Mixed model-based Jinks and Pooni method to predict segregating populations in wheat breeding

Abstract

The improvement of superior wheat cultivars depends on the identification of promising segregating populations to derive superior lines. A lattice model (8×8) involving 56 F 2 populations and eight parents was conducted in the 2020 cropping season, and grain yield per plant was evaluated for every F 2 population, with further analysis of the population potential by Jinks and Pooni method via REML/BLUP. A total of 5,410 F 2 plants were evaluated in this study. The results showed that the use of genetic variance associated with the individual genotypic value (BLUP) was superior compared with the use of variance and traditional phenotypic values.

Early selection of resilient progenies to seed yield in soybean populations

“…LEITE et al (2016) point out that the selection of progenies with greater potential is complex, as most exhibit low heritability. (1976) method is shown to be efficient in a study carried out by MEZZOMO et al (2021), in the wheat crop, which identified promising populations. Thus, it can be inferred that these populations have potential for selection and prominence in the context of genetic improvement programs, since they have a high probability of surpassing the average of the best control cultivar by 20%.…”

“…Some methodologies make it possible to estimate the probability of extracting superior lines from segregating populations that outperform a standard genotype, such as the Jinks and Pooni method (JINKS & POONI, 1976). This makes genotype selection efficient, since there is a probability associated with selection (MEZZOMO et al, 2021). Based on this, the combined use of methodologies for genotype selection results in greater assertiveness in the estimation of genetic parameters, prediction of genotypic values and the identification of the best genotypes; consequently, the greatest simultaneous gain for the traits of interest (RESENDE, 2016;SANTOS et al, 2019).…”

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

“…The use of practical methodologies that allow the dynamic determination of the number of genotypes to be selected in a set of populations, such as BLUPIS and BLUPISM, should be preferred over the methods that require the evaluation of individual plants. Mezzomo et al (2021) reported success on predicting the genetic potential and selecting superior genotypes in segregating tropical wheat populations through the association of mixed model and by using Jinks and Pooni (1976) methodology; however, it was necessary to carry out the individual evaluation of all plants, which made the process laborious. In addition, obtaining genetic components of variance by evaluating individual plants may result in biased estimates due to the high environmental influence on each plant, which leads, in many cases, to negative variance component estimates when estimated via mean squared method (ANOVA).…”

“…The experiment was conducted from June 2020 to October 2020. Agronomic management was performed as described by Mezzomo et al (2021).…”

“…In Brazil, the selection of genotypes in public wheat breeding programs is made by evaluating segregating populations in the field and collecting phenotypic data that, in turn, are used to estimate the genetic parameters and predict genotypic values to indicate the best populations (Mezzomo et al, 2021). The ideal method for the selection would be through the genotypic values predicted by the Individual best linear unbiased prediction (BLUPI) (Resende, 2002).…”