Copy number variations represent a substantial source of genetic variation and are associated with a plethora of physiological and pathophysiological conditions. Joint copy number and allelic variations (CNAVs) are difficult to analyze and require new strategies to unravel the properties of genotype distributions. We developed a Bayesian hidden Markov model (HMM) approach that allows dissecting intrinsic properties and metastructures of the distribution of CNAVs within populations, in particular haplotype phases of genes with varying copy numbers. As a key feature, this approach incorporates an extension of the Hardy-Weinberg equilibrium, allowing both a comprehensive and parsimonious model design. We demonstrate the quality of performance and applicability of the HMM approach with a real data set describing the Fcγ receptor (FcγR) gene region. Our concept, using a dynamic process to analyze a static distribution, establishes the basis for a novel understanding of complex genomic data sets.