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Data from: Finding the right coverage: The impact of coverage and sequence quality on SNP genotyping error rates


Fountain, Emily D. et al. (2016), Data from: Finding the right coverage: The impact of coverage and sequence quality on SNP genotyping error rates, Dryad, Dataset,


Restriction-enzyme-based sequencing methods enable the genotyping of thousands of single nucleotide polymorphism (SNP) loci in non-model organisms. However, in contrast to traditional genetic markers, genotyping error rates in SNPs derived from restriction-enzyme-based methods remain largely unknown. Here, we estimated genotyping error rates in SNPs genotyped with double digest RAD sequencing from Mendelian incompatibilities in known mother-offspring dyads of Hoffman's two-toed sloth (Choloepus hoffmanni) across a range of coverage and sequence quality criteria, for both reference-aligned and de novo-assembled datasets. Genotyping error rates were more sensitive to coverage than sequence quality and low coverage yielded high error rates, particularly in de novo-assembled datasets. For example, coverage ≥5 yielded median genotyping error rates of ≥0.03 and ≥0.11 in reference-aligned- and de novo-assembled datasets, respectively. Genotyping error rates declined to ≤0.01 in reference-aligned datasets with a coverage >30, but remained >0.04 in the de novo-assembled datasets. We observed approximately 10- and 13-fold declines in the number of loci sampled in the reference-aligned and de novo-assembled datasets when coverage was increased from >5 to >30 at quality score ≥30, respectively. Finally, we assessed the effects of genotyping coverage on a common population genetic application, parentage assignments, and showed that the proportion of incorrectly assigned maternities was relatively high at low coverage. Overall, our results suggest that the tradeoff between sample size and genotyping error rates be considered prior to building sequencing libraries, reporting genotyping error rates become standard practice, and that effects of genotyping errors on inference be evaluated in restriction-enzyme-based SNP studies.

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