Publications by authors named "Willem Kruijer"

26 Publications

  • Page 1 of 1

Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes.

Front Genet 2021 24;12:667358. Epub 2021 May 24.

Biometris, Wageningen University and Research, Wageningen, Netherlands.

In the past decades, genomic prediction has had a large impact on plant breeding. Given the current advances of high-throughput phenotyping and sequencing technologies, it is increasingly common to observe a large number of traits, in addition to the target trait of interest. This raises the important question whether these additional or "secondary" traits can be used to improve genomic prediction for the target trait. With only a small number of secondary traits, this is known to be the case, given sufficiently high heritabilities and genetic correlations. Here we focus on the more challenging situation with a large number of secondary traits, which is increasingly common since the arrival of high-throughput phenotyping. In this case, secondary traits are usually incorporated through additional relatedness matrices. This approach is however infeasible when secondary traits are not measured on the test set, and cannot distinguish between genetic and non-genetic correlations. An alternative direction is to extend the classical selection indices using penalized regression. So far, penalized selection indices have not been applied in a genomic prediction setting, and require plot-level data in order to reliably estimate genetic correlations. Here we aim to overcome these limitations, using two novel approaches. Our first approach relies on a dimension reduction of the secondary traits, using either penalized regression or random forests (LS-BLUP/RF-BLUP). We then compute the bivariate GBLUP with the dimension reduction as secondary trait. For simulated data (with available plot-level data), we also use bivariate GBLUP with the penalized selection index as secondary trait (SI-BLUP). In our second approach (GM-BLUP), we follow existing multi-kernel methods but replace secondary traits by their genomic predictions, with the advantage that genomic prediction is also possible when secondary traits are only measured on the training set. For most of our simulated data, SI-BLUP was most accurate, often closely followed by RF-BLUP or LS-BLUP. In real datasets, involving metabolites in Arabidopsis and transcriptomics in maize, no method could substantially improve over univariate prediction when secondary traits were only available on the training set. LS-BLUP and RF-BLUP were most accurate when secondary traits were available also for the test set.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fgene.2021.667358DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8181460PMC
May 2021

Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in .

Front Genet 2020 20;11:609117. Epub 2021 Jan 20.

Bioinformatics Group, Wageningen University, Wageningen, Netherlands.

Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (Φ ) and projected leaf area (PLA) in . To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both Φ and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fgene.2020.609117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855462PMC
January 2021

Machine learning in plant science and plant breeding.

iScience 2021 Jan 5;24(1):101890. Epub 2020 Dec 5.

Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen 6708 PB, the Netherlands.

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.isci.2020.101890DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750553PMC
January 2021

Natural variation of photosynthetic efficiency in Arabidopsis thaliana accessions under low temperature conditions.

Plant Cell Environ 2020 08 28;43(8):2000-2013. Epub 2020 Jun 28.

Laboratory of Genetics, Wageningen University and Research, Droevendaalsesteeg 1, Wageningen, 6708 PB, The Netherlands.

Low, but non-freezing, temperatures have negative effects on plant growth and development. Despite some molecular signalling pathways being known, the mechanisms causing different responses among genotypes are still poorly understood. Photosynthesis is one of the processes that are affected by low temperatures. Using an automated phenotyping platform for chlorophyll fluorescence imaging the steady state quantum yield of photosystem II (PSII) electron transport (Φ ) was measured and used to quantify the effect of moderately low temperature on a population of Arabidopsis thaliana natural accessions. Observations were made over the course of several weeks in standard and low temperature conditions and a strong decrease in Φ upon the cold treatment was found. A genome wide association study identified several quantitative trait loci (QTLs) that are associated with changes in Φ in low temperature. One candidate for a cold specific QTL was validated with a mutant analysis to be one of the genes that is likely involved in the PSII response to the cold treatment. The gene encodes the PSII associated protein PSB27 which has already been implicated in the adaptation to fluctuating light.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/pce.13811DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497054PMC
August 2020

Reconstruction of Networks with Direct and Indirect Genetic Effects.

Genetics 2020 04 3;214(4):781-807. Epub 2020 Feb 3.

Biometris, Wageningen University and Research, 6708 PB Wageningen, Netherlands.

Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, , through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example, when trying to improve crop yield and simultaneously control plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most current methods require all genetic variance to be explained by a small number of quantitative trait loci (QTL) with fixed effects. Only a few authors have considered the "missing heritability" case, where contributions of many undetectable QTL are modeled with random effects. Usually, these are treated as nuisance terms that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such an MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here, we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits; and (2) we can test the existence of direct genetic effects, and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1534/genetics.119.302949DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153926PMC
April 2020

Reciprocal cybrids reveal how organellar genomes affect plant phenotypes.

Nat Plants 2020 01 13;6(1):13-21. Epub 2020 Jan 13.

Laboratory of Genetics, Wageningen University & Research, Wageningen, the Netherlands.

Assessment of 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). We generated a panel comprising all possible cybrids of seven Arabidopsis thaliana accessions and extensively phenotyped these lines for 1,859 phenotypes under both stable and fluctuating conditions. We show that natural variation in the plasmotype results in both additive and epistatic effects across all phenotypic categories. Plasmotypes that induce more additive phenotypic changes also cause more 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 multi-level nucleotype-plasmotype-environment interactions and, as such, the plasmotype is likely to serve as a reservoir of variation that is predominantly exposed under certain conditions. The production of cybrids using haploid inducers is a rapid and precise method for assessment of 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 these combinations and identify favourable combinations to enhance plant performance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41477-019-0575-9DOI Listing
January 2020

Imputation of 3 million SNPs in the Arabidopsis regional mapping population.

Plant J 2020 05 11;102(4):872-882. Epub 2020 Feb 11.

Biometris, Wageningen University & Research, Wageningen, Netherlands.

Natural variation has become a prime resource to identify genetic variants that contribute to phenotypic variation. The regional mapping (RegMap) population is one of the most important populations for studying natural variation in Arabidopsis thaliana, and has been used in a large number of association studies and in studies on climatic adaptation. However, only 413 RegMap accessions have been completely sequenced, as part of the 1001 Genomes (1001G) Project, while the remaining 894 accessions have only been genotyped with the Affymetrix 250k chip. As a consequence, most association studies involving the RegMap are either restricted to the sequenced accessions, reducing power, or rely on a limited set of SNPs. Here we impute millions of SNPs to the 894 accessions that are exclusive to the RegMap, using the 1135 accessions of the 1001G Project as the reference panel. We assess imputation accuracy using a novel cross-validation scheme, which we show provides a more reliable measure of accuracy than existing methods. After filtering out low accuracy SNPs, we obtain high-quality genotypic information for 2029 accessions and 3 million markers. To illustrate the benefits of these imputed data, we reconducted genome-wide association studies on five stress-related traits and could identify novel candidate genes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/tpj.14659DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318218PMC
May 2020

Genomic prediction of maize yield across European environmental conditions.

Nat Genet 2019 06 20;51(6):952-956. Epub 2019 May 20.

LEPSE, INRA, Université Montpellier, SupAgro, Montpellier, France.

The development of germplasm adapted to changing climate is required to ensure food security. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios (genotype × environment interaction), in spite of promising results for flowering time. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields. We present a new strategy for germplasm evaluation under genotype × environment interaction. Yield was dissected in grain weight and number and genotype × environment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotype × environment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-019-0414-yDOI Listing
June 2019

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding.

Plant Sci 2019 May 30;282:23-39. Epub 2018 Jun 30.

CSIRO Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, QLD 4067, Australia; School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD 4343, Australia.

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.plantsci.2018.06.018DOI Listing
May 2019

Assessment of heterosis in two Arabidopsis thaliana common-reference mapping populations.

PLoS One 2018 12;13(10):e0205564. Epub 2018 Oct 12.

Laboratory of Genetics, Wageningen University and Research, Wageningen, the Netherlands.

