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Activity Number: 699
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #318556 View Presentation
Title: False Discovery Rate Control for Identifying Simultaneous Signals
Author(s): Sihai Zhao*
Companies: University of Illinois at Urbana-Champaign
Keywords: Copula ; Integrative genomics ; Multiple testing ; Pleiotropy ; Simultaneous signal analysis

It is frequently of interest to jointly analyze multiple sequences of multiple tests in order to identify simultaneous signals, defined as features tested in two or more independent studies that are significant in each. This paper proposes a false discovery rate control procedure for the two-study setting. Error control is difficult due to the composite nature of a non-discovery, as one of the tests in the pair can still be non-null. This paper proposes a simple, fast, tuning parameter-free nonparametric procedure that can be shown to provide asymptotically conservative false discovery rate control. Surprisingly, the procedure does not require knowledge of either the null or the alternative distributions of the test statistics. In simulations, the proposed method had higher power and better error control than existing procedures. In an analysis of genome-wide association study results from five psychiatric disorders, it identified more simultaneously significant genetic variants compared to other methods. The proposed method is available in the R package ssa.

Authors who are presenting talks have a * after their name.

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