Replication Data for: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Marco Lopez-Cruz Yoseph Beyene Manje Gowda Jose Crossa Paulino Pérez-Rodríguez Gustavo de los Campos (2021)

Genomic prediction models may be used in plant breeding pipelines. They are often calibrated using multi-generation data and there is an open question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Therefore, a study was undertaken to determine whether combining sparse selection indexes (SSIs) and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. This dataset contains the genotypic and phenotypic data from CIMMYT maize doubled haploid lines that were used to perform the analyses. The results of the analyses are presented in the accompanying article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Sparse kernel models provide optimization of training set design for genome-based prediction in multi-year wheat breeding data

Marco Lopez-Cruz Susanne Dreisigacker Leonardo Abdiel Crespo Herrera Alison Bentley Ravi Singh Suchismita Mondal Paulino Pérez-Rodríguez Jose Crossa (2021)

When genomic selection (GS) is used in breeding schemes, data from multiple generations can provide opportunities to increase sample size and thus the likelihood of extracting useful information from the training data. The Sparse Selection Index (SSI), is is a method for optimizing training data selection. The data files provided with this study include a large multigeneration wheat dataset of grain yield for 68,836 lines generated across eight cycles (years) as well as genotypic data that were analyzed to test this method. The results of the analysis are published in the corresponding journal article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA