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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, N. T.; Lage, K.; Bergholdt, R.; Brunak, S. and Pociot, Flemming LU (2009) In Diabetes, Obesity and Metabolism 11(S1). p.60-66
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
complex, protein interaction networks, type 1 diabetes, major histocompatibility, genetic association, integrative genomics
in
Diabetes, Obesity and Metabolism
volume
11
issue
S1
pages
60 - 66
publisher
Wiley-Blackwell
external identifiers
  • wos:000262489600009
  • scopus:58549112217
ISSN
1462-8902
DOI
10.1111/j.1463-1326.2008.01004.x
language
English
LU publication?
yes
id
e4a898f1-8dd4-4c97-bb9a-2f99b03e4868 (old id 1312833)
date added to LUP
2010-02-23 16:09:46
date last changed
2017-07-23 03:40:55
@article{e4a898f1-8dd4-4c97-bb9a-2f99b03e4868,
  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.},
  author       = {Brorsson, C. and Hansen, N. T. and Lage, K. and Bergholdt, R. and Brunak, S. and Pociot, Flemming},
  issn         = {1462-8902},
  keyword      = {complex,protein interaction networks,type 1 diabetes,major histocompatibility,genetic association,integrative genomics},
  language     = {eng},
  number       = {S1},
  pages        = {60--66},
  publisher    = {Wiley-Blackwell},
  series       = {Diabetes, Obesity and Metabolism},
  title        = {Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data},
  url          = {http://dx.doi.org/10.1111/j.1463-1326.2008.01004.x},
  volume       = {11},
  year         = {2009},
}