A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data

TL;DR

This paper introduces a new focusing framework for testing bi-directional causal effects between traits using GWAS summary data, addressing challenges posed by pleiotropy and assumptions violations in Mendelian randomization.

Contribution

The paper proposes a novel focusing framework that enhances bi-directional causal inference in MR, compatible with existing methods and robust to pleiotropy.

Findings

Framework maintains control of Type I error

Demonstrates robustness through simulations

Effective on real GWAS datasets

Abstract

Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Although recent years have seen many extensions of basic MR methods to be robust to certain violations of assumptions, few methods were proposed to infer bi-directional causal relationships, especially for phenotypes with limited biological understandings. The presence of horizontal pleiotropy adds another layer of complexity. In this article, we show that assumptions for common MR methods are often impossible or too stringent in the existence of bi-directional relationships. We then propose a new focusing framework for testing bi-directional causal effects between two traits with possibly pleiotropic genetic variants. Our proposal can be coupled with many state-of-art MR methods. We provide theoretical guarantees on the Type I error and power of the proposed methods. We demonstrate the robustness of the proposed methods using several simulated and real datasets.