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Data from: Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters

Citation

Zhang, Haibin; Hare, Matthew P. (2011), Data from: Identifying and reducing AFLP genotyping error: an example of tradeoffs when comparing population structure in broadcast spawning versus brooding oysters, Dryad, Dataset, https://doi.org/10.5061/dryad.4f64ck01

Abstract

Phylogeographic inferences about gene flow are strengthened through comparison of co-distributed taxa, but also depend on adequate genomic sampling. Amplified Fragment Length Polymorphisms (AFLP) provide a rapid and inexpensive source of multilocus allele frequency data for making genomically robust inferences. Every AFLP study initially generates markers with a range of locus-specific genotyping error rates and applies criteria to select a subset for analysis. However, there has been very little empirical evaluation of the best tradeoff between culling all but the lowest-error loci to minimize overall genotyping error versus the potential for increasing population genetic signal by retaining more loci. Here, we used AFLPs to compare population structure in co-distributed broadcast spawning (Crassostrea virginica) and brooding (Ostrea equestris) oyster species. Using existing methods for almost entirely automated marker selection and scoring, genotyping error tradeoffs were evaluated by comparing results across a nested series of datasets with mean mismatch errors of 0, 1, 2, 3, 4 and >4%. Artifactual population structure was diagnosed in high-error datasets and we assessed the low-error point at which expected population substructure signal was lost. In both species we identified substructure patterns deemed to be inaccurate at error rates {less than or equal to}2% and >4%. In the species comparison, the optimum datasets showed higher gene flow for the brooding oyster with more oceanic salinity tolerances. AFLP tradeoffs may differ among studies, but our results suggest that important signal may be lost in the pursuit of 'acceptable' error levels and our procedures provide a general method for empirically exploring these tradeoffs.

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