Methods for estimation of genetic parameters in soybeans: an alternative to adjust residual variability

Abstract

Selection practices are maximized when plant breeders have the availability of consolidated parameters, which will guide direct and indirect selection methods. This study aimed to apply a biometric alternative to minimize residual variance and maximize selection parameters by parent-progeny regression, interim controls, and mixed linear models intrinsic to breeding. The obtained data were subjected to the assumptions of the statistical model, which identified the normality and homogeneity of the residual variances and model additivity. Subsequently, two analysis scenarios were created. The first preserved all information obtained in the experiment, both from segregating families and pure-line cultivars, and was called original scenario. The other scenario preserved progeny data, but the residual variability of controls was restricted using as criterion observations contained between the interval of the first sample standard deviation. Thereby, an acceptable residue limit could be obtained. Both scenarios were submitted to three consolidated frequentist methods (genitor-progeny regression; sum of squares of augmented block design with interim controls; and mixed linear models, wherein random genetic effects are taken as weighted genetic parameters by the genealogical matrix). Restricting residual variation in parents or controls can maximize genetic parameters and genetic gains in soybean breeding. Significant heritability estimate gains were obtained in the augmented blocks with interim control approach. Mixed linear models with random genetic effects can be considered a great tool to obtain genetic parameters in experiments with a high magnitude of common and regular treatments.