Accounting for disease model uncertainty in mapping heterogeneous traits--a Bayesian model averaging approach.

ABSTRACT:

Background

Locus heterogeneity, wherein a disease can be caused in different individuals by different genes and/or environmental factors, is a ubiquitous feature of complex traits. A Bayesian approach has been proposed to account for variable rates of heterogeneity across families in a parametric linkage analysis setup [Biswas and Lin: J Am Stat Assoc 2006;101:1341-1351]. As with any parametric approach, its application requires specification of the disease model, which limits its practical utility.

Methods

We address this limitation by proposing a Bayesian model averaging (BMA) approach. We consider a finite number of disease models and treat the model as an unknown parameter. In practice, we use simple single-locus disease models as various categories for model.

Results

SUBMITTER: Biswas S 

PROVIDER: S-EPMC2889260 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature