PENDULUM—a guideline-based approach to the interpretation of STR mixtures

Bill, Martin; Gill, Peter; Curran, James M.; Clayton, Tim; Pinchin, R.; Healy, Marcus; Buckleton, John

doi:10.1016/j.forsciint.2004.06.037

Cited by 78 publications

(61 citation statements)

References 19 publications

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“…In this paper we demonstrate methods for exact computation in statistical analysis of DNA mixtures, where the need for summation over the space of possible DNA profiles for unknown contributors traditionally has involved some degree of approximation (Bill et al, 2005;Tvedebrink et al, 2010;Puch-Solis et al, 2012) and has only been made for two or three unknown contributors (Cowell et al, 2011). In contrast, using the methodology presented here and the corresponding implementation by Graversen (2013) in the R-package DNAmixtures, Cowell et al (2013) were able to perform exact evaluation and subsequent numerical maximisation of the likelihood function for up to six unknown contributors.…”

Section: Introductionmentioning

confidence: 99%

Computational aspects of DNA mixture analysis

Graversen

Lauritzen

2014

Stat Comput

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Statistical analysis of DNA mixtures is known to pose computational challenges due to the enormous state space of possible DNA profiles. We propose a Bayesian network representation for genotypes, allowing computations to be performed locally involving only a few alleles at each step. In addition, we describe a general method for computing the expectation of a product of discrete random variables using auxiliary variables and probability propagation in a Bayesian network, which in combination with the genotype network allows efficient computation of the likelihood function and various other quantities relevant to the inference. Lastly, we introduce a set of diagnostic tools for assessing the adequacy of the model for describing a particular dataset.

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Section: Introductionmentioning

confidence: 99%

Computational aspects of DNA mixture analysis

Graversen

Lauritzen

2014

Stat Comput

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“…Nevertheless, the suspect's profile that was thus identified as being "compatible" with the crime scene stains was formally accused and stood trial, in spite of Tó Jó's statements that he had nothing to do with the crime. The case dates from 1999 and since then rules and guidelines have been developed for the interpretation of mixture profiles (Bill et al 2005). What is relevant here for the "evidentiary pragmatism" perspective is that the "compatibility" of Nuno's profile could be fitted to the criminal narrative, given the existing contextual information (Thompson 2011).…”

Section: Evidentiary Pragmatismmentioning

confidence: 99%

Making sense of the story – the dialogues between the police and forensic laboratories in the construction of DNA evidence

Santos

2014

New Genetics and Society

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Abstract:The use of DNA technologies for criminal investigation purposes illuminates an interplay of knowledge and expertise where meaning and relevance of biological traces are negotiated. Through the analysis of five criminal cases that took place in Portugal between 1995 and 2010, and where DNA technologies were used, this article will focus on the dialogues established between the police and the forensic laboratories. I will argue that, on the one hand, the police investigators uses of DNA technologies seek to legitimate and provide an external source of neutrality and objectivity to the constructed narratives surrounding the commission of a crime. On the other hand, laboratories and forensic experts engage in the delimitation and preservation of their professional autonomy by developing boundary work around their scientific expertise through the translation and conversion of criminal traces into scientific artifacts.

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“…Other square deviation data models can be used [38,39], as well as nonnormal distributions [40]. We show an example data signal (Figure 1a) from the Penta D locus of sample C3, described below in the Data section.…”

Section: Mixture Data Modelmentioning

confidence: 99%

An Information Gap in DNA Evidence Interpretation

Perlin

Sinelnikov²

2009

SSRN Journal

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Forensic DNA evidence often contains mixtures of multiple contributors, or is present in low template amounts. The resulting data signals may appear to be relatively uninformative when interpreted using qualitative inclusion-based methods. However, these same data can yield greater identification information when interpreted by computer using quantitative data-modeling methods. This study applies both qualitative and quantitative interpretation methods to a wellcharacterized DNA mixture and dilution data set, and compares the inferred match information. The results show that qualitative interpretation loses identification power at low culprit DNA quantities (below 100 pg), but that quantitative methods produce useful information down into the 10 pg range. Thus there is a ten-fold information gap that separates the qualitative and quantitative DNA mixture interpretation approaches. With low quantities of culprit DNA (10 pg to 100 pg), computer-based quantitative interpretation provides greater match sensitivity.

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PENDULUM—a guideline-based approach to the interpretation of STR mixtures

Cited by 78 publications

References 19 publications

Computational aspects of DNA mixture analysis

Computational aspects of DNA mixture analysis

Making sense of the story – the dialogues between the police and forensic laboratories in the construction of DNA evidence

An Information Gap in DNA Evidence Interpretation

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