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Network-based Analysis of Genome Wide Association Data Provides Novel Candidate Genes for Lipid and Lipoprotein Traits

Sharma, Amitabh LU ; Gulbahce, Natali ; Pevzner, Samuel J. ; Menche, Joerg ; Ladenvall, Claes LU ; Folkersen, Lasse ; Eriksson, Per ; Orho-Melander, Marju LU and Barabasi, Albert-Laszlo (2013) In Molecular & Cellular Proteomics 12(11). p.3398-3408
Abstract
Genome wide association studies (GWAS) identify susceptibility loci for complex traits, but do not identify particular genes of interest. Integration of functional and network information may help in overcoming this limitation and identifying new susceptibility loci. Using GWAS and comorbidity data, we present a network-based approach to predict candidate genes for lipid and lipoprotein traits. We apply a prediction pipeline incorporating interactome, co-expression, and comorbidity data to Global Lipids Genetics Consortium (GLGC) GWAS for four traits of interest, identifying phenotypically coherent modules. These modules provide insights regarding gene involvement in complex phenotypes with multiple susceptibility alleles and low effect... (More)
Genome wide association studies (GWAS) identify susceptibility loci for complex traits, but do not identify particular genes of interest. Integration of functional and network information may help in overcoming this limitation and identifying new susceptibility loci. Using GWAS and comorbidity data, we present a network-based approach to predict candidate genes for lipid and lipoprotein traits. We apply a prediction pipeline incorporating interactome, co-expression, and comorbidity data to Global Lipids Genetics Consortium (GLGC) GWAS for four traits of interest, identifying phenotypically coherent modules. These modules provide insights regarding gene involvement in complex phenotypes with multiple susceptibility alleles and low effect sizes. To experimentally test our predictions, we selected four candidate genes and genotyped representative SNPs in the Malmo Diet and Cancer Cardiovascular Cohort. We found significant associations with LDL-C and total-cholesterol levels for a synonymous SNP (rs234706) in the cystathionine beta-synthase (CBS) gene (p = 1 x 10(-5) and adjusted-p = 0.013, respectively). Further, liver samples taken from 206 patients revealed that patients with the minor allele of rs234706 had significant dysregulation of CBS (p = 0.04). Despite the known biological role of CBS in lipid metabolism, SNPs within the locus have not yet been identified in GWAS of lipoprotein traits. Thus, the GWAS-based Comorbidity Module (GCM) approach identifies candidate genes missed by GWAS studies, serving as a broadly applicable tool for the investigation of other complex disease phenotypes. (Less)
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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Molecular & Cellular Proteomics
volume
12
issue
11
pages
3398 - 3408
publisher
American Society for Biochemistry and Molecular Biology
external identifiers
  • wos:000328816000031
  • scopus:84886753395
  • pmid:23882023
ISSN
1535-9484
DOI
10.1074/mcp.M112.024851
language
English
LU publication?
yes
id
8bb8c579-682f-455e-960d-20a0a433c1d5 (old id 4261671)
date added to LUP
2016-04-01 11:06:29
date last changed
2024-04-08 01:28:02
@article{8bb8c579-682f-455e-960d-20a0a433c1d5,
  abstract     = {{Genome wide association studies (GWAS) identify susceptibility loci for complex traits, but do not identify particular genes of interest. Integration of functional and network information may help in overcoming this limitation and identifying new susceptibility loci. Using GWAS and comorbidity data, we present a network-based approach to predict candidate genes for lipid and lipoprotein traits. We apply a prediction pipeline incorporating interactome, co-expression, and comorbidity data to Global Lipids Genetics Consortium (GLGC) GWAS for four traits of interest, identifying phenotypically coherent modules. These modules provide insights regarding gene involvement in complex phenotypes with multiple susceptibility alleles and low effect sizes. To experimentally test our predictions, we selected four candidate genes and genotyped representative SNPs in the Malmo Diet and Cancer Cardiovascular Cohort. We found significant associations with LDL-C and total-cholesterol levels for a synonymous SNP (rs234706) in the cystathionine beta-synthase (CBS) gene (p = 1 x 10(-5) and adjusted-p = 0.013, respectively). Further, liver samples taken from 206 patients revealed that patients with the minor allele of rs234706 had significant dysregulation of CBS (p = 0.04). Despite the known biological role of CBS in lipid metabolism, SNPs within the locus have not yet been identified in GWAS of lipoprotein traits. Thus, the GWAS-based Comorbidity Module (GCM) approach identifies candidate genes missed by GWAS studies, serving as a broadly applicable tool for the investigation of other complex disease phenotypes.}},
  author       = {{Sharma, Amitabh and Gulbahce, Natali and Pevzner, Samuel J. and Menche, Joerg and Ladenvall, Claes and Folkersen, Lasse and Eriksson, Per and Orho-Melander, Marju and Barabasi, Albert-Laszlo}},
  issn         = {{1535-9484}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{3398--3408}},
  publisher    = {{American Society for Biochemistry and Molecular Biology}},
  series       = {{Molecular & Cellular Proteomics}},
  title        = {{Network-based Analysis of Genome Wide Association Data Provides Novel Candidate Genes for Lipid and Lipoprotein Traits}},
  url          = {{http://dx.doi.org/10.1074/mcp.M112.024851}},
  doi          = {{10.1074/mcp.M112.024851}},
  volume       = {{12}},
  year         = {{2013}},
}