Stage-wise selection of tropical wheat populations using univariate and multivariate BLUP models

Abstract

Plant breeding programs often involve several segregating populations that must be selected for multiple traits. This study aimed to identify tropical wheat populations combining earliness and high grain yield (GY) using univariate and multivariate best linear unbiased prediction (BLUP)-based models within a stage-wise approach. Fifty-six F? and F? populations were evaluated in two environments for days to heading (DH) and GY. In the first stage, two modeling strategies were used: a univariate and multivariate model per generation. Genetic parameters and empirical genotypic values were estimated and used in the second stage for combined selection across generations. Both strategies yielded similar results in terms of genetic gains, genotype selection, and ranking, likely due to the low correlation between the traits. Populations 4H, 2F, 2D, 2A, 2E, 3E, 1G, 3A, 3B, 2G, 3F, 1D, and 1B were selected for earliness and yield and will be advanced to derive superior inbred lines.