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

Lundby A, Rossin EJ, Steffensen AB, Acha MR, Newton-Cheh C, Pfeufer A, Lynch SN, QT Interval International GWAS Consortium (QT-IGC)Olesen SP, Brunak S, Ellinor PT, Jukema JW, Trompet S, Ford I, Macfarlane PW, Krijthe BP, Hofman A, Uitterlinden AG, Stricker BH, Nathoe HM, Spiering W, Daly MJ, Asselbergs FW, van der Harst P, Milan DJ, de Bakker PI, Lage K, Olsen JV - Nat. Methods (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: 1] 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].

ABSTRACT
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 (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 confirmed in Xenopus laevis oocytes and zebrafish. We used the LQTS 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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Quantitative interaction proteomics of five Mendelian LQTS proteinsa) Hierarchical cluster analysis of proteins identified in immunoprecipitation experiments visualizes the experimental specificity and reproducibility. Proteins are color-coded according to their mass-spectrometry signal intensity. Triplicates of the LQTS protein immunoprecipitations (a-c) are shown. The highlighted yellow areas indicate that each group of triplicate experiments immunoprecipitates a specific cluster of proteins. b) Volcano plots, representing the LQTS protein IPs versus IgG control IPs, show negative logarithmized t-test derived P-values (-log10(P)) as function of logarithmized ratios of average protein intensities (log2) for the LQTS protein relative to control. A hyperbolic curve indicates a false discovery rate cut-off of 0.05 and separates specific from nonspecific interactors. All points represent a protein. Purple indicates a LQTS protein, green represent proteins specifically interacting with the LQTS proteins, and blue represents nonspecific interactors.
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Figure 2: Quantitative interaction proteomics of five Mendelian LQTS proteinsa) Hierarchical cluster analysis of proteins identified in immunoprecipitation experiments visualizes the experimental specificity and reproducibility. Proteins are color-coded according to their mass-spectrometry signal intensity. Triplicates of the LQTS protein immunoprecipitations (a-c) are shown. The highlighted yellow areas indicate that each group of triplicate experiments immunoprecipitates a specific cluster of proteins. b) Volcano plots, representing the LQTS protein IPs versus IgG control IPs, show negative logarithmized t-test derived P-values (-log10(P)) as function of logarithmized ratios of average protein intensities (log2) for the LQTS protein relative to control. A hyperbolic curve indicates a false discovery rate cut-off of 0.05 and separates specific from nonspecific interactors. All points represent a protein. Purple indicates a LQTS protein, green represent proteins specifically interacting with the LQTS proteins, and blue represents nonspecific interactors.

Mentions: We chose five LQTS proteins as the starting point of our analysis (i.e., KCNQ1, KCNH2, CACNA1C, SNTA1, CAV3)5–9. The proteins were immunoprecipitated from pooled lysates of cardiac tissue from male mice, the precipitates were separated by SDS-PAGE followed by in-gel trypsin digestion and analysis of the resulting peptide mixtures by nanoflow high-performance liquid chromatography and subjected to tandem mass spectrometry (HPLC-MS/MS)10–12 on a LTQ-Orbitrap Velos instrument using Higher-Collisional Dissociation (HCD) fragmentation (Supplementary Figures 1 to 5)13. The complete set of raw MS files were processed using the MaxQuant software suite (www.maxquant.org), where peptides and proteins were identified using the Andromeda search engine at a false discovery rate (FDR) below 0.01 and quantified using the label-free quantitation approach (all quantified proteins and modification specific peptides are provided in Supplementary Tables 1 and 2). We performed triplicate immunoprecipitations (IPs) of all LQTS proteins and compared them to matched IgG control IPs, separating specific from nonspecific interactors by applying a FDR cutoff of 0.0510,14 (Fig. 2a and b). As expected, the experimental triplicates yielded highly reproducible results for protein signal intensities (Pearson r>0.8, Supplementary Figure 6), and the LQTS proteins were among the most abundant proteins in their respective protein networks (Fig 2b).


