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

Hum Hered. 2012;74(3-4):184-95. doi: 10.1159/000346021. Epub 2013 Apr 11.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • BRCA1 Protein / genetics*
  • Bayes Theorem
  • Breast Neoplasms / genetics*
  • Colonic Neoplasms / genetics*
  • Computer Simulation
  • DNA Repair / genetics
  • Female
  • Genetic Association Studies*
  • Genetic Predisposition to Disease*
  • Genetic Variation*
  • Humans
  • Models, Genetic
  • Models, Statistical*


  • BRCA1 Protein
  • BRCA1 protein, human