Asthma is the most common chronic childhood disease and has seen increasing prevalence worldwide. While there is existing evidence of familial and other risk factors for pediatric asthma, there is a need for further studies to explore and understand interactions among these risk factors. The goal of this study was to develop an approach for mining, visualizing, and evaluating association rules representing pairwise interactions among potential familial risk factors based on information documented as part of a patient's family history in the electronic health record. As a case study, 10,260 structured family history entries for a cohort of 1,531 pediatric asthma patients were extracted and analyzed to generate family history associations at different levels of granularity. The preliminary results highlight the potential of this approach for validating known knowledge and suggesting opportunities for further investigation that may contribute to improving prediction of asthma risk in children.