Hybrid vigour, or heterosis, has been of tremendous importance in agriculture for the improvement of both crops and livestock. Notwithstanding large efforts to study the phenomenon of heterosis in the last decades, the identification of common molecular mechanisms underlying hybrid vigour remain rare. Here, we conducted a systematic survey of the degree of heterosis in Arabidopsis thaliana hybrids. For this purpose, two overlapping Arabidopsis hybrid populations were generated by crossing a large collection of naturally occurring accessions to two common reference lines. In these Arabidopsis hybrid populations the range of heterosis for several developmental and yield related traits was examined, and the relationship between them was studied. The traits under study were projected leaf area at 17 days after sowing, flowering time, height of the main inflorescence, number of side branches from the main stem or from the rosette base, total seed yield, seed weight, seed size and the estimated number of seeds per plant. Predominantly positive heterosis was observed for leaf area and height of the main inflorescence, whereas mainly negative heterosis was observed for rosette branching. For the other traits both positive and negative heterosis was observed in roughly equal amounts. For flowering time and seed size only low levels of heterosis were detected. In general the observed heterosis levels were highly trait specific. Furthermore, no correlation was observed between heterosis levels and the genetic distance between the parental lines. Since all selected lines were a part of the Arabidopsis genome wide association (GWA) mapping panel, a genetic mapping approach was applied to identify possible regions harbouring genetic factors causal for heterosis, with separate calculations for additive and dominance effects. Our study showed that the genetic mechanisms underlying heterosis were highly trait specific in our hybrid populations and greatly depended on the genetic background, confirming the elusive character of heterosis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205564PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6185836PMC
April 2019

Natural variation of YELLOW SEEDLING1 affects photosynthetic acclimation of Arabidopsis thaliana.

Nat Commun 2017 11 10;8(1):1421. Epub 2017 Nov 10.

Laboratory of Genetics, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.

Exploiting genetic variation for more efficient photosynthesis is an underexplored route towards new crop varieties. This study demonstrates the genetic dissection of higher plant photosynthesis efficiency down to the genomic DNA level, by confirming that allelic sequence variation at the Arabidopsis thaliana YELLOW SEEDLING1 (YS1) gene explains natural diversity in photosynthesis acclimation to high irradiance. We use a genome-wide association study to identify quantitative trait loci (QTLs) involved in the Arabidopsis photosynthetic acclimation response. Candidate genes underlying the QTLs are prioritized according to functional clues regarding gene ontology, expression and function. Reverse genetics and quantitative complementation confirm the candidacy of YS1, which encodes a pentatrico-peptide-repeat (PPR) protein involved in RNA editing of plastid-encoded genes (anterograde signalling). Gene expression analysis and allele sequence comparisons reveal polymorphisms in a light-responsive element in the YS1 promoter that affect its expression, and that of its downstream targets, resulting in the variation in photosynthetic acclimation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-017-01576-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5680337PMC
November 2017

SIEVE ELEMENT-LINING CHAPERONE1 Restricts Aphid Feeding on Arabidopsis during Heat Stress.

Plant Cell 2017 Oct 28;29(10):2450-2464. Epub 2017 Sep 28.

Bioscience, Wageningen University & Research, 6708 PB Wageningen, The Netherlands.

The role of phloem proteins in plant resistance to aphids is still largely elusive. By genome-wide association mapping of aphid behavior on 350 natural accessions, we identified the small heat shock-like (). Detailed behavioral studies on near-isogenic and knockout lines showed that SLI1 impairs phloem feeding. Depending on the haplotype, aphids displayed a different duration of salivation in the phloem. On mutants, aphids prolonged their feeding sessions and ingested phloem at a higher rate than on wild-type plants. The largest phenotypic effects were observed at 26°C, when expression is upregulated. At this moderately high temperature, mutants suffered from retarded elongation of the inflorescence and impaired silique development. Fluorescent reporter fusions showed that SLI1 is confined to the margins of sieve elements where it lines the parietal layer and colocalizes in spherical bodies around mitochondria. This localization pattern is reminiscent of the clamp-like structures observed in previous ultrastructural studies of the phloem and shows that the parietal phloem layer plays an important role in plant resistance to aphids and heat stress.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1105/tpc.16.00424DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5774557PMC
October 2017

Natural variation in Arabidopsis thaliana reveals shoot ionome, biomass, and gene expression changes as biomarkers for zinc deficiency tolerance.

J Exp Bot 2017 06;68(13):3643-3656

Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands.

