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Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics


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.

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Figure 1: General design and experimental workflow of our integrated genetics and proteomics study.
Figure 2: Quantitative interaction proteomics of five Mendelian LQTS proteins.
Figure 3: Proteomic annotation of GWAS loci coupled to experimental follow-up identifies ATP1B1 as a QT-variation candidate gene.
Figure 4: Integrative analysis of the LQTS protein network and GWAS data.

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  1. Morita, H., Wu, J. & Zipes, D.P. The QT syndromes: long and short. Lancet 372, 750–763 (2008).

    Article  CAS  Google Scholar 

  2. Newton-Cheh, C. et al. Common variants at ten loci influence QT interval duration in the QTGEN Study. Nat. Genet. 41, 399–406 (2009).

    Article  CAS  Google Scholar 

  3. Pfeufer, A. et al. Common variants at ten loci modulate the QT interval duration in the QTSCD Study. Nat. Genet. 41, 407–414 (2009).

    Article  CAS  Google Scholar 

  4. 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).

  5. Curran, M.E. et al. A molecular basis for cardiac arrhythmia: HERG mutations cause long QT syndrome. Cell 80, 795–803 (1995).

    Article  CAS  Google Scholar 

  6. Splawski, I. et al. CaV1.2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. Cell 119, 19–31 (2004).

    Article  CAS  Google Scholar 

  7. 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).

    Article  CAS  Google Scholar 

  8. Vatta, M. et al. Mutant caveolin-3 induces persistent late sodium current and is associated with long-QT syndrome. Circulation 114, 2104–2112 (2006).

    Article  CAS  Google Scholar 

  9. Wang, Q. et al. Positional cloning of a novel potassium channel gene: KVLQT1 mutations cause cardiac arrhythmias. Nat. Genet. 12, 17–23 (1996).

    Article  Google Scholar 

  10. Hubner, N.C. et al. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 189, 739–754 (2010).

    Article  CAS  Google Scholar 

  11. 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).

    Article  CAS  Google Scholar 

  12. Olsen, J.V. et al. Higher-energy C-trap dissociation for peptide modification analysis. Nat. Methods 4, 709–712 (2007).

    Article  CAS  Google Scholar 

  13. Lundby, A. & Olsen, J.V. GeLCMS for in-depth protein characterization and advanced analysis of proteomes. Methods Mol. Biol. 753, 143–155 (2011).

    Article  CAS  Google Scholar 

  14. 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).

    Article  CAS  Google Scholar 

  15. 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).

    Article  CAS  Google Scholar 

  16. Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309–316 (2007).

    Article  CAS  Google Scholar 

  17. 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).

    Article  Google Scholar 

  18. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Milan, D.J. et al. Drug-sensitized zebrafish screen identifies multiple genes, including GINS3, as regulators of myocardial repolarization. Circulation 120, 553–559 (2009).

    Article  Google Scholar 

  20. 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).

    Article  CAS  Google Scholar 

  21. 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).

    Article  CAS  Google Scholar 

  22. Lundby, A. et al. In vivo phosphoproteomics analysis reveals the cardiac targets of β-adrenergic receptor signaling. Sci. Signal. 6, rs11 (2013).

    Article  Google Scholar 

  23. 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).

    Article  CAS  Google Scholar 

  24. 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).

    Article  CAS  Google Scholar 

  25. 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).

    Article  CAS  Google Scholar 

  26. 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).

    Article  CAS  Google Scholar 

  27. Lundby, A. et al. Quantitative maps of protein phosphorylation sites across 14 different rat organs and tissues. Nat. Commun. 3, 876 (2012).

    Article  Google Scholar 

  28. 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).

    Article  Google Scholar 

  29. 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).

    Article  CAS  Google Scholar 

  30. 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).

    Article  Google Scholar 

  31. 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).

