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

Nature Methods volume 11, pages 868–874 (2014)Cite this article

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Abstract

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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Acknowledgements

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).

Author information

Author notes

  1. Alicia Lundby and Elizabeth J Rossin: These authors contributed equally to this work.

  2. Paul I W de Bakker, Kasper Lage and Jesper V Olsen: These authors jointly directed this work.

Authors and Affiliations

  1. Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    Alicia Lundby, Søren Brunak, Kasper Lage & Jesper V Olsen

  2. The Danish National Research Foundation Centre for Cardiac Arrhythmia, Copenhagen, Denmark

    Alicia Lundby, Annette B Steffensen & Søren-Peter Olesen

  3. The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Alicia Lundby, Elizabeth J Rossin, Christopher Newton-Cheh, Mark J Daly, Paul I W de Bakker & Kasper Lage

  4. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Elizabeth J Rossin, Mark J Daly & Kasper Lage

  5. MD/PhD Program and Health Sciences and Technology Program, Harvard Medical School, Boston, USA

    Elizabeth J Rossin

  6. Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA

    Moshe Rav Acha, Christopher Newton-Cheh, Stacey N Lynch, Patrick T Ellinor & David J Milan

  7. Center for Human Genetics Research, Massachusetts General Hospital, Boston, Massachusetts, USA

    Moshe Rav Acha, Christopher Newton-Cheh, Stacey N Lynch, Patrick T Ellinor & David J Milan

  8. Institute of Human Genetics, Technical University of Munich, Munich, Germany

    Arne Pfeufer

  9. Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark

    Søren Brunak & Kasper Lage

  10. Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands

    J Wouter Jukema & Stella Trompet

  11. Durrer Center for Cardiogenetic Research, ICIN - Netherlands Heart Institute, Utrecht, the Netherlands

    J Wouter Jukema & Folkert W Asselbergs

  12. Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands

    Stella Trompet

  13. Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK

    Ian Ford

  14. Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK

    Peter W Macfarlane

  15. Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands

    Bouwe P Krijthe, Albert Hofman, André G Uitterlinden & Bruno H Stricker

  16. Netherlands Consortium for Healthy Aging (NCHA), Leiden, the Netherlands

    Bouwe P Krijthe, Albert Hofman, André G Uitterlinden & Bruno H Stricker

  17. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands

    André G Uitterlinden & Bruno H Stricker

  18. Inspectorate for Health Care, The Hague, the Netherlands

    Bruno H Stricker

  19. Department of Medical Informatics, Erasmus Medical Center, Rotterdam, the Netherlands

    Bruno H Stricker

  20. Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands

    Bruno H Stricker & Wilko Spiering

  21. Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, the Netherlands

    Hendrik M Nathoe & Folkert W Asselbergs

  22. Faculty of Population Health Sciences, Institute of Cardiovascular Science, University College London, London, UK

    Folkert W Asselbergs

  23. University Medical Center Groningen, University of Groningen, Groningen, the Netherlands

    Pim van der Harst

  24. Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands

    Paul I W de Bakker

  25. Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands

    Paul I W de Bakker

  26. Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Boston, Massachusetts, USA

    Kasper Lage

Authors
  1. Alicia Lundby
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  2. Elizabeth J Rossin
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Consortia

The QT Interval International GWAS Consortium (QT-IGC)

Contributions

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). https://doi.org/10.1038/nmeth.2997

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