JSM 2004 - Toronto

Abstract #300122

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Activity Number: 8
Type: Invited
Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #300122
Title: A Nonparametric Confidence Set Inference Procedure for Gene-mapping
Author(s): Shili Lin*+
Companies: Ohio State University
Address: Department of Statistics, Columbus, OH, 43210-1247,
Keywords: coverage probability ; relative risks ; disease gene localization ; linkage analysis ; multiple tests
Abstract:

In genome scan studies, tests are often performed to search for signals of linkage throughout the genome for hundreds or even thousands of genetic markers. This practice has raised several important statistical questions, including multiplicity adjustment, confidence inference, and asymptotic properties. I will present an alternative approach for gene mapping based on Confidence Set Inference (CSI). This procedure constructs a confidence set for the location of a disease locus directly. The confidence set is constructed in such a way that multiplicity adjustment is unnecessary, no matter how many markers are tested. Furthermore, our formulation enables us to localize the disease gene to a small genomic region, an attractive feature for fine mapping. Simulation studies are carried out to demonstrate the advantages of CSI and to evaluate its performance for several nonparametric test statistics. The effects of violations of two key assumptions are also evaluated. An application of CSI to a dataset made available by the Genetic Analysis Workshop 13 confirms its practical utility, as the results compare favorably to those obtained from a standard nonparametric approach.

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