1932

Abstract

Forensic science has experienced a period of rapid change because of the tremendous evolution in DNA profiling. Problems of forensic identification from DNA evidence can become extremely challenging, both logically and computationally, in the presence of complicating features, such as in mixed DNA trace evidence. Additional complicating aspects are possible, such as missing data on individuals, heterogeneous populations, and kinship. In such cases, there is considerable uncertainty involved in determining whether or not the DNA of a given individual is actually present in the sample. We begin by giving a brief introduction to the genetic background needed for understanding forensic DNA mixtures, including the artifacts that commonly occur in the DNA amplification process. We then review different methods and software based on qualitative and quantitative information and give details on a quantitative method that uses Bayesian networks as a computational device for efficiently computing likelihoods. This method allows for the possibility of combining evidence from multiple samples to make inference about relationships from DNA mixtures and other more complex scenarios.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-031219-041306
2020-03-07
2024-06-23
Loading full text...

Full text loading...

/deliver/fulltext/statistics/7/1/annurev-statistics-031219-041306.html?itemId=/content/journals/10.1146/annurev-statistics-031219-041306&mimeType=html&fmt=ahah

Literature Cited

  1. Aitken C, Taroni F 2004. Statistics and the Evaluation of Evidence for Forensic Scientists New York: Wiley
    [Google Scholar]
  2. Andersen MM, Eriksen PS, Mogensen HS, Morling N. 2015. Identifying the most likely contributors to a Y-STR mixture using the discrete Laplace method. Forensic Sci. Int. Genet. 15:76–83
    [Google Scholar]
  3. Balding DJ 2005. Weight-of-Evidence for Forensic DNA Profiles New York: Wiley
    [Google Scholar]
  4. Balding DJ. 2013. Evaluation of mixed-source, low-template DNA profiles in forensic science. PNAS 110:12241–46
    [Google Scholar]
  5. Balding DJ, Buckleton J. 2009. Interpreting low template DNA profiles. Forensic Sci. Int. Genet. 4:1–10
    [Google Scholar]
  6. Balding DJ, Nichols RA. 1995. A method for quantifying differentiation between populations at multi-allelic loci and its implications for investigating identity and paternity. Genetica 96:3–12
    [Google Scholar]
  7. Biedermann A, Bozza S, Konis K, Taroni F. 2012. Inference about the number of contributors to a DNA mixture: comparative analyses of a Bayesian network approach and the maximum allele count method. Forensic Sci. Int. Genet. 6:689–96
    [Google Scholar]
  8. Bill M, Gill P, Curran J, Clayton T, Pinchin R, et al. 2005. PENDULUM—a guideline-based approach to the interpretation of STR mixtures. Forensic Sci. Int. 148:181–89
    [Google Scholar]
  9. Bleka Ø, Storvik G, Gill P. 2016. EuroForMix: an open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. Forensic Sci. Int. Genet. 21:35–44
    [Google Scholar]
  10. Bright JA, Taylor D, Curran JM, Buckleton JS. 2013. Developing allelic and stutter peak height models for a continuous method of DNA interpretation. Forensic Sci. Int. Genet. 7:296–304
    [Google Scholar]
  11. Butler JM 2005. Forensic DNA Typing: Biology, Technology, and Genetics of STR Markers Amsterdam: Elsevier. 2nd ed.
    [Google Scholar]
  12. Chung Y, Hu YQ, Fung W. 2010. Familial database search on two-person mixture. Comput. Stat. Data Anal. 54:2046–51
    [Google Scholar]
  13. Corradi F, Ricciardi F. 2013. Evaluation of kinship identification systems based on short tandem repeat DNA profiles. J. R. Stat. Soc. C 62:649–68
    [Google Scholar]
  14. Cowell RG. 2009. Validation of an STR peak area model. Forensic Sci. Int. Genet. 3:193–99
    [Google Scholar]
  15. Cowell RG, Dawid AP, Lauritzen SL, Spiegelhalter DJ 1999. Probabilistic Networks and Expert Systems New York: Springer
    [Google Scholar]
  16. Cowell RG, Graversen T, Lauritzen SL, Mortera J. 2015. Analysis of DNA mixtures with artefacts (with discussion). J. R. Stat. Soc. C 64:1–48
    [Google Scholar]
  17. Cowell RG, Lauritzen SL, Mortera J. 2007a. A gamma model for DNA mixture analyses. Bayesian Anal. 2:333–48
    [Google Scholar]
  18. Cowell RG, Lauritzen SL, Mortera J. 2007b. Identification and separation of DNA mixtures using peak area information. Forensic Sci. Int. 166:28–34
