We illustrate data analytic concerns that arise in the context of relating genotype, as represented by amino acid sequence, to phenotypes (outcomes). The present application examines whether peptides that bind to a particular major histocompatibility complex (MHC) class I molecule have characteristic amino acid sequences. However, the concerns identified and addressed are considerably more general. It is recognized that simple rules for predicting binding based solely on preferences for specific amino acids in certain (anchor) positions of the peptide’s amino acid sequence are generally inadequate and that binding is potentially influenced by all sequence positions as well as between?position interactions. The desire to elucidate these more complex prediction rules has spawned various modeling attempts, the shortcomings of which provide motivation for the methods adopted here. Because of (i) this need to model between?position interactions, (ii) amino acids constituting a highly (20) multilevel unordered categorical covariate, and (iii) there frequently being numerous such covariates (i.e., positions) comprising the sequence, standard regression/classification techniques are problematic due to the proliferation of indicator variables required for encoding the sequence position covariates and attendant interactions. These difficulties have led to analyses based on (continuous) properties (e.g., molecular weights) of the amino acids. However, there is potential information loss in such an approach if the properties used are incomplete and/or do not capture the mechanism underlying association with the phenotype. Here we demonstrate that handling unordered categorical covariates with numerous levels and accompanying interactions can be done effectively using classification trees and recently devised bump?hunting methods. We further tackle the question of whether observed associations are attributable to amino acid properties as well as addressing the assessment and implications of between?position covariation.