The aim of this study was to identify single nucleotide polymorphisms (SNPs) that are significantly associated with temperament, measured by flight speed (FS), in Nellore cattle using the single step procedure (ssGWAS). Temperament was assessed by the speed (m/s) at which each animal exited the crush after yearling weighing. Data were from 16,119 animals with phenotypes and a pedigree file with 162,644 animals. A total of 1,405 animals were genotyped with BovineHD BeadChip. Quality control was performed to exclude SNP markers with unknown genomic position, located on sex chromosomes, monomorphic, MAF < 1%, call rate < 90%, and animal call rate (with less than 90% of SNPs called). After edits, 455,374 SNPs and 1,384 genotyped animals remained. The association analyze was performed by ssGBLUP, a modification of BLUP with numerator relationship matrix A⁻¹ matrix replaced by H⁻¹, that uses the GEBV to estimate the SNPs effects. Variance components and genetic parameters were estimated by Bayesian inference via Gibbs sampling using the softwares GIBBS2F90, PREGSF90 and POSTGSF90, assuming a linear animal model for FS which included direct additive genetic and residual effects as random effects and contemporary groups as fixed effect. The effects were calculated to segments of 10 sequential SNPs and results interpreted as the percentage (%) of total genetic variance explained by each SNP window. Segments that explained 1% or more of the total genetic variation were considered as canditade region associated with FS. Ten regions were associated with FS: one on BTA1 (1:73354330–73406566, 2.07%), 2 on BTA5 (5:22596661–22604723, 3.04% and 5:119291684–119306475, 1.44%, respectively), one on BTA9 (9:98759214–98767952, 3.33%), BTA11 (11:67385287–67404876, 1.39%), BTA15 (15:16598639–16662233, 1.45%), BTA17 (17:639678–671693, 4.62%), BTA18 (18:34146668–34168795, 1.22%) BTA22 (22:32886184–32904212, 1.08%) and BTA26 (26:47061401–47095621, 1.86%). This result confirms the polygenic architecture related to expression of cattle temperament, resulting from the influence of numerous genes interacting with each other and with environmental factors. Future approaches are required to identify the gene expression associated with the SNP windows found in this study.