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

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 ($\widehat{\alpha }$ = 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.

Mendelian randomization (MR) identifies causal relationships from observational data but has increased error rates when the genetic variants used as instruments come from a single region, a typical scenario when assessing molecular traits like protein or metabolite levels as risk factors. Here the authors introduce a single-region pleiotropy-robust MR method, validating the method on three ground truth sources, showing its capability to identify disease-causing molecular traits.