Pacific Rim Chinook salmon genetic stock identification baseline of SNP genotypes

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tshawytscha) have utilized GSI analyses for decades with various genetic baselines, but these have largely become outdated with advances in technology that enable more efficient genotyping.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 eff..., We assembled SNP data that was provided by the genetics laboratories of several different agencies. The data has been tested for its ability to perform genetic stock identification estimates., , # Coastwide Chinook SNP GSI Baseline

[https://doi.org/10.5061/dryad.dz08kps5b](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...,