PuSH - Publikationsserver des Helmholtz Zentrums München: MR-link-2: Pleiotropy robust cis Mendelian randomization validated in three independent reference datasets of causality.

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van der Graaf, A.* ; Warmerdam, R.* ; Auwerx, C.* ; Võsa, U.* ; Borges, M.C.* ; Franke, L.* ; eQTLGen Consortium (Farzeen, A. ; Gieger, C. ; Peters, A.)

MR-link-2: Pleiotropy robust cis Mendelian randomization validated in three independent reference datasets of causality.

Nat. Commun. 16:6112 (2025)

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Mendelian randomization (MR) identifies causal relationships from observational data but has increased Type 1 error rates (T1E) when genetic instruments are limited to a single associated region, a typical scenario for molecular exposures. We developed MR-link-2, which leverages summary statistics and linkage disequilibrium (LD) to estimate causal effects and pleiotropy in a single region. We compare MR-link-2 to other cis MR methods: i) In simulations, MR-link-2 has calibrated T1E and high power. ii) We reidentify metabolic reactions from three metabolic pathway references using four independent metabolite quantitative trait locus studies. MR-link-2 often (76%) outperforms other methods in area under the receiver operator characteristic curve (AUC) (up to 0.80). iii) For canonical causal relationships between complex traits, MR-link-2 has lower per-locus T1E (0.096 vs. min. 0.142, at 5% level), identifying all but one of the true causal links, reducing cross-locus causal effect heterogeneity to almost half. iv) Testing causal direction between blood cell compositions and marker gene expression shows MR-link-2 has superior AUC (0.82 vs. 0.68). Finally, analyzing causality between metabolites not directly connected by canonical reactions, only MR-link-2 identifies the causal relationship between pyruvate and citrate ( α ̂ = 0.11, P = 7.2⋅10^-7), a key citric acid cycle reaction. Overall, MR-link-2 identifies pleiotropy-robust causality from summary statistics in single associated regions, making it well suited for applications to molecular phenotypes.

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