@article {Bank009654, author = {Claudia Bank and Gregory B. Ewing and Anna Ferrer-Admettla and Matthieu Foll and Jeffrey D. Jensen}, title = {Thinking too positive? Revisiting current methods of population-genetic selection inference}, elocation-id = {009654}, year = {2014}, doi = {10.1101/009654}, publisher = {Cold Spring Harbor Laboratory}, abstract = {In the age of next-generation sequencing, the availability of increasing amounts and quality of data at decreasing cost ought to allow for a better understanding of how natural selection is shaping the genome than ever before. Yet, alternative forces such as demography and background selection obscure the footprints of positive selection that we would like to identify. Here, we illustrate recent developments in this area, and outline a roadmap for improved selection inference. We argue (1) that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, (2) that genomic information from multiple-time points will enhance the power of inference, and (3) that results from experimental evolution should be utilized to better inform population-genomic studies.Background selectionreduction of genetic diversity due to selection against deleterious mutations at linked sites.Coalescent simulatorsimulation tool that reconstructs the genealogical history of a sample backwards in time. This greatly reduces computational effort, but only models in which mutations are independent of the sample{\textquoteright}s genealogy can be implemented.Cost of adaptationthe deleterious effect that a beneficial mutation can have in a different environment. Prominent examples are antibiotic resistance mutations, which have often been observed to cause reduced growth rates (as compared with the wild type) in the absence of antibiotics.Demographic historythe population history of a sample of individuals, which can include population size changes, differing sex ratios, migration rates, splitting and reconnection of the population, as well as variation over time in these parameters.Distribution of fitness effects (DFE)The statistical distribution of selection coefficients of all possible new mutations, as compared with a reference genotype.Epistasisthe interaction of mutational effects, resulting in a dependence of the effect of a mutation on the background it appears in.Forward simulatorsimulation tool that models the evolution of populations forward in time. This allows for implementation of complex models, but also usually results in much longer computation times because all individuals/haplotypes must be tracked.Non-equilibrium modelany model that incorporates violations of the assumptions of the standard neutral model (see below).Selection coefficientA measure of the strength of selection on a selected genotype. Usually, the selection coefficient is measured as the relative difference between the reproductive success of the selected and the ancestral genotypes.Selective sweepthe process of a beneficial mutation (and its closely linked chromosomal vicinity) being driven ({\textquotedblleft}swept{\textquotedblright}) to high frequency or fixation by natural selection. Selective sweeps result in a genomic signature including a local reduction in genetic variation, and skews in the SFS.Standard neutral modelunder this model [67], the population resides in an equilibrium of allele frequencies determined by the (constant) mutation rate and population size. The model assumptions include random mating, binomial sampling of offspring, and no selection.}, URL = {https://www.biorxiv.org/content/early/2014/09/25/009654}, eprint = {https://www.biorxiv.org/content/early/2014/09/25/009654.full.pdf}, journal = {bioRxiv} }