Table 2. Computing time (sec) required for the estimation of marker effects with different GWP approaches.

	Homoscedastic Marker Variances			Heteroscedastic Marker Variances
	RIR	BLUP	rrBLUPM6	RMLV	RRWA	RMLA	BL	HEM
Simulated data, 500 individuals
330 markers	0.03	0.16	0.91	5.07	0.05	0.16	5.14	39.92
810 markers	0.05	3.18	1.55	50.30	0.13	3.38	7.99	49.56
1610 markers	0.23	32.11	1.68	330.60	0.30	28.22	11.77	63.65
Crossa et al. (2010), 264 maize lines
1135 SNP markers	0.10	9.08	0.37	118.20	0.14	9.17	11.10	8.79
Pérez-Rodríguez et al. (2012), 306 wheat lines
1717 DArT markers	0.23	61.8	0.62	405.60	0.37	60.60	8.96	12.49
Hofheinz et al. (2012), 310 sugar beet lines
300 SNP markers	0.01	0.12	0.35	3.72	0.04	0.11	5.51	3.69

For the maize data set, the trait GY-WW was investigated, for the wheat data set the trait GY, and for the sugar beet data set the trait SC. GWP, genome-wide prediction; RIR, ridge regression employing preliminary estimates of the heritability; BLUP, best linear unbiased prediction; RMLV, modification of the restricted maximum likelihood procedure that yields heteroscedastic variances; RRWA, ridge regression with weighing factors according to analysis of variance components; RMLA, estimation of the error and genetic variance components with restricted maximum likelihood and partitioning according to analysis of variance components; BL, Bayesian LASSO; HEM, heteroscedastic effects model; SNP, single-nucleotide polymorphism; DArT, diversity array technology; GY, grain yield; WW, well-watered; SC, sugar content.