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<p>Tissue-specific gene expression is critical in complex disease etiology, but traditional transcriptome-wide Mendelian Randomization (TWMR) often overlooks tissue heterogeneity and is constrained by limited valid instrumental variables (IVs) per tissue. We propose Multi-Tissue TWMR (MT-TWMR), which selects cis-eQTLs with consistent effects across tissues as IVs and estimates tissue-specific causal effects via a penalized regression model combining L1 regularization for sparsity with a weighted tissue-difference penalty based on tissue similarity to enable cross-tissue information sharing. Simulations under varying IV numbers, pleiotropy levels, and tissue counts showed MT-TWMR consistently achieved higher power, lower root mean square error, and better type I error control than existing univariate and multivariable MR methods, particularly when IVs were scarce. Applied to major depressive disorder and primary hypertension, MT-TWMR identified 28 and 57 causal genes respectively, with enriched signals in biologically relevant tissues and strong colocalization and pathway evidence. MT-TWMR offers an effective and interpretable framework for integrating multi-tissue eQTL data to construct tissue-specific disease gene maps.</p>

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A multi-tissue mendelian randomization method based on eQTL data for mapping tissue-specific disease genes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Shuaiyi Wang,
  • Mengni Xu,
  • Yuxin Tang,
  • Kaixuan Wang,
  • Jinbao Wen,
  • Yu Cheng,
  • Fangrong Yan,
  • Tiantian Liu

摘要

Tissue-specific gene expression is critical in complex disease etiology, but traditional transcriptome-wide Mendelian Randomization (TWMR) often overlooks tissue heterogeneity and is constrained by limited valid instrumental variables (IVs) per tissue. We propose Multi-Tissue TWMR (MT-TWMR), which selects cis-eQTLs with consistent effects across tissues as IVs and estimates tissue-specific causal effects via a penalized regression model combining L1 regularization for sparsity with a weighted tissue-difference penalty based on tissue similarity to enable cross-tissue information sharing. Simulations under varying IV numbers, pleiotropy levels, and tissue counts showed MT-TWMR consistently achieved higher power, lower root mean square error, and better type I error control than existing univariate and multivariable MR methods, particularly when IVs were scarce. Applied to major depressive disorder and primary hypertension, MT-TWMR identified 28 and 57 causal genes respectively, with enriched signals in biologically relevant tissues and strong colocalization and pathway evidence. MT-TWMR offers an effective and interpretable framework for integrating multi-tissue eQTL data to construct tissue-specific disease gene maps.