Milk yield (MY) and fat yield (FY) are economically important traits for Thai dairy businesses. Genetic prediction for MY and FY in Thailand uses only pedigree and phenotypic information. Combining SNP genotypes of individual animals with pedigree and phenotypes would be expected to increase the accuracy of genetic predictions and speed up selection progress. The objectives of this study were to estimate the fraction of the genetic variance accounted for by 8,257 SNP from GeneSeek GGP-LD BeadChip and to compare the rankings of animals evaluated with a genomic-polygenic (GP), genomic (G), and polygenic (P) models for MY and FY. The data set consisted of first-lactation MY and FY records from 600 cows from 56 farms in Central Thailand collected from 2000 to 2013. The mixed model contained herd-year-season, Holstein fraction and age at first calving as fixed effects (all models). Random effects were SNP genomic (GP and G), animal polygenic (GP and P) and residual. Variance components were estimated using GS3 software (option VCE; GP and P). Additive genetic predictions were computed with GS3 (option BLUP) for all models. The fraction of additive genetic variances explained by the 8,257 SNP from GGP-LD and computed with the GP model were 46% for MY and 45% for FY. Heritability estimates with the GP model were higher (0.37 for MY and 0.40 for FY) than those with the P model (0.28 for MY and 0.30 for FY). Rank correlations between GP and G models were the highest (0.99 for both MY and FY; P < 0.0001), followed by correlations between GP and P models (0.91 for MY and 0.75 for FY), and the lowest correlations were between G and P models (0.89 for MY and 0.73 for FY; P < 0.0001). Thus, SNP from GeneSeek GGP-LD not only accounted for a sizeable fraction of the additive genetic variance for MY and FY, but they also yielded animal genomic EBV whose ranking was highly correlated with rankings of both genomic-polygenic and polygenic EBV. These results indicated that utilization of GGP-LD, and perhaps higher density genotyping chips, would be advantageous for genomic-polygenic evaluation and selection in Central Thailand.