Data from: Relative accuracy of three common methods of parentage analysis in natural populations

Parentage studies and family reconstructions have become increasingly
popular for investigating a range of evolutionary, ecological and
behavioral processes in natural populations. However, a number of
different assignment methods have emerged in common use, and the accuracy
of each may differ in relation to the number of loci examined, allelic
diversity, incomplete sampling of all candidate parents, and the presence
of genotyping errors. Here we examine how these factors affect the
accuracy of three popular parentage inference methods (COLONY, FaMoz and
an exclusion-Bayes’ theorem approach by Christie et al. (2010a)) to
resolve true parent-offspring pairs using simulated data. Our findings
demonstrate that accuracy increases with the number and diversity of loci.
These were clearly the most important factors in obtaining accurate
assignments explaining 75-90% of variance in overall accuracy across 60
simulated scenarios. Furthermore, the proportion of candidate parents
sampled had a small but significant impact on the susceptibility of each
method to either false positive or false negative assignments. Within the
range of values simulated, COLONY outperformed FaMoz, which outperformed
the exclusion-Bayes’ theorem method. However, with 20 or more highly
polymorphic loci, all methods could be applied with confidence. Our
results show that for parentage inference in natural populations, careful
consideration of the number and quality of markers will increase the
accuracy of assignments and mitigate the effects of incomplete sampling of
parental populations.