Integrating landscape genomics and spatially explicit approaches to detect loci under selection in clinal populations.

Uncovering the genetic basis of adaptation hinges on the ability to detect loci under selection. However, population genomics outlier approaches to detect selected loci may be inappropriate for clinal populations or those with unclear population structure because they require that individuals be clustered into populations. An alternate approach, landscape genomics, uses individual-based approaches to detect loci under selection and reveal potential environmental drivers of selection. We tested four landscape genomics methods on a simulated clinal population to determine their effectiveness at identifying a locus under varying selection strengths along an environmental gradient. We found all methods produced very low type I error rates across all selection strengths, but elevated type II error rates under "weak" selection. We then applied these methods to an AFLP genome scan of an alpine plant, Campanula barbata, and identified five highly supported candidate loci associated with precipitation variables. These loci also showed spatial autocorrelation and cline patterns indicative of selection along a precipitation gradient. Our results suggest that landscape genomics in combination with other spatial analyses provides a powerful approach for identifying loci potentially under selection and explaining spatially complex interactions between species and their environment.