Data from: The relative power of genome scans to detect local adaptation depends on sampling design and statistical method

Although genome scans have become a popular approach towards understanding
the genetic basis of local adaptation, the field still does not have a
firm grasp on how sampling design and demographic history affect the
performance of genome scans on complex landscapes. To explore these
issues, we compared 20 different sampling designs in equilibrium (i.e.
island model and isolation by distance) and nonequilibrium (i.e. range
expansion from one or two refugia) demographic histories in spatially
heterogeneous environments. We simulated spatially complex landscapes,
which allowed us to exploit local maxima and minima in the environment in
‘pair’ and ‘transect’ sampling strategies. We compared FST outlier and
genetic–environment association (GEA) methods for each of two approaches
that control for population structure: with a covariance matrix or with
latent factors. We show that while the relative power of two methods in
the same category (FST or GEA) depended largely on the number of
individuals sampled, overall GEA tests had higher power in the island
model and FST had higher power under isolation by distance. In the refugia
models, however, these methods varied in their power to detect local
adaptation at weakly selected loci. At weakly selected loci, paired
sampling designs had equal or higher power than transect or random designs
to detect local adaptation. Our results can inform sampling designs for
studies of local adaptation and have important implications for the
interpretation of genome scans based on landscape data.