Most common chronic diseases are multifactorial and characteristically involve the responses and influences of susceptibility and modifier genes that are subject to environmental factors. These interactions, mechanisms and phenotypic consequences can be richly represented using scale-free networks with semantically definable nodes and edges. Genomic studies using linkage analyses detect quantitative trait loci that encompass a large number of disease candidate genes. Similarly, transcriptomic studies using differential gene expression profiling generate hundreds of potential disease candidate genes that themselves may not include genetically variant genes that are responsible for the expression pattern signature. Hypothesizing that the majority of disease causal genes are biochemically known to play functionally important roles and whose mutations produce