Skip to main content

Exploring statistical weight estimates for mitochondrial DNA matches involving heteroplasmy


Massively parallel sequencing (MPS) of mitochondrial (mt) DNA allows forensic laboratories to report heteroplasmy on a routine basis. Statistical approaches will be needed to determine the relative frequency of observing an mtDNA haplotype when including the presence of a heteroplasmic site. Here, we examined 1301 control region (CR) sequences, collected from individuals in four major population groups (European, African, Asian, and Latino), and covering 24 geographically distributed haplogroups, to assess the rates of point heteroplasmy (PHP) on an individual and nucleotide position (np) basis. With a minor allele frequency (MAF) threshold of 2%, the data was similar across population groups, with an overall PHP rate of 37.7%, and the majority of heteroplasmic individuals (77.3%) having only one site of heteroplasmy. The majority (75.2%) of identified PHPs had an MAF of 2–10%, and were observed at 12.6% of the nps across the CR. Both the broad and phylogenetic testing suggested that in many cases the low number of observations of heteroplasmy at any one np results in a lack of statistical association. The posterior frequency estimates, which skew conservative to a degree depending on the sample size in a given haplogroup, had a mean of 0.152 (SD 0.134) and ranged from 0.031 to 0.83. As expected, posterior frequency estimates decreased in accordance with 1/n as the sample size (n) increased. This provides a proposed conservative statistical framework for assessing haplotype/heteroplasmy matches when applying an MPS technique in forensic cases and will allow for continual refinement as more data is generated, both within the CR and across the mitochondrial genome.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Data availability

A portion (n = 731) of the MPS data files associated with this study are available on EMPOP (accession number EMP00747). The remaining MPS data files cannot be made available as consent to do so was not obtained.

This study was approved by The Pennsylvania State University internal review board (IRB) protocol STUDY00000970 and #HRB-588.


  1. Amorim A, Fernandes T, Taveira N (2019) Mitochondrial DNA in human identification: a review. PeerJ 7.

  2. Bertoglio B, Grignani P, Di Simone P et al (2020) Disaster victim identification by kinship analysis: the Lampedusa October 3rd, 2013 shipwreck. Forensic Sci Int Genet 44.

  3. Hampikian G, West E, Akselrod O (2011) The genetics of innocence: analysis of 194 U.S. DNA exonerations. Annu Rev Genomics Hum Genet 12.

  4. Merheb M, Matar R, Hodeify R et al (2019) Mitochondrial DNA, a powerful tool to decipher ancient human civilization from domestication to music, and to uncover historical murder cases. Cells 8.

  5. Just RS, Irwin JA, Parson W (2015) Mitochondrial DNA heteroplasmy in the emerging field of massively parallel sequencing. Forensic Sci Int Genet 18:131–139.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Lyons EA, Scheible MK, Sturk-Andreaggi K et al (2013) A high-throughput Sanger strategy for human mitochondrial genome sequencing. BMC Genomics 14.

  7. Holland M, Parsons T (1999) Mitochondrial DNA sequence analysis - validation and use for forensic casework. Forensic Sci Rev 11:21–50

    CAS  PubMed  Google Scholar 

  8. Melton T, Dimick G, Higgins B et al (2012) Mitochondrial DNA analysis of 114 hairs measuring less than 1 cm from a 19-year-old homicide. Investig Genet 3.

  9. Forsythe B, Melia L, Harbison S (2021) Methods for the analysis of mitochondrial DNA. WIREs Forensic Sci 3.

  10. Canale LC, Parson W, Holland MM (2021) The time is now for ubiquitous forensic mtMPS analysis. WIREs Forensic Sci 4(1):1431.

  11. Bruijns B, Tiggelaar R, Gardeniers H (2018) Massively parallel sequencing techniques for forensics: a review. Electrophoresis 39.

  12. King TE, Fortes GG, Balaresque P et al (2014) Identification of the remains of King Richard III. Nat Commun 5.

  13. McElhoe JA, Holland MM, Makova KD et al (2014) Development and assessment of an optimized next-generation DNA sequencing approach for the mtgenome using the Illumina MiSeq. Forensic Sci Int Genet 13.

  14. Peck MA, Sturk-Andreaggi K, Thomas JT et al (2018) Developmental validation of a Nextera XT mitogenome Illumina MiSeq sequencing method for high-quality samples. Forensic Sci Int Genet 34.

