JSM 2005 - Toronto

Abstract #302559

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 4
Type: Invited
Date/Time: Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #302559
Title: Sequential Multiple Decision Procedures (SMDP) to Screen and Validate Signals in Genome-wide Association Scans
Author(s): Michael A. Province*+ and Qunyuan Zhang
Companies: Washington University School of Medicine and Washington University School of Medicine
Address: Box 8067 Division of Biostatistics, St. Louis, MO, 63110,
Keywords: sequential ; genome-wide ; gene by environment interaction ; gene discovery
Abstract:

SMDP theory (Bechhofer, Kiefer, and Sobel 1968) provides a single, sequential, statistical test for gene discovery (Province 2000). Traditional fixed-sample statistics must trade power for the inflated experiment-wise type I error arising from multiple testing. Since the 100K chip is in use and the 500K chip is coming, the problem is already acute. It grows exponentially with higher order interactions between genes and environmental exposures, eroding power severely. Sequential tests allow tight control of both type I and type II errors without trading one for the other because sample size varies. Sequential tests are known to be extremely efficient, requiring smaller N on average than fixed sample ones. Further, SMDP avoids multiple testing entirely. It is a single screening test to identify the entire set of signals simultaneously, instead of individual tests for each effect. It automatically defines an optimal way to split a fixed sample of chips between a hypothesis-generating training set and a validation set. The method is demonstrated via simulation, and in a pharmacogenetic experiment searching for cheomtoxicity genes in CEPH cell lines using HapMap genotypes.

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Revised March 2005