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Autor: J. Jesús Cerón Rojas
J. Jesús Cerón Rojas Jose Crossa (2019)
Multistage selection is a cost-saving strategy for improving several traits because it is not necessary to measure all traits at each stage. A combined linear genomic selection index is a linear combination of phenotypic and genomic estimated breeding values useful to predict the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The main combined multistage linear genomic selection indices are the optimum and decorrelated indices. Using real and simulated data, we compared the efficiency of both indices to predict the net genetic merit in plants in a two-stage breeding context. The criteria used to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real data set, the total decorrelated and optimum index selection responses explained 90% and 97.5%, respectively, of the estimated single-stage combined selection response. In addition, at stage two, the correlation of the optimum and decorrelated indices with the net genetic merit were 0.84 and 0.63, respectively.
Dataset
J. Jesús Cerón Rojas Sergio Pérez-Elizalde Jose Crossa (2020)
In breeding, the net genetic merit of candidates for selection is a linear combination of the breeding values of the traits of interest weighted by their respective economic values. This dataset contains the R code that accompanies a publication that describes an evaluation of linear phenotypic selection indices (LPSI) and Bayesian linear phenotypic selection indices (BLPSI).
Dataset
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