Patterns of Genetic Variation in Lahontan Cutthroat Trout: A Comparison of SNP and Microsatellite Loci

Wednesday, September 11, 2013: 4:40 PM
Miller (Statehouse Convention Center)
Mary M. Peacock , Biology, University of Nevada, Reno, Reno, NV
Victoria Pritchard , Fish Ecology, Southwest Fisheries Science Center, Santa Cruz, CA
John Carlos Garza , Fisheries Ecology, Southwest Fisheries Science Center, Santa Cruz, CA
Lahontan cutthroat trout (Oncorhynchus clarkii henshawi),  one of 14 subspecies of cutthroat trout found in the intermountain western United States, is listed as threatened under ESA. Three Geographic Management Units (GMUs) have been identified within the subspecies range.  Here we compare patterns of genetic structure observed at 8 microsatellite markers to patterns at 35 single nucleotide polymorphisms (SNPs) among populations sampled from across the three GMUs.  We used Bayesian genotype clustering analysis to examine patterns of divergence within and among the three GMUs. Two genotype clusters was the best fit of the data for the SNP dataset. However, we had statistical support for both k = 2 and 4 genotype clusters and report results for both analyses here. At k = 2 the SNP markers clearly delineated the western GMU from both the eastern and northwestern GMUs. For k = 4 most of the major river systems formed distinct genotype clusters with the exception of the Quinn and Humboldt rivers which formed a single genotype cluster.  There was little variation in the pattern of genetic structure among replicates for the SNP analyses.  Three genotype clusters was the best fit of the data for the microsatellite loci. Although the k = 3 analysis did not cleanly delineate the three GMUs, genotype clusters did tend to form for the major rivers.  As with the SNP data the Quinn and Humboldt rivers tended to form a single genotype cluster. We had additional statistical support for both k = 2 and 5.  Interestingly in the k = 5 analysis the Quinn River samples did not form a distinct genotype cluster but tended to cluster with the Truckee River samples, which reflects their shared history of inundation by pluvial Lake Lahontan. Unlike the SNP analysis we observed more variation in clustering patterns across replicates for all “k“ for which we had statistical support at the microsatellite loci. Combining the SNP and microsatellite datasets did not improve our precision.  These results suggest that SNP markers may be more appropriate for large spatial scale analyses in this subspecies.