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

Lundby A, Rossin EJ, Steffensen AB, Acha MR, Newton-Cheh C, Pfeufer A, Lynch SN, QT Interval International GWAS Consortium (QT-IGC)Olesen SP, Brunak S, Ellinor PT, Jukema JW, Trompet S, Ford I, Macfarlane PW, Krijthe BP, Hofman A, Uitterlinden AG, Stricker BH, Nathoe HM, Spiering W, Daly MJ, Asselbergs FW, van der Harst P, Milan DJ, de Bakker PI, Lage K, Olsen JV - Nat. Methods (2014)

Quantitative interaction proteomics of five Mendelian LQTS proteinsa) Hierarchical cluster analysis of proteins identified in immunoprecipitation experiments visualizes the experimental specificity and reproducibility. Proteins are color-coded according to their mass-spectrometry signal intensity. Triplicates of the LQTS protein immunoprecipitations (a-c) are shown. The highlighted yellow areas indicate that each group of triplicate experiments immunoprecipitates a specific cluster of proteins. b) Volcano plots, representing the LQTS protein IPs versus IgG control IPs, show negative logarithmized t-test derived P-values (-log10(P)) as function of logarithmized ratios of average protein intensities (log2) for the LQTS protein relative to control. A hyperbolic curve indicates a false discovery rate cut-off of 0.05 and separates specific from nonspecific interactors. All points represent a protein. Purple indicates a LQTS protein, green represent proteins specifically interacting with the LQTS proteins, and blue represents nonspecific interactors.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4117722&req=5

Figure 2: Quantitative interaction proteomics of five Mendelian LQTS proteinsa) Hierarchical cluster analysis of proteins identified in immunoprecipitation experiments visualizes the experimental specificity and reproducibility. Proteins are color-coded according to their mass-spectrometry signal intensity. Triplicates of the LQTS protein immunoprecipitations (a-c) are shown. The highlighted yellow areas indicate that each group of triplicate experiments immunoprecipitates a specific cluster of proteins. b) Volcano plots, representing the LQTS protein IPs versus IgG control IPs, show negative logarithmized t-test derived P-values (-log10(P)) as function of logarithmized ratios of average protein intensities (log2) for the LQTS protein relative to control. A hyperbolic curve indicates a false discovery rate cut-off of 0.05 and separates specific from nonspecific interactors. All points represent a protein. Purple indicates a LQTS protein, green represent proteins specifically interacting with the LQTS proteins, and blue represents nonspecific interactors.
Mentions: We chose five LQTS proteins as the starting point of our analysis (i.e., KCNQ1, KCNH2, CACNA1C, SNTA1, CAV3)5–9. The proteins were immunoprecipitated from pooled lysates of cardiac tissue from male mice, the precipitates were separated by SDS-PAGE followed by in-gel trypsin digestion and analysis of the resulting peptide mixtures by nanoflow high-performance liquid chromatography and subjected to tandem mass spectrometry (HPLC-MS/MS)10–12 on a LTQ-Orbitrap Velos instrument using Higher-Collisional Dissociation (HCD) fragmentation (Supplementary Figures 1 to 5)13. The complete set of raw MS files were processed using the MaxQuant software suite (www.maxquant.org), where peptides and proteins were identified using the Andromeda search engine at a false discovery rate (FDR) below 0.01 and quantified using the label-free quantitation approach (all quantified proteins and modification specific peptides are provided in Supplementary Tables 1 and 2). We performed triplicate immunoprecipitations (IPs) of all LQTS proteins and compared them to matched IgG control IPs, separating specific from nonspecific interactors by applying a FDR cutoff of 0.0510,14 (Fig. 2a and b). As expected, the experimental triplicates yielded highly reproducible results for protein signal intensities (Pearson r>0.8, Supplementary Figure 6), and the LQTS proteins were among the most abundant proteins in their respective protein networks (Fig 2b).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: 1] 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].

ABSTRACT
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 (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 confirmed in Xenopus laevis oocytes and zebrafish. We used the LQTS 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.

Show MeSH
Related in: MedlinePlus