Record Details

Replication Data for: A comparison between three machine learning methods for multivariate genomic prediction using the Sparse Kernels Methods (SKM) library

CIMMYT Research Data & Software Repository Network Dataverse OAI Archive

View Archive Info
 
 
Field Value
 
Title Replication Data for: A comparison between three machine learning methods for multivariate genomic prediction using the Sparse Kernels Methods (SKM) library
 
Identifier https://hdl.handle.net/11529/10548728
 
Creator Montesinos-López, Osval A.
Montesinos-López, Abelardo
Cano-Paez, Bernabé
Hernández-Suarez, Carlos Moisés
Santana Mancilla, Pedro Cesar
Crossa, Jose
 
Publisher CIMMYT Research Data & Software Repository Network
 
Description Genomic selection (GS) provides a new way for plant breeders select the best genotype. It draws upon historical phenotypic and genotypic information for training a statistical machine learning model which is used for predicting phenotypic (or breeding) values of new lines for which only genotypic information is available. Many statistical machine learning methods have been proposed for this task, but multi-trait (MT) genomic prediction models are preferred because they take advantage of correlated traits to improve the prediction accuracy.
This study contains six datasets that were used to compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least square (PLS) and the multi-trait Random Forest (RF). The data come from groundnuts, rice, and wheat. The accompanying article describes the results of the analysis.
 
Subject Agricultural Sciences
Plant Breeding
Agricultural research
Triticum aestivum
Wheat
Groundnuts
Rice
Genotypes
Days to heading
Days to maturity
Plant height
Grain yield
Plant pod number
Pod yield per plant
Grain yield
Percentage of head rice recovery
Percentage of chalky grain
 
Language English
 
Date 2022-07-13
 
Contributor Dreher, Kate
CGIAR Research Program on Wheat (WHEAT)
Genetic Resources Program (GRP)
Global Wheat Program (GWP)
Bill and Melinda Gates Foundation (BMGF)
United States Agency for International Development (USAID)
CGIAR Research Program on Maize (MAIZE)
Biometrics and Statistics Unit (BSU)
CGIAR
Global Maize Program (GMP)
Agricultural Agreement Research Fund (JA)
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AGG)
Foundation for Research Levy on Agricultural Products (FFL)
Foreign, Commonwealth and Development Office (FCDO)
 
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