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

Adriaan Graaf, Robert Warmerdam, Chiara Auwerx, Urmo Võsa, Maria Carolina Borges, Lude Franke and Zoltán Kutalik ()
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Adriaan Graaf: University of Lausanne
Robert Warmerdam: University of Groningen
Chiara Auwerx: University of Lausanne
Urmo Võsa: University of Tartu
Maria Carolina Borges: University of Bristol
Lude Franke: University of Groningen
Zoltán Kutalik: University of Lausanne

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract 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 ( $$\hat{\alpha }$$ 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.

Date: 2025
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DOI: 10.1038/s41467-025-60868-1

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