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 (LQTS). We integrated the LQTS 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 LQTS 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.
Subscribe to Journal
Get full journal access for 1 year
only $20.17 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Morita, H., Wu, J. & Zipes, D.P. The QT syndromes: long and short. Lancet 372, 750–763 (2008).
Newton-Cheh, C. et al. Common variants at ten loci influence QT interval duration in the QTGEN Study. Nat. Genet. 41, 399–406 (2009).
Pfeufer, A. et al. Common variants at ten loci modulate the QT interval duration in the QTSCD Study. Nat. Genet. 41, 407–414 (2009).
Arking, D.E. et al. Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization. Nat. Genet. 10.1038/ng.3014 (22 June 2014).
Curran, M.E. et al. A molecular basis for cardiac arrhythmia: HERG mutations cause long QT syndrome. Cell 80, 795–803 (1995).
Splawski, I. et al. CaV1.2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. Cell 119, 19–31 (2004).
Ueda, K. et al. Syntrophin mutation associated with long QT syndrome through activation of the nNOS-SCN5A macromolecular complex. Proc. Natl. Acad. Sci. USA 105, 9355–9360 (2008).
Vatta, M. et al. Mutant caveolin-3 induces persistent late sodium current and is associated with long-QT syndrome. Circulation 114, 2104–2112 (2006).
Wang, Q. et al. Positional cloning of a novel potassium channel gene: KVLQT1 mutations cause cardiac arrhythmias. Nat. Genet. 12, 17–23 (1996).
Hubner, N.C. et al. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 189, 739–754 (2010).
Olsen, J.V. et al. A dual pressure linear ion trap Orbitrap instrument with very high sequencing speed. Mol. Cell. Proteomics 8, 2759–2769 (2009).
Olsen, J.V. et al. Higher-energy C-trap dissociation for peptide modification analysis. Nat. Methods 4, 709–712 (2007).
Lundby, A. & Olsen, J.V. GeLCMS for in-depth protein characterization and advanced analysis of proteomes. Methods Mol. Biol. 753, 143–155 (2011).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Lage, K. et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc. Natl. Acad. Sci. USA 105, 20870–20875 (2008).
Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).
Müller, C.S. et al. Quantitative proteomics of the Cav2 channel nano-environments in the mammalian brain. Proc. Natl. Acad. Sci. USA 107, 14950–14957 (2010).
Rossin, E.J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).
Milan, D.J. et al. Drug-sensitized zebrafish screen identifies multiple genes, including GINS3, as regulators of myocardial repolarization. Circulation 120, 553–559 (2009).
Yoshida, M. et al. Impaired Ca2+ store functions in skeletal and cardiac muscle cells from sarcalumenin-deficient mice. J. Biol. Chem. 280, 3500–3506 (2005).
Vasile, V.C., Edwards, W.D., Ommen, S.R. & Ackerman, M.J. Obstructive hypertrophic cardiomyopathy is associated with reduced expression of vinculin in the intercalated disc. Biochem. Biophys. Res. Commun. 349, 709–715 (2006).
Lundby, A. et al. In vivo phosphoproteomics analysis reveals the cardiac targets of β-adrenergic receptor signaling. Sci. Signal. 6, rs11 (2013).
den Hoed, M. et al. Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders. Nat. Genet. 45, 621–631 (2013).
Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).
Olsen, J.V. et al. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 4, 2010–2021 (2005).
de Bakker, P.I.W. et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 17, R122–R128 (2008).
Lundby, A. et al. Quantitative maps of protein phosphorylation sites across 14 different rat organs and tissues. Nat. Commun. 3, 876 (2012).
Achterberg, S. et al. Patients with coronary, cerebrovascular or peripheral arterial obstructive disease differ in risk for new vascular events and mortality: the SMART study. Eur. J. Cardiovasc. Prev. Rehabil. 17, 424–430 (2010).
Simons, P.C., Algra, A., van de Laak, M.F., Grobbee, D.E. & van der Graaf, Y. Second manifestations of ARTerial disease (SMART) study: rationale and design. Eur. J. Epidemiol. 15, 773–781 (1999).
Stolk, R.P. et al. Universal risk factors for multifactorial diseases: LifeLines: a three-generation population-based study. Eur. J. Epidemiol. 23, 67–74 (2008).
Shepherd, J. et al. The design of a prospective study of Pravastatin in the Elderly at Risk (PROSPER). PROSPER Study Group. PROspective Study of Pravastatin in the Elderly at Risk. Am. J. Cardiol. 84, 1192–1197 (1999).
Shepherd, J. et al. Pravastatin in elderly individuals at risk of vascular disease (PROSPER): a randomised controlled trial. Lancet 360, 1623–1630 (2002).
Hofman, A. et al. The Rotterdam Study: 2010 objectives and design update. Eur. J. Epidemiol. 24, 553–572 (2009).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Lundby, A., Ravn, L.S., Svendsen, J.H., Olesen, S.-P. & Schmitt, N. KCNQ1 mutation Q147R is associated with atrial fibrillation and prolonged QT interval. Heart Rhythm 4, 1532–1541 (2007).
Blasiole, B. et al. Separate Na,K-ATPase genes are required for otolith formation and semicircular canal development in zebrafish. Dev. Biol. 294, 148–160 (2006).
