A1 Refereed original research article in a scientific journal
Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits
Authors: Eleonora Porcu, Sina Rüeger, Kaido Lepik; eQTLGen Consortium; BIOS Consortium; Federico A. Santoni, Alexandre Reymond, Zoltán Kutalik
Publisher: NATURE PUBLISHING GROUP
Publication year: 2019
Journal: Nature Communications
Journal name in source: NATURE COMMUNICATIONS
Journal acronym: NAT COMMUN
Article number: ARTN 3300
Volume: 10
Number of pages: 12
ISSN: 2041-1723
eISSN: 2041-1723
DOI: https://doi.org/10.1038/s41467-019-10936-0
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/42212312
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.
Downloadable publication This is an electronic reprint of the original article. |  |
