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

Nature Communications, vol. 16, pp. 6112

Abstract

Abstract 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.

Authors 9

1. Adriaan van der Graaf

   SIB Swiss Institute of Bioinformatics · University of Lausanne

   Affiliation as printed

   Department of Computational Biology, University of Lausanne, Lausanne, Switzerland

   Swiss Institute of Bioinformatics, Lausanne, Switzerland

2. Robert Warmerdam

   University of Groningen · Oncode Institute

   Affiliation as printed

   Department of Genetics, University of Groningen, Groningen, The Netherlands

   Oncode institute, Utrecht, The Netherlands

3. Chiara Auwerx

   SIB Swiss Institute of Bioinformatics · University of Lausanne

   Affiliation as printed

   Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland

   Department of Computational Biology, University of Lausanne, Lausanne, Switzerland

   Swiss Institute of Bioinformatics, Lausanne, Switzerland

4. Emma E. Davenport

   Wellcome Sanger Institute

   Affiliation as printed

   Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

5. Marleen M. J. van Greevenbroek

   Maastricht University

   Affiliation as printed

   CARIM, Maastricht University, Maastricht, The Netherlands

6. Mika A. P. Kähönen

   Tampere University · Tampere University Hospital · University of Tampere

   Affiliation as printed

   Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland

   Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

7. Terho J. Lehtimäki

   Tampere University · Fimlab (Finland) · University of Tampere

   Affiliation as printed

   Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

   Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland

   Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

8. Ángel Martínez-Pérez

   Institut de Recerca Sant Pau

   Affiliation as printed

   Unit of Genomic of Complex Diseases, Institut de Recerca Sant Pau (IR Sant Pau), Barcelona, Spain

9. Peter M. Visscher

   The University of Queensland · Institute for Molecular Bioscience

   Affiliation as printed

   Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia