Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions
Rincent, R. ; Malosetti, M. ; Ababaei, B. ; Touzy, G. ; Mini, A. ; Bogard, M. ; Martre, P. ; Gouis, J. Le; Eeuwijk, F. van - \ 2019
Theoretical and Applied Genetics 132 (2019)12. - ISSN 0040-5752 - p. 3399 - 3411.

Key message: We propose new methods to predict genotype × environment interaction by selecting relevant environmental covariates and using an AMMI decomposition of the interaction. Abstract: Farmers are asked to produce more efficiently and to reduce their inputs in the context of climate change. They have to face more and more limiting factors that can combine in numerous stress scenarios. One solution to this challenge is to develop varieties adapted to specific environmental stress scenarios. For this, plant breeders can use genomic predictions coupled with environmental characterization to identify promising combinations of genes in relation to stress covariates. One way to do it is to take into account the genetic similarity between varieties and the similarity between environments within a mixed model framework. Molecular markers and environmental covariates (EC) can be used to estimate relevant covariance matrices. In the present study, based on a multi-environment trial of 220 European elite winter bread wheat (Triticum aestivum L.) varieties phenotyped in 42 environments, we compared reference regression models potentially including ECs, and proposed alternative models to increase prediction accuracy. We showed that selecting a subset of ECs, and estimating covariance matrices using an AMMI decomposition to benefit from the information brought by the phenotypic records of the training set are promising approaches to better predict genotype-by-environment interactions (G × E). We found that using a different kinship for the main genetic effect and the G × E effect increased prediction accuracy. Our study also demonstrates that integrative stress indexes simulated by crop growth models are more efficient to capture G × E than climatic covariates.

Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis
Velazco, Julio G. ; Jordan, David R. ; Mace, Emma S. ; Hunt, Colleen H. ; Malosetti, Marcos ; Eeuwijk, Fred A. van - \ 2019
Frontiers in Plant Science 10 (2019). - ISSN 1664-462X
auxiliary trait - blended kinship matrix - BLUP - genomic prediction - grain yield - multi-trait analysis - sorghum - stay-green

Grain yield and stay-green drought adaptation trait are important targets of selection in grain sorghum breeding for broad adaptation to a range of environments. Genomic prediction for these traits may be enhanced by joint multi-trait analysis. The objectives of this study were to assess the capacity of multi-trait models to improve genomic prediction of parental breeding values for grain yield and stay-green in sorghum by using information from correlated auxiliary traits, and to determine the combinations of traits that optimize predictive results in specific scenarios. The dataset included phenotypic performance of 2645 testcross hybrids across 26 environments as well as genomic and pedigree information on their female parental lines. The traits considered were grain yield (GY), stay-green (SG), plant height (PH), and flowering time (FT). We evaluated the improvement in predictive performance of multi-trait G-BLUP models relative to single-trait G-BLUP. The use of a blended kinship matrix exploiting pedigree and genomic information was also explored to optimize multi-trait predictions. Predictive ability for GY increased up to 16% when PH information on the training population was exploited through multi-trait genomic analysis. For SG prediction, full advantage from multi-trait G-BLUP was obtained only when GY information was also available on the predicted lines per se, with predictive ability improvements of up to 19%. Predictive ability, unbiasedness and accuracy of predictions from conventional multi-trait G-BLUP were further optimized by using a combined pedigree-genomic relationship matrix. Results of this study suggest that multi-trait genomic evaluation combining routinely measured traits may be used to improve prediction of crop productivity and drought adaptability in grain sorghum.

A statistical framework for the detection of quantitative trait loci in plant multi-parent populations composed of crosses
Garin, Vincent - \ 2019
Wageningen University. Promotor(en): F.A. van Eeuwijk, co-promotor(en): M. Malosetti. - Wageningen : Wageningen University - ISBN 9789463950114 - 189
Combining pedigree and genomic information to improve prediction quality: an example in sorghum
Velazco, Julio G. ; Malosetti, Marcos ; Hunt, Colleen H. ; Mace, Emma S. ; Jordan, David R. ; Eeuwijk, Fred A. van - \ 2019
Theoretical and Applied Genetics 132 (2019)7. - ISSN 0040-5752 - p. 2055 - 2067.

