Development and evaluation of 200 novel SNP assays for population genetic studies of westslope cutthroat trout and genetic identification of related taxa.

DNA sequence data were collected and screened for single nucleotide polymorphisms (SNPs) in westslope cutthroat trout (Oncorhynchus clarki lewisi) and also for substitutions that could be used to genetically discriminate rainbow trout (O. mykiss) and cutthroat trout, as well as several cutthroat trout subspecies. In total, 260 expressed sequence tag-derived loci were sequenced and allelic discrimination genotyping assays developed from 217 of the variable sites. Another 50 putative SNPs in westslope cutthroat trout were identified by restriction-site-associated DNA sequencing, and seven of these were developed into assays. Twelve O. mykiss SNP assays that were variable within westslope cutthroat trout and 12 previously published SNP assays were also included in downstream testing. A total of 241 assays were tested on six westslope cutthroat trout populations (N = 32 per population), as well as collections of four other cutthroat trout subspecies and a population of rainbow trout. All assays were evaluated for reliability and deviation from Hardy-Weinberg and linkage equilibria. Poorly performing and duplicate assays were removed from the data set, and the remaining 200 assays were used in tests of population differentiation. The remaining markers easily distinguished the various subspecies tested, as evidenced by mean G(ST) of 0.74. A smaller subset of the markers (N = 86; average G(ST) = 0.40) was useful for distinguishing the six populations of westslope cutthroat trout. This study increases by an order of magnitude the number of genetic markers available for the study of westslope cutthroat trout and closely related taxa and includes many markers in genes (developed from ESTs).

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 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.

Differentiating salmon populations at broad and fine geographical scales with microsatellites and single nucleotide polymorphisms.

Single nucleotide polymorphisms (SNPs) are appealing genetic markers due to several beneficial attributes, but uncertainty remains about how many of these bi-allelic markers are necessary to have sufficient power to differentiate populations, a task now generally accomplished with highly polymorphic microsatellite markers. In this study, we tested the utility of 37 SNPs and 13 microsatellites for differentiating 29 broadly distributed populations of Chinook salmon (n = 2783). Information content of all loci was determined by In and G'(ST), and the top 12 markers ranked by In were microsatellites, but the 6 highest, and 7 of the top 10 G'(ST) ranked markers, were SNPs. The mean ratio of random SNPs to random microsatellites ranged from 3.9 to 4.1, but this ratio was consistently reduced when only the most informative loci were included. Individual assignment test accuracy was higher for microsatellites (73.1%) than SNPs (66.6%), and pooling all 50 markers provided the highest accuracy (83.2%). When marker types were combined, as few as 15 of the top ranked loci provided higher assignment accuracy than either microsatellites or SNPs alone. Neighbour-joining dendrograms revealed similar clustering patterns and pairwise tests of population differentiation had nearly identical results with each suite of markers. Statistical tests and simulations indicated that closely related populations were better differentiated by microsatellites than SNPs. Our results indicate that both types of markers are likely to be useful in population genetics studies and that, in some cases, a combination of SNPs and microsatellites may be the most effective suite of loci.