Zinc (Zn) is an essential nutrient for plants, with a crucial role as a cofactor for many enzymes. Approximately one-third of the global arable land area is Zn deficient, leading to reduced crop yield and quality. To improve crop tolerance to Zn deficiency, it is important to understand the mechanisms plants have adopted to tolerate suboptimal Zn supply. In this study, physiological and molecular aspects of traits related to Zn deficiency tolerance were examined in a panel of 19 Arabidopsis thaliana accessions. Accessions showed a larger variation for shoot biomass than for Zn concentration, indicating that they have different requirements for their minimal Zn concentration required for growth. Accessions with a higher tolerance to Zn deficiency showed an increased expression of the Zn deficiency-responsive genes ZIP4 and IRT3 in comparison with Zn deficiency-sensitive accessions. Changes in the shoot ionome, as a result of the Zn treatment of the plants, were used to build a multinomial logistic regression model able to distinguish plants regarding their Zn nutritional status. This set of biomarkers, reflecting the A. thaliana response to Zn deficiency and Zn deficiency tolerance, can be useful for future studies aiming to improve the performance and Zn status of crop plants grown under suboptimal Zn concentrations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jxb/erx191DOI Listing
June 2017

Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping.

New Phytol 2017 Feb 4;213(3):1346-1362. Epub 2016 Oct 4.

Wageningen University and Research Plant Breeding, Wageningen University and Research, PO Box 386, 6700 AJ, Wageningen, the Netherlands.

Plants are exposed to combinations of various biotic and abiotic stresses, but stress responses are usually investigated for single stresses only. Here, we investigated the genetic architecture underlying plant responses to 11 single stresses and several of their combinations by phenotyping 350 Arabidopsis thaliana accessions. A set of 214 000 single nucleotide polymorphisms (SNPs) was screened for marker-trait associations in genome-wide association (GWA) analyses using tailored multi-trait mixed models. Stress responses that share phytohormonal signaling pathways also share genetic architecture underlying these responses. After removing the effects of general robustness, for the 30 most significant SNPs, average quantitative trait locus (QTL) effect sizes were larger for dual stresses than for single stresses. Plants appear to deploy broad-spectrum defensive mechanisms influencing multiple traits in response to combined stresses. Association analyses identified QTLs with contrasting and with similar responses to biotic vs abiotic stresses, and below-ground vs above-ground stresses. Our approach allowed for an unprecedented comprehensive genetic analysis of how plants deal with a wide spectrum of stress conditions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/nph.14220DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5248600PMC
February 2017

Genome-wide association analysis reveals distinct genetic architectures for single and combined stress responses in Arabidopsis thaliana.

New Phytol 2017 Jan 8;213(2):838-851. Epub 2016 Sep 8.

Laboratory of Entomology, Wageningen University, PO Box 16, 6700 AA, Wageningen, the Netherlands.

Plants are commonly exposed to abiotic and biotic stresses. We used 350 Arabidopsis thaliana accessions grown under controlled conditions. We employed genome-wide association analysis to investigate the genetic architecture and underlying loci involved in genetic variation in resistance to: two specialist insect herbivores, Pieris rapae and Plutella xylostella; and combinations of stresses, i.e. drought followed by P. rapae and infection by the fungal pathogen Botrytis cinerea followed by infestation by P. rapae. We found that genetic variation in resistance to combined stresses by drought plus P. rapae was limited compared with B. cinerea plus P. rapae or P. rapae alone. Resistance to the two caterpillars is controlled by different genetic components. There is limited overlap in the quantitative trait loci (QTLs) underlying resistance to combined stresses by drought plus P. rapae or B. cinerea plus P. rapae and P. rapae alone. Finally, several candidate genes involved in the biosynthesis of aliphatic glucosinolates and proteinase inhibitors were identified to be involved in resistance to P. rapae and P. xylostella, respectively. This study underlines the importance of investigating plant responses to combinations of stresses. The value of this approach for breeding plants for resistance to combinatorial stresses is discussed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/nph.14165DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217058PMC
January 2017

Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios.

Plant Physiol 2016 10 19;172(2):749-764. Epub 2016 Jul 19.

INRA, Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux, 34060 Montpellier, France (E.J.M., C.W., A.C.-L., F.T.);Biometris - Applied Statistics, Department of Plant Science, Wageningen University, 6700AA Wageningen, Netherlands (W.K., F.V.E.);INRA, UMR 0320 / UMR 8120 Génétique Quantitative et Evolution, 91190 Gif-sur-Yvette, France (S.N, S.D.N., C.B., A.C.); INRA, SMH Maïs, Centre de recherche de Bordeaux Aquitaine, 40390 Saint-Martin-De-Hinx, France (J.L.); Centre de Recherche de Chappes, Biogemma, 63720 Chappes, France (S.P.); Syngenta France SAS, 12, Chemin de l'Hobit, BP 27, 31790, Saint-Sauveur, France (N.R.); KWS Saat SE, 37555 Einbeck, Germany (T.P.); Department of Agricultural Sciences, University of Bologna, 40127 Bologna, Italy (R.T.);MTA ATK/ AI CAR HAS, Martonvasar 2462, Hungary (Z.B.);UCL ELIA, 1348 Louvain-la-Neuve, Belgium (X.D.); andInstitute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany (B.U.)

Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years × 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047082PMC
http://dx.doi.org/10.1104/pp.16.00621DOI Listing
October 2016

AtWRKY22 promotes susceptibility to aphids and modulates salicylic acid and jasmonic acid signalling.

J Exp Bot 2016 05 23;67(11):3383-96. Epub 2016 Apr 23.

Plant Research International, Business Unit Bioscience, Wageningen University and Research Centre, PO Box 16, 6700 AA Wageningen, The Netherlands.

Aphids induce many transcriptional perturbations in their host plants, but the signalling cascades responsible and the effects on plant resistance are largely unknown. Through a genome-wide association (GWA) mapping study in Arabidopsis thaliana, we identified WRKY22 as a candidate gene associated with feeding behaviour of the green peach aphid, Myzus persicae The transcription factor WRKY22 is known to be involved in pathogen-triggered immunity, and WRKY22 gene expression has been shown to be induced by aphids. Assessment of aphid population development and feeding behaviour on knockout mutants and overexpression lines showed that WRKY22 increases susceptibility to M. persicae via a mesophyll-located mechanism. mRNA sequencing analysis of aphid-infested wrky22 knockout plants revealed the up-regulation of genes involved in salicylic acid (SA) signalling and down-regulation of genes involved in plant growth and cell-wall loosening. In addition, mechanostimulation of knockout plants by clip cages up-regulated jasmonic acid (JA)-responsive genes, resulting in substantial negative JA-SA crosstalk. Based on this and previous studies, WRKY22 is considered to modulate the interplay between the SA and JA pathways in response to a wide range of biotic and abiotic stimuli. Its induction by aphids and its role in suppressing SA and JA signalling make WRKY22 a potential target for aphids to manipulate host plant defences.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jxb/erw159DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4892728PMC
May 2016

Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability.

Plant Methods 2016 15;12:14. Epub 2016 Feb 15.

Laboratory of Genetics, Wageningen University, Wageningen, The Netherlands.

Background: Recent advances in genome sequencing technologies have shifted the research bottleneck in plant sciences from genotyping to phenotyping. This shift has driven the development of phenomics, high-throughput non-invasive phenotyping technologies.

Results: We describe an automated high-throughput phenotyping platform, the Phenovator, capable of screening 1440 Arabidopsis plants multiple times per day for photosynthesis, growth and spectral reflectance at eight wavelengths. Using this unprecedented phenotyping capacity, we have been able to detect significant genetic differences between Arabidopsis accessions for all traits measured, across both temporal and environmental scales. The high frequency of measurement allowed us to observe that heritability was not only trait specific, but for some traits was also time specific.

Conclusions: Such continuous real-time non-destructive phenotyping will allow detailed genetic and physiological investigations of the kinetics of plant homeostasis and development. The success and ultimate outcome of a breeding program will depend greatly on the genetic variance which is sampled. Our observation of temporal fluctuations in trait heritability shows that the moment of measurement can have lasting consequences. Ultimately such phenomic level technologies will provide more dynamic insights into plant physiology, and the necessary data for the omics revolution to reach its full potential.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13007-016-0113-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754911PMC
February 2016

Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis.

