Steelhead (Oncorhynchus mykiss) lineages and sexes show variable patterns of association of adult migration timing and age‐at‐maturity traits with two genomic regions. (27th August 2020)

• Record Type: Journal Article
• Authors: Willis, Stuart C.; Hess, Jon E.; Fryer, Jeff K.; Whiteaker, John M.; Brun, Chris; Gerstenberger, Ryan; Narum, Shawn R.
• Is Part Of: Evolutionary applications. Volume 13:Number 10(2020)
• Page Start: 2836
• Page End: 2856
• Publication Date: 2020-08-27
• Subjects: genome‐wide association study -- genotype–phenotype association -- haplotype -- rainbow trout -- salmon; Evolution (Biology) -- Periodicals; Genetics -- Periodicals; Natural selection -- Periodicals; Ecology -- Periodicals; 576.8
• DOI: 10.1111/eva.13088
• Languages: English
• ISSNs: 1752-4571

Abstract

Abstract: As life history diversity plays a critical role in supporting the resilience of exploited populations, understanding the genetic basis of those life history variations is important for conservation management. However, effective application requires a robust understanding of the strength and universality of genetic associations. Here, we examine genetic variation of single nucleotide polymorphisms in genomic regions previously associated with migration phenology and age‐at‐maturity in steelhead ( Oncorhynchus mykiss ) from the Columbia River. We found chromosome 28 markers (GREB1L, ROCK1 genes) explained significant variance in migration timing in both coastal and inland steelhead. However, strength of association was much greater in coastal than inland steelhead ( R 2 0.51 vs. 0.08), suggesting that genomic background and challenging inland migration pathways may act to moderate effects of this region. Further, we found that chromosome 25 candidate markers (SIX6 gene) were significantly associated with age and size at first return migration for inland steelhead, and this pattern was mediated by sex in a predictable pattern (males R 2 = 0.139–0.170; females R 2 = 0.096–0.111). While this encourages using these candidate regions in predicting life history characteristics, we suggest that stock‐specific associations and haplotype frequencies will be useful in guiding implementation of genetic assays to inform management.