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The consequences of not accounting for background selection in demographic inference
Author(s) -
Ewing Gregory B.,
Jensen Jeffrey D.
Publication year - 2016
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/mec.13390
Subject(s) - inference , replicate , approximate bayesian computation , bayesian inference , selection (genetic algorithm) , bayesian probability , econometrics , population , model selection , statistical physics , selection bias , statistical inference , computer science , space (punctuation) , biology , machine learning , statistics , artificial intelligence , mathematics , physics , demography , sociology , operating system
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.