Lundby, Alicia3; Rossin, Elizabeth J.4; Steffensen, Annette B.3; Acha, Moshe Ray4; Newton-Cheh, Christopher4; Pfeufer, Arne5; Lyneh, Stacey N.4; Olesen, Søren-Peter3; Brunak, Søren6; Ellinor, Patrick T.4; Jukema, J. Wouter7; Trompet, Stella7; Ford, Ian13; Macfarlane, Peter W.13; Krijthe, Bouwe P.9; Hofman, Albert9; Uitterlinden, Andre G.9; Stricker, Bruno H.9; Nathoe, Hendrik M.10; Spiering, Wilko10; Daly, Mark J.4; Asselbergs, Folkert W.11; van der Harst, Pim14; Milan, David J.4; de Bakker, Paul I. W.10; Lage, Kasper3; Olsen, Jesper V.3
1 Department of Systems Biology, Technical University of Denmark2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark3 University of Copenhagen4 Massachusetts General Hospital5 Technical University of Munich6 Department of Bio and Health Informatics, Technical University of Denmark7 Leiden University8 University of Glasgow9 Erasmus Medical Center10 University Medical Centre Utrecht11 University College London12 University of Groningen13 University of Glasgow14 University of Groningen
Genome-wide association studies (GWAS) have identified thousands of loci associated with complex traits, but it is challenging to pinpoint causal genes in these loci and to exploit subtle association signals. We used tissue-specific quantitative interaction proteomics to map a network of five genes involved in the Mendelian disorder long QT syndrome (LOTS). We integrated the LOTS network with GWAS loci from the corresponding common complex trait, QT-interval variation, to identify candidate genes that were subsequently confirmed in Xenopus laevis oocytes and zebrafish. We used the LOTS protein network to filter weak GWAS signals by identifying single-nucleotide polymorphisms (SNPs) in proximity to genes in the network supported by strong proteomic evidence. Three SNPs passing this filter reached genome-wide significance after replication genotyping. Overall, we present a general strategy to propose candidates in GWAS loci for functional studies and to systematically filter subtle association signals using tissue-specific quantitative interaction proteomics.