Abstract:

The prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP) was performed. The SNPs arrays were customized based on different length size windows per chromosome to select proportions of 10.0, 5.0, 2.5, 1.25 and 0.625% of markers from the 427k panel. The SNPs arrays were customized with densities of 40k, 20k, 10k, 5k and 2k, respectively. Two criteria were used to select the SNPs within a chromosome window: (1) highly informative SNPs; and (2) evenly distributed SNPs. The linear regression method (LR) was used to evaluate the prediction ability, inflation and bias of GEBV. The prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10k SNPs. The genomic selection with low-density customized arrays could be feasible and cost-effective in Nellore cattle.