Key message: The use of a kinship matrix integrating pedigree- and marker-based relationships optimized the performance of genomic prediction in sorghum, especially for traits of lower heritability. Abstract: Selection based on genome-wide markers has become an active breeding strategy in crops. Genomic prediction models can make use of pedigree information to account for the residual polygenic effects not captured by markers. Our aim was to evaluate the impact of using pedigree and genomic information on prediction quality of breeding values for different traits in sorghum. We explored BLUP models that use weighted combinations of pedigree and genomic relationship matrices. The optimal weighting factor was empirically determined in order to maximize predictive ability after evaluating a range of candidate weights. The phenotypic data consisted of testcross evaluations of sorghum parental lines across multiple environments. All lines were genotyped, and full pedigree information was available. The performance of the best predictive combined matrix was compared to that of models fitting the component matrices independently. Model performance was assessed using cross-validation technique. Fitting a combined pedigree–genomic matrix with the optimal weight always yielded the largest increases in predictive ability and the largest reductions in prediction bias relative to the simple G-BLUP. However, the weight that optimized prediction varied across traits. The benefits of including pedigree information in the genomic model were more relevant for traits with lower heritability, such as grain yield and stay-green. Our results suggest that the combination of pedigree and genomic relatedness can be used to optimize predictions of complex traits in crops when the additive variation is not fully explained by markers.

Heritability of growth and leaf loss compensation in a long-lived tropical understorey palm
Jansen, Merel ; Zuidema, Pieter A. ; Ast, Aad van; Bongers, Frans ; Malosetti, Marcos ; Martínez-Ramos, Miguel ; Núñez-Farfán, Juan ; Anten, Niels P.R. - \ 2019
PLoS ONE 14 (2019)5. - ISSN 1932-6203

Introduction Defoliation and light competition are ubiquitous stressors that can strongly limit plant performance. Tolerance to defoliation is often associated with compensatory growth, which could be positively or negatively related to plant growth. Genetic variation in growth, tolerance and compensation, in turn, plays an important role in the evolutionary adaptation of plants to changing disturbance regimes but this issue has been poorly investigated for long-lived woody species. We quantified genetic variation in plant growth and growth parameters, tolerance to defoliation and compensation for a population of the understorey palm Chamaedorea elegans. In addition, we evaluated genetic correlations between growth and tolerance/compensation. Methods We performed a greenhouse experiment with 711 seedlings from 43 families with twelve or more individuals of C. elegans. Seeds were collected in southeast Mexico within a 0.7 ha natural forest area. A two-third defoliation treatment (repeated every two months) was applied to half of the individuals to simulate leaf loss. Compensatory responses in specific leaf area, biomass allocation to leaves and growth per unit leaf area were quantified using iterative growth models. Results We found that growth rate was highly heritable and that plants compensated strongly for leaf loss. However, genetic variation in tolerance, compensation, and the individual compensatory responses was low. We found strong correlations between family mean growth rates in control and defoliation treatments. We did not find indications for growth-tolerance/ compensation trade-offs: genetic correlation between tolerance/compensation and growth rate were not significant. Implications The high genetic variation in growth rate, but low genetic variation in tolerance and compensation observed here suggest high ability to adapt to changes in environment that require different growth rates, but a low potential for evolutionary adaptation to changes in damage or herbivory. The strong correlations between family mean growth rates in control and defoliation treatments suggest that performance differences among families are also maintained under stress of disturbance.

Genome-wide association study for kernel composition and flour pasting behavior in wholemeal maize flour
Alves, Mara Lisa ; Carbas, Bruna ; Gaspar, Daniel ; Paulo, Manuel ; Brites, Cláudia ; Mendes-Moreira, Pedro ; Brites, Carla Moita ; Malosetti, Marcos ; Eeuwijk, Fred Van; Vaz Patto, Maria Carlota - \ 2019
BMC Plant Biology 19 (2019)1. - ISSN 1471-2229
Bread - Candidate genes - Nutritional quality - Pasting behavior - Plant breeding - Portuguese maize germplasm - Zea mays L.

