environmental inputs is a long-standing challenge in genetics and plant breeding. Deep neural networks form a promising approach to this task, due to their capacity to approximate nonlinear biological processes. Despite initial expectations, recent studies have found deep neural networks under-performing in comparison to linear methods, even for continent-scale trial datasets. We attribute this to several failure modes of deep learning, including greedy learning, the tendency of deep neural networks to over-emphasize a single type of input data. As a solution, we present the Structured Interaction Neural Network (SINN), which combines statistical decomposition of genetic, environmental and interaction effects with deep neural networks. SINN dissects phenotype prediction into isolated component modeling tasks, revealing poor generalization of learned representations to new environments to be the main limitation for both prediction of genotype-by-environment interactions and yield prediction overall. By balancing model complexity and regularization per component, we reach competitive performance on yield prediction in the next cycle of a maize multi-environment trial, including both new genotypes and new environmental conditions. SINN achieved a higher accuracy (0.63) than BLUP-based methods (0.43) and a neural network from previous literature (0.48), and surpassed the top-performing models in a public benchmark dataset with a lower RMSE (2.41 Mg/ha versus 2.46 Mg/ha, with mean yield of 9.51 Mg/ha) and higher genetic correlation (0.38 versus 0.36). By combining statistical genetics and modern deep learning, SINN enables accurate, modular and scalable genomic prediction in new environments.

genetics↗

bioRxiv2024

Flower bud cooling protects pollen development and improves fertility during heatwaves

Early pollen development is a bottleneck for plant fertility in heatwave conditions, thus affecting yield stability. Mechanisms that protect this process and explain variation in tolerance level between genotypes are poorly understood. Here we show that sepal transpiration in young, still closed, flower buds reduces the impact of heat on developing tomato pollen and that this mechanism is enhanced by the major tomato pollen thermotolerance QTL, qPV11. By direct measurement of the flower bud core temperature and transpiration we show this process, which we term flower bud cooling, depends on heat-induced opening of sepal stomata and that the transpiration enhancing effect of qPV11 requires functional stomatal regulation and is specific to the sepals. Large-scale evaluation of populations in both a production field and greenhouse showed that qPV11 improves pollen viability and fruit set in heatwave-affected complex cultivation environments. These findings highlight enhanced flower bud cooling as a naturally evolved protection mechanism against heatwaves and qPV11 as genetic component in the differential regulation of transpiration between reproductive and vegetative tissues and candidate variant for the breeding of climate-resilient tomato cultivars.

plant biology↗

bioRxiv2024

Species wide inventory of Arabidopsis thaliana organellar variation reveals ample phenotypic variation for photosynthetic performance

Efforts to improve photosynthetic performance are increasingly employing natural genetic variation. However, genetic variation in the organellar genomes (plasmotypes) is often disregarded due to the difficulty of studying the plasmotypes and the lack of evidence that this is a worthwhile investment. Here, we systematically phenotyped plasmotype diversity using Arabidopsis thaliana as a model species. A reanalysis of whole genome resequencing data of 1,531 representative accessions shows that the genetic diversity amongst the mitochondrial genomes is eight times lower than amongst the chloroplast genomes. Plasmotype diversity of the accessions divides the species into two major phylogenetic clusters, within which highly divergent subclusters are distinguished. We combined plasmotypes from 60 A. thaliana accessions with the nuclear genomes (nucleotypes) of four A. thaliana accessions to create a panel of 232 novel cytonuclear genotypes (cybrids). The cybrid plants were grown in a range of different light and temperature conditions and phenotyped using high-throughput phenotyping platforms. Analysis of the phenotypes showed that several plasmotypes alone or in interaction with the nucleotypes have significant effects on photosynthesis, and that the effects are highly dependent on the environment. Moreover, we introduce Plasmotype Association Studies (PAS) as a novel method to reveal plasmotypic effects. Within A. thaliana, several organellar variants can influence photosynthetic phenotypes, which emphasizes the valuable role this variation has on improving photosynthetic performance. The increasing feasibility of producing cybrids in various species calls for further research into how these phenotypes may support breeding goals in crop species. Significance statementPhotosynthesis is one of the few crop traits that has been largely unaddressed which can contribute to increasing crop yield potential. Exploiting genetic variation within organellar genomes presents a promising, yet untapped resource to improve photosynthesis. However, the extent of organellar variation and its impact on photosynthesis within a species remains largely unknown. Using Arabidopsis thaliana as a model species, we revealed highly divergent clusters of organellar variation. We constructed 232 novel combinations of species representative organellar and nuclear genomes, referred to as cybrids. High-throughput phenotyping of these cybrids revealed that organellar variants can substantially impact photosynthesis in different environments. These findings indicate that organellar genomes may be a valuable resource for improving photosynthesis in crops.

plant biology↗

bioRxiv2023

Local adaptation of Arabidopsis thaliana in a small geographic region with mild environmental clines

