Genome Wide Association Studies (GWAS) identify susceptibility loci for complex traits. However, interpretation of these results through integration of molecular networks and functional gene annotation holds promise for identifying particular genes of interest as well as new susceptibility loci. We present a network-based approach using GWAS and comorbidity data to predict additional candidate genes for lipid and lipoprotein traits. Starting with Global Lipids Genetics Consortium (GLGC) GWAS for four traits of interest, we apply a prediction pipeline incorporating interactome, co-expression and comorbidity data to identify 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=1x10-5 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.