Thinking too positive? Revisiting current methods of population genetic selection inference

Abstract: In the age of next-generation sequencing, the availability of increasing amounts and improved quality of data at decreasing cost ought to allow for a better understand- ing of how natural selection is shaping the genome than ever before. However, alternative forces, such as demog- raphy and background selection (BGS), obscure the footprints of positive selection that we would like to identify. In this review, we illustrate recent develop- ments in this area, and outline a roadmap for improved selection inference. We argue (i) that the development and obligatory use of advanced simulation tools is nec- essary for improved identification of selected loci, (ii) that genomic information from multiple time points will enhance the power of inference, and (iii) that results from experimental evolution should be utilized to better inform population genomic studies.