Data from: Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing

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.