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BWGS: A R package for genomic selection and its application to a wheat breeding programme

PloS one, 2020-04, Vol.15 (4), p.e0222733-e0222733

2020

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Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar

G3 : genes - genomes - genetics, 2018-12, Vol.8 (12), p.3961-3972

2018

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Wheat individual grain-size variance originates from crop development and from specific genetic determinism

PloS one, 2020-03, Vol.15 (3), p.e0230689-19

2020

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Training set optimization of genomic prediction by means of EthAcc

PloS one, 2019-02, Vol.14 (2), p.e0205629-e0205629

2019

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Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions

Frontiers in plant science, 2020-06, Vol.11, p.827-11

2020

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Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection

Theoretical and applied genetics, 2022-03, Vol.135 (3), p.895-914

2022

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Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm

Statistics and computing, 2024-02, Vol.34 (1), Article 53

2024

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Correction to: Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm

Statistics and computing, 2024, Vol.34 (3), Article 113

2024

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Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits

Frontiers in plant science, 2022-03, Vol.13, p.853601-853601

2022

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Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

Frontiers in plant science, 2021-06, Vol.12, p.658978-658978

2021

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Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

Frontiers in plant science, 2023-02, Vol.13, p.1075077-1075077

2023

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Genetic Variation for Cold Tolerance in Two Nested Association Mapping Populations

Agronomy (Basel), 2023-01, Vol.13 (1), p.195

2023

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Phenomic Selection: A New and Efficient Alternative to Genomic Selection

Methods in molecular biology (Clifton, N.J.), 2022, Vol.2467, p.397

2022

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Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations

Theoretical and applied genetics, 2017-11, Vol.130 (11), p.2231-2247

2017

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MegaLMM improves genomic predictions in new environments using environmental covariates

Genetics (Austin), 2024-10

2024

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Plasticity QTLs specifically contribute to the genotype x water availability interaction in maize

Theoretical and applied genetics, 2023-11, Vol.136 (11)

2023

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Plasticity QTLs specifically contribute to the genotype × water availability interaction in maize

Theoretical and applied genetics, 2023-11, Vol.136 (11), p.228-228, Article 228

2023

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

Theoretical and applied genetics, 2019-12, Vol.132 (12), p.3399-3411

2019

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Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars

Journal of experimental botany, 2024-04, Vol.75 (8), p.2510-2526

2024

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Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.)

Genetics (Austin), 2012-10, Vol.192 (2), p.715-728

2012

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Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality

Theoretical and applied genetics, 2020-07, Vol.133 (7), p.2197-2212

2020

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Optimization of multi-environment trials for genomic selection based on crop models

Theoretical and applied genetics, 2017-08, Vol.130 (8), p.1735-1752

2017

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Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.)

Theoretical and applied genetics, 2019-10, Vol.132 (10), p.2859-2880

2019

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Training population optimization for genomic selection improves the predictive ability of a costly measure in bread wheat, the gliadin to glutenin ratio

Euphytica, 2022-08, Vol.218 (8), Article 111

2022

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Different wheat loci are associated to heritable free asparagine content in grain grown under different water and nitrogen availability

Theoretical and applied genetics, 2024-02, Vol.137 (2), p.46-46, Article 46

2024

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BWGS: A R package for genomic selection and its application to a wheat breeding programme

Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar

Wheat individual grain-size variance originates from crop development and from specific genetic determinism

Training set optimization of genomic prediction by means of EthAcc

Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions

Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection

Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm

Correction to: Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm

Genetic Analysis of Platform-Phenotyped Root System Architecture of Bread and Durum Wheat in Relation to Agronomic Traits

Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

Genetic Variation for Cold Tolerance in Two Nested Association Mapping Populations

Phenomic Selection: A New and Efficient Alternative to Genomic Selection

Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations

MegaLMM improves genomic predictions in new environments using environmental covariates

Plasticity QTLs specifically contribute to the genotype x water availability interaction in maize

Plasticity QTLs specifically contribute to the genotype × water availability interaction in maize

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

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars

Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.)

Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality

Optimization of multi-environment trials for genomic selection based on crop models

Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.)

Training population optimization for genomic selection improves the predictive ability of a costly measure in bread wheat, the gliadin to glutenin ratio

Different wheat loci are associated to heritable free asparagine content in grain grown under different water and nitrogen availability

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