Unbroken: RADseq remains a powerful tool for understanding the genetics of adaptation in natural populations

• Published in: Molecular ecology resources 2017-05, Vol.17 (3), p.362-365
• Main Authors: Catchen, Julian M., Hohenlohe, Paul A., Bernatchez, Louis, Funk, W. Chris, Andrews, Kimberly R., Allendorf, Fred W.
• Format: Article
• Language: English
• Subjects: Adaptation; Computer simulation; Genetics; genome scan; Genomes; Linkage disequilibrium; Markers; Natural populations; Populations; RADseq; Researchers; selection; Studies; Tags
• ISSN: 1755-098X; 1755-0998
• DOI: 10.1111/1755-0998.12669

Recently, Lowry et al. addressed the ability of RADseq approaches to detect loci under selection in genome scans. While the authors raise important considerations, such as accounting for the extent of linkage disequilibrium in a study system, we strongly disagree with their overall view of the ability of RADseq to inform our understanding of the genetic basis of adaptation. The family of RADseq protocols has radically improved the field of population genomics, expanding by several orders of magnitude the number of markers available while substantially reducing the cost per marker. Researchers whose goal is to identify regions of the genome under selection must consider the LD of the experimental system; however, there is no magical LD cutoff below which researchers should refuse to use RADseq. Lowry et al. further made two major arguments: a theoretical argument that modeled the likelihood of detecting selective sweeps with RAD markers, and gross summaries based on an anecdotal collection of RAD studies. Unfortunately, their simulations were off by two orders of magnitude in the worst case, while their anecdotes merely showed that it is possible to get widely divergent densities of RAD tags for any particular experiment, either by design or due to experimental efficacy. We strongly argue that RADseq remains a powerful and efficient approach that provides sufficient marker density for studying selection in many natural populations. Given limited resources, we argue that researchers should consider a wide range of trade‐offs among genomic techniques, in light of their study question and the power of different techniques to answer it.