Rare diseases are challenging to diagnose and collectively affect a large fraction of the population. This work sought to develop an approach to generate models for probabilistic reasoning focused on the presence of a specified phenotypic abnormality. The approach generates a Bayesian network, a graphical AI model that uses probability to reason under uncertainty, that includes all diseases that can cause the specified abnormality as well as all phenotypic abnormalities caused by those diseases. The approach efficiently computes the probabilities of the possible diagnoses and evaluates the impact of additional evidence. One can use the model to identify the observations that yield the greatest information to reduce uncertainty. An example model for diagnosis of a finding of enlarged kidney is presented to demonstrate the feasibility and advantages of the approach. Further work includes incorporation of age of onset and inheritance pattern of the diseases, hierarchical relationships among diseases and phenotypic abnormalities to allow diagnosis based on information at varying levels of granularity, and user interfaces to simplify interaction with the models.