    [Google Scholar]
  19. Cowell RG, Lauritzen SL, Mortera J. 2011. Probabilistic expert systems for handling artefacts in complex DNA mixtures. Forensic Sci. Int. Genet. 5:202–9
    [Google Scholar]
  20. Curran J, Gill P, Bill M. 2005. Interpretation of repeat measurement DNA evidence allowing for multiple contributors and population substructure. Forensic Sci. Int. 148:47–53
    [Google Scholar]
  21. Dawid AP. 1979. Conditional independence in statistical theory (with discussion). J. R. Stat. Soc. B 41:1–31
    [Google Scholar]
  22. Dawid AP, Mortera J, Pascali VL, Van Boxel D. 2002. Probabilistic expert systems for forensic inference from genetic markers. Scand. J. Stat. 29:577–95
    [Google Scholar]
  23. Dawid AP, Mortera J, Vicard P. 2007. Object-oriented Bayesian networks for complex forensic DNA profiling problems. Forensic Sci. Int. 169:195–205
    [Google Scholar]
  24. Dørum G, Kaur N, Gysi M. 2017. Pedigree-based relationship inference from complex DNA mixtures. Int. J. Legal Med. 131:629–41
    [Google Scholar]
  25. Dotto F, Mortera J, Baldasarri L, Pascali V 2020. Analysis of a DNA mixture case involving Romani reference populations. Forensic Sci. Int. Genet. 44:102168
    [Google Scholar]
  26. Evett I, Gill P, Lambert J. 1998. Taking account of peak areas when interpreting mixed DNA profiles. J. Forensic Sci. 43:62–69
    [Google Scholar]
  27. Gill P, Curran J, Elliot K. 2005. A graphical simulation model of the entire DNA process associated with the analysis of short tandem repeat loci. Nucleic Acids Res. 33:632–43
    [Google Scholar]
  28. Gill P, Curran J, Neumann C, Kirkham A, Clayton T, et al. 2008. Interpretation of complex DNA profiles using empirical models and a method to measure their robustness. Forensic Sci. Int. Genet. 2:91–103
    [Google Scholar]
  29. Gill P, Gusmão L, Haned H, Mayr W, Morling N, et al. 2012. DNA commission of the International Society of Forensic Genetics: recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods. Forensic Sci. Int. Genet. 6:679–88
    [Google Scholar]
  30. Gill P, Kirkham A, Curran J. 2007. LoComatioN: a software tool for the analysis of low copy number DNA profiles. Forensic Sci. Int. 166:128–38
    [Google Scholar]
  31. Gill P, Whitaker J, Flaxman C, Brown N, Buckleton J. 2000. An investigation of the rigor of interpretation rules for STRs derived from less than 100 pg of DNA. Forensic Sci. Int. 112:17–40
    [Google Scholar]
  32. Good IJ 1950. Probability and the Weighing of Evidence London: Griffin
    [Google Scholar]
  33. Good IJ. 1979. Studies in the history of probability and statistics. XXXVII. A.M. Turing's statistical work in World War II. Biometrika 66:393–96
    [Google Scholar]
  34. Graversen T 2013. DNAmixtures: statistical inference for mixed traces of DNA. R package version 0.1-4. http://dnamixtures.r-forge.r-project.org
    [Google Scholar]
  35. Graversen T, Lauritzen S. 2015. Computational aspects of DNA mixture analysis. Stat. Comput. 25:527–41
    [Google Scholar]
  36. Graversen T, Mortera J, Lago G. 2019. The Yara Gambirasio case: combining evidence in a complex DNA mixture case. Forensic Sci. Int. Genet. 40:52–63
    [Google Scholar]
  37. Green PJ, Mortera J. 2009. Sensitivity of inferences in forensic genetics to assumptions about founder genes. Ann. Appl. Stat. 3:731–63
    [Google Scholar]
  38. Green PJ, Mortera J. 2017. Paternity testing and other inference about relationships from DNA mixtures. Forensic Sci. Int. Genet. 28:128–37
    [Google Scholar]
  39. Haned H, Gill P. 2011. Analysis of complex DNA mixtures using the Forensim package. Forensic Sci. Int. Genet. Suppl. Ser. 3:e79–80
    [Google Scholar]
  40. Haned H, Slooten K, Gill P. 2012. Exploratory data analysis for the interpretation of low template DNA mixtures. Forensic Sci. Int. Genet. 6:762–74
    [Google Scholar]
  41. Hwa H, Chung W, Chen P, Lin C, Li H, et al. 2018. A 1204-single nucleotide polymorphism and insertion-deletion polymorphism panel for massively parallel sequencing analysis of DNA mixtures. Forensic Sci. Int. Genet. 32:94–101
    [Google Scholar]
  42. Jeffreys AJ, Wilson AJ, Thein SL. 1985. Individual-specific ‘fingerprints’ of human DNA. Nature 316:76–79
    [Google Scholar]
  43. Kaur N, Bouzga M, Dørum G, Egeland T. 2016. Relationship inference based on DNA mixtures. Int. J. Legal Med. 130:323–29
