A novel method for genomic-enabled prediction of cultivars in new environments

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Jose Crossa (2023)

Artículo

Genomic Best Linear Unbiased Prediction Gains in Accuracy Genomic Prediction Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOTYPE ENVIRONMENT INTERACTION METHODS ENVIRONMENT

Statistical machine-learning methods for genomic prediction using the SKM library

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Jose Crossa (2023)

Artículo

Sparse Kernel Methods R package Statistical Machine Learning Genomic Selection CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MARKER-ASSISTED SELECTION MACHINE LEARNING GENOMICS METHODS

Replication Data for: Multi-trait genome prediction of new environments with partial least squares

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González Marco Alberto Valenzo-Jimenez Jose Crossa (2022)

The genomic selection (GS) methodology has revolutionized plant breeding. This methodology makes predictions for genotyped candidate lines based on statistical machine learning algorithms that are trained with phenotypic and genotypic data of a reference population. GS can save significant resources in the selection of candidate individuals. However, plant breeders can face challenges when trying to implement it practically to make predictions for future seasons or new locations and/or environments. To help address this challenge, this study seeks to explore the use of the multi-trait partial least square (MT-PLS) regression methodology and to compare its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. A benchmarking process was performed with five actual data sets contained in this study. The results of the analysis are reported in the accompanying article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Sparse multi-trait genomic prediction under incomplete block designs

Osval Antonio Montesinos-Lopez Brandon Alejandro Mosqueda González JOSAFHAT SALINAS RUIZ Abelardo Montesinos Jose Crossa (2022)

The efficiency of genomic selection methodologies can be increased by sparse testing where a subset of materials are evaluated in different environments. Seven different multi-environment plant breeding datasets were used to evaluate four different methods for allocating lines to environments in a multi-trait genomic prediction problem. The results of the analysis are presented in the accompanying article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Optimizing sparse testing for genomic prediction of plant breeding crops

Osval Antonio Montesinos-Lopez Carolina Saint Pierre Brandon Alejandro Mosqueda González Alison Bentley Yoseph Beyene Manje Gowda Leonardo Abdiel Crespo Herrera Jose Crossa (2022)

In plant breeding, sparse testing methods have been suggested to improve the efficiency of the genomic selection methodology. The data provided in this dataset were used to evaluate four methods for allocating lines to environments for sparse testing in multi-environment trials. The analysis was conducted using a multi-trait and uni-trait framework. The accompanying article describes the results of the evaluation as well as a cost-benefit analysis to identify the benefits that can be obtained using sparse testing methods.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA