4.7 Article

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

期刊

MOLECULAR & CELLULAR PROTEOMICS
卷 12, 期 11, 页码 3398-3408

出版社

AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
DOI: 10.1074/mcp.M112.024851

关键词

-

资金

  1. Swedish Research Council
  2. Swedish Heart and Lung Foundation
  3. Region Skane
  4. Skane University Hospital
  5. Novo Nordic Foundation
  6. Albert Pahlsson Research Foundation
  7. Crafoord foundation equipment grant from the Knut and Alice Wallenberg Foundation
  8. Linneus for the Lund University Diabetes Center (LUDC)
  9. National Institutes of Health (NIH) [P50-HG004233 CEGS]

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据