MR-link-2: pleiotropy robust cis Mendelian randomization validated in four independent gold-standard datasets of causality

Mendelian randomization (MR) can identify 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. To address this, we developed MR-link-2, which uses summary statistics and linkage disequilibrium (LD) information to simultaneously estimate a causal effect and pleiotropy in a single associated region. We extensively compare MR-link-2 to other cis MR methods: i) In realistic simulations, MR-link-2 has calibrated T1E and high power. ii) We replicate causal relationships derived from three metabolic pathway references using four independent metabolite quantitative trait locus studies as input to MR. Compared to other methods, MR-link-2 has a superior area under the receiver operator characteristic curve (AUC) (up to 0.80). iii) Applied to canonical causal relationships between complex traits, MR-link-2 has a lower per-locus T1E rate than competing methods (0.09 vs 0.15, at a nominal 5% level) and has several fold less heterogeneous causal effect estimates. iv) Testing the correct causal direction between blood cell type compositions and gene expression of their marker genes reveals that MR-link has superior AUC 0.90 (best competing: 0.67). Finally, when testing for causality between metabolites that are not connected by canonical reactions, MR-link-2 exclusively identifies a link between glycine and pyrroline-5-carboxylate, corroborating results for hypomyelinating leukodystrophy-10, otherwise only found in model systems. Overall, MR-link-2 is the first method to identify pleiotropy-robust causality from summary statistics in single associated regions, making it ideally suited for applications on molecular phenotypes. ### Competing Interest Statement The main authors of this study do not declare a competing interest. The authors of the eQTLGen consortium declare the following competing interests: B.M.P. serves on the Steering Committee for the Yale Open Data Access Project funded by Johnson & Johnson. This activity is unrelated to this work. M.I. is a trustee of the Public Health Genomics (PHG) Foundation, a member of the Scientific Advisory Board of Open Targets, and has a research collaboration with AstraZeneca that is unrelated to this study. D.S.P. is an employee and stockholder of AstraZeneca. The other authors of the eQTLGen consortium do not declare competing interests. ### Funding Statement This study was funded by the Swiss National Science Foundation (310030_189147, Zoltan Kutalik) and the Department of Computational Biology of the University of Lausanne (Zoltan Kutalik) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All data used in this study is publicly available except for newly generated data from the 2 cohorts in the eQTLgen consortium. The details for the previously available eQTLGen cohorts used in the preliminary meta-analysis freeze are detailed in the Vosa & Claringbould et al., 2021 publication, and corresponding original publications. Below we provide the details of additional INTERVAL cohort which was not part of Vosa & Claringbould et al., 2021. The eQTLGen phase II research activities involving Estonian Biobank participant data (two EstBB cohorts) have been carried out under the ethical approval nr. 1.1-12/655 and its extension 1.1-12/490 by the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs), using data according to release application number S54 from the Estonian Biobank. The INTERVAL study is a prospective cohort study of approximately 50,000 participants nested within a randomized trial of varying blood donation intervals. All participants gave informed consent before joining the study and the United Kingdom National Research Ethics Service approved this study (11/EE/0538). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data and materials availability: The code for simulations and working examples for MR- link-2 and the other cis- causal inference methods are available at: https://github.com/adriaan-vd-graaf/mrlink2. The summary statistics for the metabolite analysis, the complex trait analysis and the blood cell type composition phenotypes are available from the respective source publications (Data S15). The data availability of each of 15 the eQTLGen Consortium cohorts is listed in the (Supplementary Text). The genotype information underlying the LD matrices for the UK10K data resource were downloaded from (EGAD00001000740, EGAD00001000741).