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B1851
Title: Modelling ethnic differences in metabolic associations via Bayesian nonparametric processes Authors:  Marco Molinari - University College London (United Kingdom) [presenting]
Abstract: A novel approach is proposed for the estimation of multiple Gaussian Graphical Models (GGMs) to analyse patterns of association among a set of metabolites, under different conditions. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. We model the relationship between a set of metabolites and a set of covariates through a Sparse Seemingly Unrelated Regressions model and we use GGMs to represent the conditional dependence structure among metabolites. We specify a Dependent Generalised Dirichlet Process prior on the edge inclusion probabilities to borrow strength across groups and we adopt the Horseshoe prior to identify important biomarkers. Inference is performed via Markov Chain Monte Carlo (MCMC).