Background: Maize is a crop in high demand for food purposes and consumers worldwide are increasingly concerned with food quality. However, breeding for improved quality is a complex task and therefore developing tools to select for better quality products is of great importance. Kernel composition, flour pasting behavior, and flour particle size have been previously identified as crucial for maize-based food quality. In this work we carried out a genome-wide association study to identify genomic regions controlling compositional and pasting properties of maize wholemeal flour. Results: A collection of 132 diverse inbred lines, with a considerable representation of the food used Portuguese unique germplasm, was trialed during two seasons, and harvested samples characterized for main compositional traits, flour pasting parameters and mean particle size. The collection was genotyped with the MaizeSNP50 array. SNP-trait associations were tested using a mixed linear model accounting for genetic relatedness. Fifty-seven genomic regions were identified, associated with the 11 different quality-related traits evaluated. Regions controlling multiple traits were detected and potential candidate genes identified. As an example, for two viscosity parameters that reflect the capacity of the starch to absorb water and swell, the strongest common associated region was located near the dull endosperm 1 gene that encodes a starch synthase and is determinant on the starch endosperm structure in maize. Conclusions: This study allowed for identifying relevant regions on the maize genome affecting maize kernel composition and flour pasting behavior, candidate genes for the majority of the quality-associated genomic regions, or the most promising target regions to develop molecular tools to increase efficacy and efficiency of quality traits selection (such as "breadability") within maize breeding programs.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding
Eeuwijk, Fred A. van; Bustos-Korts, Daniela ; Millet, Emilie J. ; Boer, Martin P. ; Kruijer, Willem ; Thompson, Addie ; Malosetti, Marcos ; Iwata, Hiroyoshi ; Quiroz, Roberto ; Kuppe, Christian ; Muller, Onno ; Blazakis, Konstantinos N. ; Yu, Kang ; Tardieu, Francois ; Chapman, Scott C. - \ 2019
Plant Science 282 (2019). - ISSN 0168-9452 - p. 23 - 39.
Crop growth model - Genomic prediction - Genotype-by-environment-interaction - Genotype-to-phenotype model - Mixed model - Multi-environment model - Multi-trait model - Phenotyping - Phenotyping platform - Physiology - Plant breeding - Prediction - Reaction norm - Response surface - Statistical genetics

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.

QTL detection in a breeding pedigree of diploid potato
Korontzis, G. ; Malosetti, Marcos ; Mulder, H.A. ; Maliepaard, C.A. ; Víquez Zamora, A.M. ; Veerkamp, R.F. ; Eeuwijk, F.A. van - \ 2018
Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics
Bustos-Korts, Daniela - \ 2017
Wageningen University. Promotor(en): F.A. Eeuwijk, co-promotor(en): M. Malosetti. - Wageningen : Wageningen University - ISBN 9789463436694 - 340
crops - applied statistics - genotype environment interaction - complex loci - quantitative genetics - gewassen - toegepaste statistiek - genotype-milieu interactie - complexe loci - kwantitatieve genetica

