Journal article

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

  • Porcu E Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. eleonora.porcu@unil.ch.
  • Rüeger S Swiss Institute of Bioinformatics, Lausanne, Switzerland.
  • Lepik K University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland.
  • Santoni FA Swiss Institute of Bioinformatics, Lausanne, Switzerland. zoltan.kutalik@unil.ch.
  • Reymond A
  • Kutalik Z
Show more…
  • 2019-07-26
Published in:
  • Nature communications. - 2019
English 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.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://folia.unifr.ch/global/documents/115562
Statistics

Document views: 18 File downloads:
  • fulltext.pdf: 0