Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score

TL;DR

This paper develops robust statistical methods for two-sample summary-data Mendelian randomization, addressing pleiotropy issues to improve causal inference accuracy in genetic epidemiology.

Contribution

It introduces a robust adjusted profile score estimator that accounts for systematic and idiosyncratic pleiotropy, enhancing inference in MR studies.

Findings

The proposed methods are robust to pleiotropy in real datasets.

The estimators are consistent and asymptotically normal.

Simulation studies demonstrate improved accuracy over existing methods.

Abstract

Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in epidemiology and other related areas of population science. In this paper, we study statistical inference in the increasingly popular two-sample summary-data MR design. We show a linear model for the observed associations approximately holds in a wide variety of settings when all the genetic variants satisfy the exclusion restriction assumption, or in genetic terms, when there is no pleiotropy. In this scenario, we derive a maximum profile likelihood estimator with provable consistency and asymptotic normality. However, through analyzing real datasets, we find strong evidence of both systematic and idiosyncratic pleiotropy in MR, echoing the omnigenic model of complex traits that is recently proposed in genetics. We model the systematic pleiotropy by a random effects model, where no genetic variant satisfies the exclusion restriction condition exactly. In this case we propose a consistent and asymptotically normal estimator by adjusting the profile score. We then tackle the idiosyncratic pleiotropy by robustifying the adjusted profile score. We demonstrate the robustness and efficiency of the proposed methods using several simulated and real datasets.