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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Lundby et al. compared the list of 584 proteins with genes in 35 GWAS loci for QT-interval variation and identified 12 overlapping genes.
“They then compared this list of proteins with the genes located in 35 GWAS loci for common forms of QT-interval variation, and identified 12 genes that overlapped between the two sets.”
From Multi-omics approaches to disease (2017) · cites this paper as [29]
Lundby et al. identified 584 proteins that co-precipitated with five target proteins underlying Mendelian long QT syndrome, reflecting potential protein-protein interactions.
“Using mass spectrometry (MS), they then identified 584 proteins that co-precipitated with the five target proteins, reflecting potential protein–protein interactions.”
From Multi-omics approaches to disease (2017) · cites this paper as [29]
Lundby and colleagues used quantitative tissue-specific interaction proteomics combined with GWAS data to identify a network of genes involved in cardiac arrhythmias.
“In another example, Lundby and colleagues [29] used quantitative tissue-specific interaction proteomics, combined with data from GWAS studies, to identify a network of genes involved in cardiac arrhythmias.”
From Multi-omics approaches to disease (2017) · cites this paper as [29]
The Lundby study provides a mechanistic link between specific genes in some GWAS loci and the genotype, suggesting a causative link in the locus.
“This study provides a mechanistic link between specific genes in some of the GWAS loci to the genotype in question, which suggests a causative link in the locus.”
From Multi-omics approaches to disease (2017) · cites this paper as [29]
Using PPIs derived from cardiac tissue identified new human loci for long QT syndrome.
“A particularly salient example of the utility of defining tissue-relevant networks is the power obtained by using PPIs derived from cardiac tissue to identify new human loci for long QT syndrome54.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [54]
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Reconciling proteomics with next generation sequencing
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Self-citationGWAS for identification of genomic regions and candidate genes in vegetable crops
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Gene co-opening network deciphers gene functional relationships
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The Notch Interactome: Complexity in Signaling Circuitry
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The architecture of brain co-expression reveals the brain-wide basis of disease susceptibilityPreprint
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PKD Phosphorylation as Novel Pathway of KV11.1 Regulation
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Distinguished Minimal Topological Lassos
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- Authors
- The QT Interval International GWAS Consortium (QT-IGC), Lundby, A., Rossin, E. J., Steffensen, A. B., Acha, M. R., Newton-Cheh, C., Pfeufer, A., Lynch, S. N., Olesen, S.-P., Brunak, S., Ellinor, P. T., Jukema, J. W., Trompet, S., Ford, I., Macfarlane, P. W., Krijthe, B. P., Hofman, A., Uitterlinden, A. G., Stricker, B. H., Nathoe, H. M., Spiering, W., Daly, M. J., Asselbergs, F. W., van der Harst, P., Milan, D. J., de Bakker, P. I. W., Lage, K., & Olsen, J. V.
- Affiliations
- Broad Institute (US)
- Danish National Research Foundation (DK)
- Institut für Humangenetik (DE)
- Massachusetts General Hospital (US)
- University of Copenhagen (DK)
- Leiden University Medical Center (NL)
- University of Glasgow (GB)
- Erasmus MC (NL)
- University Medical Center Utrecht (NL)
- Corresponding author
- Lage, Kasper
- Olsen, Jesper V
- ORCID
- Lundby, Alicia
- Rossin, Elizabeth J
- Steffensen, Annette B
- Olesen, Søren-Peter
- Brunak, Søren
- Ellinor, Patrick T
- Jukema, J Wouter
- Trompet, Stella
- Ford, Ian
- Macfarlane, Peter W
- Uitterlinden, André G
- Stricker, Bruno H
- Nathoe, Hendrik M
- Spiering, Wilko
- Daly, Mark J
- Asselbergs, Folkert W
- Milan, David J
- de Bakker, Paul I W
- Lage, Kasper
- Olsen, Jesper V
- Venue
- Nature Methods
- Published
- 2014-06-22
- Volume
- 11
- Issue
- 8
- Pages
- 868-874
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC4117722
- OpenAlex
- W2103514539
- Cited by
- 71
- References
- 39
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 8 · 2022: 1 · 2021: 2 · 2020: 13 · 2019: 9 · 2018: 9 · 2017: 5 · 2016: 4 · 2015: 10 · 2014: 2
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Site cant be reached (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- ZonMw (90700342)
- National Heart, Lung, and Blood Institute (K24HL105780, R01HL092577)
- National Institute of General Medical Sciences (T32GM007753)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P01HD068250)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- genome-wide association study, long QT syndrome, complex traits, causal gene identification, protein-protein interaction network, single nucleotide polymorphisms, candidate genes, Xenopus oocytes, zebrafish, monogenic disorders
- MeSH terms
- Animals, Humans, Long QT Syndrome, Xenopus laevis, Zebrafish, Proteomics, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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