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Evaluation of forensic DNA profiles while accounting for one and two repeat less and two repeat more stutters

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FOSW03 - Statistical modelling of scientific evidence

Co-author: Dr Therese Graversen (University of Copenhagen)

Current forensic DNA profile technology is very sensitive and can produce profiles from a minute amount of DNA , e.g. from one cell. A profile from a stain recovered from a crime scene is represented through an electropherogram (epg), which consists of peaks located in positions corresponding to alleles. Peak heights are related to the originating amount of DNA : the more DNA the sample contains, the taller the peaks are.

An epg also tends to contain artefactual peaks of different kinds. Some of these artefacts originate during PCR duplication and are usually called ‘stutters’. The most predominant of the stutter appears one STR less to the corresponding alleles and it is about 10% of the height of the allelic peak, although this percentage vary from locus to locus. Given the sensitivity of the DNA systems, other stutters also tend to appear in the epg: one located two STR less and the other one STR more of the allelic peak. They tend to be much smaller than their corresponding one STR less stutters.

Many stain profiles from samples taken from a scene of a crime originate from more than one person where each of them contributes different amounts of DNA . The peaks of minor contributors can be about the same height of the stutters of a major contributor. A stutter could also combine with an allelic peak or with other stutters, making an evaluation more complicated. Caseworkers are also scrutinised on their stutters designations in court.

Graversen & Lauritzen (2015) introduced an efficient method for calculating likelihood ratios using Bayesian Networks. In this talk, this method is extended to consider two STR less and one STR more stutters, and the complexities of the extension is discussed.


Graversen T. & Lauritzen S. (2015). Computational aspects of DNA mixture analysis: exact inference using auxiliary variables in a Bayesian network. Statistics & Computing 25, pp. 527-541. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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