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

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  • Eleonora Porcu

    (Center for Integrative Genomics, University of Lausanne
    Swiss Institute of Bioinformatics)

  • Sina Rüeger

    (Swiss Institute of Bioinformatics
    University Center for Primary Care and Public Health, University of Lausanne, Switzerland)

  • Kaido Lepik

    (University Center for Primary Care and Public Health, University of Lausanne, Switzerland
    Institute of Computer Science, University of Tartu)

  • 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
    University Center for Primary Care and Public Health, University of Lausanne, Switzerland)

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.

Suggested Citation

  • Eleonora Porcu & Sina Rüeger & Kaido Lepik & Federico A. Santoni & Alexandre Reymond & Zoltán Kutalik, 2019. "Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10936-0
    DOI: 10.1038/s41467-019-10936-0
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    Cited by:

    1. Marie C. Sadler & Chiara Auwerx & Kaido Lepik & Eleonora Porcu & Zoltán Kutalik, 2022. "Quantifying the role of transcript levels in mediating DNA methylation effects on complex traits and diseases," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Richard Howey & So-Youn Shin & Caroline Relton & George Davey Smith & Heather J Cordell, 2020. "Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data," PLOS Genetics, Public Library of Science, vol. 16(3), pages 1-35, March.

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