Advertisement

International Journal of Legal Medicine

, Volume 133, Issue 3, pp 719–729 | Cite as

Mixture deconvolution by massively parallel sequencing of microhaplotypes

  • Lindsay Bennett
  • Fabio Oldoni
  • Kelly Long
  • Selena Cisana
  • Katrina Madella
  • Sharon Wootton
  • Joseph Chang
  • Ryo Hasegawa
  • Robert Lagacé
  • Kenneth K. Kidd
  • Daniele PodiniEmail author
Original Article

Abstract

Short tandem repeat polymorphisms (STRs) are the standard markers for forensic human identification. STRs are highly polymorphic loci analyzed using a direct PCR-to-CE (capillary electrophoresis) approach. However, STRs have limitations particularly when dealing with complex mixtures. These include slippage of the polymerase during amplification causing stutter fragments that can be indistinguishable from minor contributor alleles, preferential amplification of shorter alleles, and limited number of loci that can be effectively co-amplified with CE. Massively parallel sequencing (MPS), by enabling a higher level of multiplexing and actual sequencing of the DNA, provides forensic practitioners an increased power of discrimination offered by the sequence of STR alleles and access to new sequence-based markers. Microhaplotypes (i.e., microhaps or MHs) are emerging multi-allelic loci of two or more SNPs within < 300 bp that are highly polymorphic, have alleles all of the same length, and do not generate stutter fragments. The growing number of loci described in the literature along with initial mixture investigations supports the potential for microhaps to aid in mixture interpretation and the purpose of this study was to demonstrate that practically. A panel of 36 microhaplotypes, selected from a set of over 130 loci, was tested with the Ion S5™ MPS platform (Thermo Fisher Scientific) on single-source samples, synthetic two-to-six person mixtures at different concentrations/contributor ratios, and on crime scene-like samples. The panel was tested both in multiplex with STRs and SNPs and individually. The analysis of single-source samples showed that the allele coverage ratio across all loci was 0.88 ± 0.08 which is in line with the peak height ratio of STR alleles in CE. In mixture studies, results showed that the input DNA can be much higher than with conventional CE, without the risk of oversaturating the detection system, enabling an increased sensitivity for the minor contributor in imbalanced mixtures with abundant amounts of DNA. Furthermore, the absence of stutter fragments simplifies the interpretation. On casework-like samples, MPS of MHs enabled the detection of a higher number of alleles from minor donors than MPS and CE of STRs. These results demonstrated that MPS of microhaplotypes can complement STRs and enhance human identification practices when dealing with complex imbalanced mixtures.

Keywords

Microhaplotype Single-nucleotide polymorphism Massively parallel sequencing (MPS) Mixture deconvolution Forensic DNA samples 

Notes

Acknowledgments

The authors thank Dr. Moses S. Schanfield for providing the DNA samples, collected and extracted between 1993 and 2003, and used in this study.

Funding information

This study was in part supported by National Institute of Justice through grants No. 2017-DN-BX-0164 awarded to Daniele Podini and No. 2015-DN-BX-K023 awarded to Kenneth K. Kidd, and by the Swiss National Science Foundation through grant No. 2017-P2LAP3_174742 awarded to Fabio Oldoni.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

414_2019_2010_MOESM1_ESM.xlsx (10 kb)
ESM 1 (XLSX 10 kb)
414_2019_2010_MOESM2_ESM.xlsx (11 kb)
ESM 2 (XLSX 11 kb)
414_2019_2010_MOESM3_ESM.xlsx (12 kb)
ESM 3 (XLSX 11 kb)

