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Activity Number: 484 - Methods for High-Dimensional Data in Genetics and Genomics
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #311052
Title: Covariate Adaptive Family-Wise Error Rate Control for Genome-Wide Association Studies
Author(s): Huijuan Zhou* and Xianyang Zhang and Jun Chen
Companies: Texas A&M University and Texas A&M University and Mayo Clinic
Keywords: EM algorithm; External covariates; Family-wise error rate; Multiple testing

Motivated by various applications in genomics studies where there are rich covariates that are informative of either the statistical power or the prior null probability, we propose a covariate adaptive family-wise error rate control procedure. We develop an efficient algorithm to implement the proposed method. We prove its asymptotic validity and obtain the convergence rate through a novel perturbation type argument. The numerical study shows that our procedure is more powerful than competing methods and maintains robustness across different settings. Finally, we apply the method to genome-wide association studies and demonstrate that our approach is capable of dealing with the big dataset and more competent to discover signals compared with existing methods.

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

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