The study of inherited genomic variation through genome wide association studies (GWAS) promised to provide key biologic insight into common diseases of public health significance such as obesity, type II diabetes (T2D), and cancer. While many large studies of these traits have been conducted, the results have been disappointing – identifying loci of small influence which are difficult to replicate across studies. This difficulty, in part, is due to the heterogeneity of underlying trait evolutionary history and complexity of genetics underlying the trait. Analysis using biologic networks embraces this complexity. Using novel methods that examine variation integrated via networks we find that we can identify common pathways across independent data sets that have markedly higher influence. More provocatively, we find that many of these susceptibility pathways are shared across the complex traits obesity, T2D, and liver cancer. This latter observation suggests that it may be possible to both identify individuals at differential risk of developing disease and better understand why an individual’s disease progresses down specific paths.