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Table 6 Performance comparison of deepGBLUP with the other genomic prediction methods on the simulated data across different heritabilities and single QTL effects

From: deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle

Heritability

Method

QTL effect

a

d

e

0.5

GBLUP

0.633 ± 0.008

0.629 ± 0.008

0.613 ± 0.005

DGBLUP

0.627 ± 0.008

0.624 ± 0.007

0.608 ± 0.005

EGBLUP

0.630 ± 0.009

0.626 ± 0.008

0.611 ± 0.006

BayesA

0.628 ± 0.01

0.622 ± 0.007

0.606 ± 0.006

BayesB

0.626 ± 0.009

0.621 ± 0.008

0.602 ± 0.005

BayesC

0.628 ± 0.009

0.625 ± 0.008

0.608 ± 0.005

deepGBLUP

0.641 ± 0.007

0.635 ± 0.007

0.620 ± 0.006

0.3

GBLUP

0.588 ± 0.028

0.571 ± 0.026

0.566 ± 0.027

DGBLUP

0.587 ± 0.029

0.571 ± 0.027

0.567 ± 0.027

EGBLUP

0.587 ± 0.028

0.571 ± 0.026

0.565 ± 0.027

BayesA

0.569 ± 0.027

0.552 ± 0.025

0.546 ± 0.026

BayesB

0.583 ± 0.028

0.568 ± 0.026

0.564 ± 0.026

BayesC

0.581 ± 0.028

0.567 ± 0.027

0.564 ± 0.027

deepGBLUP

0.608 ± 0.028

0.594 ± 0.026

0.589 ± 0.026

0.1

GBLUP

0.457 ± 0.028

0.443 ± 0.023

0.433 ± 0.026

DGBLUP

0.454 ± 0.028

0.441 ± 0.023

0.431 ± 0.026

EGBLUP

0.462 ± 0.028

0.450 ± 0.023

0.439 ± 0.026

BayesA

0.413 ± 0.031

0.388 ± 0.029

0.394 ± 0.033

BayesB

0.446 ± 0.025

0.438 ± 0.025

0.415 ± 0.024

BayesC

0.443 ± 0.029

0.439 ± 0.025

0.421 ± 0.029

deepGBLUP

0.542 ± 0.023

0.532 ± 0.019

0.518 ± 0.022

  1. Each value in the cells are means and standard errors of the predictive abilities for 10-fold tests. We highlight the best results in italic
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Genetics Selection Evolution

ISSN: 1297-9686