Journal
NATURE METHODS
Volume 11, Issue 8, Pages 868-874Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.2997
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Funding
- National Danish Research Council
- Eleanor and Miles Shore Fellowship Program from Harvard Medical School
- Harvard Medical School Junior Faculty Development Award
- US National Institute of General Medical Sciences [T32GM007753]
- Netherlands Organization for Health Research and Development [90700342]
- EU [262067]
- Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) [050-060-810]
- Novo Nordisk Foundation
- UCL Hospitals NIHR Biomedical Research Centre
- [NWO 184.021.007]
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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 (LOTS). We integrated the LOTS 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 LOTS 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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