Data from: SNPs selected by information content outperform randomly selected microsatellite loci for delineating genetic identification and introgression in the endangered dark European honeybee (Apis mellifera mellifera)
Muñoz, Irene et al. (2016), Data from: SNPs selected by information content outperform randomly selected microsatellite loci for delineating genetic identification and introgression in the endangered dark European honeybee (Apis mellifera mellifera), Dryad, Dataset, https://doi.org/10.5061/dryad.5vp20
The honeybee (Apis mellifera) has been threatened by multiple factors, including pests and pathogens, pesticides, and loss of locally adapted gene complexes due to replacement and introgression. In western Europe, the genetic integrity of the native A.m. mellifera (M-lineage) is endangered due to trading and intensive queen breeding with commercial subspecies of eastern European ancestry (C-lineage). Effective conservation actions require reliable molecular tools to identify purebred A.m. mellifera colonies. Microsatellites have been preferred for identification of A.m. mellifera stocks across conservation centers. However, owing to high-throughput, easy transferability between laboratories and low genotyping error, SNPs promise to become popular. Here, we compared the resolving power of a widely utilized microsatellite dataset to detect structure and introgression with that of different datasets that combine a variable number of SNPs selected for their information content and genomic proximity to the microsatellites. Contrary to every SNP dataset, microsatellites were unable to clearly separate the two European lineages in the PCA space. Mean introgression proportions were identical across the two marker types, although at the individual level microsatellites’ performance was relatively poor at the upper range of introgression, a result reflected by their lower precision. Although mean accuracy was relatively high across datasets (>91%), microsatellites were the least accurate and the top-ranked informative 144 SNPs were the most accurate. Comparisons amongst the SNP datasets showed that those combining SNPs flanking microsatellites performed worst. Our results suggest that SNPs are more powerful for identification of A.m. mellifera colonies, especially when they are selected by information content.