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Protein Interactomes of Mendelian Long QT Syndrome Genes are Associated to QT Interval Variation in the General Population and Augment Genome‐Wide Association Data
Author(s) -
Lundby Alicia,
Rossin Elizabeth J.,
Steffensen Annette B.,
Lage Kasper,
Olsen Jesper V.
Publication year - 2012
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.26.1_supplement.lb605
Subject(s) - genome wide association study , genetics , biology , mendelian inheritance , gene , genetic association , genome , population , computational biology , genotyping , single nucleotide polymorphism , proteomics , genotype , medicine , environmental health
Mendelian long QT syndrome (LQTS) is caused by severe mutations in genes important for cardiac ion channel function, and genome‐wide association studies (GWAS) have identified common variant loci associated with QT interval variation in the general population. We hypothesized that genes contributing to QT interval variation encode proteins that interact with ion channel complexes causal of LQTS. By label‐free quantitative proteomics we resolved cardiac protein interaction networks (interactomes) of proteins encoded by LQTS genes. The interactomes establish functional links to genes in genome wide significant loci, suggesting that these genes are responsible for the association signals. Excluding genes from the established QT interval variation loci, the genes represented in the interactomes remain strongly enriched for genetic associations. We thus identified single nucleotide polymorphisms for replication and genotyping in independent samples confirmed our finding and led to identification of two novel genome‐wide significant loci. Combining tissue‐specific high‐resolution proteomics with GWAS datasets, we show that variants causal of QT interval variation are spread across genes represented in the protein interactomes of Mendelian LQTS genes. Our approach furthermore illustrates a strategy for augmenting GWAS data using quantitative interaction proteomics.