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

Eleonora Porcu*, Sina Rueger, Kaido Lepik, Mawusse Agbessi, Habibul Ahsan, Isabel Alves, Anand Andiappan, Wibowo Arindrarto, Philip Awadalla, Alexis Battle, Frank Beutner, Marc Jan Bonder, Dorret Boomsma, Mark Christiansen, Annique Claringbould, Patrick Deelen, Tonu Esko, Marie-Julie Fave, Lude Franke, Timothy FraylingSina A. Gharib, Gregory Gibson, Bastiaan T. Heijmans, Gibran Hemani, Rick Jansen, Mika Kahonen, Anette Kalnapenkis, Silva Kasela, Johannes Kettunen, Yungil Kim, Holger Kirsten, Peter Kovacs, Knut Krohn, Jaanika Kronberg-Guzman, Viktorija Kukushkina, Bernett Lee, Terho Lehtimaki, Markus Loeffler, Urko M. Marigorta, Hailang Mei, Lili Milani, Grant W. Montgomery, Martina Mueler-Nurasyid, Matthias Nauck, Coen D. A. Stehouwer, Aaron Isaacs, Casper G. Schalkwijk, Carla J. H. van der Kallen, Marleen M. J. van Greevenbroek, eQTLgen Consortium, BIOS Consortium, Zoltan Kutalik*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Article number3300
Number of pages12
JournalNature Communications
Publication statusPublished - 24 Jul 2019




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