References

  1. 1.
    Butler JM, Buel E, Crivellente F, McCord BR (2004) Forensic DNA typing by capillary electrophoresis using the ABI Prism 310 and 3100 genetic analyzers for STR analysis. Electrophoresis 25:1397–1412.  https://doi.org/10.1002/elps.200305822 CrossRefGoogle Scholar
  2. 2.
    Butler JM (2015) The future of forensic DNA analysis. Philos Trans R Soc Lond Ser B Biol Sci 370:577–579.  https://doi.org/10.1098/rstb.2014.0252 CrossRefGoogle Scholar
  3. 3.
    Gill P, Haned H, Bleka O, Hansson O, Dørum G, Egeland T (2015) Genotyping and interpretation of STR-DNA: low-template, mixtures and database matches—twenty years of research and development. Forensic Sci Int Genet 18:100–117.  https://doi.org/10.1016/j.fsigen.2015.03.014 CrossRefGoogle Scholar
  4. 4.
    Perlin MW, Belrose JL, Duceman BW (2013) New York State TrueAllele® casework validation study. J Forensic Sci 58:1458–1466.  https://doi.org/10.1111/1556-4029.12223 CrossRefGoogle Scholar
  5. 5.
    Bright J-A, Taylor D, McGovern C, Cooper S, Russell L, Abarno D, Buckleton J (2016) Developmental validation of STRmix™, expert software for the interpretation of forensic DNA profiles. Forensic Sci Int Genet 23:226–239.  https://doi.org/10.1016/j.fsigen.2016.05.007 CrossRefGoogle Scholar
  6. 6.
    Gettings KB, Aponte RA, Kiesler KM, Vallone PM (2015) The next dimension in STR sequencing: polymorphisms in flanking regions and their allelic associations. Forensic Sci Int Genet Suppl Ser 5:e121–e123.  https://doi.org/10.1016/j.fsigss.2015.09.049 CrossRefGoogle Scholar
  7. 7.
    Gettings KB, Aponte RA, Vallone PM (2015) STR allele sequence variation: current knowledge and future issues. Forensic Sci Int Genet 18:118–130.  https://doi.org/10.1016/j.fsigen.2015.06.005 CrossRefGoogle Scholar
  8. 8.
    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 CrossRefGoogle Scholar
  9. 9.
    van der Gaag KJ, de Leeuw RH, Hoogenboom J, Patel J, Storts DR, Laros JFJ, de Knijff P (2016) Massively parallel sequencing of short tandem repeats—population data and mixture analysis results for the PowerSeq™ system. Forensic Sci Int Genet 24:86–96.  https://doi.org/10.1016/j.fsigen.2016.05.016 CrossRefGoogle Scholar
  10. 10.
    Aponte RA, Gettings KB, Dueller DL, Coble MD, Vallone PM (2015) Sequence-based analysis of stutter at STR loci: characterization and utility. Forensic Sci Int Genet Suppl Ser 5:e456–e458.  https://doi.org/10.1016/J.FSIGSS.2015.09.181 CrossRefGoogle Scholar
  11. 11.
    Børsting C, Fordyce SL, Olofsson J, Mogensen HS, Morling N (2014) Evaluation of the Ion TorrentTM HID SNP 169-plex: a SNP typing assay developed for human identification by second generation sequencing. Forensic Sci Int Genet 12:144–154.  https://doi.org/10.1016/j.fsigen.2014.06.004 CrossRefGoogle Scholar
  12. 12.
    Ambers AD, Churchill JD, King JL, Stoljarova M, Gill-King H, Assidi M, Abu-Elmagd M, Buhmeida A, Budowle B, Budowle B (2016) More comprehensive forensic genetic marker analyses for accurate human remains identification using massively parallel DNA sequencing. BMC Genomics 17:750.  https://doi.org/10.1186/s12864-016-3087-2 CrossRefGoogle Scholar
  13. 13.
    Wendt FR, Warshauer DH, Zeng X, Churchill JD, Novroski NMM, Song B, King JL, LaRue BL, Budowle B (2016) Massively parallel sequencing of 68 insertion/deletion markers identifies novel microhaplotypes for utility in human identity testing. Forensic Sci Int Genet 25:198–209.  https://doi.org/10.1016/j.fsigen.2016.09.005 CrossRefGoogle Scholar
