Resumen

RUEDA CALDERON, M. A; BALZARINI, M  y  BRUNO, C. Meta-análisis para evaluar eficiencia de selección genómica en cereales: Meta-analysis for evaluating the efficiency of genomic selection in cereals. BAG, J. basic appl. genet. [online]. 2020, vol.31, n.1, pp.23-32. ISSN 1852-6233.

Genomic selection (GS) is used to predict the merit of a genotype with respect to a quantitative trait from molecular or genomic data. Statistically, GS requires fitting a regression model with multiple predictors associated with the molecular markers (MM) states. The model is calibrated in a population with phenotypic and genomic data. The abundance and correlation of MM information make model estimation challenging. For that reason there are diverse strategies to adjust the model: based on best linear unbiased predictors (BLUP), Bayesian regressions and machine learning methods. The correlation between the observed phenotype and the predicted genetic merit by the fitted model provides a measure of the efficiency (predictive ability) of the GS. The objective of this work was to perform a metaanalysis on the efficiency of GS in cereals. A systematic review of related GS studies and a meta-analysis, in wheat and maize, was carried out to obtain a global measure of GS efficiency under different scenarios (MM quantity and statistical models used in GS). The meta-analysis indicated an average correlation coefficient of 0.61 between observed and predicted genetic merits. There were no significant differences in the efficiency of the GS based on BLUP (RR-BLUP and GBLUP), the most common statistical approach. The increase of MM data, make GS efficiency do not vary widely.

Palabras clave : Systematic review; Random effects model; Forest plot; Predictive accuracy.

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