Whole-Genome Resequencing to Evaluate Life History Variation in Anadromous Migration of Oncorhynchus mykiss.

Anadromous fish experience physiological modifications necessary to migrate between vastly different freshwater and marine environments, but some species such as Oncorhynchus mykiss demonstrate variation in life history strategies with some individuals remaining exclusively resident in freshwater, whereas others undergo anadromous migration. Because there is limited understanding of genes involved in this life history variation across populations of this species, we evaluated the genomic difference between known anadromous (n = 39) and resident (n = 78) Oncorhynchus mykiss collected from the Klickitat River, WA, USA, with whole-genome resequencing methods. Sequencing of these collections yielded 5.64 million single-nucleotide polymorphisms that were tested for significant differences between resident and anadromous groups along with previously identified candidate gene regions. Although a few regions of the genome were marginally significant, there was one region on chromosome Omy12 that provided the most consistent signal of association with anadromy near two annotated genes in the reference assembly: COP9 signalosome complex subunit 6 (CSN6) and NACHT, LRR, and PYD domain-containing protein 3 (NLRP3). Previously identified candidate genes for anadromy within the inversion region of chromosome Omy05 in coastal steelhead and rainbow trout were not informative for this population as shown in previous studies. Results indicate that the significant region on chromosome Omy12 may represent a minor effect gene for male anadromy and suggests that this life history variation in Oncorhynchus mykiss is more strongly driven by other mechanisms related to environmental rearing such as epigenetic modification, gene expression, and phenotypic plasticity. Further studies into regulatory mechanisms of this trait are needed to understand drivers of anadromy in populations of this protected species.

Selection at a genomic region of major effect is responsible for evolution of complex life histories in anadromous steelhead.

BACKGROUND: Disparity in the timing of biological events occurs across a variety of systems, yet the understanding of genetic basis underlying diverse phenologies remains limited. Variation in maturation timing occurs in steelhead trout, which has been associated with greb1L, an oestrogen target gene. Previous techniques that identified this gene only accounted for about 0.5-2.0% of the genome and solely investigated coastal populations, leaving uncertainty on the genetic basis of this trait and its prevalence across a larger geographic scale. RESULTS: We used a three-tiered approach to interrogate the genomic basis of complex phenology in anadromous steelhead. First, fine scale mapping with 5.3 million SNPs from resequencing data covering 68% of the genome confirmed a 309-kb region consisting of four genes on chromosome 28, including greb1L, to be the genomic region of major effect for maturation timing. Second, broad-scale characterization of candidate greb1L genotypes across 59 populations revealed unexpected patterns in maturation phenology for inland fish migrating long distances relative to those in coastal streams. Finally, genotypes from 890 PIT-tag tracked steelhead determined associations with early versus late arrival to spawning grounds that were previously unknown. CONCLUSIONS: This study clarifies the genetic bases for disparity in phenology observed in steelhead, determining an unanticipated trait association with premature versus mature arrival to spawning grounds and identifying multiple candidate genes potentially contributing to this variation from a single genomic region of major effect. This illustrates how dense genome mapping and detailed phenotypic characterization can clarify genotype to phenotype associations across geographic ranges of species.

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.