1932

Abstract

The evaluation of weight of evidence for forensic DNA profiles has been a subject of controversy since their introduction over 20 years ago. Substantial progress has been made for standard DNA profiles, but new issues have arisen in recent years with the advent of more sensitive profiling techniques, allowing profiles to be recovered from minuscule amounts of possibly degraded DNA. These low-template DNA profiles suffer from enhanced stochastic effects, including dropin, dropout, and stutter, which pose problems for DNA profile evaluation. These problems are now beginning to be overcome with the emergence of several statistical models and software. We first review the general principles of statistical evaluation of DNA profile evidence, and we then focus on low-template DNA profiles, briefly reviewing the main statistical models and software. We cover methods that use allele presence/absence and those that use electropherogram peak heights, focusing on the likelihood ratio as measure of evidential weight.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-022513-115602
2014-01-03
2024-06-14
Loading full text...

Full text loading...

/deliver/fulltext/statistics/1/1/annurev-statistics-022513-115602.html?itemId=/content/journals/10.1146/annurev-statistics-022513-115602&mimeType=html&fmt=ahah

Literature Cited

  1. Balding DJ. 1995. Estimating products in forensic identification using DNA profiles. J. Am. Stat. Assoc. 90:839–44 [Google Scholar]
  2. Balding DJ. 2005. Weight-of-Evidence for Forensic DNA Profiles New York: Wiley [Google Scholar]
  3. Balding DJ. 2013. Evaluation of mixed-source, low-template DNA profiles in forensic science. Proc. Natl. Acad. Sci. USA 110:12241–46 [Google Scholar]
  4. Balding DJ, Buckleton J. 2009. Interpreting low template DNA profiles. Forensic Sci. Int. Genet. 4:1–10 [Google Scholar]
  5. Ballantyne J, Hanson EK, Perlin MW. 2013. DNA mixture genotyping by probabilistic computer interpretation of binomially-sampled laser captured cell populations: combining quantitative data for greater identification information. Sci. Justice 53:103–14 [Google Scholar]
  6. Benschop C, van der Beek C, Meiland H, van Gorp A, Westen A, Sijen T. 2011. Low template STR typing: effect of replicate number and consensus method on genotyping reliability and DNA database search results. Forensic Sci. Int. Genet. 5:316–28 [Google Scholar]
  7. Bille T, Bright JA, Buckleton J. 2013. Application of random match probability calculations to mixed STR profiles. J. Forensic Sci. 58:474–85 [Google Scholar]
  8. Bright JA, Taylor D, Curran J, Buckleton J. 2013a. Degradation of forensic DNA profiles. Aust. J. Forensic Sci. In press. doi: 10.1080/00450618.2013.772235 [Google Scholar]
  9. Bright JA, Taylor D, Curran J, Buckleton J. 2013b. Developing allelic and stutter peak height models for a continuous method of DNA interpretation. Forensic Sci. Int. Genet. 7:296–304 [Google Scholar]
  10. Brookes C, Bright JA, Harbison S, Buckleton J. 2012. Characterising stutter in forensic STR multiplexes. Forensic Sci. Int. Genet. 6:58–63 [Google Scholar]
  11. Buckleton J, Curran J. 2008. A discussion of the merits of random man not excluded and likelihood ratios. Forensic Sci. Int. Genet. 2:343–48 [Google Scholar]
  12. Buckleton J, Triggs CM, Walsh SJ. 2004. Forensic DNA Evidence Interpretation Boca Raton, FL: CRC Press [Google Scholar]
  13. Cowell RG. 2009. Validation of an STR peak model. Forensic Sci. Int. Genet. 3:193–99 [Google Scholar]
  14. Cowell RG, Graversen T, Lauritzen SL, Mortera J. 2013. Analysis of DNA mixtures with artefacts. arXiv:1302.4404v1 [stat.ME]
  15. Cowell RG, Lauritzen SL, Mortera J. 2007a. A gamma model for DNA mixture analyses. Bayesian Anal. 2:333–48 [Google Scholar]
  16. 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]
  17. Cowell RG, Lauritzen SL, Mortera J. 2011. Probabilistic expert systems for handling artifacts in complex DNA mixtures. Forensic Sci. Int. Genet. 5:202–9 [Google Scholar]
  18. 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]
  19. Evett I, Buffery C, Wilcott G, Stoney D. 1991. A guide to interpreting single locus profiles of DNA mixtures in forensic cases. J. Forensic Sci. Soc. 31:41–47 [Google Scholar]
  20. 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]
  21. Evett I, Weir B. 1998. Interpreting DNA Evidence: Statistical Genetics for Forensic Scientists Sunderland, MA: Sinauer [Google Scholar]
  22. Fung WK, Hu YQ. 2008. Statistical DNA Forensics: Theory, Methods and Computation Sussex, UK: Wiley [Google Scholar]
