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

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

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Title Replication Data for: Multi-trait genome prediction of new environments with partial least squares
 
Identifier https://hdl.handle.net/11529/10548705
 
Creator Montesinos-López, Osval A.
Montesinos-López, Abelardo
Bernal Sandoval, David Alejandro
Mosqueda-Gonzalez, Brandon Alejandro
Valenzo-Jiménez, Marco Alberto
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description 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.
 
Subject Agricultural Sciences
Wheat
Groundnuts
Rice
Triticum aestivum
Agricultural research
Plant Breeding
Genotypes
Days to heading
Days to maturity
Plant height
Grain yield
Seed yield per plant
Plant pod number
Pod yield per plant
Pyrenophora tritici-repentis
Parastagonospora nodorum
Bipolaris sorokiniana
Grain yield
Plant height
Percentage of chalky grain
Percentage of head rice recovery
 
Language English
 
Date 2022-07-04
 
Contributor Dreher, Kate
Foreign, Commonwealth and Development Office (FCDO)
Foundation for Research Levy on Agricultural Products (FFL)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
Agricultural Agreement Research Fund (JA)
CGIAR
Biometrics and Statistics Unit (BSU)
CGIAR Research Program on Maize (MAIZE)
United States Agency for International Development (USAID)
Bill and Melinda Gates Foundation (BMGF)
Global Wheat Program (GWP)
Genetic Resources Program (GRP)
CGIAR Research Program on Wheat (WHEAT)
 
Type Dataset
 
Source Eliana Monteverde, Lucía Gutierrez, Pedro Blanco, Fernando Pérez de Vida, Juan E Rosas, Victoria Bonnecarrère, Gastón Quero, Susan McCouch, Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativa L.) Grown in Subtropical Areas, G3 Genes|Genomes|Genetics, Volume 9, Issue 5, 1 May 2019, Pages 1519–1531, https://doi.org/10.1534/g3.119.400064
Pandey, M.K., Chaudhari, S., Jarquin, D. et al. Genome-based trait prediction in multi- environment breeding trials in groundnut. Theor Appl Genet 133, 3101–3117 (2020). https://doi.org/10.1007/s00122-020-03658-1
Juliana, P., Singh, R.P., Poland, J., Mondal, S., Crossa, J., Montesinos-López, O.A., Dreisigacker, S., Pérez-Rodríguez, P., Huerta-Espino, J., Crespo-Herrera, L. and Govindan, V. (2018), Prospects and Challenges of Applied Genomic Selection—A New Paradigm in Breeding for Grain Yield in Bread Wheat. The Plant Genome, 11: 180017. https://doi.org/10.3835/plantgenome2018.03.0017