Data from: The search for loci under selection: trends, biases and progress

Detecting genetic variants under selection using FST outlier analysis (OA)
and environmental association analyses (EAA) are popular approaches that
provide insight into the genetic basis of local adaptation. Despite the
frequent use of OA and EAA approaches and their increasing attractiveness
for detecting signatures of selection, their application to field-based
empirical data have not been synthesized. Here, we review 66 empirical
studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We
report trends and biases across biological systems, sequencing methods,
approaches, parameters, environmental variables and their influence on
detecting signatures of selection. We found striking variability in both
the use and reporting of environmental data and statistical parameters.
For example, linkage disequilibrium among SNPs and numbers of unique SNP
associations identified with EAA were rarely reported. The proportion of
putatively adaptive SNPs detected varied widely among studies, and
decreased with the number of SNPs analyzed. We found that genomic sampling
effort had a greater impact than biological sampling effort on the
proportion of identified SNPs under selection. OA identified a higher
proportion of outliers when more individuals were sampled, but this was
not the case for EAA. To facilitate repeatability, interpretation and
synthesis of studies detecting selection, we recommend that future studies
consistently report geographic coordinates, environmental data, model
parameters, linkage disequilibrium, and measures of genetic structure.
Identifying standards for how OA and EAA studies are designed and reported
will aid future transparency and comparability of SNP-based selection
studies and help to progress landscape and evolutionary genomics.