Genotype and phenotype data for Columbia River steelhead.
Data files
Aug 20, 2020 version files 6.66 MB
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bonn_2013_2018_allLOCI.map
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bonn_2013_2018_allLOCI.ped
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bonn_2013_2018_neutral_ULnO.ped
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Omy_367_marker_meta.txt
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Omy_BNV2013-2018_sex_1male_2female_0unk.txt
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Omy_hood_GTseq_Chr28rt_10pMISS.map.haploview
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Omy_hood_GTseq_Chr28rt_10pMISS.ped
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Omy_hood_NEUTRAL_ADAPTIVE_Chr28_10Pmiss_hapmap.txt
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Omy_hood_NEUTRAL_ADAPTIVE_phenotype_10Pmiss.txt
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Omy_pit_pings_Bonneville_2013_2018_marked_arrived_MADEITS_filteredID_age_length_lineage_HUC-BPA-YEAR-LAG-SPEED.txt
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Omy_pit_pings_Bonneville_2013_2018_marked_arrived_MADEITS_filteredID_length_lineage_HUC-BPA-YEAR-LAG-SPEED_ocean_total.txt
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SFile1.sort_PITtag_hits_pub2.R
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SFile2.sort_PITtag_hits_hood_pub2.R
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SFile3.Gapit-Haplostats-Hood-run-timing-pub.R
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SFile4.Gapit-Haplostats-2013-2018-bonneville-run-timing-pub.R
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SFile5.Gapit-Haplostats-2013-2018-bonneville-age-length-pub.R
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 (R2 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 R2 = 0.139-0.170; females R2 = 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.
Methods
genotype-by-thousands (GTseq) genotypes; observation/measurement at Bonneville AFF, PIT recordings from PTAGIS