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

Leonardo Volpato,Willian Hytalo Ludke,Felipe Lopes Da Silva,Ana Carolina Campana Nascimento,Aluízio Borém,Paulo Eduardo Teodoro,Marcos Deon Vilela De Resende,Rodrigo Silva Alves,Moysés Nascimento,David A Lightfoot

doi:10.1371/journal.pone.0215315

Leonardo Volpato, Willian Hytalo Ludke + Show 8 more

Open Access

https://doi.org/10.1371/journal.pone.0215315

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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 F2:4 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.

Highlights

Soybean [Glycine max (L.) Merrill] is the fourth most widely grown crop in the world
In view of the above-described situation, the present study proposes to examine the efficiency and applicability of multi-trait multi-environment (MTME) models in the selection of segregating soybean progeny, using phenotypic data, by the frequentist (FMTME) and Bayesian (BMTME) methodologies
According to the Akaike information criteria (AIC) from the results obtained with the frequentist single-trait multi-environment (FSTME) model, the model including the G and genotype × environment (G×E) interaction effects showed the best fit for all traits (Table 2)

Summary

Methods

Three populations (Pop) belonging to the Soybean Breeding Program at the Federal University of Vicosa (UFV) were obtained from crosses between divergent inbred lines (Pop: TMG123RR/M7211RR; Pop: UFVSCitrinoRR/UFVSTurquezaRR; and Pop: M7908RR/ M7211RR). These lines were classified into different relative maturity groups according to the Genetic selection of segregating soybean progeny soybean crop management classification [27], aiming to exploit genetic variability for the selection of productive progeny. One sample was collected from each F2:3 progeny to compose the 203 F2:4 progeny that were used in this study by the withinprogeny bulk method [28,29]. One of them took place in Capinopolis—MG, Brazil (18 ̊40’48" S latitude, 49 ̊33’58" W longitude; 530 m altitude) and the other in Vicosa—MG, Brazil (20o45’45" S latitude, 42o49’27" W longitude; 647 m altitude)

Results

Discussion

Conclusion