Optimizing selection of wheat genotypes through simulated individual BLUP and modified simulated individual BLUP

Abstract

The early stages of wheat breeding programs often contain large number of plants in segregating populations that must be selected. Selecting the best populations and plants in those stages is not an easy task, making it necessary to use robust methodologies that facilitate the selection process. The goals of this work were to evaluate different methods for the selection of individual plants in early wheat breeding stages, compare the efficiency of each method, and suggest the most suitable one for implementation in wheat breeding programs. An experiment with 56 F 2 segregant populations derived from a complete diallel, together with eight parents, was conducted and evaluated for grain yield (g pL −1 ). Analysis of variance and mixed model analysis were applied to obtain the genetic parameters and predict genotypic values. Four selection strategies were designed: mass selection and other three mixed model-based methods (individual best linear unbiased prediction [BLUPI]; simulated individual best linear unbiased prediction [BLUPIS]; and modified simulated individual best linear unbiased [BLPISM]). The genotypic variability among and within populations allowed the selection of superior genotypes. Simple linear regression analysis and Spearman correlation analysis allowed to conclude that BLUPIS and BLUPISM are equivalent to BLUPI. The BLUPIS and BLUPISM should be used because they easily indicate for the selection of plants in populations with positive genetic effects, dismissing the evaluation of individual plants.

Application of Artificial Neural Networks to Predict Genotypic Values of Soybean Derived from Wide and Restricted Crosses for Relative Maturity Groups

“…Genetic variability is estimated using variance components utilizing the restricted maximum likelihood method (REML), and obtaining the best unbiased linear prediction (BLUP) of genetic values is preferred because it maximizes selective accuracy compared to parametric statistical methodologies [8]. Using parametric statistical methods necessitates assumptions related to the probability distribution of variables, often assuming the linear nature of the phenomenon under study.…”