    [Google Scholar]
  44. Konis K 2014. Rhugin: R API for the HUGIN Decision Engine. R package version 7.8. http://rhugin.r-forge.r-project.org/
    [Google Scholar]
  45. Lauritzen SL, Mortera J. 2002. Bounding the number of contributors to mixed DNA stains. Forensic Sci. Int. 130:125–26
    [Google Scholar]
  46. Lauritzen SL, Spiegelhalter DJ. 1988. Local computations with probabilities on graphical structures and their application to expert systems. J. R. Stat. Soc. B 50:157–94
    [Google Scholar]
  47. Lindley D. 1977. A problem in forensic science. Biometrika 64:207–13
    [Google Scholar]
  48. McKinley J 2016. Potsdam boy's murder case may hinge on minuscule DNA sample from fingernail. New York Times July 24. https://www.nytimes.com/2016/07/25/nyregion/potsdam-boys-murder-case-may-hinge-on-statistical-analysis.html
    [Google Scholar]
  49. Mortera J. 2003. Analysis of DNA mixtures using Bayesian networks. In Highly Structured Stochastic Systems PJ Green, S Richardson, NL Hjort39–44 Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  50. Mortera J, Dawid AP, Lauritzen SL. 2003. Probabilistic expert systems for DNA mixture profiling. Theor. Popul. Biol. 63:191–205
    [Google Scholar]
  51. Mortera J, Vecchiotti C, Zoppis S, Merigioli S. 2016. Paternity testing that involves a DNA mixture. Forensic Sci. Int. Genet. 23:50–54
    [Google Scholar]
  52. New York v. Oral Nicholas Hillary, Decis. Ord., Ind. No. 2015-15 (St. Lawrence Cty. Ct. Aug. 26 2016. www.northcountrypublicradio.org/assets/files/08-26-16DecisionandOrder-DNAAnalysisAdmissibility.pdf
  53. PCAST (Pres. Counc. Advisors Sci. Technol.) 2016. Report to the President: forensic science in criminal courts: ensuring scientific validity of feature-comparison methods. Rep., Exec. Off. Pres., Washington, DC
    [Google Scholar]
  54. Perlin M 2016. STRmix v. Buckleton. Misinterpretation of DNA evidence in: People of New York v. Oral (Nick) Hillary Rep., Cybergenetics, Pittsburgh, PA
    [Google Scholar]
  55. Perlin MW. 2011. Combining DNA evidence for greater match information. Forensic Sci. Int. Genet. Suppl. Ser. 3:e510–11
    [Google Scholar]
  56. Perlin MW, Szabady B. 2001. Linear mixture analysis: a mathematical approach to resolving mixed DNA samples. J. Forensic Sci. 46:1372–78
    [Google Scholar]
  57. Puch-Solis R, Rodgers L, Mazumder A, Pope S, Evett I, et al. 2013. Evaluating forensic DNA profiles using peak heights, allowing for multiple donors, allelic dropout and stutters. Forensic Sci. Int. Genet. 7:555–63
    [Google Scholar]
  58. Ryan K, Williams DG, Balding DJ. 2016. Encoding of low-quality DNA profiles as genotype probability matrices for improved profile comparisons, relatedness evaluation and database searches. Forensic Sci. Int. Genet. 25:227–39
    [Google Scholar]
  59. Slooten K. 2016. Familial searching on DNA mixtures with dropout. Forensic Sci. Int. Genet. 22:128–38
    [Google Scholar]
  60. Slooten K. 2017. Identifying common donors in DNA mixtures, with applications to database searches. Forensic Sci. Int. Genet. 26:40–47
    [Google Scholar]
  61. Sun F. 1995. The polymerase chain reaction and branching processes. J. Comput. Biol. 2:63–86
    [Google Scholar]
  62. Tvedebrink T, Eriksen PS, Mogensen HS, Morling N. 2009. Estimating the probability of allelic drop-out of STR alleles in forensic genetics. Forensic Sci. Int. Genet. 3:222–26
    [Google Scholar]
  63. Tvedebrink T, Eriksen PS, Mogensen HS, Morling N. 2010. Evaluating the weight of evidence by using quantitative short tandem repeat data in DNA mixtures. Appl. Stat. 59:855–74
    [Google Scholar]
  64. Tvedebrink T, Eriksen PS, Mogensen HS, Morling N. 2012. Statistical model for degraded DNA samples and adjusted probabilities for allelic drop-out. Forensic Sci. Int. Genet. 6:97–101
    [Google Scholar]
  65. Voskoboinik L, Ayers SB, LeFebvre AK, Darvasi A. 2015. SNP-microarrays can accurately identify the presence of an individual in complex forensic DNA mixtures. Forensic Sci. Int. Genet. 16:208–15
    [Google Scholar]
  66. Wright S. 1951. The genetical structure of populations. Ann. Eugen. 15:323–54
    [Google Scholar]
/content/journals/10.1146/annurev-statistics-031219-041306
Loading
/content/journals/10.1146/annurev-statistics-031219-041306
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error