Network-based analysis of genome wide association data provides novel candidate genes for lipid and lipoprotein traits.

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 sel

SUBMITTER: Sharma A 

PROVIDER: S-EPMC3820950 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature