Inclusion of Dominance Effects in the Multivariate GBLUP Model

Santos, José Fortunato Ferreira; Vasconcellos, Renato Coelho de Castro; Pires, Luíz Paulo Miranda; Balestre, Márcio; Pinho, Renzo Garcia Von

doi:10.1371/journal.pone.0152045

Cited by 44 publications

(47 citation statements)

References 39 publications

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“…Studies using simulated datasets have suggested that multitrait models can be used to increase predictive ability for low-heritability traits that are correlated with higherheritability traits, or when a trait is simply too difficult or expensive to measure in all individuals within a population (Calus and Veerkamp, 2011;Jia and Jannink, 2012;Hayashi and Iwata, 2013;Guo et al, 2014). Several studies have subsequently assessed multitrait GS in datasets consisting of nonsimulated phenotypic data (Jia and Jannink, 2012;Pszczola et al, 2013;dos Santos et al, 2016;Rutkoski et al, 2016;Schulthess et al, 2016;Wang et al, 2016).…”

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confidence: 99%

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

et al. 2019

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The majority of studies evaluating genomic selection (GS) for plant breeding have used single‐trait, single‐site models that ignore genotype × environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study's goal was to test GS methods for prediction in scenarios that simulate early‐generation yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across‐environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low‐heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments.

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confidence: 99%

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

et al. 2019

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“…For comparison to the three Bayesian models, we also evaluated two different formulations of the multivariate GBLUP model (Henderson and Quaas 1976; dos Santos et al 2016b; Fernandes et al 2018) that recovered information between traits and/or time points. In the first formulation, only PH measurements across time points were used (MTi-GBLUP).…”

Section: Methodsmentioning

confidence: 99%

“…The vast majority of GP studies conducted in crop species have only tested models for predicting individual traits. However, recent studies have shown the advantages of combining multiple correlated traits in a GP model (Calus and Veerkamp 2011; Jia and Jannink 2012; Fernandes et al 2018), allowing genetic correlations among secondary traits to be leveraged for improving predictions of a target trait (dos Santos et al 2016b; Okeke et al 2017). Most of these efforts used multi-trait GBLUP—a type of multivariate mixed linear model that incorporates a genomic relationship matrix (Gianola et al 2015).…”

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Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

Santos

Fernandes

Lozano

et al. 2019

Preprint

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2The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench] lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In 5-fold cross-validation, prediction accuracies ranged from 0.48 (PBN) to 0.51 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.74 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection

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“…Models assuming dominance and epistatic effects have been proposed (Su et al, 2012;Vitezica et al, 2013;dos Santos et al, 2016) but are still rarely used in plant breeding, despite strong evidence that these models are important in understanding the genetic architecture of quantitative traits (Carlborg and Haley, 2004). As mentioned above, all these genetic models vary with the addition or removal of genetic effects or in the form of relatedness estimations between individuals of a current population, which have shown relationships relative to a reference population (Zeng et al, 2005;Van Raden, 2008;Powell et al, 2010;de Los Campos et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Assessing non-additive effects in GBLUP model

Vieira

Santos²,

Pires

et al. 2017

Genet. Mol. Res.

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ABSTRACT. Understanding non-additive effects in the expression of quantitative traits is very important in genotype selection, especially in species where the commercial products are clones or hybrids. The use of molecular markers has allowed the study of non-additive genetic effects on a genomic level, in addition to a better understanding of its importance in quantitative traits. Thus, the purpose of this study was to evaluate the behavior of the GBLUP model in different genetic models and relationship matrices and their influence on the estimates of genetic parameters. We used real data of the circumference at breast height in Eucalyptus spp and simulated data from a population of F 2 . Three commonly reported kinship structures in the literature were adopted. The simulation results showed that the inclusion of epistatic kinship improved prediction estimates of genomic breeding values. However, the non-additive effects were not accurately recovered. The 2 I.C. Vieira et al. Genetics and Molecular Research 16 (2): gmr16029632Fisher information matrix for real dataset showed high collinearity in estimates of additive, dominant, and epistatic variance, causing no gain in the prediction of the unobserved data and convergence problems. Estimates presented differences of genetic parameters and correlations considering the different kinship structures. Our results show that the inclusion of non-additive effects can improve the predictive ability or even the prediction of additive effects. However, the high distortions observed in the variance estimates when the Hardy-Weinberg equilibrium assumption is violated due to the presence of selection or inbreeding can converge at zero gains in models that consider epistasis in genomic kinship.

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Inclusion of Dominance Effects in the Multivariate GBLUP Model

Cited by 44 publications

References 39 publications

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early‐Generation Wheat Yield Trials

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

Assessing non-additive effects in GBLUP model

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