Related references
Note: Only part of the references are listed.TrainSel: An R Package for Selection of Training Populations
Deniz Akdemir et al.
FRONTIERS IN GENETICS (2021)
Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview
Julio Isidro y Sanchez et al.
FRONTIERS IN PLANT SCIENCE (2021)
Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality
S. Ben-Sadoun et al.
THEORETICAL AND APPLIED GENETICS (2020)
Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs
Theresa Ankamah-Yeboah et al.
FRONTIERS IN PLANT SCIENCE (2020)
The Design of Early-Stage Plant Breeding Trials Using Genetic Relatedness
Brian R. Cullis et al.
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS (2020)
Genomic Prediction Enhanced Sparse Testing for Multi-environment Trials
Diego Jarquin et al.
G3-GENES GENOMES GENETICS (2020)
Design of training populations for selective phenotyping in genomic prediction
Deniz Akdemir et al.
SCIENTIFIC REPORTS (2019)
Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions
R. Rincent et al.
THEORETICAL AND APPLIED GENETICS (2019)
A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data
Osval A. Montesinos-Lopez et al.
G3-GENES GENOMES GENETICS (2019)
Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel
Simon Rio et al.
THEORETICAL AND APPLIED GENETICS (2019)
Mega-Environmental Design: Using Genotype x Environment Interaction to Optimize Resources for Cultivar Testing
Pablo Gonzalez-Barrios et al.
CROP SCIENCE (2019)
Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture
Abelardo Montesinos-Lopez et al.
G3-GENES GENOMES GENETICS (2018)
Optimization of multi-environment trials for genomic selection based on crop models
R. Rincent et al.
THEORETICAL AND APPLIED GENETICS (2017)
Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations
R. Rincent et al.
THEORETICAL AND APPLIED GENETICS (2017)
Genomic Prediction Within and Across Biparental Families: Means and Variances of Prediction Accuracy and Usefulness of Deterministic Equations
Pascal Schopp et al.
G3-GENES GENOMES GENETICS (2017)
Extending the Marker x Environment Interaction Model for Genomic-Enabled Prediction and Genome-Wide Association Analysis in Durum Wheat
Jose Crossa et al.
CROP SCIENCE (2016)
What Should Students in Plant Breeding Know About the Statistical Aspects of Genotype x Environment Interactions?
Fred A. van Eeuwijk et al.
CROP SCIENCE (2016)
Genomic selection in a commercial winter wheat population
Sang He et al.
THEORETICAL AND APPLIED GENETICS (2016)
On the Accuracy of Genomic Selection
Charles-Elie Rabier et al.
PLOS ONE (2016)
Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker x Environment Interaction Genomic Selection Model
Marco Lopez-Cruz et al.
G3-GENES GENOMES GENETICS (2015)
Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation
Frank Technow et al.
PLOS ONE (2015)
Training set optimization under population structure in genomic selection
Julio Isidro et al.
THEORETICAL AND APPLIED GENETICS (2015)
Empirical and deterministic accuracies of across-population genomic prediction
Yvonne C. J. Wientjes et al.
GENETICS SELECTION EVOLUTION (2015)
Optimal Design of Preliminary Yield Trials with Genome-Wide Markers
Jeffrey B. Endelman et al.
CROP SCIENCE (2014)
Genome-Wide Regression and Prediction with the BGLR Statistical Package
Paulino Perez et al.
GENETICS (2014)
On the Design of Field Experiments with Correlated Treatment Effects
David G. Butler et al.
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS (2014)
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
Diego Jarquin et al.
THEORETICAL AND APPLIED GENETICS (2014)
Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions
Nicolas Heslot et al.
THEORETICAL AND APPLIED GENETICS (2014)
Efficiency of augmented p-rep designs in multi-environmental trials
Jens Moehring et al.
THEORETICAL AND APPLIED GENETICS (2014)
Accuracy of Across-Environment Genome-Wide Prediction in Maize Nested Association Mapping Populations
Zhigang Guo et al.
G3-GENES GENOMES GENETICS (2013)
Resource Allocation for Maximizing Prediction Accuracy and Genetic Gain of Genomic Selection in Plant Breeding: A Simulation Experiment
Aaron J. Lorenz
G3-GENES GENOMES GENETICS (2013)
The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis
Marcos Malosetti et al.
FRONTIERS IN PHYSIOLOGY (2013)
Genomic Prediction of Breeding Values when Modeling Genotype x Environment Interaction using Pedigree and Dense Molecular Markers
Juan Burgueno et al.
CROP SCIENCE (2012)
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.)
R. Rincent et al.
GENETICS (2012)
Using the genomic relationship matrix to predict the accuracy of genomic selection
M. E. Goddard et al.
JOURNAL OF ANIMAL BREEDING AND GENETICS (2011)
Accuracy of genomic breeding values in multi-breed dairy cattle populations
Ben J. Hayes et al.
GENETICS SELECTION EVOLUTION (2009)
Efficient Methods to Compute Genomic Predictions
P. M. VanRaden
JOURNAL OF DAIRY SCIENCE (2008)
Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach
Hans D. Daetwyler et al.
PLOS ONE (2008)
Block designs for random treatment effects
Julio S. de S. Bueno Filho et al.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE (2007)
A comparison of experimental designs for selection in breeding trials with nested treatment structure
H. P. Piepho et al.
THEORETICAL AND APPLIED GENETICS (2006)
The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches
AB Smith et al.
JOURNAL OF AGRICULTURAL SCIENCE (2005)
Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend
A Smith et al.
BIOMETRICS (2001)
Cultivar evaluation and mega-environment investigation based on the GGE biplot
WK Yan et al.
CROP SCIENCE (2000)
Share Paper
