The WZA: A window-based method for characterizing genotype-environment
Genotype environment association (GEA) studies have the potential to
elucidate the genetic basis of local adaptation in natural populations.
Specifically, GEA approaches look for a correlation between allele
frequencies and putatively selective features of the environment.
Genetic markers with extreme evidence of correlation with the
environment are presumed to be tagging the location of alleles that
contribute to local adaptation. In this study, we propose a new method
for GEA studies called the weighted-Z analysis (WZA) that combines
information from closely linked sites into analysis windows in a way
that was inspired by methods for calculating FST. We analyze simulations
modelling local adaptation to heterogeneous environments either using a
GEA method that controls for population structure or an uncorrected
approach. In the majority of cases we tested, the WZA either
outperformed single-SNP based approaches or performed similarly. The WZA
outperformed individual SNP approaches when the measured environment is
not perfectly correlated with the true selection pressure or when a
small number of individuals or demes was sampled. We apply the WZA to
previously published data from lodgepole pine and identified candidate
loci that were not found in the original study.