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

作者：Lundby Alicia; Rossin Elizabeth J; Steffensen Annette B; Acha Moshe Ray; Newton Cheh Christopher; Pfeufer Arne; Lyneh Stacey N; Olesen Soren Peter; Brunak Soren; Ellinor Patrick T; Jukema J Wouter; Trompet Stella; Ford Ian; Macfarlane Peter W; Krijthe Bouwe P; Hofman Albert; Uitterlinden Andre G; Stricker Bruno H; Nathoe Hendrik M; Spiering Wilko; Daly Mark J; Asselbergs Ikea W; van der Harst Pim; Milan David J; de Bakker Paul I W; Lage Kasper^*

来源：Nature Methods, 2014, 11(8): 868-874.

DOI：10.1038/NMETH.2997

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.

• 出版日期2014-8