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Table 4 Accuracy 1 of prediction of seven linear methods in seven training scenarios for line W1

From: Genomic prediction based on data from three layer lines: a comparison between linear methods

 

Training dataset

Model

B1

B2

W1

B1 + B2

B1 + W1

B2 + W1

B1 + B2 + W1

BLUP2

-

-

0.599

-

-

-

-

GBLUP_VR

-0.339

-0.161

0.768

-0.393

0.747

0.764

0.748

GBLUP_%id

-0.342

-0.141

0.764

-0.378

0.747

0.758

0.746

RRBLUP

-0.252

-0.192

0.761

-0.393

0.743

0.762

0.742

RRPCA

-0.247

-0.249

0.775

-0.352

0.748

0.772

0.747

BSSVS

-0.241

-0.212

0.757

-0.382

0.734

0.758

0.742

BayesC

-0.235

-0.224

0.759

-0.355

0.737

0.757

0.739

  1. BLUP: conventional BLUP using a pedigree based relationship matrix; G-BLUP: Genome-enabled Best Linear Unbiased Prediction (G-BLUP); RRBLUP: Ridge Regression BLUP; RRPCA: Ridge Regression with PCA reduction; BayesSSVS: Bayesian Stochastic Search Variable Selection; BayesC; 1approximated SE of the accuracies of the genomic prediction models ranged from 0.054-0.064; 2for BLUP, only the analysis including the line itself was performed, because there are no pedigree relations between lines.