Mediation Analysis with Mendelian Randomization and Efficient Multiple GWAS Integration
arxiv(2023)
摘要
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 variations 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 employs carefully crafted estimating
equations, allowing for different sets of genetic variations to instrument the
exposure and the mediator, to efficiently integrate information stored in three
independent GWAS. As part of this endeavor, we demonstrate that in mediation
analysis, the challenge raised by instrument selection goes beyond the
well-known winner's curse issue, and therefore, addressing it requires special
treatment. We then develop bias correction techniques to address the instrument
selection issue and commonly encountered measurement error bias issue.
Collectively, through our theoretical investigations, we show that our
framework provides valid statistical inference for both direct and mediation
effects with enhanced statistical efficiency compared to existing methods. We
further illustrate the finite-sample performance of our approach through
simulation experiments and a case study.
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