Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits.
Fiche du document
24 juillet 2019
- doi: 10.1038/s41467-019-10936-0
- issn: 2041-1723
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-019-10936-0
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/31341166
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/2041-1723
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_5BC4DEF5ED276
info:eu-repo/semantics/openAccess , CC BY 4.0 , https://creativecommons.org/licenses/by/4.0/
Sujets proches
Group dynamics Association Groups, Social Genes--ExpressionCiter ce document
E. Porcu et al., « Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. », Serveur académique Lausannois, ID : 10.1038/s41467-019-10936-0
Métriques
Partage / Export
Résumé
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.