Data from: Genotyping-by-sequencing for estimating relatedness in non-model organisms: avoiding the trap of precise bias

There has been remarkably little attention to using the high resolution
provided by genotyping-by-sequencing (i.e. RADseq and similar methods)
datasets for assessing relatedness in wildlife populations. A major hurdle
is the genotyping error, especially allelic dropout, often found in this
type of dataset that could lead to downward-biased, yet precise, estimates
of relatedness. Here we assess the applicability of
genotyping-by-sequencing datasets for relatedness inferences given their
relatively high genotyping error rates. Individuals of known relatedness
were simulated under genotyping error, allelic dropout, and missing data
scenarios based on an empirical ddRAD dataset, and their true relatedness
was compared to that estimated by seven relatedness estimators. We found
that an estimator chosen through such analyses can circumvent the
influence of genotyping error, with the estimator of Ritland (1996) shown
to be unaffected by allelic dropout and to be the most accurate when there
is genotyping error. We also found that the choice of estimator should not
rely solely on the strength of correlation between estimated and true
relatedness as a strong correlation does not necessarily mean estimates
are close to true relatedness. We also demonstrated how even a large SNP
dataset with genotyping error (allelic dropout or otherwise) or missing
data still performs better than a perfectly genotyped microsatellite
dataset of tens of markers. The simulation-based approach used here can be
easily implemented by others on their own genotyping-by-sequencing
datasets to confirm the most appropriate and powerful estimator for their
dataset.