    Article  CAS  Google Scholar 

  32. Shepherd, J. et al. Pravastatin in elderly individuals at risk of vascular disease (PROSPER): a randomised controlled trial. Lancet 360, 1623–1630 (2002).

    Article  CAS  Google Scholar 

  33. Hofman, A. et al. The Rotterdam Study: 2010 objectives and design update. Eur. J. Epidemiol. 24, 553–572 (2009).

    Article  Google Scholar 

  34. 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).

    Article  CAS  Google Scholar 

  35. 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).

    Article  Google Scholar 

  36. 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).

    Article  CAS  Google Scholar 

  37. Vogel, B. et al. In-vivo characterization of human dilated cardiomyopathy genes in zebrafish. Biochem. Biophys. Res. Commun. 390, 516–522 (2009).

    Article  CAS  Google Scholar 

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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).

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Authors and Affiliations




Overall idea, concept, and project coordination: A.L., E.J.R., K.L. and J.V.O. Conceived of and designed the immunoprecipitations and proteomics experiments: A.L. and J.V.O. Performed the immunoprecipitations and proteomics experiments: A.L. Analyzed the proteomics data: A.L. and J.V.O. Contributed meta-analysis GWAS data: QT-IGC Consortium, C.N.-C. and A.P. Conceived of and designed statistical enrichment analyses and the integration of genetic and proteomic data: E.J.R. and K.L. Performed enrichment analyses: E.J.R. Identified SNPs for replication: A.L., E.J.R., P.I.W.d.B., K.L. and J.V.O. Conceived of and designed genetic replication experiments: E.J.R., M.J.D., P.I.W.d.B. and K.L. Performed genetic meta-analysis: E.J.R. Contributed input for the manuscript: S.B., S.-P.O., C.N.-C., P.v.d.H. and P.I.W.d.B. Conceived of and designed electrophysiological experiments: A.L. Performed and analyzed the electrophysiological experiments: A.B.S. Conceived of and designed zebrafish experiments: A.L., P.T.E. and D.J.M. Performed and analyzed the zebrafish experiments: M.R.A. and S.N.L. Contributed data for genetic replication: SMART, F.W.A., W.S., H.M.N. and P.I.W.d.B.; LifeLines, P.v.d.H.; PROSPER-PHASE, J.W.J., S.T., I.F. and P.W.M.; RS3, B.P.K., A.G.U., B.H.S. and A.H. Wrote the paper: A.L., E.J.R., K.L. and J.V.O.

Corresponding authors

Correspondence to Kasper Lage or Jesper V Olsen.

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Competing interests

The authors declare no competing financial interests.

Additional information

A full list of members of the QT-IGC is in the Supplementary Note

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 and Supplementary Note (PDF 2209 kb)

Supplementary Table 1

All quantified proteins (XLSX 700 kb)

Supplementary Table 2

All quantified peptides (XLSX 3015 kb)

Supplementary Table 3

Table of proteins specifically immunoprecipitating with KCNQ1 (XLSX 29 kb)

Supplementary Table 4

Table of proteins specifically immunoprecipitating with KCNH2 (XLSX 15 kb)

Supplementary Table 5

Table of proteins specifically immunoprecipitating with CACNA1C (XLSX 24 kb)

Supplementary Table 6

Table of proteins specifically immunoprecipitating with CAV3 (XLSX 68 kb)

Supplementary Table 7

Table of proteins specifically immunoprecipitating with SNTA1 (XLSX 27 kb)

Supplementary Table 8

Comparison with literature derived interactions in InWeb (XLSX 50 kb)

Supplementary Table 9

Proteins identified in network with DSP (XLSX 8 kb)

Supplementary Table 10

Proteins in network with ATP1A1 (XLSX 10 kb)

Supplementary Table 11

Proteins in network with DMD (XLSX 8 kb)

Supplementary Table 12

Proteins in network with RYR2 (XLSX 9 kb)

Supplementary Table 13

Proteins in network with MYBCP3 (XLSX 8 kb)

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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).

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