Data from: Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin

Genetic stock identification (GSI) is an important tool in fisheries
management. Microsatellites (µSATs) have been the dominant genetic marker
for GSI, however increasing availability and numerous advantages of single
nucleotide polymorphism (SNP) markers make them an appealing alternative.
We tested performance of 13 µSAT versus 92 SNP loci in a fine-scale
application of GSI, using a new baseline for Chinook salmon consisting of
49 collections (n=4014) distributed across the Columbia River Basin. In
GSI, baseline genotypes for both marker sets were used independently to
analyze a real fishery mixture (n=2731) representing the total run of
Chinook salmon passing Bonneville Dam in the Columbia River. Marker sets
were evaluated using three criteria: 1) ability to differentiate reporting
groups, 2) proportion of correct assignment in mixture simulation tests
and baseline leave-one-out analyses, and 3) individual assignment and
confidence intervals around estimated stock proportions of a real fishery
mixture. The µSATs outperformed the SNPs in resolving fine-scale
relationships, but all 105 markers combined provided greatest power for
GSI. SNPs were ranked by relative information content based on both an
iterative procedure that optimized correct assignment to the baseline and
ranking by minor allele frequency. For both methods, we identified a
subset of the top 50 ranked loci, which were similar in assignment
accuracy, and both reached maximum available power of the total 92 SNP
loci (correct assignment=73%). Our estimates indicate that between 100-200
highly informative SNP loci are required to meet management standards
(correct assignment>90%) for resolving stocks in finer-scale GSI
applications.