  15. Cihlar JC, Strobl C, Lagacé R et al (2020) Distinguishing mitochondrial DNA and NUMT sequences amplified with the precision ID mtDNA whole genome panel. Mitochondrion 55.

  16. Strobl C, Churchill Cihlar J, Lagacé R et al (2019) Evaluation of mitogenome sequence concordance, heteroplasmy detection, and haplogrouping in a worldwide lineage study using the Precision ID mtDNA Whole Genome Panel. Forensic Sci Int Genet 42.

  17. Brandhagen MD, Just RS, Irwin JA (2020) Validation of NGS for mitochondrial DNA casework at the FBI Laboratory. Forensic Sci Int Genet 44.

  18. Desmyter S, Dognaux S, Noel F, Prieto L (2019) Base specific variation rates at mtDNA positions 16093 and 16183 in human hairs. Forensic Sci Int Genet 43.

  19. Irwin JA, Saunier JL, Niederstätter H et al (2009) Investigation of heteroplasmy in the human mitochondrial DNA control region: a synthesis of observations from more than 5000 global population samples. J Mol Evol 68.

  20. Gallimore JM, McElhoe JA, Holland MM (2018) Assessing heteroplasmic variant drift in the mtDNA control region of human hairs using an MPS approach. Forensic Sci Int Genet 32.

  21. Kim BM, Hong SR, Chun H et al (2020) Comparison of whole mitochondrial genome variants between hair shafts and reference samples using massively parallel sequencing. Int J Legal Med 134.

  22. Sturk-Andreaggi K, Parson W, Allen M, Marshall C (2020) Impact of the sequencing method on the detection and interpretation of mitochondrial DNA length heteroplasmy. Forensic Sci Int Genet 44.

  23. McElhoe JA, Holland MM (2020) Characterization of background noise in MiSeq MPS data when sequencing human mitochondrial DNA from various sample sources and library preparation methods. Mitochondrion 52:40–55.

    CAS  Article  PubMed  Google Scholar 

  24. González M del M, Ramos A, Aluja MP, Santos C (2020) Sensitivity of mitochondrial DNA heteroplasmy detection using next generation sequencing. Mitochondrion 50.

  25. Holland MM, Makova KD, McElhoe JA (2018) Deep-coverage MPS analysis of heteroplasmic variants within the mtgenome allows for frequent differentiation of maternal relatives. Genes (Basel) 9.

  26. Li M, Stoneking M (2012) A new approach for detecting low-level mutations in next-generation sequence data. Genome Biol 13.

  27. Ivanov PL, Wadhams MJ, Roby RK et al (1996) Mitochondrial DNA sequence heteroplasmy in the Grand Duke of Russia Georgij Romanov establishes the authenticity of the remains of Tsar Nicholas II. Nat Genet 12:417–420.

    CAS  Article  PubMed  Google Scholar 

  28. Parson W, Gusmão L, Hares DR et al (2014) DNA Commission of the International Society for Forensic Genetics: revised and extended guidelines for mitochondrial DNA typing. Forensic Sci Int Genet 13.

  29. Carracedo A, Bär W, Lincoln P et al (2000) DNA Commission of the International Society for Forensic Genetics: guidelines for mitochondrial DNA typing. Forensic Sci Int 110.

  30. Forster L, Forster P, Gurney SMR et al (2010) Evaluating length heteroplasmy in the human mitochondrial DNA control region. Int J Legal Med 124.

  31. Huber N, Parson W, Dür A (2018) Next generation database search algorithm for forensic mitogenome analyses. Forensic Sci Int Genet 37.

  32. Parson W, Dür A (2007) EMPOP—a forensic mtDNA database. Forensic Sci Int Genet 1.

  33. Wallace DC, Chalkia D (2013) Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease. Cold Spring Harb Perspect Biol 5.

  34. Weissensteiner H, Pacher D, Kloss-Brandstätter A et al (2016) HaploGrep 2: mitochondrial haplogroup classification in the era of high-throughput sequencing. Nucleic Acids Res 44:W58–W63.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. van Oven M (2015) PhyloTree Build 17: Growing the human mitochondrial DNA tree. Forensic Sci Int Genet Suppl Ser 5.

  36. Ye J, Coulouris G, Zaretskaya I et al (2012) Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics 13.

  37. Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215.

