As knowledge of human genetic polymorphisms grows, so does the opportunity and challenge of identifying those polymorphisms that may impact the health or disease risk of an individual person. A critical need is to organize large-scale polymorphism analyses and to prioritize candidate non-synonymous coding SNPs (nsSNPs) that should be tested in experimental and epidemiological studies to establish their context-specific impacts on protein function. In addition, with emerging high-resolution clinical genetics testing, new polymorphisms must be analyzed in the context of all available protein feature knowledge including other known mutations and polymorphisms. To approach this, we developed PolyDoms (http://polydoms.cchmc.org/) as a database to integrate the results of multiple algorithmic procedures and functional criteria applied to the entire Entrez dbSNP dataset. In addition to predicting structural and functional impacts of all nsSNPs, filtering functions enable group-based identification of potentially harmful nsSNPs among multiple genes associated with specific diseases, anatomies, mammalian phenotypes, gene ontologies, pathways or protein domains. PolyDoms, thus, provides a means to derive a list of candidate SNPs to be evaluated in experimental or epidemiological studies for impact on protein functions and disease risk associations. PolyDoms will continue to be curated to improve its usefulness.