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

Date Published: April 18, 2019

Publisher: Public Library of Science

Author(s): Leonardo Volpato, Rodrigo Silva Alves, Paulo Eduardo Teodoro, Marcos Deon Vilela de Resende, Moysés Nascimento, Ana Carolina Campana Nascimento, Willian Hytalo Ludke, Felipe Lopes da Silva, Aluízio Borém, David A. Lightfoot.

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

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; hprog2) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of hprog2. 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.

Partial Text

Soybean [Glycine max (L.) Merrill] is the fourth most widely grown crop in the world. This species is originally from China and is the major crop in the USA, Brazil, Argentina, and many other countries [1]. Soybean is currently grown from low to high latitudes, where it is used as a source of oil, protein, biodiesel, etc. [2]. In this scenario, because the genotype × environment (G×E) interaction plays an essential role in genotypic expression, it must be considered in the evaluation and selection of superior genotypes [3–5].

For our data set, the average BMTME processing time using an Intel(R) i7-5500U (2.4 GHz) processor with 8 GB of RAM was 1 h 40 min and 35 s, corresponding to approximately 0.006 s for each MCMC iteration. Silva et al. [72] considered this performance plausible, but pointed out that improvements can be obtained using the conditional decompositions proposed by Hallander et al. [92]. For the same purpose, in addition to improving the prior information, Montesinos-López et al. [13] proposed a Bayesian model for analyzing multiple traits and multiple environments for the whole-genome prediction model. The authors also developed an R-software package that offers specialized and optimized routines to efficiently perform the analyses under the proposed model. By contrast, the FMTME model took approximately 14 s to converge. Despite the considerable difference in processing time of the analysis and output size of the results (around 1.03 GB) due to the high number of interactions adopted, the Bayesian model showed to be efficient for the proposed objective. Furthermore, it provided additional results to those obtained by the frequentist approach, with noteworthy credibility intervals.

 

Source:

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

 

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