title: The consequences of not accounting for background selection in demographic inference.
abstract: 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.
authors.affiliation: Ecole Polytechnique Fédérale de Lausanne (EPFL), EPFL SV IBI-SV UPJENSEN, AAB 0 46, Station 15, CH 1015, Lausanne, Switzerland.|Swiss Institute of Bioinformatics (SIB), EPFL SV IBI-SV UPJENSEN, AAB 0 46, Station 15, CH 1015, Lausanne, Switzerland.
authors.forename: Gregory B
authors.initials: GB
authors.lastname: Ewing
authors.affiliation: Ecole Polytechnique Fédérale de Lausanne (EPFL), EPFL SV IBI-SV UPJENSEN, AAB 0 46, Station 15, CH 1015, Lausanne, Switzerland.|Swiss Institute of Bioinformatics (SIB), EPFL SV IBI-SV UPJENSEN, AAB 0 46, Station 15, CH 1015, Lausanne, Switzerland.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Jensen
mesh_terms: D001499:Bayes Theorem; D005828:Genetics, Population; D008957:Models, Genetic; D012641:Selection, Genetic
keywords: evolutionary theory; natural selection and contemporary evolution; population dynamics; population genetics-theoretical