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Activity Number: 217 - Studying Psychiatric Disorders Using Statistical and Machine Learning Methods
Type: Invited
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #326651
Title: Statistical Methods for Integrative Analysis of Multi-Omics Data with Applications to Psychiatric Disorders
Author(s): Hongyuan Cao* and Jun Chen and Xianyang Zhang
Companies: University of Missouri-Columbia and Mayo Clinic and Texas A&M University
Keywords: Multiple testing; Auxiliary information

Genome-wise complex trait analysis (GCTA) was developed and applied to heritability analyses on complex traits and more recently extended to mental disorders. However, besides the intensive computation, previous literature also limits the scope to univariate phenotype, which ignores mutually informative but partially independent pieces of information provided in other phenotypes. Our goal is to use such auxiliary information to improve power. We show that the proposed method leads to a large power increase, while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method over several state-of-the-art methods. We illustration our methods on dataset from a schizophrenia study.

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

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