Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions

[1]  V. Allard,et al.  Wheat individual grain-size variance originates from crop development and from specific genetic determinism , 2020, PloS one.

[2]  Jill T. Anderson,et al.  Small spaces, big impacts: contributions of micro-environmental variation to population persistence under climate change , 2020, AoB PLANTS.

[3]  S. Praud,et al.  Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.) , 2019, Theoretical and Applied Genetics.

[4]  C. Müller,et al.  Climate change impact and adaptation for wheat protein , 2018, Global change biology.

[5]  C. Bastien,et al.  Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar , 2018, G3: Genes, Genomes, Genetics.

[6]  M. Semenov,et al.  Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time , 2018, Proceedings of the National Academy of Sciences.

[7]  Paul H. C. Eilers,et al.  Correcting for spatial heterogeneity in plant breeding experiments with P-splines , 2018 .

[8]  S. Huet,et al.  Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression , 2018 .

[9]  K. Eversole,et al.  High throughput SNP discovery and genotyping in hexaploid wheat , 2018, PloS one.

[10]  K. Chenu,et al.  Nitrogen nutrition index predicted by a crop model improves the genomic prediction of grain number for a bread wheat core collection , 2017 .

[11]  P. Martre,et al.  A Model of Leaf Coordination to Scale-Up Leaf Expansion from the Organ to the Canopy1 , 2017, Plant Physiology.

[12]  V. Allard,et al.  Optimization of multi-environment trials for genomic selection based on crop models , 2017, Theoretical and Applied Genetics.

[13]  P. Pérez-Rodríguez,et al.  Extending the Marker × Environment Interaction Model for Genomic‐Enabled Prediction and Genome‐Wide Association Analysis in Durum Wheat , 2016 .

[14]  Fred A. van Eeuwijk,et al.  Predicting responses in multiple environments : Issues in relation to genotype × Environment interactions , 2016 .

[15]  Alain Charcosset,et al.  Genome-Wide Analysis of Yield in Europe: Allelic Effects Vary with Drought and Heat Scenarios1[OPEN] , 2016, Plant Physiology.

[16]  Marco Lopez-Cruz,et al.  Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker × Environment Interaction Genomic Selection Model , 2015, G3: Genes, Genomes, Genetics.

[17]  M. Malosetti,et al.  A weighted AMMI algorithm to study genotype-by-environment interaction and QTL-by-environment interaction , 2014 .

[18]  D. Akdemir,et al.  Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions , 2014, Theoretical and Applied Genetics.

[19]  M. Calus,et al.  A reaction norm model for genomic selection using high-dimensional genomic and environmental data , 2013, Theoretical and Applied Genetics.

[20]  Hans-Peter Piepho,et al.  Genomic selection allowing for marker‐by‐environment interaction , 2013 .

[21]  M. Semenov,et al.  Simulation of environmental and genotypic variations of final leaf number and anthesis date for wheat , 2012 .

[22]  José Crossa,et al.  Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers , 2012 .

[23]  Ky L. Mathews,et al.  Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern Australia. , 2011, Journal of experimental botany.

[24]  David Gouache,et al.  Why are wheat yields stagnating in Europe? A comprehensive data analysis for France , 2010 .

[25]  M. Goddard,et al.  LASSO with cross-validation for genomic selection. , 2009, Genetics research.

[26]  P. VanRaden,et al.  Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.

[27]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[28]  Brian R. Cullis,et al.  On the design of early generation variety trials with correlated data , 2006 .

[29]  J. Porter,et al.  Modelling protein content and composition in relation to crop nitrogen dynamics for wheat , 2006 .

[30]  B. Cullis,et al.  Applications: The Analysis of Crop Variety Evaluation Data in Australia , 2001 .

[31]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[32]  J. Denis,et al.  Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression , 2000, Theoretical and Applied Genetics.

[33]  M. Brancourt-Hulmel.  Crop diagnosis and probe genotypes for interpreting genotype environment interaction in winter wheat trials , 1999, Theoretical and Applied Genetics.

[34]  P. Jamieson,et al.  Sirius: a mechanistic model of wheat response to environmental variation , 1998 .

[35]  E. Justes,et al.  Determination of a Critical Nitrogen Dilution Curve for Winter Wheat Crops , 1994 .

[36]  J. Zadoks.  A decimal code for the growth stages of cereals , 1974 .

[37]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[38]  K. Chenu.  Characterising the crop environment – nature, significance and applications , 2015 .

[39]  D. P. Stone.  The Intergovernmental Panel on Climate Change (IPCC) , 2015 .

[40]  François Tardieu,et al.  Any trait or trait-related allele can confer drought tolerance: just design the right drought scenario. , 2012, Journal of experimental botany.

[41]  Robin Thompson,et al.  ASREML user guide release 1.0 , 2002 .