Data from: Evaluation of demographic history and neutral parameterization on the performance of Fst outlier tests

FST outlier tests are a potentially powerful way to detect genetic loci
under spatially divergent selection. Unfortunately, the extent to which
these tests are robust to non-equilibrium demographic histories has been
under-studied. We developed a landscape-genetics simulator to test the
effects of isolation by distance (IBD) and range expansion on FST outlier
methods. We evaluated the two most commonly used methods for the
identification of FST outliers (FDIST2 and BayeScan, which assume samples
are evolutionarily independent) and two recent methods (FLK and Bayenv2,
which estimate and account for evolutionary non-independence).
Parameterization with a set of neutral loci (“neutral parameterization”)
always improved the performance of FLK and Bayenv2, while neutral
parameterization caused FDIST2 to actually perform worse in the cases of
IBD or range expansion. BayeScan was improved when the prior odds on
neutrality was increased, regardless of the true odds in the data. On
their best performance, however, the widely-used methods had high
false-positive rates for IBD and range expansion and were outperformed by
methods that accounted for evolutionary non-independence. In addition,
default settings in FDIST2 and BayeScan resulted in many false positives
under balancing selection. However, all methods did very well if a large
set of neutral loci is available to create empirical p-values. We conclude
that in species that exhibit IBD or have undergone range expansion, many
of the published FST outliers based on FDIST2 and BayeScan are probably
false positives, but FLK and Bayenv2 show great promise for accurately
identifying loci under spatially-divergent selection.