TABLE 5.

Prediction accuracies for seven end-use quality traits using four different uni- and multi-trait genomic prediction models for the across-location predictions. 2019_Pullman_Lind represents the scenario where predictions were made on 2019_Pullman by training models on the Lind dataset.

		Uni-trait models				Multi-trait models				Multi-trait multi-environment models
Location	Trait	GBLUP	BayesB	RF	MLP	GBLUP	BayesB	RF	MLP	BMTME
2019_Pullman_Lind	GPC	0.25	0.23	0.30	0.31	0.32	0.28	0.33	0.31	0.31
	FPROT	0.35	0.34	0.40	0.40	0.40	0.29	0.39	0.44	0.47
	FASH	0.40	0.41	0.41	0.41	0.42	0.45	0.44	0.43	0.45
	MSCOR	0.27	0.23	0.30	0.30	0.33	0.27	0.35	0.38	0.36
	FYELD	0.41	0.42	0.48	0.50	0.42	0.45	0.51	0.50	0.52
	CODI	0.40	0.43	0.45	0.46	0.47	0.44	0.49	0.53	0.56
	FSDS	0.36	0.30	0.44	0.43	0.38	0.34	0.47	0.48	0.46
2019_Lind_Pullman	GPC	0.27	0.29	0.30	0.28	0.31	0.33	0.37	0.36	0.40
	FPROT	0.34	0.37	0.42	0.42	0.37	0.39	0.42	0.47	0.38
	FASH	0.41	0.38	0.42	0.42	0.48	0.46	0.44	0.45	0.47
	MSCOR	0.28	0.28	0.29	0.31	0.31	0.28	0.31	0.34	0.31
	FYELD	0.43	0.42	0.47	0.50	0.47	0.43	0.52	0.51	0.55
	CODI	0.42	0.45	0.44	0.46	0.43	0.44	0.41	0.46	0.49
	FSDS	0.38	0.35	0.41	0.40	0.42	0.39	0.45	0.45	0.42
Average		0.37	0.35	0.40	0.40	0.40	0.37	0.42	0.44	0.42

Highest prediction accuracies are bolded for each trait.