JSM 2005 - Toronto

Abstract #302676

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 84
Type: Invited
Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #302676
Title: Genetic Association Studies Using False Discovery Control with P-value Weighting
Author(s): Kathryn Roeder*+
Companies: Carnegie Mellon University
Address: 5000 Forbes Avenue, Pittsburgh, PA, 15213-3890,
Keywords: genetics ; FDR ; multiple testing ; association
Abstract:

In genetic epidemiology, tens of thousands of genomic regions may be tested in a genetic association study to locate alleles that increase the risk for complex diseases. Testing such a large number of hypotheses exacerbates the tradeoff between power and Type I error control, making it more difficult to detect small but important signals in the data. Multiple testing problems of this nature are well suited for application of the false discovery rate (FDR) principle, which can improve power somewhat. To further enhance the power, we consider a new approach that involves weighting the hypotheses based on prior knowledge. We conjecture that typically some of the hypotheses under investigation can be considered more likely to be non-null than others, based on partial knowledge of genetic function available from the human genome project. Similarly, linkage studies can provide useful guidance for choosing weights in an association study. We present a method for multiple hypothesis testing that maintains control of FDR while incorporating prior information about the hypotheses.

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