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

Lee, Hyo-Jun; Lee, Jun Heon; Gondro, Cedric; Koh, Yeong Jun; Lee, Seung Hwan

doi:10.1186/s12711-023-00825-y

Genetics Selection Evolution

Table 3 Performance comparison of deepGBLUP with the other genomic prediction methods on the Korean native cattle dataset across different traits and marker densities

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

Density	Method	CWT	BF	EMA	MS
50K	GBLUP	0.729 ± 0.015	0.647 ± 0.009	0.726 ± 0.017	0.670 ± 0.014
	DGBLUP	0.731 ± 0.016	0.639 ± 0.01	0.729 ± 0.017	0.668 ± 0.013
	EGBLUP	0.724 ± 0.016	0.641 ± 0.01	0.721 ± 0.019	0.664 ± 0.014
	BayesA	0.730 ± 0.015	0.658 ± 0.009	0.720 ± 0.016	0.667 ± 0.014
	BayesB	0.746 ± 0.015	0.667 ± 0.009	0.723 ± 0.019	0.670 ± 0.013
	BayesC	0.737 ± 0.015	0.662 ± 0.01	0.726 ± 0.018	0.668 ± 0.014
	deepGBLUP	0.752 ± 0.016	0.673 ± 0.009	0.746 ± 0.017	0.672 ± 0.012
10K	GBLUP	0.676 ± 0.015	0.577 ± 0.008	0.678 ± 0.018	0.613 ± 0.011
	DGBLUP	0.675 ± 0.015	0.571 ± 0.009	0.678 ± 0.018	0.607 ± 0.01
	EGBLUP	0.684 ± 0.016	0.585 ± 0.009	0.684 ± 0.019	0.619 ± 0.012
	BayesA	0.700 ± 0.015	0.59 ± 0.008	0.682 ± 0.019	0.620 ± 0.011
	BayesB	0.695 ± 0.015	0.585 ± 0.007	0.675 ± 0.018	0.612 ± 0.012
	BayesC	0.689 ± 0.016	0.589 ± 0.008	0.681 ± 0.018	0.616 ± 0.012
	deepGBLUP	0.713 ± 0.017	0.612 ± 0.008	0.705 ± 0.018	0.626 ± 0.012
5K	GBLUP	0.638 ± 0.015	0.543 ± 0.01	0.631 ± 0.019	0.548 ± 0.011
	DGBLUP	0.632 ± 0.016	0.533 ± 0.011	0.633 ± 0.019	0.544 ± 0.011
	EGBLUP	0.653 ± 0.016	0.556 ± 0.011	0.646 ± 0.02	0.564 ± 0.012
	BayesA	0.668 ± 0.016	0.557 ± 0.009	0.650 ± 0.019	0.568 ± 0.013
	BayesB	0.658 ± 0.016	0.543 ± 0.008	0.643 ± 0.018	0.562 ± 0.013
	BayesC	0.655 ± 0.017	0.555 ± 0.008	0.647 ± 0.019	0.567 ± 0.013
	deepGBLUP	0.681 ± 0.016	0.58 ± 0.01	0.672 ± 0.019	0.582 ± 0.011
1K	GBLUP	0.535 ± 0.017	0.429 ± 0.014	0.537 ± 0.021	0.424 ± 0.013
	DGBLUP	0.519 ± 0.015	0.401 ± 0.012	0.529 ± 0.023	0.405 ± 0.014
	EGBLUP	0.552 ± 0.017	0.444 ± 0.014	0.555 ± 0.022	0.443 ± 0.014
	BayesA	0.568 ± 0.016	0.442 ± 0.014	0.557 ± 0.022	0.443 ± 0.014
	BayesB	0.564 ± 0.016	0.437 ± 0.012	0.556 ± 0.021	0.441 ± 0.013
	BayesC	0.551 ± 0.017	0.440 ± 0.013	0.552 ± 0.021	0.441 ± 0.014
	deepGBLUP	0.581 ± 0.016	0.467 ± 0.014	0.584 ± 0.022	0.466 ± 0.013

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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ISSN: 1297-9686

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