Vogel, B. et al. In-vivo characterization of human dilated cardiomyopathy genes in zebrafish. Biochem. Biophys. Res. Commun. 390, 516–522 (2009).
We thank M.B. Thomsen, N. Schmitt, H. Poulsen and P. Nissen for experimental input; S. Pulit, S. Ripke and J. Cox for help with data analysis; S. Raychaudhuri, P.K. Donahoe and members of the NNF Center for Protein Research for their input on the manuscript. Research reported in this publication was supported in part by the research career programme Sapere Aude from The National Danish Research Council (A.L. and J.V.O.), the Eleanor and Miles Shore Fellowship Program from Harvard Medical School and the Harvard Medical School Junior Faculty Development Award (K.L.), US National Institute of General Medical Sciences award T32GM007753 (E.J.R.), ZonMw grant 90700342 from the Netherlands Organization for Health Research and Development (FAW) and the EU 7th framework programme grant PRIME-XS (contract no. 262067). The research was also partially supported by the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) project no. 050-060-810 and the generous donation by the Novo Nordisk Foundation to Center for Protein Research. F.W.A. is supported by UCL Hospitals NIHR Biomedical Research Centre. SMART: SMART was financially supported by BBMRI_NL from the Dutch government (NWO 184.021.007). LifeLines: The LifeLines Cohort Study, and generation and management of GWAS genotype data for the LifeLines Cohort Study, is supported by the NWO (grant 175.010.2007.006), Economic Structure Enhancing Fund (FES) of the Dutch government, Ministry of Economic Affairs, Ministry of Education, Culture and Science, Ministry for Health, Welfare and Sports, Northern Netherlands Collaboration of Provinces (SNN), Province of Groningen, University Medical Center Groningen, University of Groningen, Dutch Kidney Foundation and Dutch Diabetes Research Foundation. We thank B. Alizadeh, A. Boesjes, M. Bruinenberg, N. Festen, I. Nolte, L. Franke and M. Valimohammadi for their help in creating the GWAS database; R. Bieringa, J. Keers, R. Oostergo, R. Visser and J. Vonk for their work related to data collection and validation; the study participants; the staff from the LifeLines Cohort Study and Medical Biobank Northern Netherlands; and the participating general practitioners and pharmacists. LifeLines scientific protocol preparation: R. de Boer, H. Hillege, M. van der Klauw, G. Navis, H. Ormel, D. Postma, J. Rosmalen, J. Slaets, R. Stolk and B. Wolffenbuttel; LifeLines GWAS Working Group: B. Alizadeh, M. Boezen, M. Bruinenberg, N. Festen, L. Franke, P. van der Harst, G. Navis, D. Postma, H. Snieder, C. Wijmenga and B. Wolffenbuttel. PROSPER: PROSPER is supported by an investigator-initiated grant from Bristol-Myers Squibb, the Netherlands Heart Foundation (grant 2001 D 032, J.W.J.), the EU 7th framework programme (grant 223004) and the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810). RS3: The Rotterdam Study (RS) is supported by the Erasmus Medical Center and Erasmus University Rotterdam, Netherlands Organization for Scientific Research, Netherlands Organization for Health Research and Development (ZonMw), Research Institute for Diseases in the Elderly, Netherlands Heart Foundation, Ministry of Education, Culture and Science, Ministry of Health Welfare and Sports, European Commission and Municipality of Rotterdam. Support for genotyping was provided by the NWO (175.010.2005.011, 911.03.012) and Research Institute for Diseases in the Elderly (RIDE).
The authors declare no competing financial interests.
A full list of members of the QT-IGC is in the Supplementary Note
Supplementary Figures 1–13 and Supplementary Note (PDF 2209 kb)
All quantified proteins (XLSX 700 kb)
All quantified peptides (XLSX 3015 kb)
Table of proteins specifically immunoprecipitating with KCNQ1 (XLSX 29 kb)
Table of proteins specifically immunoprecipitating with KCNH2 (XLSX 15 kb)
Table of proteins specifically immunoprecipitating with CACNA1C (XLSX 24 kb)
Table of proteins specifically immunoprecipitating with CAV3 (XLSX 68 kb)
Table of proteins specifically immunoprecipitating with SNTA1 (XLSX 27 kb)
Comparison with literature derived interactions in InWeb (XLSX 50 kb)
Proteins identified in network with DSP (XLSX 8 kb)
Proteins in network with ATP1A1 (XLSX 10 kb)
Proteins in network with DMD (XLSX 8 kb)
Proteins in network with RYR2 (XLSX 9 kb)
Proteins in network with MYBCP3 (XLSX 8 kb)
About this article
Cite this article
Lundby, A., Rossin, E., Steffensen, A. et al. Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics. Nat Methods 11, 868–874 (2014) doi:10.1038/nmeth.2997
Disruption of Ca 2+ i Homeostasis and Connexin 43 Hemichannel Function in the Right Ventricle Precedes Overt Arrhythmogenic Cardiomyopathy in Plakophilin-2–Deficient Mice
Reevaluation of genetic variants previously associated with arrhythmogenic right ventricular cardiomyopathy integrating population‐based cohorts and proteomics data
Clinical Genetics (2019)
Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data
Genome Medicine (2019)
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
Information Fusion (2019)
Oncogenic Mutations Rewire Signaling Pathways by Switching Protein Recruitment to Phosphotyrosine Sites