Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Eleonora Porcu*, Zoltán Kutalik, eQTLGen Consortium, BIOS Consortium, D.I. Boomsma, Michel G. Nivard, Jouke Jan Hottenga, René Pool, Jenny van Dongen

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene–trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2, known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.

Original languageEnglish
Article number3300
JournalNature Communications
Issue number1
Publication statusPublished - 24 Jul 2019


This work was supported by grants from the Swiss National Science Foundation (31003A_143914 and 32003B_173092 to ZK and 31003A_160203 to AR) and the Horizon2020 Twinning project ePerMed (692145 to AR). This research has been conducted using the UK Biobank Resource (#16389). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 6Computational Biology, Ontario Institute for Cancer Research, Toronto, ON, Canada. 7Department of Public Health Sciences, University of Chicago, Chicago, IL, USA. 8Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore. 9Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. 10Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. 11Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. 12Heart Center Leipzig, Universität Leipzig, Leipzig, Germany. 13Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands. 14European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. 15Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany. 16Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 17Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA. 18Genomics Coordination Center, University Medical Centre Groningen, Groningen, The Netherlands. 19Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu 51010, Estonia. 20Exeter Medical School, University of Exeter, Exeter, EX2 5DW, UK. 21Department of Medicine, University of Washington, Seattle, WA, USA. 22School of Biological Sciences, Georgia Tech, Atlanta, GA, USA. 23MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

FundersFunder number
Horizon 2020 Framework Programme692145
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung31003A_160203, 32003B_173092, 31003A_143914

    Cohort Studies

    • Netherlands Twin Register (NTR)


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