  38. Smith B, Fisher D, Weedn V et al (1993) A systematic approach to the sampling of dental DNA. J Forensic Sci 38:1194–1209

    CAS  PubMed  Google Scholar 

  39. Stoneking M, Hedgecock D, Higuchi RG, et al (1991) Population variation of human mtDNA control region sequences detected by enzymatic amplification and sequence-specific oligonucleotide probes. Am J Hum Genet 48:370–382

  40. Rathbun MM, McElhoe JA, Parson W, Holland MM (2017) Considering DNA damage when interpreting mtDNA heteroplasmy in deep sequencing data. Forensic Sci Int Genet 26.

  41. Brandstätter A, Niederstätter H, Pavlic M et al (2007) Generating population data for the EMPOP database—an overview of the mtDNA sequencing and data evaluation processes considering 273 Austrian control region sequences as example. Forensic Sci Int 166.

  42. Zimmermann B, Röck AW, Dür A, Parson W (2014) Improved visibility of character conflicts in quasi-median networks with the EMPOP NETWORK software. Croat Med J 55.

  43. Free Software Foundation (2007) Bash [Unix Shell Program]

  44. RStudio Team (2020) RStudio: Integrated Development for RStudio, PBC, Boston, MA.

  45. Fritz SA, Purvis A (2010) Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conserv Biol 24.

  46. Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30.

  47. Hewson P (2015) Bayesian data analysis 3rd edn A. Gelman, J. B.Carlin, H. S.Stern, D. B.Dunson, A.Vehtari and D. B.Rubin, 2013 Boca Raton, Chapman and Hall-CRC 676 pp., £44.99 ISBN 1–439–84095–4. J R Stat Soc Ser A (Statistics Soc 178.

  48. Just RS, Scheible MK, Fast SA et al (2014) Development of forensic-quality full mtGenome haplotypes: success rates with low template specimens. Forensic Sci Int Genet 10.

  49. Santibanez-Koref M, Griffin H, Turnbull DM et al (2019) Assessing mitochondrial heteroplasmy using next generation sequencing: a note of caution. Mitochondrion 46.

  50. Li M, Schroeder R, Ko A, Stoneking M (2012) Fidelity of capture-enrichment for mtDNA genome sequencing: influence of NUMTs. Nucleic Acids Res 40.

  51. Ring JD, Sturk-Andreaggi K, Alyse Peck M, Marshall C (2018) Bioinformatic removal of NUMT-associated variants in mitotiling next-generation sequencing data from whole blood samples. Electrophoresis 39.

  52. Umbria M, Ramos A, Aluja MP, Santos C (2020) The role of control region mitochondrial DNA mutations in cardiovascular disease: stroke and myocardial infarction. Sci Rep 10.

  53. Goios A, Prieto L, Amorim A, Pereira L (2008) Specificity of mtDNA-directed PCR—influence of NUclear MTDNA insertion (NUMT) contamination in routine samples and techniques. Int J Legal Med 122.

  54. Ramos A, Barbena E, Mateiu L et al (2011) Nuclear insertions of mitochondrial origin: database updating and usefulness in cancer studies. Mitochondrion 11.

  55. Ramos A, Santos C, Alvarez L et al (2009) Human mitochondrial DNA complete amplification and sequencing: a new validated primer set that prevents nuclear DNA sequences of mitochondrial origin co-amplification. Electrophoresis 30.

  56. Maude H, Davidson M, Charitakis N et al (2019) NUMT confounding biases mitochondrial heteroplasmy calls in favor of the reference allele. Front Cell Dev Biol 7.

  57. Loman NJ, Misra RV, Dallman TJ et al (2012) Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol 30.

  58. Holland CA, McElhoe JA, Gaston-Sanchez S, Holland MM (2021) Damage patterns observed in mtDNA control region MPS data for a range of template concentrations and when using different amplification approaches. Int J Legal Med 135.

  59. Ross MG, Russ C, Costello M et al (2013) Characterizing and measuring bias in sequence data. Genome Biol 14.

  60. Schirmer M, Ijaz UZ, D’Amore R et al (2015) Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res 43.

  61. Dür A, Huber N, Parson W (2021) Fine-tuning phylogenetic alignment and haplogrouping of mtDNA sequences. Int J Mol Sci 22.

  62. Klimentidis YC, Miller GF, Shriver MD (2009) Genetic admixture, self-reported ethnicity, self-estimated admixture, and skin pigmentation among Hispanics and Native Americans. Am J Phys Anthropol 138.

  63. Secher B, Fregel R, Larruga JM et al (2014) The history of the North African mitochondrial DNA haplogroup U6 gene flow into the African, Eurasian and American continents. BMC Evol Biol 14.

