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

Eleonora Porcu (), Sina Rüeger, Kaido Lepik, Federico A. Santoni, Alexandre Reymond and Zoltán Kutalik ()
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Eleonora Porcu: Center for Integrative Genomics, University of Lausanne
Sina Rüeger: Swiss Institute of Bioinformatics
Kaido Lepik: University Center for Primary Care and Public Health, University of Lausanne, Switzerland
Federico A. Santoni: Endocrine, Diabetes, and Metabolism Service, CHUV and University of Lausanne
Alexandre Reymond: Center for Integrative Genomics, University of Lausanne
Zoltán Kutalik: Swiss Institute of Bioinformatics

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: Abstract 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.

Date: 2019
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DOI: 10.1038/s41467-019-10936-0

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