Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.
Recently, there has been increased awareness of the role of background selection (BGS) in both data analysis and modelling advances. However, BGS is still difficult to take into account because of tractability issues with simulations and difficulty with nonequilibrium demographic models. Often, simple rescaling adjustments of effective population size are used. However, there has been neither a proper characterization of how BGS could bias or shift inference when not properly taken into account, nor a thorough analysis of whether rescaling is a sufficient solution. Here, we carry out extensive simulations with BGS to determine biases and behaviour of demographic inference using an approximate Bayesian approach. We find that results can be positively misleading with significant bias, and describe the parameter space in which BGS models replicate observed neutral nonequilibrium expectations.
Anne-Florence Raphaëlle Bitbol, Richard Marie Servajean
Anthony Christopher Davison, Ophélia Mireille Anna Miralles