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

• Novel technologies provide us with data of vast dimensions in terms of amount, resolution, and design.
• To improve selection inference from polymorphism data, it is crucial to begin better accounting for factors including demography and background selection.
• Analysis of data from multiple time points increases power to identify beneficial variants, as well as to quantify the strength and timing of selection.
• Experimental evolution studies provide us with detailed information of statistical distributions of selection coefficients, and allude to the prevalence of epistasis – yet reconciling experimental results with natural population data remains an open challenge.

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 understanding of how natural selection is shaping the genome than ever before. However, alternative forces, such as demography and background selection (BGS), obscure the footprints of positive selection that we would like to identify. In this review, we illustrate recent developments 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 necessary 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.