Estimation of the number of contributors to mixed samples of DNA by mitochondrial DNA analyses using massively parallel sequencing
We evaluated whether the number of contributors to mixed DNA samples can be estimated by analyzing the D-loop of mitochondrial DNA using massively parallel sequencing. The A- (positions 16,209–16,400) and B- (positions 30–284) amplicons in hypervariable regions 1 and 2, respectively, were sequenced using MiSeq with 2 × 251 cycles. Sequence extraction and trimming were performed using CLC Genomics Workbench 11 and the number of observed haplotypes was counted for each amplicon type using Microsoft Excel. The haplotype ratios were calculated by dividing the number of counted reads of the corresponding haplotype by the total number of sequence reads. Haplotypes that were over the threshold (5%) were defined as positive haplotypes. The number of larger positive haplotypes in either of the two amplicon types was defined as the number of contributors. Samples were collected from seven individuals. Seventeen mixed samples were prepared by mixing DNA from two to five contributors at various ratios. The number of contributors was correctly estimated from almost all of the mixed samples containing equal amounts of DNA from two to five people. In mixed samples of two or three people, the minor components were detected down to a ratio of 20:1 or 8:2:1. However, heteroplasmy, base deletions, and sharing of the same haplotypes caused incorrect estimations of the number of contributors. Although this method still has room for improvement, it may be useful for estimating the number of contributors in a mixed sample, as it does not rely on forensic mathematics.
KeywordsNumber of contributors Mixed DNA Mitochondrial DNA Massively parallel sequencing
The authors thank Dr. Hajime Miyaguchi (National Research Institute of Police Science) for his help with the MiSeq analyses.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
Informed consent was obtained from all of the participants.
This study was approved by the Ethics Committee of Juntendo University School of Medicine (No. 2016134).
- 5.Inman K, Rudin N, Cheng K, Robinson C, Kirschner A, Inman-Semerau L, Lohmueller KE (2015) Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles. BMC Bioinformatics 16:298. https://doi.org/10.1186/s12859-015-0740-8 CrossRefPubMedPubMedCentralGoogle Scholar
- 7.Parson W, Ballard D, Budowle B, Butler JM, Gettings KB, Gill P, Gusmão L, Hares DR, Irwin JA, King JL, Knijff P, Morling N, Prinz M, Schneider PM, Neste CV, Willuweit S, Phillips C (2016) Massively parallel sequencing of forensic STRs: considerations of the DNA commission of the International Society for Forensic Genetics (ISFG) on minimal nomenclature requirements. Forensic Sci. Int. Genet. 22:54–63. https://doi.org/10.1016/j.fsigen.2016.01.009 CrossRefPubMedGoogle Scholar
- 9.Jäger AC, Alvarez ML, Davis CP, Guzmán E, Han Y, Way L, Walichiewicz P, Silva D, Pham N, Caves G, Bruand J, Schlesinger F, Pond SJK, Varlaro J, Stephens KM, Holt CL (2017) Developmental validation of the MiSeq FGx forensic genomics system for targeted next generation sequencing in forensic DNA casework and database laboratories. Forensic Sci Int Genet 28:52–70. https://doi.org/10.1016/j.fsigen.2017.01.011 CrossRefPubMedGoogle Scholar
- 12.Nakanishi H, Pereira V, Børsting C, Yamamoto T, Tvedebrink T, Hara M, Takada A, Saito K, Morling N (2018) Analysis of mainland Japanese and Okinawan Japanese populations using the precision ID Ancestry Panel. Forensic Sci Int Genet 33:106–109. https://doi.org/10.1016/j.fsigen.2017.12.004 CrossRefPubMedGoogle Scholar
- 13.Parson W, Strobl C, Huber G, Zimmermann B, Gomes SM, Souto L, Fendt L, Delport R, Langit R, Wootton S, Lagacé R, Irwin J (2013) Reprint of: Evaluation of next generation mtGenome sequencing using the Ion Torrent Personal Genome Machine (PGM). Forensic Sci Int Genet 7:632–639. https://doi.org/10.1016/j.fsigen.2013.09.007 CrossRefPubMedGoogle Scholar
- 14.King JL, LaRue BL, Novroski NM, Stoljarova M, Seo SB, Zeng X, Warshauer DH, Davis CP, Parson W, Sajantila A, Budowle B (2014) High-quality and high-throughput massively parallel sequencing of the human mitochondrial genome using the Illumina MiSeq. Forensic Sci Int Genet 12:128–135. https://doi.org/10.1016/j.fsigen.2014.06.001 CrossRefPubMedGoogle Scholar
- 15.Davis C, Peters D, Warshauer D, King J, Budowle B (2015) Sequencing the hypervariable regions of human mitochondrial DNA using massively parallel sequencing: Enhanced data acquisition for DNA samples encountered in forensic testing. Legal Med 17:123–127. https://doi.org/10.1016/j.legalmed.2014.10.004 CrossRefPubMedGoogle Scholar
- 16.McElhoe JA, Holland MM, Makova KD, Su MS, Paul IM, Baker CH, Faith SA, Young B (2014) Development and assessment of an optimized next-generation DNA sequencing approach for the mtgenome using the Illumina MiSeq. Forensic Sci Int Genet 13:20–29. https://doi.org/10.1016/j.fsigen.2014.05.007 CrossRefPubMedPubMedCentralGoogle Scholar
- 20.Peck MA, Sturk-Andreaggi K, Thomas JT, Oliver RS, Barritt-Ross S, Marshall C (2018) Developmental validation of a Nextera XT mitogenome Illumina MiSeq sequencing method for high-quality samples. Forensic Sci Int Genet 34:25–36. https://doi.org/10.1016/j.fsigen.2018.01.004 CrossRefPubMedGoogle Scholar
- 21.Peck MA, Brandhagen MD, Marshall C, Diegoli TM, Irwin JA, Sturk-Andreaggi K (2016) Concordance and reproducibility of a next generation mtGenome sequencing method for high-quality samples using the Illumina MiSeq. Forensic Sci Int Genet 24:103–111. https://doi.org/10.1016/j.fsigen.2016.06.003 CrossRefPubMedGoogle Scholar
- 27.Wang DY, Chang CW, Lagacé RE, Calandro LM, Hennessy LK (2012) Developmental validation of the AmpFℓSTR® Identifiler® Plus PCR Amplification Kit: an established multiplex assay with improved performance. J Forensic Sci 57:453–465. https://doi.org/10.1111/j.1556-4029.2011.01963.x CrossRefPubMedGoogle Scholar