Title: A novel algorithmic approach to Bayesian logic regression
Authors: Florian Frommlet - Medical University Vienna (Austria) [presenting]
Abstract: Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates. It has been mainly used to model epistatic effects in genetic association studies, which is very appealing due to the intuitive interpretation of logic expressions to describe the interaction between genetic variations. Nevertheless logic regression has remained less well known than other approaches to epistatic association mapping. The aim is to introduce an advanced evolutionary algorithm called GMJMCMC (Genetically modified Mode Jumping Markov Chain Monte Carlo) to perform Bayesian model selection in the space of logic regression models. Comprehensive simulation studies illustrate its performance given logic regression terms of various complexity. Specifically GMJMCMC is shown to be able to identify three-way and even four-way interactions with relatively large power, a level of complexity which has not been achieved by previous implementations of logic regression. GMJMCMC is applied to reanalyze QTL mapping data for Recombinant Inbred Lines in Arabidopsis thaliana and for a backcross population in Drosophila.