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Activity Number: 658 - Recent Statistical Advances in Genomic and Genetic Data Analysis
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #329896 Presentation
Title: A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics
Author(s): Yu-Ru Su* and Li Hsu and Chongzhi Di
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center, USA and Fred Hutchinson Cancer Research Center
Keywords: Aggregate test; Transcriptome-wide analysis; Mixed effects score test; Multi-omics data

Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants for many complex diseases; however, these variants explain only a small fraction of the heritability. PrediXcan, a recent development in transcriptome-wide association studies, has shown promises for discovering novel variants by leveraging functional information from external multi-omics data. However, it suffers from potential power loss as a consequence of testing only the association of imputed gene expression with the phenotype. To tackle these challenges, we consider a unified mixed effects model that formulates the association of imputed gene expression through fixed effects, while allowing residual effects of individual variants to be random. We consider a set-based score testing framework, MiST (Mixed effects Score Test), and propose two data-driven combination approaches to jointly test for the fixed and random effects. We establish the asymptotic distributions, which enable rapid calculation of p-values for genome-wide analyses. Extensive simulations the application to real data demonstrate that the proposed approaches are more powerful than existing ones.

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

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