ECC-MR: an error-correcting-code framework for robust Mendelian randomization and pleiotropy decoding on correlated instruments
- 1Nanjing Medical University
Abstract
Mendelian randomization (MR) uses genetic variants as instrumental variables for causal inference, but its validity is often undermined by horizontal pleiotropy and linkage disequilibrium (LD) among instruments, which current methods typically address by discarding correlated or outlying variants. Here we present ECC-MR, an MR framework that brings ideas from error-correcting codes into causal inference: a parity-check matrix constructed from LD structure places the causal signal in its null space, so that pleiotropic effects become a decodable sparse error syndrome rather than a nuisance. We establish theoretical guarantees for consistency and stable recovery under sparse pleiotropy, and show in simulations that ECC-MR outperforms existing methods in bias, efficiency and Type I error control, while flagging regimes where its assumptions fail. Applied to three exposure-outcome pairs spanning weak to strong pleiotropic burden, ECC-MR yields concordant causal estimates while retaining all correlated instruments, and its decoded pleiotropic variants recover known hotspots and drug targets, corroborated by colocalisation and phenome-wide association analyses. ECC-MR thus turns LD and pleiotropy from liabilities into resources for robust causal inference and mechanistic discovery.
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BibTeX
@misc{jiang2026eccmr,
title = {{ECC-MR: an error-correcting-code framework for robust Mendelian randomization and pleiotropy decoding on correlated instruments}},
author = {J. Jiang and D. Hu and Q. Zhang and X. Li and S. Qin and Q. Ling and M. Tang and S. Sun and Y. Liu and X. Li and Q. Xu and G. Cao and L. Dong and Z. Lin},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.22.26363654},
url = {https://www.medrxiv.org/content/10.64898/2026.09.22.26363654v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - ECC-MR: an error-correcting-code framework for robust Mendelian randomization and pleiotropy decoding on correlated instruments
AU - J. Jiang
AU - D. Hu
AU - Q. Zhang
AU - X. Li
AU - S. Qin
AU - Q. Ling
AU - M. Tang
AU - S. Sun
AU - Y. Liu
AU - X. Li
AU - Q. Xu
AU - G. Cao
AU - L. Dong
AU - Z. Lin
PY - 2026
DA - 2026/09/22
PB - medRxiv
DO - 10.64898/2026.09.22.26363654
UR - https://www.medrxiv.org/content/10.64898/2026.09.22.26363654v1
AB - Mendelian randomization (MR) uses genetic variants as instrumental variables for causal inference, but its validity is often undermined by horizontal pleiotropy and linkage disequilibrium (LD) among instruments, which current methods typically address by discarding correlated or outlying variants. Here we present ECC-MR, an MR framework that brings ideas from error-correcting codes into causal inference: a parity-check matrix constructed from LD structure places the causal signal in its null space, so that pleiotropic effects become a decodable sparse error syndrome rather than a nuisance. We establish theoretical guarantees for consistency and stable recovery under sparse pleiotropy, and show in simulations that ECC-MR outperforms existing methods in bias, efficiency and Type I error control, while flagging regimes where its assumptions fail. Applied to three exposure-outcome pairs spanning weak to strong pleiotropic burden, ECC-MR yields concordant causal estimates while retaining all correlated instruments, and its decoded pleiotropic variants recover known hotspots and drug targets, corroborated by colocalisation and phenome-wide association analyses. ECC-MR thus turns LD and pleiotropy from liabilities into resources for robust causal inference and mechanistic discovery.
ER -