  14. 14.
    Brown H, Thompson R, Murphy G, Peters D, La Rue B, King J, Montgomery AH, Carroll M, Baus J, Sinha S, Wendt FR, Song B, Chakraborty R, Budowle B, Sinha SK (2017) Development and validation of a novel multiplexed DNA analysis system, InnoTyper® 21. Forensic Sci Int Genet 29:80–99.  https://doi.org/10.1016/j.fsigen.2017.03.017 CrossRefGoogle Scholar
  15. 15.
    Westen AA, Matai AS, Laros JFJ, Meiland HC, Jasper M, de Leeuw WJF, de Knijff P, Sijen T (2009) Tri-allelic SNP markers enable analysis of mixed and degraded DNA samples. Forensic Sci Int Genet 3:233–241.  https://doi.org/10.1016/j.fsigen.2009.02.003 CrossRefGoogle Scholar
  16. 16.
    Phillips C, Amigo J, Carracedo Á, Lareu MV (2015) Tetra-allelic SNPs: informative forensic markers compiled from public whole-genome sequence data. Forensic Sci Int Genet 19:100–106.  https://doi.org/10.1016/j.fsigen.2015.06.011 CrossRefGoogle Scholar
  17. 17.
    Oldoni F, Kidd KK, Podini D (2019) Microhaplotypes in forensic genetics. Forensic Sci Int Genet 38:54–69.  https://doi.org/10.1016/j.fsigen.2018.09.009 CrossRefGoogle Scholar
  18. 18.
    Kidd KK, Pakstis AJ, Speed WC, Lagace R, Chang J, Wootton S, Ihuegbu N (2013) Microhaplotype loci are a powerful new type of forensic marker. Forensic Sci Int Genet Suppl Ser 4:e123–e124.  https://doi.org/10.1016/J.FSIGSS.2013.10.063 CrossRefGoogle Scholar
  19. 19.
    Kidd KK, Pakstis AJ, Speed WC, Lagacé R, Chang J, Wootton S, Haigh E, Kidd JR (2014) Current sequencing technology makes microhaplotypes a powerful new type of genetic marker for forensics. Forensic Sci Int Genet 12:215–224.  https://doi.org/10.1016/j.fsigen.2014.06.014 CrossRefGoogle Scholar
  20. 20.
    Kidd KK, Speed WC (2015) Criteria for selecting microhaplotypes: mixture detection and deconvolution. Investig Genet 6:1.  https://doi.org/10.1186/s13323-014-0018-3 CrossRefGoogle Scholar
  21. 21.
    Kidd KK, Speed WC, Pakstis AJ, Podini DS, Lagacé R, Chang J, Wootton S, Haigh E, Soundararajan U (2017) Evaluating 130 microhaplotypes across a global set of 83 populations. Forensic Sci Int Genet 29:29–37.  https://doi.org/10.1016/j.fsigen.2017.03.014 CrossRefGoogle Scholar
  22. 22.
    Børsting C, Morling N (2015) Next generation sequencing and its applications in forensic genetics. Forensic Sci Int Genet 18:78–89.  https://doi.org/10.1016/j.fsigen.2015.02.002 CrossRefGoogle Scholar
  23. 23.
    Bulbul O, Pakstis AJ, Soundararajan U, Gurkan C, Brissenden JE, Roscoe JM, Evsanaa B, Togtokh A, Paschou P, Grigorenko EL, Gurwitz D, Wootton S, Lagace R, Chang J, Speed WC, Kidd KK (2017) Ancestry inference of 96 population samples using microhaplotypes. Int J Legal Med 132:703–711.  https://doi.org/10.1007/s00414-017-1748-6 CrossRefGoogle Scholar
  24. 24.
    Kidd KK, Pakstis AJ, Speed WC, Lagace R, Wootton S, Chang J (2018) Selecting microhaplotypes optimized for different purposes. Electrophoresis 39:2815–2823.  https://doi.org/10.1002/elps.201800092 CrossRefGoogle Scholar
  25. 25.
    van der Gaag KJ, de Leeuw RH, Laros JFJ, den Dunnen JT, de Knijff P (2018) Short hypervariable microhaplotypes: a novel set of very short high discriminating power loci without stutter artefacts. Forensic Sci Int Genet 35:169–175.  https://doi.org/10.1016/j.fsigen.2018.05.008 CrossRefGoogle Scholar
