Incorporating prior biologic information for high-dimensional rare variant association studies.

ABSTRACT:

Background

Given the increasing scale of rare variant association studies, we introduce a method for high-dimensional studies that integrates multiple sources of data as well as allows for multiple region-specific risk indices.

Methods

Our method builds upon the previous Bayesian risk index by integrating external biological variant-specific covariates to help guide the selection of associated variants and regions. Our extension also incorporates a second level of uncertainty as to which regions are associated with the outcome of interest.

Results

Using a set of study-based simulations, we show that our approach leads to an increase in power to detect true associations in comparison to several commonly used alternatives. Additionally, the method provides multi-level i

SUBMITTER: Quintana MA 

PROVIDER: S-EPMC4058572 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature