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

TY - JOUR

T1 - Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics

AU - Lundby, Alicia

AU - Rossin, Elizabeth J

AU - Steffensen, Annette B

AU - Acha, Moshe Rav

AU - Newton-Cheh, Christopher

AU - Pfeufer, Arne

AU - Lynch, Stacey N

AU - Olesen, Søren-Peter

AU - Brunak, Søren

AU - Ellinor, Patrick T

AU - Jukema, J Wouter

AU - Trompet, Stella

AU - Ford, Ian

AU - Macfarlane, Peter W

AU - Krijthe, Bouwe P

AU - Hofman, Albert

AU - Uitterlinden, André G

AU - Stricker, Bruno H

AU - Nathoe, Hendrik M

AU - Spiering, Wilko

AU - Daly, Mark J

AU - Asselbergs, Folkert W

AU - van der Harst, Pim

AU - Milan, David J

AU - de Bakker, Paul I W

AU - Hansen, Kasper Lage

AU - Olsen, Jesper V

AU - The QT Interval International GWAS Consortium (QT-IGC)

PY - 2014/8

Y1 - 2014/8

N2 - Genome-wide association studies (GWAs) have identifed 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-specifc quantitative interaction proteomics to map a network of fve 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 confrmed in Xenopus laevis oocytes and zebrafsh. We used the lQts protein network to flter 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 flter reached genome-wide signifcance after replication genotyping. overall, we present a general strategy to propose candidates in GWAs loci for functional studies and to systematically flter subtle association signals using tissue-specifc quantitative interaction proteomics.

AB - Genome-wide association studies (GWAs) have identifed 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-specifc quantitative interaction proteomics to map a network of fve 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 confrmed in Xenopus laevis oocytes and zebrafsh. We used the lQts protein network to flter 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 flter reached genome-wide signifcance after replication genotyping. overall, we present a general strategy to propose candidates in GWAs loci for functional studies and to systematically flter subtle association signals using tissue-specifc quantitative interaction proteomics.

U2 - 10.1038/nmeth.2997

DO - 10.1038/nmeth.2997

M3 - Journal article

C2 - 24952909

SN - 1548-7091

VL - 11

SP - 868

EP - 874

JO - Nature Methods

JF - Nature Methods

IS - 8

ER -