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Activity Number: 302 - Statistical Methods for Data Integration
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: International Chinese Statistical Association
Abstract #309667
Title: Statistical Inference of Genetic Pathway Analysis Under High Dimensions
Author(s): Chad He* and Yang Liu and Wei Sun and Alexander P. Reiner and Charles Kooperberg
Companies: Fred Hutchinson Cancer Research Center and Wright State University and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
Keywords: genetic pathway analysis; high dimensions; statistical inferences

Genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits. With dense genotyping and extensive imputation, the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size n?. Conducting genetic pathway analysis and statistical inference in such settings is challenging. We introduce an approach that can handle pathways whose dimension p could be greater than n?. We establish the asymptotic distribution for the proposed statistic and conduct theoretical analysis on its power. Simulation studies show that our test has excellent performance under the considered situations. An application to a genome-wide association study of high-density lipoproteins demonstrates the proposed approach.

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

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