Objective: Mitochondria drive cellular energy production and regulate key biological processes. High levels of heteroplasmic in mitochondrial DNA (mtDNA) variants can cause mitochondrial dysfunction and clinical symptoms. Third-generation sequencing overcomes the limitations of traditional mtDNA analysis methods, offering improved cost, throughput, and sensitivity. We developed an integrated approach for analyzing methylation patterns and genetic variations in mtDNA and ADME genes. Methods: We implemented Oxford Nanopore’s long-read sequencing with adaptive sampling (AS) to enrich enzymatically linearized mtDNA and absorption, distribution, metabolism, and excretion (ADME) genes without PCR amplification, enabling native sequencing in adipose-derived mesenchymal stem cells (AdMSC). Our custom algorithm preserved phase relationships between base modifications and sequence polymorphisms. Results: We identified differential methylation patterns in ADME genes correlating with specific genetic variants, suggesting epigenetic regulation of drug response. Adaptive sampling identifies a wider range of variant diversity, while whole genome sequencing (WGS) uncovers higher-frequency hotspots. Both methods offer complementary insights into mitochondrial heteroplasmy. In mtDNA, direct sequencing showed extensive hypomethylation, and low levels of non-CpG methylation were detected regardless of sequencing coverage depth. These sparse methylation patterns showed non-random distribution, correlating with functional regions and heteroplasmic sites. Conclusions: This study demonstrates the utility of adaptive sampling for the integrated analysis of mtDNA heteroplasmy and native base modifications, revealing widespread hypomethylation independent of coverage depth. The approach showcases the potential for combined pharmacoepigenomic and mitochondrial profiling in precision medicine, disease modeling, and therapeutic development.