Natural populations of Arabidopsis thaliana provide powerful systems to study adaptation of wild plant species. Previous research has predominantly focused on global populations or accessions collected from regions with diverse climates. However, little is known about the genetics underlying adaptation in regions with mild environmental clines. We have examined a diversity panel consisting of 192 A. thaliana accessions collected from the Netherlands, a region with limited climatic variation. Despite the relatively uniform climate, we identified compelling evidence of local adaptation within this population. Notably, semidwarf accessions, due to mutation of the GIBBERELLIC ACID REQUIRING 5 (GA5) gene, occur at a relatively high frequency near the coast and these displayed enhanced tolerance to high wind velocities. Additionally, we evaluated the performance of the population under iron deficiency conditions and found that allelic variation in the FE SUPEROXIDE DISMUTASE 3 (FSD3) gene affects tolerance to low iron levels. Moreover, we explored patterns of local adaptation to environmental clines in temperature and precipitation, observing that allelic variation at LA RELATED PROTEIN 1C (LARP1c) likely affects drought tolerance. Not only is the genetic variation observed in a diversity panel of A. thaliana collected in a region with mild environmental clines comparable to that in collections sampled over larger geographic ranges, it is also sufficiently rich to elucidate the genetic and environmental factors underlying natural plant adaptation.

plant biology↗

bioRxiv2023

A one-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

This work is motivated by the need to accurately estimate genetic effects over time when analysing data from high-throughput phenotyping (HTP) experiments. The HTP data we deal with here are characterised by phenotypic traits measured multiple times in the presence of spatial and temporal noise and a hierarchical organisation at three levels (populations, genotypes within populations, and plants within genotypes). We propose a feasible one-stage spatio-temporal P-spline-based hierarchical approach to model the evolution of the genetic signal over time on a given phenotype while accounting for spatio-temporal noise. We provide the user with appealing tools that take advantage of the sparse model matrices structure to reduce computational complexity. We illustrate the performance of our method using spatio-temporal simulated data and data from the PhenoArch greenhouse platform at INRAE Montpellier. In the plant breeding context, we show that information extracted for genomic selection purposes from our fitted genotypic curves is similar to those obtained using a comparable two-stage P-spline-based approach.

genetics↗

bioRxiv2022

High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase

Crop breeders increasingly need to mitigate the effects of climate change. Ideally, their selection strategies are based on an understanding of crop responses to environmental covariates such as temperature. In this study, the height of 352 varieties (European and Swiss) was repeatedly measured in multiple years. P-splines were used to model plant height as a function of time, from which the phenology parameters jointing (start) and end of stem elongation were derived. An asymptotic model was used to estimate the base-temperature of growth (Tmin), the steepness of the response (lrc), and the growth at optimum temperature (rmax). Parameter rmax had the largest effect on final height, whereas the temperature-response parameters in the narrow sense (Tmin, lrc) were closely connected to phenology. Final height and rmax decreased from the more continental, eastern European countries towards the more maritime, western countries. For genotypes registered in Great Bitain, Tmin was distinctly lower compared to most other regions. Integrating such analysis of in-season responsiveness to fluctuating environmental conditions in breeding will help to improve the genetic gain for climate adaptation. It can only be achieved based on high-throughput assessment of phenotypes in the field throughout the season. HighlightWheat phenology and environmental responses have been affected by breeders selections since 1970. High throughput field phenotyping methods reveal these developments, allowing to better adapt future varieties to climate change.

physiology↗

bioRxiv2021

Little heterosis found in Diploid Hybrid Potato

Hybrid potato breeding has become a novel alternative to con-ventional potato breeding allowing breeders to overcome intractable barriers (e.g. tetrasomic inheritance, masked deleterious alleles, obligate clonal propagation) with the benefit of seed-based propagule, flexible population design, and the potential of hybrid vigour. Until now, however, no formal inquiry has adequately examined the relevant genetic components for complex traits in hybrid potato populations. In this present study, we use a two-step modelling approach to estimate the relevant variance components to assess the magnitude of the general and specific combining abilities (GCA and SCA, respectively) in diploid hybrid potato (DHP). SCA effects were identified for all yield components studied here warranting evidence of non-additive genetic effects in hybrid potato yield. However, the estimated GCA effects were on average two times larger than their respective SCA quantile across all yield phenotypes. Tuber number GCAs and SCAs were found to be highly correlated with total yields genetic components. Tuber volume appearing under-selected in this population. The prominence of additive effects found for all traits presents evidence that breeders can perform hybrid potato evaluation using the mid-parent value alone. Heterotic vigour stands be useful in bolstering simpler traits but this will be very dependent on the target market of a population. This study represents the first diallel analysis of its kind in diploid potato using material derived from a commercial hybrid breeding programme.

genetics↗

bioRxiv2021

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zurich.

genetics↗

bioRxiv2018

A complete chromosome substitution mapping panel reveals genome-wide epistasis in Arabidopsis

Chromosome substitution lines (CSLs) are tentatively supreme resources to investigate non-allelic genetic interactions. However, the difficulty of generating such lines in most species largely yielded imperfect CSL panels, prohibiting a systematic dissection of epistasis. Here, we present the development and use of a unique and complete panel of CSLs in Arabidopsis thaliana, allowing the full factorial analysis of epistatic interactions. A first comparison of reciprocal single chromosome substitutions revealed a dependency of QTL detection on different genetic backgrounds. The subsequent analysis of the complete panel of CSLs enabled the mapping of the genetic interactors and identified multiple two- and three-way interactions for different traits. Some of the detected epistatic effects were as large as any observed main effect, illustrating the impact of epistasis on quantitative trait variation. We, therefore, have demonstrated the high power of detection and mapping of genome-wide epistasis, confirming the assumed supremacy of comprehensive CSL sets.\n\nOne sentence summaryDevelopment of a complete panel of chromosome substitution lines enables high power mapping of epistatic interactions in Arabidopsis thaliana.

genetics↗