## 3 Results and Discussion
### Phylogenetic Clustering and Population-Level Analysis of Novel Mutations in SE-Z-DNA Loci  
To assess the representativeness of our mutation set, we compared the frequencies of similar mutations in population-scale datasets and performed phylogenetic clustering of individuals with novel mutations in Z-DNA-associated super-enhancer loci (see details in Section 2.6 of Materials and Methods). As individuals possess mutations at various sites, and mutation scores for each position are calculated independently (see Table S4 in the Supplementary Materials for detailed per-individual information, including mutation status, reference and alternative alleles, affected motifs, transcription factor binding changes, and intersections with pathogenic variants), the use of the Cumulative Allele Frequency Score (CAFS) is proposed. CAFS is a derived metric that represents the average allele frequency across multiple unrelated mutations for a given individual. This score is calculated by averaging the values for allele frequency in both the population's and the cohort of 100 individuals' variant frequency data. The purpose of this approach is to provide a cumulative evaluation of allele frequencies across individuals in order to reflect the landscape of selected genetic variations within the studied cohort in comparison with populational data. Taking into account that mutations considered for each individual are unrelated, meaning they occur at different genomic positions and may have distinct biological implications averaging of frequencies gives a generalized measure of overall allele frequency profile which allows for comparisons across individuals. For each individual, the CAFS is calculated as follows:  
\[CAFS_{P}=\frac{\sum_{i=1}^{n}(Frequency_{i})}{n}, \tag{1}\]  
where \(CAFS_{P}\) is the cohort allele frequency score for individual \(P\), \(n\) is the number of mutations for individual \(P\), and \(Frequency_{i}\) is either the populational or cohort of 100 allele frequency value for mutation \(i\). The obtained tree revealed distinct groups separated by deep clades, reflecting divergent trajectories within analyzed sequences (Figure 5). The CAFSs of novel mutations, as well as those intersecting with known pathogenic variants, revealed a consistent distribution pattern across all individuals, suggesting a shared evolutionary mechanism for the studied loci. The Spearman correlation coefficient of 0.95 between the CAFSs across clades reflects a strong similarity in mutation rates and suggests that the mutation frequencies are consistent and predictable within the European population. Revealed consistency enables the approximation of mutation frequencies of cohort and their potential functional significance in the European population. The showed branching patterns were significantly influenced by specific mutations, which served as key markers for cluster formation. Overall, we successfully clustered over 90% of the analyzed fragments. Six clusters were identified, each comprising regions with specific and closely related mutation patterns. The majority of genomes were grouped into clusters 1, 3, and 5, which included 23, 11, and 14 genomes, respectively (Table 1). In these clusters, no more than 1 mutation out of the 5 studied positions was observed, with the exception of genomes NA20512 and NA20581, whose inclusion in cluster 5 remains debatable. Clusters 2, 4, and 6, comprising 11, 5, and 7 genome fragments, respectively, were characterized by the presence of 1-3 alternative alleles at the loci of interest. The genome fragment with ID HG01603 was the most distinct from the others, exhibiting alternative alleles at all studied positions except for chr10:119394546. Among all clusters, the most variable region was the locus chr10:119394275, where an alternative allele was observed in 38% of the genomes. In these genomes, a long deletion at this locus resulted in the formation of new transcription factor binding motifs: RGKGGGGCGKGGC, GGGGGTGTCC, and DGGGYGKGGC.