The main objective of plant breeders is to create and identify genotypes that are well-adapted to the target population of environments (TPE). The TPE corresponds to the future growing conditions in which the varieties produced by a breeding program will be grown. All possible genotypes that could be considered as selection candidates for a specific TPE are said to belong to the target population of genotypes, TPG. Genotypes commonly show different sensitivities to environmental gradients and then genotype by environment interaction (GxE) is observed. GxE can lead to changes in genotypic ranking, complicating the breeding process. The main aim of this thesis was to investigate statistical models and the combination of statistical and crop growth models to improve phenotype prediction across multiple environments. One aspect that determines the quality of phenotype prediction is the set of genotypes used to train the prediction model, especially when the TPG is structured. We proposed a method that uniformly covers the genetic space of the TPG, leading to a larger prediction accuracy than random sampling. We produced positive results for wheat, maize and rice. A second aspect that influences the accuracy of phenotype predictions is the choice of environments used to train the prediction model, which should capture the heterogeneity in the TPE. When accounting for heterogeneity in environmental quality, it is important to distinguish between repeatable and well predictable elements in the environmental conditions from those that are badly predictable. We proposed statistical methods based on the AMMI model and on mixed models to identify groups of environments that show repeatable GxE, illustrating our ideas with multi-environment wheat data in North-Western Europe. The importance of training set construction strategies and multi-environment genomic prediction models was also demonstrated for barley data. If breeders are interested in identifying the genetic basis of the target traits, it is advantageous to have a higher SNP density. In this thesis, we used exome sequence data of the EU-Whealbi-barley germplasm, which corresponds to a unique set of genotypes with a diverse origin, growth habit and breeding history. For this diverse data, we assessed the effects of QTLs and haplotypes across multiple environments for awn length, grain weight, heading date and plant height. Our results show that the EU-Whealbi-barley collection possesses a large diversity of promising alleles regulating the four traits we analysed. The last major topic addressed in this thesis is the use of a combination of statistical-genetic models and crop growth models (APSIM) as a strategy to assess the traits and phenotyping schemes to improve the prediction accuracy of a target trait like yield. We assess the potential of the combined modelling approach to characterize a sample of the TPG and TPE, and illustrate how trait correlations are modified by environmental conditions and by the genetic architecture of the sample of the TPE. We discuss the topics mentioned above, from a didactical perspective, proposing a list of subjects that should be covered in a GxE course for plant breeders. Finally, we discuss challenges and opportunities presented by the characterization of the TPE and TPG when using simulations based on statistical and crop growth models.

Dissecting the old Mediterranean durum wheat genetic architecture for phenology, biomass and yield formation by association mapping and QTL meta-analysis
Soriano, Jose Miguel ; Malosetti Zunin, Marcos ; Roselló, Martina ; Sorrells, Mark Earl ; Royo, Conxita - \ 2017
PLoS ONE 12 (2017)5. - ISSN 1932-6203

Association mapping was used to identify genome regions affecting yield formation, crop phenology and crop biomass in a collection of 172 durum wheat landraces representative of the genetic diversity of ancient local durum varieties from the Mediterranean Basin. The collection was genotyped with 1, 149 DArT markers and phenotyped in Spanish northern and southern locations during three years. A total of 245 significant marker trait associations (MTAs) (P<0.01) were detected. Some of these associations confirmed previously identified quantitative trait loci (QTL) and/or candidate genes, and others are reported for the first time here. Eighty-six MTAs corresponded with yield and yield component traits, 70 to phenology and 89 to biomass production. Twelve genomic regions harbouring stable MTAs (significant in three or more environments) were identified, while five and two regions showed specific MTAs for northern and southern environments, respectively. Sixty per cent of MTAs were located on the B genome and 29% on the A genome. The marker wPt-9859 was detected in 12 MTAs, associated with six traits in four environments and the mean across years. To refine QTL positions, a meta-analysis was performed. A total of 477 unique QTLs were projected onto a durum wheat consensus map and were condensed to 71 meta-QTLs and left 13 QTLs as singletons. Sixty-one percent of QTLs explained less than 10% of the phenotypic variance confirming the high genetic complexity of the traits analysed.