  23. Gill P, Brenner CH, Buckleton JS, Carracedo A, Krawczak M. et al. 2006. DNA commission of the International Society of Forensic Genetics: recommendations on the interpretation of mixtures. Forensic Sci. Int. 160:90–101 [Google Scholar]
  24. 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]
  25. 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]
  26. Gill P, Haned H. 2013. A new methodological framework to interpret complex DNA profiles using likelihood ratios. Forensic Sci. Int. Genet. 7:251–63 [Google Scholar]
  27. 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]
  28. Gill P, Sparkes R, Pinchin R, Clayton T, Whitaker J, Buckleton J. 1998. Interpreting simple STR mixtures using allele peak areas. Forensic Sci. Int. 91:41–53 [Google Scholar]
  29. 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]
  30. Good IJ. 1979. Studies in the history of probability and statistics. XXXVII AM. Turing's statistical work in World War II. Biometrika 66:393–96 [Google Scholar]
  31. Graversen T, Lauritzen S. 2013a. Computational aspects of DNA mixture analysis. arXiv:1307.4956v1 [stat.ME]
  32. Graversen T, Lauritzen S. 2013b. Estimation of parameters in DNA mixture analysis. J. Appl. Stat 402423–36 [Google Scholar]
  33. Grisedale K, van Daal A. 2012. Comparison of STR profiling from low template DNA extracts with and without the consensus profiling method. Investig. Genet. 3:1–9 [Google Scholar]
  34. 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]
  35. Lohmueller K, Rudin N. 2012. Calculating the weight of evidence in low-template forensic DNA casework. J. Forensic Sci. 12017:1–7 [Google Scholar]
  36. Mitchell AA, Tamariz J, O'Connell K, Ducasse N, Budimlija Z. et al. 2012. Validation of a DNA mixture statistics tool incorporating allelic drop-out and drop-in. Forensic. Sci. Int. Genet. 6:749–61 [Google Scholar]
  37. Natl. Res. Counc 1996. The Evaluation of Forensic DNA Evidence. Washington, DC: Natl. Acad. Press [Google Scholar]
  38. Pascali VL, Merigioli S. 2012. Joint Bayesian analysis of forensic mixtures. Forensic Sci. Int. Genet 6:735–748 [Google Scholar]
  39. Pascali VL, Merigioli S. 2014. ‘Stochastic’ effects at balanced mixtures: a calibration study. Forensic Sci. Int. Genet 8:113–125 [Google Scholar]
  40. Perlin MW, Belrose JL, Duceman BW. 2013. New York State TrueAllele Casework Validation Study. J. Forensic Sci. 581458–66 [Google Scholar]
  41. Perlin MW, Legler MM, Spencer CE, Smith JL, Allan WP. et al. 2011. Validating TrueAllele® DNA mixture interpretation. J. Forensic Sci. 56:1430–47 [Google Scholar]
  42. Perlin MW, Sinelnikov A. 2009. An information gap in DNA evidence interpretation. PLoS ONE 4:e8327 [Google Scholar]
  43. Perlin MW, Szabady B. 2001. Linear mixture analysis: a mathematical approach to resolving mixed DNA samples. J. Forensic Sci. 46:1372–78 [Google Scholar]
  44. Pfeifer CM, Klein-Unseld R, Klintschar M, Wiegand P. 2012. Comparison of different interpretation strategies for low template DNA mixtures. Forensic Sci. Int. Genet. 6:716–22 [Google Scholar]
  45. Puch-Solis R, Kirkham AJ, Gill P, Read J, Watson S, Drew D. 2011. Practical determination of the low template DNA threshold. Forensic Sci. Int. Genet. 5:422–27 [Google Scholar]
  46. 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]
  47. Robertson B, Vignaux T. 1995. Interpreting Evidence: Evaluating Forensic Science in the Courtroom Chichester, UK: Wiley [Google Scholar]
  48. Taylor D, Bright JA, Buckleton J. 2013. The interpretation of single source and mixed DNA profiles. Forensic Sci. Int. Genet. 7:516–28 [Google Scholar]
  49. Tvedebrink T, Eriksen PS, Asplund M, Mogensen HS, Morling N. 2012a. Allelic drop-out probabilities estimated by logistic regression—further considerations and practical implementation. Forensic Sci. Int. Genet. 6:263–67 [Google Scholar]
  50. Tvedebrink T, Eriksen PS, Mogensen HS, Morling N. 2012b. Statistical model for degraded DNA samples and adjusted probabilities for allelic drop-out. Forensic Sci. Int. Genet. 6:97–101 [Google Scholar]
  51. 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]
  52. 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. 89:855–74 [Google Scholar]
  53. Van Nieuwerburgh F, Goetghebeur E, Vandewoestyne M, Deforce D. 2009. Impact of allelic dropout on evidential value of forensic DNA profiles using RMNE. Bioinformatics 25:225–29 [Google Scholar]
/content/journals/10.1146/annurev-statistics-022513-115602
Loading
/content/journals/10.1146/annurev-statistics-022513-115602
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