Replication Data for: Multi-trait genome prediction of new environments with partial least squares
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.
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Genotypic data biblioteca |
| Language: | English |
| Published: |
CIMMYT Research Data & Software Repository Network
2022
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| Subjects: | 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, Percentage of chalky grain, Percentage of head rice recovery, |
| Online Access: | https://hdl.handle.net/11529/10548705 |
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