Table 6. Mean correlations between the predicted grain yield values for three models (BL, RKHS regression and GBLUP) when the numbers of individuals in the training sets randomly taken from the entire population (50 different times) are 30, 40, 50, 70 and 90 for different numbers of SNPs.

Model_ number of individuals in the training population	Number of SNPs
	238 SNPs	100 SNPs	50 SNPs
Bi-parental population 1(249 F2individuals; 238 SNPs)
BL_30	0.1348	0.1009	0.0640
RKHS-KA_30	0.1520	0.1144	0.0703
GBLUP_30	0.1643	0.1173	0.0672
BL_40	0.1539	0.1286	0.0899
RKHS-KA_40	0.1685	0.1380	0.0925
GBLUP_40	0.1789	0.1441	0.0906
BL_50	0.2093	0.1598	0.0918
RKHS-KA_50	0.2004	0.1555	0.0944
GBLUP_50	0.2165	0.1612	—
BL_70	0.2236	0.1814	0.1152
RKHS-KA_70	0.2153	0.1756	0.1147
GBLUP_70	0.2338	0.1839	—
BL_90	0.2484	0.2000	0.1251
RKHS-KA_90	0.2386	0.1952	0.1284
GBLUP_90	0.2574	0.2024	—
Bi-parental population 2 (250 F2individuals; 271 SNPs)
	271 SNPs	100 SNPs	50 SNPs
BL_30	0.2994	0.2765	0.2165
RKHS-KA_30	0.2924	0.2725	0.2136
GBLUP_30	0.2982	0.2770	0.2161
BL_40	0.3367	0.3156	0.2447
RKHS-KA_40	0.3371	0.3172	0.2471
GBLUP_40	0.3378	0.3146	—
BL_50	0.3471	0.3264	0.2585
RKHS-KA_50	0.3486	0.3271	0.2621
GBLUP_50	0.3467	—	—
BL_70	0.3717	0.3549	0.2818
RKHS-KA_70	0.3770	0.3605	0.2866
GBLUP_70	0.3714	—	—
BL_90	0.3919	0.3725	0.2998
RKHS-KA_90	0.4066	0.3859	0.3199
GBLUP_90	0.3903	—	—

Abbreviations: BL, Bayesian LASSO; RKHS, reproducing kernel Hilbert space; SNP, single-nucleotide polymorphism.

For each bi-parental population and for each number of markers (columns) the best predictive model is underlined.