Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

Abstract

At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F 2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; ) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of . Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.

Genetic and phenotypic parameters associated with soybean progenies in a recurrent selection program

“…From this result, it can be inferred that in the soybean breeding programs in Brazil, quantification of the effect of the interaction is of crucial importance aiming at selection of superior genotypes. The genotype × environment interaction has been widely reported for the soybean crop in Brazil (Soares et al 2015, Silva et al 2016, Volpato et al 2019 and confirms the results of the present study.…”

SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

“…In the case of the pedigree-based method [80], the heritability (hfalse^a2) in a narrow sense was calculated as follows:hfalse^a2=σfalse^a2σfalse^a2+σfalse^ε2 where σfalse^a2 and σfalse^ε2 correspond to the additive genetic and residual variances, respectively. In the case of Bayesian genomic prediction models (SNP/haplotypes), genomic heritability (hfalse^g2), genomic variance (σfalse^g2) and the residual variance (σfalse^ε2) were estimated using the marginal posterior distributions of each estimated parameter [81,82,83]. The genomic variance was estimated for each model as follows:…”

Linear, Generalized, Hierarchical, Bayesian and Random Regression Mixed Models in Genetics/Genomics in Plant Breeding

“…Another approach for computing the Bayesian accuracy was proposed by Resende et al (2012a; and applied by Volpato et al (2019) showing coherent and consistent results. The formula is given by ̂= 1 − ( )⁄ , where ( ) is the standard deviation of the estimated genetic value .…”