How do the type of QTL effect and the form of the residual term influence QTL detection in multi-parent populations? A case study in the maize EU-NAM population
Garin, Vincent ; Wimmer, Valentin ; Mezmouk, Sofiane ; Malosetti Zunin, Marcos ; Eeuwijk, Fred van - \ 2017
Theoretical and Applied Genetics 130 (2017)8. - ISSN 0040-5752 - p. 1753 - 1764.
Key message: In the QTL analysis of multi-parent populations, the inclusion of QTLs with various types of effects can lead to a better description of the phenotypic variation and increased power.Abstract: For the type of QTL effect in QTL models for multi-parent populations (MPPs), various options exist to define them with respect to their origin. They can be modelled as referring to close parental lines or to further away ancestral founder lines. QTL models for MPPs can also be characterized by the homo- or heterogeneity of variance for polygenic effects. The most suitable model for the origin of the QTL effect and the homo- or heterogeneity of polygenic effects may be a function of the genetic distance distribution between the parents of MPPs. We investigated the statistical properties of various QTL detection models for MPPs taking into account the genetic distances between the parents of the MPP. We evaluated models with different assumptions about the QTL effect and the form of the residual term using cross validation. For the EU-NAM data, we showed that it can be useful to mix in the same model QTLs with different types of effects (parental, ancestral, or bi-allelic). The benefit of using cross-specific residual terms to handle the heterogeneity of variance was less obvious for this particular data set.
Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model
Velazco, Julio G. ; Rodríguez-Álvarez, María Xosé ; Boer, Martin P. ; Jordan, David R. ; Eilers, Paul H.C. ; Malosetti, Marcos ; Eeuwijk, Fred A. van - \ 2017
Theoretical and Applied Genetics 130 (2017)7. - ISSN 0040-5752 - p. 1375 - 1392.
Key message: A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Abstract: Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.
Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space
Bustos-Korts, D. ; Malosetti Zunin, Marcos ; Chapman, S. ; Biddulph, B. ; Eeuwijk, F. Van - \ 2016
G3 : Genes Genomes Genetics 6 (2016)11. - ISSN 2160-1836 - p. 3733 - 3747.
Genome enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under represented in the training set, whereas other parts will be over represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD), proposed by Rincent et al. (2012). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTLs and GBLUPs, and Reproducing Kernel Hilbert Spaces (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel.
Ascertainment bias from imputation methods evaluation in wheat
Brandariz, Sofía P. ; González Reymúndez, Agustín ; Lado, Bettina ; Malosetti Zunin, Marcos ; Garcia, Antonio Augusto Franco ; Quincke, Martín ; Zitzewitz, Jarislav von; Castro, Marina ; Matus, Iván ; Pozo, Alejandro del; Castro, Ariel J. ; Gutiérrez, Lucía - \ 2016
BMC Genomics 17 (2016)1. - ISSN 1471-2164
False positive - GBS - GWAS - Power - QTL

Background: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. Results: In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. Conclusions: Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.

What should students in plant breeding know about the statistical aspects of genotype × Environment interactions?
Eeuwijk, Fred A. Van; Bustos-Korts, Daniela V. ; Malosetti Zunin, Marcos - \ 2016
Crop Science 56 (2016)5. - ISSN 0011-183X - p. 2119 - 2140.

A good statistical analysis of genotype × environment interactions (G × E) is a key requirement for progress in any breeding program. Data for G × E analyses traditionally come from multi-environment trials. In recent years, increasingly data are generated from managed stress trials, phenotyping platforms, and high throughput phenotyping techniques in the field. Simultaneously, and complementary to the phenotyping, more elaborate genotyping and envirotyping occur. All of these developments further increase the importance of a sound statistical framework for analyzing G × E. This paper presents considerations on such a framework from the point of view of the choices that need to be made with respect to the content of short academic courses on statistical methods for G × E. Based on our experiences in teaching statistical methods to plant breeders, for specialized G × E courses between three and 5 d are reserved. The audience in such courses includes MSc students, PhD students, postdocs, and researchers at breeding companies. For such specialized courses, we propose a collection of topics to be covered. Our outlook on G × E analyses is two-fold. On the one hand, we see the G × E problem as the building of predictive models for genotype-specific reaction norms. On the other hand, the G × E problem consists in the identification of suitable variance-covariance models to describe heterogeneity of genetic variance and correlations across environments. Our preferred class of statistical models is the class of mixed linear-bilinear models. These statistical models allow us to answer breeding questions on adaptation, adaptability, stability, and the identification and subdivision of the target population of environments. By a citation analysis of the literature on G × E, we show that our preference for mixed linear-bilinear models for analyzing G × E is supported by recent trends in the types of methods for G × E analysis that are most frequently cited.

Predicting responses in multiple environments : Issues in relation to genotype × Environment interactions
Malosetti Zunin, Marcos ; Bustos-Korts, Daniela ; Boer, Martin P. ; Eeuwijk, Fred A. van - \ 2016
Crop Science 56 (2016)5. - ISSN 0011-183X - p. 2210 - 2222.

