A Bayesian Approach for Incorporating Variable Rates of Heterogeneity in Linkage Analysis

• Activity Number: 328
• Type: Contributed
• Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Swati Biswas*+ and Shili Lin
• Companies: University of North Texas Health Sciences Center and The Ohio State University
• Keywords: Linkage analysis ; Heterogeneity ; Markov chain Monte Carlo ; HLOD ; admixture approach ; reversible jump MCMC

Abstract

A widely used approach for dealing with locus heterogeneity in linkage analysis is based on mixture likelihood formulated using a single heterogeneity (mixing) parameter. However, in general, different types of families exhibit different levels of heterogeneity. To incorporate this variability, we propose a Bayesian approach wherein each family has its own heterogeneity parameter representing the probability that it is of linked type. These parameters are nuisance parameters while the main parameter of interest is the location of the disease gene, if there is any. We use the reversible jump Markov chain Monte Carlo methodology to allow moves between the two models: linkage and no linkage. We first estimate the posterior probability of linkage on a chromosome and the corresponding Bayes factor. If linkage is inferred, the location of the disease gene along with its credible set is estimated. The asymptotic joint distribution of the estimators is derived. We show this approach is more powerful than the currently used approach in detecting linkage while the two approaches have comparable false positive rates. The proposed method was applied to lung cancer and asthma datasets.