Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

C. Brorsson, Niclas Tue Hansen, Kasper Lage Hansen, R. Bergholdt, Søren Brunak, F. Pociot

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1 genes. We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation. A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in beta-cell development and diabetic complications. The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D.
    Original languageEnglish
    JournalDiabetes, Obesity and Metabolism
    Volume11
    Issue numberSuppl. 1
    Pages (from-to)60-66
    ISSN1462-8902
    DOIs
    Publication statusPublished - 2009

    Keywords

    • protein interaction networks
    • integrative genomics
    • type 1 diabetes
    • genetic association
    • major histocompatibility complex

    Cite this

    Brorsson, C. ; Hansen, Niclas Tue ; Hansen, Kasper Lage ; Bergholdt, R. ; Brunak, Søren ; Pociot, F. / Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data. In: Diabetes, Obesity and Metabolism. 2009 ; Vol. 11, No. Suppl. 1. pp. 60-66.
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    abstract = "To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1 genes. We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation. A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in beta-cell development and diabetic complications. The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D.",
    keywords = "protein interaction networks, integrative genomics, type 1 diabetes, genetic association, major histocompatibility complex",
    author = "C. Brorsson and Hansen, {Niclas Tue} and Hansen, {Kasper Lage} and R. Bergholdt and S{\o}ren Brunak and F. Pociot",
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    Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data. / Brorsson, C.; Hansen, Niclas Tue; Hansen, Kasper Lage; Bergholdt, R.; Brunak, Søren; Pociot, F.

    In: Diabetes, Obesity and Metabolism, Vol. 11, No. Suppl. 1, 2009, p. 60-66.

    Research output: Contribution to journalJournal articleResearchpeer-review

    TY - JOUR

    T1 - Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

    AU - Brorsson, C.

    AU - Hansen, Niclas Tue

    AU - Hansen, Kasper Lage

    AU - Bergholdt, R.

    AU - Brunak, Søren

    AU - Pociot, F.

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    N2 - To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1 genes. We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation. A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in beta-cell development and diabetic complications. The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D.

    AB - To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1 genes. We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein modules were statistically evaluated using permutation. A total of 151 genes could be mapped to nodes within the protein interaction network and their interaction partners were identified. Five protein interaction modules reached statistical significance using this approach. The identified proteins are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in beta-cell development and diabetic complications. The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D.

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