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Porcu, E.* ; Rüeger, S.* ; Lepik, K.* ; eQTLGen Consortium (Müller-Nurasyid, M. ; Strauch, K. ; Prokisch, H.) ; Santoni, F.A.* ; Reymond, A.* ; Kutalik, Z.*

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

Nat. Commun. 10:3300 (2019)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Instrumental Variables; Variants; Disease; Association; Mutation; Statistics; Expression; Pleiotropy; Obesity; Family
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 10, Heft: 1, Seiten: , Artikelnummer: 3300 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Genetic Epidemiology (IGE)
Institute of Human Genetics (IHG)