  26. 26.
    Chen P, Yin C, Li Z, Pu Y, Yu Y, Zhao P, Chen D, Liang W, Zhang L, Chen F (2018) Evaluation of the microhaplotypes panel for DNA mixture analyses. Forensic Sci Int Genet 35:149–155.  https://doi.org/10.1016/j.fsigen.2018.05.003 CrossRefGoogle Scholar
  27. 27.
    Oldoni F, Hart R, Long K, Maddela K, Cisana S, Schanfield M, Wootton S, Chang J, Lagace R, Hasegawa R, Kidd K, Podini D (2017) Microhaplotypes for ancestry prediction. Forensic Sci Int Genet Suppl Ser 6:e513–e515.  https://doi.org/10.1016/j.fsigss.2017.09.209 CrossRefGoogle Scholar
  28. 28.
    Zhu J, Lv M, Zhou N, Chen D, Jiang Y, Wang L, He W, Peng D, Li Z, Qu S, Wang Y, Wang H, Luo H, An G, Liang W, Zhang L (2018) Genotyping polymorphic microhaplotype markers through the Illumina® MiSeq platform for forensics. Forensic Sci Int Genet 39:1–7.  https://doi.org/10.1016/j.fsigen.2018.11.005 CrossRefGoogle Scholar
  29. 29.
    Chen P, Zhu W, Tong F, Pu Y, Yu Y, Huang S, Li Z, Zhang L, Liang W, Chen F (2018) Identifying novel microhaplotypes for ancestry inference. Int J Legal Med.  https://doi.org/10.1007/s00414-018-1881-x
  30. 30.
    Butler JM, Decker AE, Kline MC, Reid TM, Vallone PM (2007) New autosomal and Y-chromosome STR loci: characterization and potential uses. In: 18th Int. Symp. Hum. Identification, Hollywood, CA, PromegaGoogle Scholar
  31. 31.
    Karafet TM, Mendez FL, Meilerman MB, Underhill PA, Zegura SL, Hammer MF (2008) New binary polymorphisms reshape and increase resolution of the human Y chromosomal haplogroup tree. Genome Res 18:830–838.  https://doi.org/10.1101/gr.7172008 CrossRefGoogle Scholar
  32. 32.
    Pakstis AJ, Speed WC, Kidd JR, Kidd KK (2007) Candidate SNPs for a universal individual identification panel. Hum Genet 121:305–317.  https://doi.org/10.1007/s00439-007-0342-2 CrossRefGoogle Scholar
  33. 33.
    Phillips C, Fang R, Ballard D, Fondevila M, Harrison C, Hyland F, Musgrave-Brown E, Proff C, Ramos-Luis E, Sobrino B, Carracedo A, Furtado MR, Court DS, Schneider PM (2007) Evaluation of the Genplex SNP typing system and a 49plex forensic marker panel. Forensic Sci Int Genet 1:180–185.  https://doi.org/10.1016/j.fsigen.2007.02.007 CrossRefGoogle Scholar
  34. 34.
    Holland MM, Fisher DL, Lee DA, Bryson CK, Weedn VW (1993) Short tandem repeat loci: application to forensic and human remains identification. In: Pena SDJ, Chakraborty R, Epplen JT, Jeffreys AJ (eds) DNA Fingerprinting State Sci. Birkhäuser Basel, Basel, pp 267–274.  https://doi.org/10.1007/978-3-0348-8583-6_24 CrossRefGoogle Scholar
  35. 35.
    Cheung KH, Osier MV, Kidd JR, Pakstis AJ, Miller PL, Kidd KK (2000) ALFRED: an allele frequency database for diverse populations and DNA polymorphisms. Nucleic Acids Res 28:361–363 https://www.ncbi.nlm.nih.gov/pubmed/10592274 CrossRefGoogle Scholar
  36. 36.
    Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29:308–311 https://www.ncbi.nlm.nih.gov/pubmed/11125122 CrossRefGoogle Scholar
  37. 37.
    Buchard A, Kampmann M-L, Poulsen L, Børsting C, Morling N (2016) ISO 17025 validation of a next-generation sequencing assay for relationship testing. Electrophoresis 37:2822–2831.  https://doi.org/10.1002/elps.201600269 CrossRefGoogle Scholar
  38. 38.
