Abstract
Most studies on genomic selection in plant breeding compare different statistical methods of univariate approach. However, multi-trait methodologies should be considered since they allow the simultaneous selection of superior genotypes in several economic traits. Here, the aims were to compare the selection accuracy and efficiency of the multivariate partial least square (MPLS) method compared with random regression best linear unbiased predictor (rrBLUP), Bayesian Lasso (Blasso) and univariate partial least square (UPLS) and to develop genomic selection indexes efficient for superior genotypes identification in plant breeding. Ten F2 populations with 800 individuals were simulated, considering four traits with different heritabilities. Genomic selection analyses using rrBLUP, Blasso, UPLS, and MPLS were conducted. Four genomic selection indexes were elaborated by the sum of the marker effects obtained for each trait, weighted by the respective residual variance. Multi-trait indexes were developed based on the assumptions of each methodology mentioned (rrBLUP, Blasso, UPLS, and MPLS), and were denominated I-rrBLUP, I-Blasso, I-UPLS, and I-MPLS. Processing time, selective accuracy, selection gains, and selection coincidence were used to compare the methods and the selection indexes proposed. The MPLS method had similar results compared to UPLS method for the low heritability traits and was less efficient than the rrBLUP and Blasso. The genome selection indexes provided the highest total genetic gains. The I-rrBLUP and I-MPLS indexes stood out for high efficiency in selecting superior genotypes in the shortest processing time. Results suggest that the genomic selection indexes proposed in this study may be promising for plant breeding programs.
Highlights
Genomic selection (Meuwissen et al 2001) is based on the prediction of the genomic estimated breeding value (GEBV) from the relationship between the individuals’ phenotype and thousands of molecular markers widely distributed in the genome
The aims were to compare the selection accuracy and efficiency of the multivariate partial least square (MPLS) method compared with random regression best linear unbiased predictor, Bayesian Lasso (Blasso) and univariate partial least square (UPLS) and to develop genomic selection indexes efficient for superior genotypes identification in plant breeding
Considering the lower computational demand, these results suggest that the regression best linear unbiased predictor (rrBLUP), UPLS, and MPLS methods were more advantageous
Summary
Genomic selection (Meuwissen et al 2001) is based on the prediction of the genomic estimated breeding value (GEBV) from the relationship between the individuals’ phenotype and thousands of molecular markers widely distributed in the genome. This high density of markers increases the probability of each quantitative trait loci (QTL) of the target trait to be in linkage disequilibrium with at least one marker. Studies proposing and testing the efficiency of multi-trait genomic selection in plant breeding programs should be developed
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