Abstract Number:
1877
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Qiuran Lyu (1), Chong Wu (2), Jingshen Wang (3), Xinwei Ma (4)
Institutions:
(1) N/A, N/A, (2) The University of Texas MD Anderson Cancer Center, N/A, (3) UC Berkeley, N/A, (4) University of California San Diego, N/A
Co-Author(s):
Chong Wu
The University of Texas MD Anderson Cancer Center
First Author:
Presenting Author:
Abstract Text:
Mediation analysis is a powerful tool for studying causal pathways between exposure, mediator, and outcome variables of interest. While classical mediation analysis using observational data often requires strong and sometimes unrealistic assumptions, such as unconfoundedness, Mendelian Randomization (MR) avoids unmeasured confounding bias by employing genetic variants as instrumental variables. We develop a novel MR framework for mediation analysis with
genome-wide associate study (GWAS) summary data, and provide solid statistical guarantees. Our framework efficiently integrates information stored in three independent GWAS summary data and mitigates not only the commonly encountered winner's curse and measurement error bias in MR, but also the loser's
curse and the imperfect IV selection issue, which are tailored to mediation analysis. Our method is also immune to measurement error bias as the estimating equations are carefully adjusted by incorporating estimated conditional variances of the Rao-Blackwellized association effects. Through our theoretical investigations, we show that the proposed method delivers consistent and asymptotically normally distributed effect estimates.
Keywords:
Inverse Variance Weighting|Post-selection Inference|Instrumental Variable|Causal Mediation Analysis|Multivariable Mendelian Randomization|
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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