An immobilized sequence-specific oligonucleotide (SSO) probe system consisting of 16 SSO probes that detect sequence polymorphisms within five regions of the mtDNA control region was used to investigate the frequency of heteroplasmy in human mtDNA. Five regions of hypervariable region II (HVII) of the control region were studied in blood-, muscle-, heart-, and brain-tissue samples collected from 43 individuals during autopsy. An initial search for heteroplasmy was conducted by use of the SSO probe system. Samples in which multiple probe signals were detected within a region were sequenced for the HVII region, to verify the typing-strip results. The frequency of heteroplasmy was 5 of 43 individuals, or 11.6%. The frequency of heteroplasmy differed across tissue types, being higher in muscle tissue. The difference in the frequency of heteroplasmy across different age groups was statistically significant, which suggests that heteroplasmy increases with age. As a test for contamination and to confirm heteroplasmy, the samples were sequenced for the HVI region and were typed by use of a panel of five polymorphic nuclear markers. Portions of the tissues that appeared to be heteroplasmic were extracted at least one additional time; all gave identical results. The results from these tests indicate that the multiple sequences present in individual samples result from heteroplasmy and not from contamination.

Massively parallel (next-generation) sequencing provides a powerful method to analyze DNA from many different sources, including degraded and trace samples. A common challenge, however, is that many forensic samples are often known or suspected mixtures of DNA from multiple individuals. Haploid lineage markers, such as mitochondrial (mt) DNA, are useful for analysis of mixtures because, unlike nuclear genetic markers, each individual contributes a single sequence to the mixture. Deconvolution of these mixtures into the constituent mitochondrial haplotypes is challenging as typical sequence read lengths are too short to reconstruct the distinct haplotypes completely. We present a powerful computational approach for determining the constituent haplotypes in massively parallel sequencing data from potentially mixed samples. At the heart of our approach is an expectation maximization based algorithm that co-estimates the overall mixture proportions and the source haplogroup for each read individually. This approach, implemented in the software package mixemt, correctly identifies haplogroups from mixed samples across a range of mixture proportions. Furthermore, our method can separate fragments in a mixed sample by the most likely originating contributor and generate reconstructions of the constituent haplotypes based on known patterns of mtDNA diversity.