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

原文摘要

A widely used approach for dealing with locus heterogeneity in linkage analysis is based on mixture likelihood, in which a single mixing (heterogeneity) parameter represents the probability that each family is of linked type. However, in general, different types of families exhibit different heterogeneity levels. To incorporate this variability, we propose a new approach wherein each family has its own heterogeneity parameter representing the probability that it is of linked type. These parameters are nuisance parameters, whereas the main parameter of interest is the location of the disease gene, if there is any. We model the problem in the Bayesian framework and implement it using the Markov chain Monte Carlo (MCMC) methodology. In particular, we use the reversible-jump MCMC sampler to move 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, then the location of the disease gene along with its credible set is estimated. The asymptotic joint distribution of the estimators is derived. We show that this approach is more powerful than the currently used approach in detecting linkage, whereas the two approaches have comparable false-positive rates. The proposed method was applied to a lung cancer dataset of Genetic Epidemiology of Lung Cancer Consortium and an asthma dataset consisting of three samples from Genetic Analysis Workshop 12. Since both lung cancer and asthma are complex traits with heterogeneous genetic predisposition, they provide suitable applications for the proposed method.

摘要来源：OpenAlex 索引摘要（稿件版本未注明）