Abstract
Crime scene traces of various types are routinely sent to forensic laboratories for analysis, generally with the aim of addressing questions about the source of the trace. The laboratory may choose to analyse the samples in different ways depending on the type and quality of the sample, the importance of the case and the cost and performance of the available analysis methods. Theoretically well-founded guidelines for the choice of analysis method are, however, lacking in most situations. In this paper, it is shown how such guidelines can be created using Bayesian decision theory. The theory is applied to forensic DNA analysis, showing how the information from the initial qPCR analysis can be utilized. It is assumed the alternatives for analysis are using a standard short tandem repeat (STR) DNA analysis assay, using the standard assay and a complementary assay, or the analysis may be cancelled following quantification. The decision is based on information about the DNA amount and level of DNA degradation of the forensic sample, as well as case circumstances and the cost for analysis. Semi-continuous electropherogram models are used for simulation of DNA profiles and for computation of likelihood ratios. It is shown how tables and graphs, prepared beforehand, can be used to quickly find the optimal decision in forensic casework.
Similar content being viewed by others
References
Butler JM (2012) Advanced topics in forensic DNA typing: methodology. Elsevier/Academic Press, San Diego
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. doi:10.1016/S0379-0738(00)00158-4
Kloosterman AD, Kersbergen P (2003) Efficacy and limits of genotyping low copy number (LCN) DNA samples by multiplex PCR of STR loci. J Soc Biol 197:351–359
Butler JM, Shen Y, McCord BR (2003) The development of reduced size STR amplicons as tools for analysis of degraded DNA. J Forensic Sci 48:1054–1064. doi:10.1520/JFS2003043
Holt A, Wootton SC, Mulero JJ, Brzoska PM, Langit E, Green RL (2016) Developmental validation of the Quantifiler HP and trio kits for human DNA quantification in forensic samples. Forensic Sci Int Genet 21:145–157. doi:10.1016/j.fsigen.2015.12.007
Tucker VC, Hopwood AJ, Sprecher CJ, McLaren RS, Rabbach DR, Ensenberger MG, Thompson JM, Storts DR (2011) Developmental validation of the PowerPlex ESI 16 and PowerPlex ESI 17 systems: STR multiplexes for the new European standard. Forensic Sci Int Genet 5:436–448. doi:10.1016/j.fsigen.2010.09.004
Tucker VC, Hopwood AJ, Sprecher CJ, McLaren RS, Rabbach DR, Ensenberger MG, Thompson JM, Storts DR (2012) Developmental validation of the PowerPlex ESX 16 and PowerPlex ESX 17 systems. Forensic Sci Int Genet 6:124–131. doi:10.1016/j.fsigen.2011.03.009
Gittelson S, Bozza S, Biedermann A, Taroni F (2013) Decision-theoretic reflections on processing a fingermark. Forensic Sci Int 226:42–47. doi:10.1016/j.forsciint.2013.01.019
Biedermann A, Bozza S, Garbolino P, Taroni F (2012) Decision-theoretic analysis of forensic sampling criteria using Bayesian decision networks. Forensic Sci Int 223:217–227. doi:10.1016/j.forsciint.2012.09.003
Gittelson S, Steffen CR, Coble MD (2016) Low-template DNA: a single DNA analysis or two replicates? Forensic Sci Int 264:139–145. doi:10.1016/j.forsciint.2016.04.012
Taroni F, Bozza S, Bernard M, Champod C (2007) Value of DNA tests: a decision perspective. J Forensic Sci 52:31–39. doi:10.1111/j.1556-4029.2006.00302.x
Tillmar AO, Mostad P (2014) Choosing supplementary markers in forensic casework. Forensic Sci Int Genet 13:128–133. doi:10.1016/j.fsigen.2014.06.019
Mazumder A (2010) Planning in forensic DNA identification using probabilistic expert systems. Dissertation, Department of Statistics, University of Oxford
Ceci SJ, Friedman RD (2000) The suggestibility of children: scientific research and legal implications. Cornell L Rev 86:33–108
Taroni F, Bozza S, Biedermann A, Garbolino P, Aitken C (2010) Data analysis in forensic science: a Bayesian decision perspective. Wiley, Chichester
Quantifiler HP, Trio DNA Quantification kits user guide, revision C. Thermo Fisher Scientific, Waltham
Gill P, Gusmão L, Haned H, Mayr WR, Morling N, Parson W, Prieto L, Prinz M, Schneider H, Schneider PM, Weir BS (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 6:679–688. doi:10.1016/j.fsigen.2012.06.002
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. doi:10.1016/j.fsigen.2011.03.001
van Oorschot RAH, Ballantyne KN, Mitchell JR (2010) Forensic trace DNA: a review. Investig Genet 1:14. doi:10.1186/2041-2223-1-14
Haned H, Slooten K, Gill P (2012) Exploratory data analysis for the interpretation of low template DNA mixtures. Forensic Sci Int Genet 6:762–774. doi:10.1016/j.fsigen.2012.08.008
Balding DJ, Buckleton J (2009) Interpreting low template DNA profiles. Forensic Sci Int Genet 4:1–10. doi:10.1016/j.fsigen.2009.03.003
Tvedebrink T, Eriksen PS, Asplund M, Mogensen HS, Morling N (2012) Allelic drop-out probabilities estimated by logistic regression - further considerations and practical implementation. Forensic Sci Int Genet 6:263–267. doi:10.1016/j.fsigen.2011.06.004
Stan Development Team (2016) RStan: the R interface to Stan. R package version 2.14.1. http://mc-stan.org
Gelman A, Carlin JB, Stern HS, Rubin DB (2003) Bayesian Data Analysis, second edn. Chapman and Hall, London
Vehtari A, Gelman A, Gabry J (2016) Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. J Stat Comput. doi:10.1007/s11222-016-9696-4
Core Team R (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna https://www.R-project.org/
Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Soft 67:1–48. doi:10.18637/jss.v067.i01
Kelly H, Bright JA, Buckleton JS, Curran JM (2014) A comparison of statistical models for the analysis of complex forensic DNA profiles. Sci Justice 54:66–70. doi:10.1016/j.scijus.2013.07.003
Curran J, Gill P, Bill MR (2015) Interpretation of repeat measurement DNA evidence allowing for multiple contributors and population substructure. Forensic Sci Int 148:47–53. doi:10.1016/j.forsciint.2004.04.077
Albinsson L, Norén L, Hedell R, Ansell R (2011) Swedish population data and concordance for the kits PowerPlex ESX 16 system, PowerPlex ESI 16 system, AmpFlSTR NGM, AmpFlSTR SGM plus and investigator ESSplex. Forensic Sci Int Genet 5:89–92. doi:10.1016/j.fsigen.2010.11.005
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, Second edn. Springer-Verlag, New York
Wood SN (2006) Generalized additive models: an introduction with R. Chapman and Hall/CRC, Boca Raton
Kruijver M (2015) Efficient computations with the likelihood ratio distribution. Forensic Sci Int Genet 14:116–124. doi:10.1016/j.fsigen.2014.09.018
Acknowledgements
Lina Boiso and Malin Sanga at the Swedish National Forensic Centre are acknowledged for laboratory work and for compilation of data. RH was partly financed by the Swedish Civil Contingencies Agency (MSB), project: MSB-SäkProv.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hedell, R., Hedman, J. & Mostad, P. Determining the optimal forensic DNA analysis procedure following investigation of sample quality. Int J Legal Med 132, 955–966 (2018). https://doi.org/10.1007/s00414-017-1635-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00414-017-1635-1