  64. Bedoya G, Montoya P, Garcia J et al (2006) Admixture dynamics in Hispanics: a shift in the nuclear genetic ancestry of a South American population isolate. Proc Natl Acad Sci 103.

  65. Bryc K, Durand EY, Macpherson JM et al (2015) The genetic ancestry of African Americans, Latinos, and European Americans across the United States. Am J Hum Genet 96.

  66. Allard MW, Wilson MR, Monson KL, Budowle B (2004) Control region sequences for East Asian individuals in the Scientific Working Group on DNA Analysis Methods forensic mtDNA data set. Leg Med 6.

  67. Alves-Silva J, da Silva SM, Guimarães PEM et al (2000) The ancestry of Brazilian mtDNA lineages. Am J Hum Genet 67.

  68. Wood MR, Sturk-Andreaggi K, Ring JD et al (2019) Resolving mitochondrial haplogroups B2 and B4 with next-generation mitogenome sequencing to distinguish Native American from Asian haplotypes. Forensic Sci Int Genet 43.

  69. Yao Y-G, Kong Q-P, Bandelt H-J et al (2002) Phylogeographic differentiation of mitochondrial DNA in Han Chinese. Am J Hum Genet 70.

  70. Forster P, Harding R, Torroni A, Bandelt HJ (1996) Origin and evolution of Native American mtDNA variation: a reappraisal. Am J Hum Genet 59:935–945

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Perego UA, Angerhofer N, Pala M et al (2010) The initial peopling of the Americas: a growing number of founding mitochondrial genomes from Beringia. Genome Res 20.

  72. Li M, Schröder R, Ni S et al (2015) Extensive tissue-related and allele-related mtDNA heteroplasmy suggests positive selection for somatic mutations. Proc Natl Acad Sci U S A 112:2491–2496.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  73. Rebolledo-Jaramillo B, Su MS-W, Stoler N et al (2014) Maternal age effect and severe germ-line bottleneck in the inheritance of human mitochondrial DNA. Proc Natl Acad Sci U S A 111.

  74. Skonieczna K, Malyarchuk B, Jawień A et al (2015) Heteroplasmic substitutions in the entire mitochondrial genomes of human colon cells detected by ultra-deep 454 sequencing. Forensic Sci Int Genet 15.

  75. Tully LA, Parsons TJ, Steighner RJ et al (2000) A Sensitive denaturing gradient-gel electrophoresis assay reveals a high frequency of heteroplasmy in hypervariable region I of the human mtDNA control region. Am J Hum Genet 67.

  76. Tamura K, Nei M (1993) Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol.

  77. Vigilant L, Stoneking M, Harpending H, et al. (1991) African populations and the evolution of human mitochondrial DNA. Science (80- ) 253.

  78. Naue J, Hörer S, Sänger T et al (2015) Evidence for frequent and tissue-specific sequence heteroplasmy in human mitochondrial DNA. Mitochondrion 20.

  79. Gaag KJV, Desmyter S, Smit S et al (2020) Reducing the number of mismatches between hairs and buccal references when analysing mtDNA heteroplasmic variation by massively parallel sequencing. Genes (Basel) 11:1355.

Download references


We gratefully acknowledge Mark Shriver and Corey Liebowitz for collection of saliva samples (The Pennsylvania State University ADAPT2 study; IRB#45727). The authors would also like to thank Molly Rathbun and Emmy Demchak for assisting in Illumina processing, Troy Adams for logistical support, Nicole Huber for assisting in quality control and EMPOP, Arne Dür for haplogroup estimation using a refined set of PhyloTree motifs and alignment algorithm, and Charla Marshall and Kim Sturk-Andreaggi for discussion and guidance on NUMT evaluation of our dataset.


This study was funded by the National Institute of Justice—NIJ Grant Numbers 2014-DN-BX-K022 and 2016-DN-BX-0171.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jennifer A. McElhoe.

Ethics declarations

Consent to participate

Informed consent was obtained from all individual participants/donors included in this study.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

McElhoe, J.A., Wilton, P.R., Parson, W. et al. Exploring statistical weight estimates for mitochondrial DNA matches involving heteroplasmy. Int J Legal Med 136, 671–685 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Forensic mtDNA
  • Control region
  • Massively parallel sequencing
  • MiSeq
  • Rates of mtDNA heteroplasmy
  • Forensic statistics