Journal
HUMAN HEREDITY
Volume 74, Issue 3-4, Pages 184-195Publisher
KARGER
DOI: 10.1159/000346021
Keywords
Genetic association studies; Bayesian model uncertainty; Bayes factors; Sequence analysis; Rare variant analysis
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Funding
- National Institute of Health [R01 ES016813, U01 DA020830, R21HL115606, R01CA14561]
- WECARE Study Collaborative Group [R01 CA097397, U01 CA083178]
- Kentucky Colorectal Cancer Study [U54 CA116867, K22 CA120545]
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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 inference at the pathway, region and variant levels. Conclusion: To demonstrate the flexibility of the method to incorporate various types of information and the applicability to high-dimensional data, we apply our method to a single region within a candidate gene study of second primary breast cancer and to multiple regions within a candidate pathway study of colon cancer. Copyright (C) 2013 S. Karger AG, Basel
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