Prediction of the phenotypes for a set of genotypes across multiple environments is a fundamental task in any plant breeding program. Genomic prediction (GP) can assist selection decisions by combining incomplete phenotypic information over multiple environments (MEs) with dense sets of markers. We compared a range of ME-GP models differing in the way environment-specific genetic effects were modeled. Information among environments was shared either implicitly via the response variable, or by the introduction of explicit environmental covariables. We discuss the models not only in the light of their accuracy, but also in their ability to predict the different parts of the incomplete genotype × environment interaction (G × E) table: (Gt; Et), (Gu; Et), (Gt; Eu), and (Gu; Eu), where G is genotype, E is environment, both tested (t; in one or more instances) and untested (u). Using the ‘Steptoe’ × ‘Morex’ barley (Hordeum vulgare L.) population as an example, we show the advantage of ME-GP models that account for G × E. In addition, for our example data set, we show that for prediction in the most challenging scenario of untested environments (Eu), the use of explicit environmental information is preferable over the simpler approach of predicting from a main effects model. Besides producing the most general ME-GP model, the use of environmental covariables naturally links with ecophysiological and crop-growth models (CGMs) for G × E. We conclude with a list of future research topics in ME-GP, where we see CGMs playing a central role.

Comparison of phenotyping methods for resistance to stem rot and aggregated sheath spot in rice
Rosas, Juan E. ; Martínez, Sebastián ; Bonnecarrère, Victoria ; Pérez de Vida, Fernando ; Blanco, Pedro ; Malosetti Zunin, Marcos ; Jannink, Jean Luc ; Gutiérrez, Lucía - \ 2016
Crop Science 56 (2016)4. - ISSN 0011-183X - p. 1619 - 1627.

Stem and sheath diseases caused by Sclerotium oryzae Cattaneo (SCL) and Rhizoctonia oryzae-sativae Sawada Mordue (ROS) can severely reduce rice (Oryza sativa L.) yield and grain quality. Genetic resistance is the best strategy to control them. Phenotypic selection for resistance is hampered due to a heterogeneous distribution of the inoculum in the soil that generates high environmental variability and decreases genetic gain. To have higher selection accuracy it is necessary to develop phenotyping methods with high repeatability and discriminative power. Comparison of greenhouse methods have been reported for Rhizoctonia solani Kühn, a more invasive pathogen than SCL and ROS, and for SCL, but no such comparisons are reported for ROS. Our study compares five inoculation methods for SCL and ROS to identify the more discriminant and repeatable method and to apply it for high-throughput phenotyping of hundreds of rice lines. A method that uses an agar disc with growing mycelium attached to the base of stems was found to have the best balance between discrimination among genotypes and variability among replicates of the same genotype for both pathogens. This method was used in five greenhouse experiments for phenotyping resistance to SCL and ROS in a population of 641 rice advanced breeding lines. Heritabilities of resistance ranged from 0.36 to 0.71 in these experiments. These findings have a direct application in screening for resistance of rice to SCL and ROS, and in high-throughput phenotyping for mapping loci associated to disease resistance.

Back to acid soil fields : The citrate transporter sbmate is a major asset for sustainable grain yield for sorghum cultivated on acid soils
Carvalho, Geraldo ; Schaffert, Robert Eugene ; Malosetti Zunin, Marcos ; Eeuwijk, Fred van - \ 2016
G3 : Genes Genomes Genetics 6 (2016)2. - ISSN 2160-1836 - p. 475 - 484.
Al tolerance - Alt - Field trials - QTL mapping - Sorghum bicolor

