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Nat Commun. 2019 Jul 24;10(1):3300. doi: 10.1038/s41467-019-10936-0.

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

Collaborators (129)

Agbessi M, Ahsan H, Alves I, Andiappan A, Arindrarto W, Awadalla P, Battle A, Beutner F, Jan Bonder M, Boomsma D, Christiansen M, Claringbould A, Deelen P, Esko T, Favé MJ, Franke L, Frayling T, Gharib SA, Gibson G, Heijmans BT, Hemani G, Jansen R, Kähönen M, Kalnapenkis A, Kasela S, Kettunen J, Kim Y, Kirsten H, Kovacs P, Krohn K, Kronberg-Guzman J, Kukushkina V, Lee B, Lehtimäki T, Loeffler M, Marigorta UM, Mei H, Milani L, Montgomery GW, Müller-Nurasyid M, Nauck M, Nivard M, Penninx B, Perola M, Pervjakova N, Pierce BL, Powell J, Prokisch H, Psaty BM, Raitakari OT, Ripatti S, Rotzschke O, Saha A, Scholz M, Schramm K, Seppälä I, Slagboom EP, Stehouwer CDA, Stumvoll M, Sullivan P, 't Hoen PAC, Teumer A, Thiery J, Tong L, Tönjes A, van Dongen J, van Iterson M, van Meurs J, Veldink JH, Verlouw J, Visscher PM, Völker U, Võsa U, Westra HJ, Wijmenga C, Yaghootkar H, Yang J, Zeng B, Zhang F, Arindrarto W, Beekman M, Boomsma DI, Bot J, Deelen J, Deelen P, Franke L, Heijmans BT, 't Hoen PAC, Hofman BA, Hottenga JJ, Isaacs A, Bonder MJ, Jhamai PM, Jansen R, Kielbasa SM, Lakenberg N, Luijk R, Mei H, Moed M, Nooren I, Pool R, Schalkwijk CG, Slagboom PE, Stehouwer CDA, Suchiman HED, Swertz MA, Tigchelaar EF, Uitterlinden AG, van den Berg LH, van der Breggen R, van der Kallen CJH, van Dijk F, van Dongen J, van Duijn CM, van Galen M, van Greevenbroek MMJ, van Heemst D, van Iterson M, van Meurs J, van Rooij J, Van't Hof P, van Zwet EW, Vermaat M, Veldink JH, Verbiest M, Verkerk M, Wijmenga C, Zhernakova DV, Zhernakova S.

Author information

1
Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. eleonora.porcu@unil.ch.
2
Swiss Institute of Bioinformatics, Lausanne, Switzerland. eleonora.porcu@unil.ch.
3
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
4
University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland.
5
Institute of Computer Science, University of Tartu, Tartu, Estonia.
6
Endocrine, Diabetes, and Metabolism Service, CHUV and University of Lausanne, Lausanne, Switzerland.
7
Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
8
Swiss Institute of Bioinformatics, Lausanne, Switzerland. zoltan.kutalik@unil.ch.
9
University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland. zoltan.kutalik@unil.ch.

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.

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