Comparison of SNPs and microsatellites for fine‐scale application of genetic stock identification of Chinook salmon in the Columbia River Basin

Abstract

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 vs. 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 analyse 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: (i) ability to differentiate reporting groups, (ii) proportion of correct assignment in mixture simulation tests and baseline leave-one-out analyses and (iii) 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 and 200 highly informative SNP loci are required to meet management standards (correct assignment > 90%) for resolving stocks in finer-scale GSI applications.

Empirical comparison of single nucleotide polymorphisms and microsatellites for population and demographic analyses of bowhead whales

“…Berlin et al 2008, Narum et al 2008, Hefti-Gautschi et al 2009, Amend et al 2010, Keller et al 2010, Mims et al 2010, Quintela et al 2010, Freamo et al 2011, Mesnick et al 2011. For non-model organisms, there are still few empirical studies that directly compare the utility or power of SNPs relative to microsatellites for detecting population structure (Narum et al 2008, Smith & Seeb 2008, Coates et al 2009, Glover et al 2010, Hess et al 2011. Previous comparisons of SNPs and microsatellites have typically been limited to 1 or 2 types of analysis (e.g.…”

A Comparison of Stock and Individual Identification for Chinook Salmon in British Columbia Provided by Microsatellites and Single‐Nucleotide Polymorphisms

“…Although a number of techniques are available to survey SNP variation, we are aware of none that will allow well over 100 SNPs to be analyzed in our laboratory at a cost comparable to that of analyzing up to 16 microsatellites with three injections on an automated DNA sequencer. For the present, the combined microsatellite-SNP approach outlined by Narum et al (2008) and Hess et al (2011) may be a practical approach to incorporating the power of both classes of markers. R o b e r t s o n S a n J u a n S a r it a T a h s is T o q u a r t …”

“…Single-nucleotide polymorphism arrays containing 100-200 loci were also estimated to be required to meet management standards for fine-scale resolution of Columbia River Chinook salmon (Hess et al 2011). If individual assignment is part of the stock identification application, then no enhancement of the existing SNP baseline is required if the regional levels of accuracy provided by the DFO and GAPS microsatellites is acceptable, and only about 20 additional SNPs would be required if the enhanced microsatellite baselines were utilized.…”

Population and individual identification of coho salmon in British Columbia through parentage-based tagging and genetic stock identification: an alternative to coded-wire tags

“…However, should differentiation among populations be sufficient to allow GSI to be conducted reliably, then a combined PBT-GSI approach may provide accurate estimates of stock composition. Genetically based stock identification employs genetic markers, and for this purpose SNPs have been applied in a number of ecological, conservation, and fishery management settings (Habicht et al 2010;Hess et al 2011). SNP panels need to be applied rapidly and inexpensively to genotype many individuals ), incorporating sufficient numbers of SNPs to provide confidence in the accuracy of information produced.…”