Landscape features along migratory routes influence adaptive genomic variation in anadromous steelhead (Oncorhynchus mykiss).

Organisms typically show evidence of adaptation to features within their local environment. However, many species undergo long-distance dispersal or migration across larger geographic regions that consist of highly heterogeneous habitats. Therefore, selection may influence adaptive genetic variation associated with landscape features at residing sites and along migration routes in migratory species. We tested for genomic adaptation to landscape features at natal spawning sites and along migration paths to the ocean of anadromous steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin. Results from multivariate ordination, gene-environment association and outlier analyses using 24,526 single nucleotide polymorphisms (SNPs) provided evidence that adaptive allele frequencies were more commonly associated with landscape features along migration paths than features at natal sites (91.8% vs. 8.2% of adaptive loci, respectively). Among the 45 landscape variables tested, migration distance to the ocean and mean annual precipitation along migration paths were significantly associated with adaptive genetic variation in three distinct genetic groups. Additionally, variables such as minimum migration water temperature and mean migration slope were significant only in inland stocks of steelhead that migrate up to 1,200 km farther than those near the coast, indicating regional differences in migratory selective pressures. This study provides novel approaches for investigating migratory corridors and some of the first evidence that environment along migration paths can lead to substantial divergent selection. Consequently, our approach to understand genetic adaptation to migration conditions can be applied to other migratory species when migration or dispersal paths are generally known.

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. We used both univariate analyses and a 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 Salmo salar genome provided evidence that all three SNPs localize within a 46 kb region overlapping GREB1-like (an oestrogen target gene) on chromosome Ssa03. Additionally, multivariate analyses with RF identified that these three 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.