Plant Physiol 2016 04 11;170(4):2187-203. Epub 2016 Feb 11.

Laboratory of Plant Physiology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands (R.K., R.B., A.K., H.B., D.V.); Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands (R.K., F.B., J.J.B.K.); Centre for Biosystems Genomics, Wageningen Campus, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands (R.K., H.v.d.G., D.V., J.J.B.K); Biometris, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands (W.K.); PRI Bioinformatics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands (H.v.d.G.); and Keygene, Agro Business Park 90, 6708 PW Wageningen, the Netherlands (J.B., T.D., J.G.)

Quantitative traits in plants are controlled by a large number of genes and their interaction with the environment. To disentangle the genetic architecture of such traits, natural variation within species can be explored by studying genotype-phenotype relationships. Genome-wide association studies that link phenotypes to thousands of single nucleotide polymorphism markers are nowadays common practice for such analyses. In many cases, however, the identified individual loci cannot fully explain the heritability estimates, suggesting missing heritability. We analyzed 349 Arabidopsis accessions and found extensive variation and high heritabilities for different morphological traits. The number of significant genome-wide associations was, however, very low. The application of genomic prediction models that take into account the effects of all individual loci may greatly enhance the elucidation of the genetic architecture of quantitative traits in plants. Here, genomic prediction models revealed different genetic architectures for the morphological traits. Integrating genomic prediction and association mapping enabled the assignment of many plausible candidate genes explaining the observed variation. These genes were analyzed for functional and sequence diversity, and good indications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained. For ACS11, an ethylene biosynthesis gene, haplotype differences explaining variation in the ratio of petiole and leaf length could be identified.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1104/pp.15.00997DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4825126PMC
April 2016

Novel Genes Affecting the Interaction between the Cabbage Whitefly and Arabidopsis Uncovered by Genome-Wide Association Mapping.

PLoS One 2015 23;10(12):e0145124. Epub 2015 Dec 23.

Wageningen UR Plant Breeding, Wageningen University, Wageningen, The Netherlands.

Plants have evolved a variety of ways to defend themselves against biotic attackers. This has resulted in the presence of substantial variation in defense mechanisms among plants, even within a species. Genome-wide association (GWA) mapping is a useful tool to study the genetic architecture of traits, but has so far only had limited exploitation in studies of plant defense. Here, we study the genetic architecture of defense against the phloem-feeding insect cabbage whitefly (Aleyrodes proletella) in Arabidopsis thaliana. We determined whitefly performance, i.e. the survival and reproduction of whitefly females, on 360 worldwide selected natural accessions and subsequently performed GWA mapping using 214,051 SNPs. Substantial variation for whitefly adult survival and oviposition rate (number of eggs laid per female per day) was observed between the accessions. We identified 39 candidate SNPs for either whitefly adult survival or oviposition rate, all with relatively small effects, underpinning the complex architecture of defense traits. Among the corresponding candidate genes, i.e. genes in linkage disequilibrium (LD) with candidate SNPs, none have previously been identified as a gene playing a role in the interaction between plants and phloem-feeding insects. Whitefly performance on knock-out mutants of a number of candidate genes was significantly affected, validating the potential of GWA mapping for novel gene discovery in plant-insect interactions. Our results show that GWA analysis is a very useful tool to gain insight into the genetic architecture of plant defense against herbivorous insects, i.e. we identified and validated several genes affecting whitefly performance that have not previously been related to plant defense against herbivorous insects.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145124PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4689410PMC
July 2016

Misspecification in Mixed-Model-Based Association Analysis.

Authors:
Willem Kruijer

Genetics 2016 Jan 19;202(1):363-6. Epub 2015 Nov 19.

Biometris, Wageningen University and Research Centre, 6702AG Wageningen, The Netherlands

Additive genetic variance in natural populations is commonly estimated using mixed models, in which the covariance of the genetic effects is modeled by a genetic similarity matrix derived from a dense set of markers. An important but usually implicit assumption is that the presence of any nonadditive genetic effect increases only the residual variance and does not affect estimates of additive genetic variance. Here we show that this is true only for panels of unrelated individuals. In the case that there is genetic relatedness, the combination of population structure and epistatic interactions can lead to inflated estimates of additive genetic variance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1534/genetics.115.177212DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701099PMC
January 2016

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits.

