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Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics
Alicia Lundby1, Elizabeth J Rossin2, Annette B Steffensen3
11] Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark. [2] The Danish National Research Foundation Centre for Cardiac Arrhythmia, Copenhagen, Denmark. [3] The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. [4].
This study introduces a novel strategy combining proteomics and genome-wide association studies (GWAS) to identify causal genes for complex traits. The approach successfully pinpointed significant genetic variants for QT-interval variation, improving disease gene discovery.
Area of Science:
- Genetics and Genomics
- Proteomics
- Systems Biology
Background:
- Genome-wide association studies (GWAS) identify numerous loci for complex traits but struggle with pinpointing causal genes and leveraging subtle signals.
- Understanding the genetic architecture of complex traits like QT-interval variation requires integrating diverse data types.
Purpose of the Study:
- To develop and validate a general strategy for identifying candidate genes within GWAS loci.
- To systematically filter and prioritize subtle association signals using tissue-specific quantitative interaction proteomics.
- To investigate the genetic basis of QT-interval variation and its associated Mendelian disorder, long QT syndrome (LQTS).
Main Methods:
- Mapped a five-gene interaction network for long QT syndrome (LQTS) using tissue-specific quantitative interaction proteomics.
- Integrated the LQTS protein network with GWAS loci data for QT-interval variation.
- Validated candidate genes using Xenopus laevis oocytes and zebrafish models.
- Filtered weak GWAS signals by identifying single-nucleotide polymorphisms (SNPs) proximal to network genes with strong proteomic evidence.
Main Results:
- Identified and confirmed candidate genes for QT-interval variation by integrating proteomic networks with GWAS data.
- Successfully filtered subtle GWAS signals, leading to the identification of three SNPs reaching genome-wide significance after replication.
- Demonstrated the utility of tissue-specific quantitative interaction proteomics in refining GWAS findings.
Conclusions:
- The presented strategy effectively proposes candidate genes in GWAS loci for functional studies.
- Tissue-specific quantitative interaction proteomics provides a powerful tool for systematically filtering subtle GWAS signals.
- This integrated approach enhances the discovery of genes underlying complex traits and associated disorders.
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