Meta-Analysis for Evaluating the Efficiency of Genomic Selection in Cereals

Abstract

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. Key words: Systematic review; Random effects model; Forest plot; Predictive accuracy.

Relationships between the Visual Evaluation of Soil Structure (VESS) and soil properties in agriculture: A meta-analysis

“…Heterogeneity between studies was evaluated with the 𝐼 2 statistic, which allows quantifying how much of the total variability in the statistic of interest should be attributed to the variation between studies (Higgins et al, 2003). It is an independent measure of the number of studies included in the meta-analysis and the unit of measurement used to quantify the effect studied (Rueda-Calderón et al 2020). The 𝐼 2 statistic is expressed as a proportion, a value close to zero indicates that the observed variance is spurious, and therefore the primary studies can be considered homogeneous.…”

“…Very rarely has a single study contributed definitively to the advancement of knowledge represented by this type of visual soil assessment. On the contrary, it is common to find studies with similar objectives presenting inconsistent or contradictory results (Rossi et al, 2019;Rueda-Calderón et al, 2020). The individualized analysis of each of these studies and the synthesis of their results can be very useful in the theoretical framework of scientific knowledge and the agricultural practice and productivity of agri-food systems.…”

“…Meta-analysis (MA) encompasses a series of statistical methods that can be applied to a dataset, which have been compiled from several independent studies around an object of study. The objective of the MA is to identify consistent patterns and sources of variation between studies (Rueda-Calderón et al, 2020). Its application in various fields makes it a widely used methodology since it provides a single result of the evidence collected in a systematic review.…”