Aluminum (Al) toxicity damages plant roots and limits crop production on acid soils, which comprise up to 50% of the world's arable lands. A major Al tolerance locus on chromosome 3, AltSB, controls aluminum tolerance in sorghum [Sorghum bicolor (L.) Moench] via SbMATE, an Al-activated plasma membrane transporter that mediates Al exclusion from sensitive regions in the root apex. As is the case with other known Al tolerance genes, SbMATE was cloned based on studies conducted under controlled environmental conditions, in nutrient solution. Therefore, its impact on grain yield on acid soils remains undetermined. To determine the real world impact of SbMATE, multi-trait quantitative trait loci (QTL) mapping in hydroponics, and, in the field, revealed a large-effect QTL colocalized with the Al tolerance locus AltSB, where SbMATE lies, conferring a 0.6 ton ha-1 grain yield increase on acid soils. A second QTL for Al tolerance in hydroponics, where the positive allele was also donated by the Al tolerant parent, SC283, was found on chromosome 9, indicating the presence of distinct Al tolerance genes in the sorghum genome, or genes acting in the SbMATE pathway leading to Al-activated citrate release. There was no yield penalty for AltSB, consistent with the highly localized Al regulated SbMATE expression in the root tip, and Al-dependent transport activity. A female effect of 0.5 ton ha-1 independently demonstrated the effectiveness of AltSB in hybrids. Al tolerance conferred by AltSB is thus an indispensable asset for sorghum production and food security on acid soils, many of which are located in developing countries.

Modelling of Genotype by Environment Interaction and Prediction of Complex Traits across Multiple Environments as a Synthesis of Crop Growth Modelling, Genetics and Statistics
Bustos Korts, Daniela ; Malosetti, M. ; Chapman, S. ; Eeuwijk, Fred van - \ 2016
In: Crop Systems Biology : Narrowing the Gaps Between Crop Modelling and Genetics / Yin, X., Struik, P.C., Springer International Publishing - ISBN 9783319205618 - p. 55 - 82.
Selection processes in plant breeding depend critically on the quality of phenotype predictions. The phenotype is classically predicted as a function of genotypic and environmental information. Models for phenotype prediction contain a mixture of statistical, genetic and physiological elements. In this chapter, we discuss prediction from linear mixed models (LMMs), with an emphasis on statistics, and prediction from crop growth models (CGMs), with an emphasis on physiology. Three modalities of prediction are distinguished: predictions for new genotypes under known environmental conditions, predictions for known genotypes under new environmental conditions, and predictions for new genotypes under new environmental conditions. For LMMs, the genotypic input information includes molecular marker variation, while the environmental input can consist of meteorological, soil and management variables. However, integrated types of environmental characterizations obtained from CGMs can also serve as environmental covariable in LMMs. LMMs consist of a fixed part, corresponding to the mean for a particular genotype in a particular environment, and a random part defined by genotypic and environmental variances and correlations. For prediction via the fixed part, genotypic and/or environmental covariables are required as in classical regression. For predictions via the random part, correlations need to be estimated between observed and new genotypes, between observed and new environments, or both. These correlations can be based on similarities calculated from genotypic and environmental covariables. A simple type of covariable assigns genotypes to sub-populations and environments to regions. Such groupings can improve phenotype prediction. For a second type of phenotype prediction, we consider CGMs. CGMs predict a target phenotype as a non-linear function of underlying intermediate phenotypes. The intermediate phenotypes are outcomes of functions defined on genotype dependent CGM parameters and classical environmental descriptors. While the intermediate phenotypes may still show some genotype by environment interaction, the genotype dependent CGM parameters should be consistent across environmental conditions. The CGM parameters are regressed on molecular marker information to allow phenotype prediction from molecular marker information and standard physiologically relevant environmental information. Both LMMs and CGMs require extensive characterization of genotypes and environments. High-throughput technologies for genotyping and phenotyping provide new opportunities for upscaling phenotype prediction and increasing the response to selection in the breeding process.
Quantitative trait loci and candidate genes underlying genotype by environment interaction in the response of Arabidopsis thaliana to drought
El-Soda, M. ; Kruijer, Willem ; Malosetti, M. ; Koornneef, M. ; Aarts, M.G.M. - \ 2015
Plant, Cell & Environment 38 (2015)3. - ISSN 0140-7791 - p. 585 - 599.
genome-wide association - natural variation - abiotic stress - inbred lines - qtl analysis - growth - reveals - protein - genetics - adaptation
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
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