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Genet Epidemiol. 2011 Nov;35(7):638-49. doi: 10.1002/gepi.20613. Epub 2011 Aug 26.

Incorporating model uncertainty in detecting rare variants: the Bayesian risk index.

Author information

  • 1Department of Preventive Medicine, Division of Biostatistics, University of Southern California, Los Angeles, California, USA. wilsonme@usc.edu

Abstract

We are interested in investigating the involvement of multiple rare variants within a given region by conducting analyses of individual regions with two goals: (1) to determine if regional rare variation in aggregate is associated with risk; and (2) conditional upon the region being associated, to identify specific genetic variants within the region that are driving the association. In particular, we seek a formal integrated analysis that achieves both of our goals. For rare variants with low minor allele frequencies, there is very little power to statistically test the null hypothesis of equal allele or genotype counts for each variant. Thus, genetic association studies are often limited to detecting association within a subset of the common genetic markers. However, it is very likely that associations exist for the rare variants that may not be captured by the set of common markers. Our framework aims at constructing a risk index based on multiple rare variants within a region. Our analytical strategy is novel in that we use a Bayesian approach to incorporate model uncertainty in the selection of variants to include in the index as well as the direction of the associated effects. Additionally, the approach allows for inference at both the group and variant-specific levels. Using a set of simulations, we show that our methodology has added power over other popular rare variant methods to detect global associations. In addition, we apply the approach to sequence data from the WECARE Study of second primary breast cancers.

© 2011 Wiley Periodicals, Inc.

PMID:
22009789
[PubMed - indexed for MEDLINE]
PMCID:
PMC3936341
Free PMC Article

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