Capturing the rapidly evolving study of adaptation.

Research on the genomics of adaptation is rapidly changing. In the last few decades, progress in this area has been driven by methodological advances, not only in the way increasingly large amounts of molecular data are generated (e.g. with high-throughput sequencing), but also in the way these data are analysed. This includes a growing appreciation and quantitative treatment of covariation among units within the same data type (e.g. genes) or across data types (e.g. genes and phenotypes). The development and adoption of more and more integrative tools have resulted in richer and more interesting empirical work. This special issue - comprising methodological, empirical, and review papers - aims to capture a 'snapshot' of this rapidly evolving field. We discuss in particular three important themes in the study of adaptation: the genetic architecture of adaptive variation, protein-coding and regulatory changes, and parallel evolution. We highlight how more traditional key themes in the study of genetic architecture (e.g. the number of loci underlying adaptive traits and the distribution of their effects) are now being complemented by other factors (e.g. how patterns of linkage and number of loci interact to affect the ability to adapt). Similarly, apart from addressing the relative importance of protein-coding and regulatory changes, we now have the tools to look in-depth at specific types of regulatory variation to gain a clearer picture of regulatory networks. Finally, parallel evolution has always been central to the study of adaptation, but now we are often able to address the question of whether - and to what extent - parallelism at the organismal or phenotypic level is matched by parallelism at the genetic level. Perhaps most importantly, we can now determine what mechanisms are driving parallelism (or lack thereof) across levels of biological organization. All these recent methodological developments open up new directions for future studies of adaptive changes across traits, levels of biological organization, demographic contexts and time scales.