| Autor: |
Quintana, Melanie A., Schumacher, Fredrick R., Casey, Graham, Bernstein, Jonine L., Li, Li, Conti, David V. |
| Zdroj: |
Human Heredity; Apr2013, Vol. 74 Issue 3/4, p184-195, 12p, 2 Charts, 4 Graphs |
| Abstrakt: |
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 © 2013 S. Karger AG, Basel [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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