Leveraging pleiotropic clustering to address high proportion correlated horizontal pleiotropy in Mendelian randomization studies

Bin Tang, Nan Lin, Junhao Liang, Guorong Yi, Liubin Zhang, Wenjie Peng, Chao Xue, Hui Jiang and Miaoxin Li ()
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Bin Tang: Sun Yat-Sen University
Nan Lin: Sun Yat-Sen University
Junhao Liang: Sun Yat-Sen University
Guorong Yi: Sun Yat-Sen University
Liubin Zhang: Sun Yat-Sen University
Wenjie Peng: Sun Yat-Sen University
Chao Xue: Sun Yat-Sen University
Hui Jiang: Sun Yat-Sen University
Miaoxin Li: Sun Yat-Sen University

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Mendelian randomization harnesses genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. However, certain genetic variants can affect both the exposure and the outcome through a shared factor. This phenomenon, called correlated horizontal pleiotropy, may result in false-positive causal findings. Here, we propose a Pleiotropic Clustering framework for Mendelian randomization, PCMR. PCMR detects correlated horizontal pleiotropy and extends the zero modal pleiotropy assumption to enhance causal inference in trait pairs with correlated horizontal pleiotropic variants. Simulations show that PCMR can effectively detect correlated horizontal pleiotropy and avoid false positives in the presence of correlated horizontal pleiotropic variants, even when they constitute a high proportion of the variants connecting both traits (e.g., 30–40%). In datasets consisting of 48 exposure-common disease pairs, PCMR detects horizontal correlated pleiotropy in 7 out of the exposure-common disease pairs, and avoids detecting false positive causal links. Additionally, PCMR can facilitate the integration of biological information to exclude correlated horizontal pleiotropic variants, enhancing causal inference. We apply PCMR to study causal relationships between three common psychiatric disorders as examples.

Date: 2025
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DOI: 10.1038/s41467-025-57912-5

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