PLoS Genet 2015 Oct 23;11(10):e1005594. Epub 2015 Oct 23.

Biometris, Wageningen University, Wageningen, The Netherlands.

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pgen.1005594DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4619680PMC
October 2015

Genotype-phenotype modeling considering intermediate level of biological variation: a case study involving sensory traits, metabolites and QTLs in ripe tomatoes.

Mol Biosyst 2015 Nov 7;11(11):3101-10. Epub 2015 Sep 7.

Biometris, Wageningen University and Research Centre, PO Box 16, 6700AA Wageningen, The Netherlands.

Modeling genotype-phenotype relationships is a central objective in plant genetics and breeding. Commonly, variations in phenotypic traits are modeled directly in relation to variations at the DNA level, regardless of intermediate levels of biological variation. Here we present an integrative method for the simultaneous modeling of a set of multilevel phenotypic responses to variations at the DNA level. More specifically, for ripe tomato fruits, we use Gaussian graphical models and causal inference techniques to learn the dependencies of 24 sensory traits on 29 metabolites and the dependencies of those sensory and metabolic traits on 21 QTLs. The inferred dependency network which, though not essentially representing biological pathways, suggests how the effects of allele substitutions propagate through multilevel phenotypes. Such simultaneous study of the underlying genetic architecture and multifactorial interactions is expected to enhance the prediction and manipulation of complex traits.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1039/c5mb00477bDOI Listing
November 2015

Marker-based estimation of heritability in immortal populations.

Genetics 2015 Feb 19;199(2):379-98. Epub 2014 Dec 19.

Biometris, Wageningen University and Research Centre, 6700AC Wageningen, The Netherlands.

Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1534/genetics.114.167916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4317649PMC
February 2015

Quantitative trait loci and candidate genes underlying genotype by environment interaction in the response of Arabidopsis thaliana to drought.

Plant Cell Environ 2015 Mar 3;38(3):585-99. Epub 2014 Sep 3.

Laboratory of Genetics, Wageningen University, Wageningen, 6708PB, The Netherlands.

Drought stress was imposed on two sets of Arabidopsis thaliana genotypes grown in sand under short-day conditions and analysed for several shoot and root growth traits. The response to drought was assessed for quantitative trait locus (QTL) mapping in a genetically diverse set of Arabidopsis accessions using genome-wide association (GWA) mapping, and conventional linkage analysis of a recombinant inbred line (RIL) population. Results showed significant genotype by environment interaction (G×E) for all traits in response to different watering regimes. For the RIL population, the observed G×E was reflected in 17 QTL by environment interactions (Q×E), while 17 additional QTLs were mapped not showing Q×E. GWA mapping identified 58 single nucleotide polymorphism (SNPs) associated with loci displaying Q×E and an additional 16 SNPs associated with loci not showing Q×E. Many candidate genes potentially underlying these loci were suggested. The genes for RPS3C and YLS7 were found to contain conserved amino acid differences when comparing Arabidopsis accessions with strongly contrasting drought response phenotypes, further supporting their candidacy. One of these candidate genes co-located with a QTL mapped in the RIL population.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/pce.12418DOI Listing
March 2015

High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement.

Curr Opin Biotechnol 2012 Apr 4;23(2):221-6. Epub 2011 Nov 4.

Horticultural Supply Chains, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands.

Marker assisted plant breeding is a powerful technique for targeted crop improvement in horticulture and agriculture. It depends upon the correlation of desirable phenotypic characteristics with specific genetic markers. This can be determined by statistical models that relate the variation in the value of genetic markers to variation in phenotypic traits. It therefore depends upon the convergence of three technologies; the creation of genetically characterised (and thus marked) populations, high throughput screening procedures, and statistical procedures. While a large number of high throughput screening technologies are available, real-time screening techniques are usually based on some kind of imaging technologies, such as chlorophyll fluorescence imaging, that offers physiological data that are eminently suitable as a quantitative trait for genetic mapping.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.copbio.2011.10.006DOI Listing
April 2012