FUBAR: A Fast, Unconstrained Bayesian AppRoximation for Inferring Selection

Model-based analyses of natural selection often categorize sites into a relatively small number of site classes. Forcing each site to belong to one of these classes places unrealistic constraints on the distribution of selection parameters, which can result in misleading inference due to model misspecification. We present an approximate hierarchical Bayesian method using a Markov chain Monte Carlo (MCMC) routine that ensures robustness against model misspecification by averaging over a large number of predefined site classes. This leaves the distribution of selection parameters essentially unconstrained, and also allows sites experiencing positive and purifying selection to be identified orders of magnitude…

• EC
  European Commission
• NR
  National Research Foundation
• UO
  University of Cape Town
• NI
  National Institutes of Health

  Awards: AI74621, GM093939, AI57167, AI47745