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

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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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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

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.

Correspondence to Kasper Lage or Jesper V Olsen.

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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) doi:10.1038/nmeth.2997

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