Migration traits are presumed to be complex and to involve interaction among multiple genes, thus we employed both univariate analyses and multivariate Random Forest (RF) machine learning algorithm to conduct association mapping of 15,239 single nucleotide polymorphisms (SNPs) for adult migration-timing phenotype in steelhead (Oncorhynchus mykiss). Our study focused on a model natural population of steelhead that exhibits two distinct migration-timing life histories with high levels of admixture in nature. Neutral divergence was limited between fish exhibiting summer- and winter-run migration owing to high levels of interbreeding, but a univariate mixed linear model found three SNPs from a major effect gene to be significantly associated with migration-timing (p < 0.000005) that explained 46% of trait variation. Alignment to the annotated S. salar genome provided evidence that all three SNPs localize within a 46 kb region overlapping GREB1-like (an estrogen target gene) on chromosome Ssa03. Additionally, multivariate analyses with RF identified that these 3 SNPs plus 15 additional SNPs explained up to 60% of trait variation. These candidate SNPs may provide the ability to predict adult migration-timing of steelhead to facilitate conservation management of this species and this study demonstrates the benefit of multivariate analyses for association studies.