Pacific Rim Chinook salmon genetic stock identification baseline of SNP genotypes

Van Doornik, Donald1

Research facility: NOAA National Marine Fisheries Service Northwest Fisheries Science Center

Published May 10, 2024 on Dryad. https://doi.org/10.5061/dryad.dz08kps5b

Data files

May 10, 2024 version files 33.54 MB

Chinook_SNP_Baseline_Tier1_Coastwide_Genotypes.csv

12.08 MB
Chinook_SNP_Baseline_Tier1_Coastwide_pop_info.csv

12.25 KB
Chinook_SNP_Baseline_Tier2_Columbia_Genotypes.csv

4.98 MB
Chinook_SNP_Baseline_Tier2_Columbia_pop_info.csv

3.11 KB
Chinook_SNP_Baseline_Tier2_Northern_Genotypes.csv

14.66 MB
Chinook_SNP_Baseline_Tier2_Northern_pop_info.csv

8.61 KB
Chinook_SNP_Baseline_Tier2_Southern_Genotypes.csv

1.78 MB
Chinook_SNP_Baseline_Tier2_Southern_pop_info.csv

719 B
README.md
3.78 KB

Abstract

Genetic stock identification (GSI) can be an effective tool for fisheries management, but development of reference baselines can be challenging for species with broad geographic distributions. Mixed stock fisheries of Chinook salmon (Oncorhynchus tshawytscha) have utilized GSI analyses for decades with various genetic baselines, but these have largely become outdated with advances in technology that enable more efficient genotyping. In this study, we compiled a large genetic baseline of single nucleotide polymorphism (SNP) markers for 391 populations that encompass the entire geographic range of Chinook salmon. Using cross validation simulations, we demonstrated that a multi-tiered assignment approach can provide high accuracy at both tier 1 (broad scale with three coastwide reporting groups; 97.8% mean accuracy) and tier 2 (fine scale regional reporting groups; up to 97.7% mean accuracy) levels. Realistic mixture simulations showed that this multi-tiered approach can provide highly effective GSI results for several common mixed stock fisheries applications in the Pacific Ocean. This new SNP baseline and multi-tiered assignment approach provides the most comprehensive GSI range-wide baseline for Chinook salmon over any previous applications and enables highly efficient genotyping solutions for these scenarios.

README: Coastwide Chinook SNP GSI Baseline

https://doi.org/10.5061/dryad.dz08kps5b

This dataset contains genotypes that can be used to make genetic stock identification estimates of Chinook salmon.

Description of the data and file structure

The files are formatted for the program Ms.GSI. There is a single Tier1 baseline file, and three Tier2 baseline files containing genotypes (file names = (*_Genotypes.csv).

These files contain the columns:

sampletype - should be "reference" for all individuals

indiv - Unique identifier for each individual

collection - the population or sample each individual belongs to

repunit - the reporting group each individual belongs to

Subsequent columns contain the genotypes for each locus (2 columns per locus)

Each baseline file also has an accompanying pop_info file (file names = "*pop_info.csv"). These files have the columns:

collection - Population or sample corresponding to those in the baseline file

repunit - Reporting group each collection belongs to

grpvec - A unique numerical value for each reporting group

The baseline files can also be used in the program Rubias.

The numeric allele designations were converted from the original base designations using the commonly used translation: A = 1, C = 2, G = 3, T = 4, "-" = 5.

Sharing/Access information

Links to other publicly accessible locations of the data:

http://fishgen.net

Data was derived from the following sources:

Templin, W.D., Seeb, J.E., Jasper, J.R., Barclay, A.W. and Seeb, L.W. 2011. Genetic differentiation of Alaska Chinook salmon: the missing link for migratory studies. Molecular Ecology Resources, 11: 226-246. https://doi.org/10.1111/j.1755-0998.2010.02968.x
Clemento, A. J., E. D. Crandall, and J. C. Garza. 2014. Evaluation of a single nucleotide polymorphism baseline for genetic stock identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current large marine ecosystem. Fishery Bulletin 112(2-3):112-130.
Larson, W. A., J. E. Seeb, C. E. Pascal, W. D. Templin, and L. W. Seeb. 2014. Single-nucleotide polymorphisms (SNPs) identified through genotyping-by-sequencing improve genetic stock identification of Chinook salmon (Oncorhynchus tshawytscha) from western Alaska. Canadian Journal of Fisheries and Aquatic Sciences 71(5):698-708.
Hess, J. E., N. R. Campbell, A. P. Matala, D. J. Hasselman, and S. R. Narum. 2016. Genetic Assesment of Columbia River Stocks, 4/1/2014 - 3/31/2015, Annual Report, 2008-907-00. Bonneville Power Administration. https://critfc.org/wp-content/uploads/2021/08/16-03.pdf
Barclay, A. W., D. F. Evenson, and C. Habicht. 2019. New Genetic Baseline for Upper Cook Inlet Chinook Salmon Allows for the Identification of More Stocks in Mixed Stock Fisheries: 413 Loci and 67 Populations. Alaska Department of Fish and Game, Fishery Manuscript Series No. 19-06. https://www.adfg.alaska.gov/FedAidPDFs/FMS19-06.pdf
McKinney, G. J., C. E. Pascal, W. D. Templin, S. E. Gilk-Baumer, T. H. Dann, L. W. Seeb, and J. E. Seeb. 2020. Dense SNP panels resolve closely related Chinook salmon populations. Canadian Journal of Fisheries and Aquatic Sciences 77(3):451-461.
Beacham, T. D., C. Wallace, K. Jonsen, B. G. Sutherland, C. Gummer, and E. B. Rondeau. 2021. Estimation of conservation unit and population contribution to Chinook salmon mixed-stock fisheries in British Columbia, Canada, using direct DNA sequencing for single nucleotide polymorphisms. Canadian Journal of Fisheries and Aquatic Sciences 78(10):1422-1434.