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Determining the optimal forensic DNA analysis procedure following investigation of sample quality

Ronny Hedell (Institutionen för matematiska vetenskaper, Tillämpad matematik och statistik) ; J. Hedman ; Petter Mostad (Institutionen för matematiska vetenskaper, Tillämpad matematik och statistik)
International Journal of Legal Medicine (0937-9827). p. 1-12. (2017)
[Artikel, refereegranskad vetenskaplig]

© 2017 Springer-Verlag GmbH Germany 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.

Nyckelord: Allele dropout , Bayesian decision theory , DNA degradation , DNA quantification , PCR

Denna post skapades 2017-12-31.
CPL Pubid: 254210


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Institutionen för matematiska vetenskaper, Tillämpad matematik och statistikInstitutionen för matematiska vetenskaper, Tillämpad matematik och statistik (GU)


Sannolikhetsteori och statistik
Biokemi och molekylärbiologi

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