We applied an information-based similarity index (IBS) [16, 17] to measure the pairwise dissimilarity of interbeat interval time series among a sample of healthy adult volunteers. The IBS method is based on rank-order frequency analysis of acceleration/ deceleration patterns of heart rate fluctuation. Because stimulus of b-AR results in acceleration of heart rate, the functional changes in genetic polymorphisms of b-AR may affect acceleration/deceleration patterns of heart rate, which can be detected by the IBS method. The analyses of the present study were two-fold: 1) a nonrandomness index [17] derived from the IBS method was applied to quantify the nonlinear aspect of HRV according to b-AR genotype and to test the correlation of this index with standard HRV indices; and 2) using agglomerative hierarchical cluster analysis, we unsupervisedly categorized these subjects into clusters based on pairwise dissimilarity among heart rate dynamics, and then we investigated the association of these clustering patterns with b-AR gene polymorphisms. We show that this bottom-up, categorization approach combining the IBS method and hierarchical cluster analysis can detect subgroups of subjects based on phenotypes that are associated with b-AR gene polymorphisms.