    Ng L-K, Ng A, Cholette F, Davis C (2007) Optimization of recovery of human DNA from envelope flaps using DNA IQ™ system for STR genotyping. Forensic Sci Int Genet 1:283–286.  https://doi.org/10.1016/j.fsigen.2007.05.004 CrossRefGoogle Scholar
  39. 39.
    Phillips K, McCallum N, Welch L (2012) A comparison of methods for forensic DNA extraction: Chelex-100® and the QIAGEN DNA Investigator Kit (manual and automated). Forensic Sci Int Genet 6:282–285.  https://doi.org/10.1016/j.fsigen.2011.04.018 CrossRefGoogle Scholar
  40. 40.
    Kidd KK (2016) Proposed nomenclature for microhaplotypes. Hum Genomics 10:16.  https://doi.org/10.1186/s40246-016-0078-y CrossRefGoogle Scholar
  41. 41.
    Wang DY, Gopinath S, Lagacé RE, Norona W, Hennessy LK, Short ML, Mulero JJ (2015) Developmental validation of the GlobalFiler® express PCR amplification kit: a 6-dye multiplex assay for the direct amplification of reference samples. Forensic Sci Int Genet 19:148–155.  https://doi.org/10.1016/j.fsigen.2015.07.013 CrossRefGoogle Scholar
  42. 42.
    Budowle B, Giusti AM, Waye JS, Baechtel FS, Fourney RM, Adams DE, Presley LA, Deadman HA, Monson KL (1991) Fixed-bin analysis for statistical evaluation of continuous distributions of allelic data from VNTR loci, for use in forensic comparisons. Am J Hum Genet 48:841–855 https://www.ncbi.nlm.nih.gov/pubmed/1673286 Google Scholar
  43. 43.
    T.I.H. 3 Consortium, Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, Peltonen L, Dermitzakis E, Schaffner SF, Yu F, Peltonen L, Dermitzakis E, Bonnen PE, Altshuler DM, Gibbs RA, de Bakker PIW (Co-leader), Deloukas P (Co-leader), Gabriel SB, Gwilliam R, Hunt S, Inouye M (Co-leader), Jia X, Palotie A, Parkin M (Co-leader), Whittaker P, Yu F (Leader), Chang K, Hawes A, Lewis LR, Ren Y, Wheeler D, Gibbs RA, Marie Muzny D, Barnes C, Darvishi K, Hurles M (Co-leader), Korn JM, Kristiansson K, Lee C, McCarroll SA (Co-leader), Nemesh J, Dermitzakis E, Keinan A (Leader), Montgomery SB, Pollack S, Price AL, Soranzo N, Bonnen PE, Gibbs RA, Gonzaga-Jauregui C, Keinan A, Price AL, Yu F (Leader), Anttila V, Brodeur W, Daly MJ, Leslie S, McVean G, Moutsianas L, Nguyen H, Schaffner SF (Leader), Zhang Q, Ghori MJR, McGinnis R (Co-leader), McLaren W, Pollack S, Price AL (Co-leader), Schaffner SF (Co-leader), Takeuchi F, Grossman SR, Shlyakhter I, Hostetter EB, Sabeti PC (Leader), Adebamowo CA, MW Foster, Gordon DR, Licinio J, Manca MC, Marshall PA, Matsuda I, Ngare D, Wang VO, Reddy D, Rotimi CN, Royal CD, Sharp RR, Zeng C, Brooks LD, McEwen JE (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467:52.  https://doi.org/10.1038/nature09298
  44. 44.
    Jorde LB, Wooding SP (2004) Genetic variation, classification and “race”. Nat Genet 36:S28–S33.  https://doi.org/10.1038/ng1435 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lindsay Bennett
    • 1
  • Fabio Oldoni
    • 2
  • Kelly Long
    • 2
  • Selena Cisana
    • 2
  • Katrina Madella
    • 2
  • Sharon Wootton
    • 3
  • Joseph Chang
    • 3
  • Ryo Hasegawa
    • 3
  • Robert Lagacé
    • 3
  • Kenneth K. Kidd
    • 4
  • Daniele Podini
    • 2
    Email author
  1. 1.Metro Nashville Police Department Crime LaboratoryMadisonUSA
  2. 2.The Department of Forensic SciencesThe George Washington UniversityWashingtonUSA
  3. 3.Thermo Fisher ScientificSan FranciscoUSA
  4. 4.Department of GeneticsYale UniversityNew HavenUSA

Personalised recommendations