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Activity Number: 137 - Statistical Methods for Analyzing Genetic Variants and QTLs
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306873
Title: Pathway Association Analysis Under High Dimensions
Author(s): Yang Liu* and Qianchuan He
Companies: Wright State University and Fred Hutchinson Cancer Research Center
Keywords: High-dimensional inference; Genetic pathway analysis; Non-sparse signal; Power analysis

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. Conducting genetic pathway analysis and statistical inference in such settings is challenging. In this talk, we introduce an approach that can handle pathways whose dimension could be greater than the sample size. The approach can detect pathways that have non-sparse weak signals. The asymptotic distribution for the proposed statistic, and conduct theoretical analysis on its power are established. Simulation studies show that our test has correct type-I error control and is more powerful than existing approaches. 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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