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Despite extensive efforts and many promising early findings, genome-wide association studies have explained only a small fraction of the genetic factors contributing to common human diseases. There are many theories about where this "missing heritability" might lie, but increasingly the prevailing view is that common variants, the target of GWAS, are not solely responsible for susceptibility to common diseases and a substantial portion of human disease risk will be found among rare variants. Relatively new, such variants have not been subject to purifying selection, and therefore may be particularly pertinent for neuropsychiatric disorders and other diseases with greatly reduced fecundity. Recently, several researchers have made great progress towards uncovering the genetics behind autism and schizophrenia. By sequencing families, they have found hundreds of de novo variants occurring only in affected individuals, both large structural copy number variants and single nucleotide variants. Despite studying large cohorts there has been little recurrence among the genes implicated suggesting that many hundreds of genes may underlie these complex phenotypes. The question becomes how to tie these rare mutations together into a cohesive picture of disease risk. Biological networks represent an intuitive answer, as different mutations which converge on the same phenotype must share some underlying biological process. Network-based analysis offers three major advantages: it allows easy integration of both common and rare variants, it allows us to assign significance to collection of genes where individual genes may not be significant due to rarity, and it allows easier identification of the biological processes underlying physical consequences. This work presents the construction of a novel phenotype network and a method for the analysis of disease-associated variants. This method has been applied to de novo mutations and GWAS results associated with both autism and schizophrenia and found clusters of genes strongly connected by shared function for both diseases. The results help elucidate the real physical consequences of putative disease mutations, leading to a better understanding of the pathophysiology of the diseases.