Author: Johannes Martini

Replication Data for: Genomic prediction within and across families in wheat pre-breeding populations

Johannes Martini Fernando Henrique Toledo Carolina Sansaloni Jose Crossa Jaime Cuevas Sivakumar Sukumaran (2020)

The genetic diversity housed in germplasm banks may provide valuable contributions to breeding efforts. It is important to understand the best way to introduce this diversity into elite breeding materials. This files in this dataset provide phenotypic and genotypic data used to compare genomic prediction approaches and different cross-validation scenarios on a set of wheat families obtained from crosses between elite materials and diverse germplasm bank accessions. The linked top cross population (LTP) materials analyzed in the study were screened under yield potential, drought, and heat stress conditions.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: A Bayesian Linear Phenotypic Selection Index to Predict the Net Genetic Merit

J. Jesús Cerón Rojas Sergio Pérez-Elizalde Jose Crossa Johannes Martini (2021)

In breeding, the plant net genetic merit may be predicted through the linear phenotypic selection index (LPSI). This paper associated with this dataset proposes a Bayesian LPSI (BLPSI). The supplemental files provided in this dataset include data that were used to compare the two indices as well as figures showing the results from these comparisons. The analysis revealed that the BLPSI is a good option when carrying out phenotypic selections in breeding programs.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Approximate kernels for large data sets In genome-based prediction

Osval Antonio Montesinos-Lopez Johannes Martini Paulino Pérez-Rodríguez Jose Crossa (2020)

The rapid development of molecular markers and sequencing technologies has made it possible to use genomic selection (GS) and genomic prediction (GP) in animal and plant breeding. However, computational difficulties arise when the number of observations is large. This five datasets provided here were used to support a comparative analysis of two genomic-enabled prediction models: the full genomic method single environment (FGSE) and the approximate kernel method for a single environment model (APSE). The data were also used to compare the full genomic method with genotype × environment model (FGGE) to the approximate kernel method with genotype × environment interaction (APGE). The results of the analyses are described in the related publication.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA