Abstract Number:
3166
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Ni Kang (1), Anny Xiang (2), Max Aung (1), David Conti (1), Juan Pablo Lewinger (1), Zhanghua Chen (1)
Institutions:
(1) Department of Population and Public Health Science, University of Southern California, Los Angeles, CA, USA, (2) Kaiser Permanente Southern California, Pasadena, CA, USA
Co-Author(s):
Max Aung
Department of Population and Public Health Science, University of Southern California
David Conti
Department of Population and Public Health Science, University of Southern California
Juan Pablo Lewinger
Department of Population and Public Health Science, University of Southern California
Zhanghua Chen
Department of Population and Public Health Science, University of Southern California
First Author:
Ni Kang
Department of Population and Public Health Science, University of Southern California
Presenting Author:
Abstract Text:
Pathway mediation analysis is widely used to identify biological mechanisms linking environmental exposures with disease outcomes. Previous methods such as HIMA1 and HIMA2 use penalized regression to identify individual omics biomarker as potential mediators among high-dimensional omics data. However, these methods overlook correlated omics biomarkers within pathway and shared omics biomarkers across multiple pathways, failing to identify key pathways.
We proposed a novel method using overlapped group lasso and principal component analysis to assess association of pathways with exposures and outcomes. Joint significance test was applied to identify mediators among overlapped and correlated pathways. Simulations were done based on correlation structure from a real study. Our method demonstrated power of 0.86, and 5-minutes computation time which showed higher power and less computation time than using HIMA1 or HIMA2 for 1000 simulations in detecting three mediation pathways under high-dimensional setting.
Our method offered higher power, and more efficient computing time in detecting mediation pathways than the existing method for both high and low dimensional data.
Keywords:
Mediation Analysis|high dimensional omics data|pathway analysis| | |
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Statistical Issues in Environmental Epidemiology
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