Data from: Effects of sample size and full sibs on genetic diversity characterization: a case study of three syntopic Iberian pond-breeding amphibians
Sánchez-Montes, Gregorio et al. (2017), Data from: Effects of sample size and full sibs on genetic diversity characterization: a case study of three syntopic Iberian pond-breeding amphibians, Dryad, Dataset, https://doi.org/10.5061/dryad.f65s7
Accurate characterization of genetic diversity is essential for understanding population demography, predicting future trends and implementing efficient conservation policies. For that purpose, molecular markers are routinely developed for nonmodel species, but key questions regarding sampling design, such as calculation of minimum sample sizes or the effect of relatives in the sample, are often neglected. We used accumulation curves and sibship analyses to explore how these 2 factors affect marker performance in the characterization of genetic diversity. We illustrate this approach with the analysis of an empirical dataset including newly optimized microsatellite sets for 3 Iberian amphibian species: Hyla molleri, Bufo calamita, and Pelophylax perezi. We studied 17–21 populations per species (total n = 547, 652, and 516 individuals, respectively), including a reference locality in which the effect of sample size was explored using larger samples (77–96 individuals). As expected, FIS and tests for Hardy–Weinberg equilibrium and linkage disequilibrium were affected by the presence of full sibs, and most initially inferred disequilibria were no longer statistically significant when full siblings were removed from the sample. We estimated that to obtain reliable estimates, the minimum sample size (potentially including full sibs) was close to 20 for expected heterozygosity, and between 50 and 80 for allelic richness. Our pilot study based on a reference population provided a rigorous assessment of marker properties and the effects of sample size and presence of full sibs in the sample. These examples illustrate the advantages of this approach to produce robust and reliable results for downstream analyses.
Sierra de Guadarrama