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

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eQTLGen Consortium , BIOS Consortium , Porcu , E , Rüeger , S , Lepik , K & Perola , M 2019 , ' Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits ' , Nature Communications , vol. 10 , 3300 . https://doi.org/10.1038/s41467-019-10936-0

Title: Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits
Author: eQTLGen Consortium; BIOS Consortium; Porcu, Eleonora; Rüeger, Sina; Lepik, Kaido; Perola, Markus
Contributor organization: University Management
Department of Public Health
Centre of Excellence in Complex Disease Genetics
Samuli Olli Ripatti / Principal Investigator
Biostatistics Helsinki
Institute for Molecular Medicine Finland
Complex Disease Genetics
Date: 2019-07-24
Language: eng
Number of pages: 12
Belongs to series: Nature Communications
ISSN: 2041-1723
DOI: https://doi.org/10.1038/s41467-019-10936-0
URI: http://hdl.handle.net/10138/306048
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.
Subject: INSTRUMENTAL VARIABLES
VARIANTS
DISEASE
ASSOCIATION
MUTATION
STATISTICS
EXPRESSION
PLEIOTROPY
OBESITY
FAMILY
1184 Genetics, developmental biology, physiology
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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