Interpretation of complex DNA profiles using empirical models and a method to measure their robustness

Gill, Peter; Curran, James M.; Neumann, Cédric; Kirkham, Amanda; Clayton, Tim; Whitaker, Jonathan; Lambert, J.

doi:10.1016/j.fsigen.2007.10.160

Cited by 80 publications

(52 citation statements)

References 18 publications

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“…As a generic example we consider a trace MC15 from Gill et al (2008), also analysed in Cowell et al (2013). The trace is believed to contain DNA from at least three contributors, and the victim, who we shall denote K 1 , is assumed present along with another contributor K 2 .…”

Section: Dna Mixture Analysismentioning

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: Dna Mixture Analysismentioning

confidence: 99%

Computational aspects of DNA mixture analysis

Graversen

Lauritzen

2014

Stat Comput

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“…We shall apply our model to the challenging example presented by Gill et al (2008), who describe the case background as follows.…”

Section: Application To a Casementioning

confidence: 99%

“…Gill et al (2006) give recommendations on DNA mixture interpretation including some general guidelines for handling artifacts such as dropout and stutter bands. In a recent paper Gill et al (2008) illustrate a method to interpret complex DNA profiles where peak height information is used in the preprocessing of PCR output to identify potential stutter bands and other artifacts. In contrast, we present a probability model that handles these artifacts simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic expert systems for handling artifacts in complex DNA mixtures

Cowell¹,

Lauritzen

Mortera

2011

Forensic Science International: Genetics

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This paper presents a coherent probabilistic framework for taking account of allelic dropout, stutter bands and silent alleles when interpreting STR DNA profiles from a mixture sample using peak size information arising from a PCR analysis. This information can be exploited for evaluating the evidential strength for a hypothesis that DNA from a particular person is present in the mixture. It extends an earlier Bayesian network approach that ignored such artifacts. We illustrate the use of the extended network on a published casework example.

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“…This was conducted in previous articles and can be found in [15,16]. Detection error trade-off (DET) and receiver operating characteristics (ROC) curves as well as Tippett plots are recommended tools to assess and compare scientifically and numerically the performance of different metrics [20][21][22][23][24]. Tippett plot are used for empirical performance assessment, in particular to assess the likelihood ratio (LR) accuracy.…”

Section: Comparison Process: Iterative Selection Of the 'Best' Metricmentioning

confidence: 99%

Forensic intelligence framework. Part II: Study of the main generic building blocks and challenges through the examples of illicit drugs and false identity documents monitoring

Baechler¹,

Morelato²,

Ribaux³

et al. 2015

Forensic Science International

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The development of forensic intelligence relies on the expression of suitable models that better represent the contribution of forensic intelligence in relation to the criminal justice system, policing and security. Such models assist in comparing and evaluating methods and new technologies, provide transparency and foster the development of new applications. Interestingly, strong similarities between two separate projects focusing on specific forensic science areas were recently observed. These observations have led to the induction of a general model (Part I) that could guide the use of any forensic science case data in an intelligence perspective. The present article builds upon this general approach by focusing on decisional and organisational issues. The article investigates the comparison process and evaluation system that lay at the heart of the forensic intelligence framework, advocating scientific decision criteria and a structured but flexible and dynamic architecture. These building blocks are crucial and clearly lay within the expertise of forensic scientists. However, it is only part of the problem. Forensic intelligence includes other blocks with their respective interactions, decision points and tensions (e.g. regarding how to guide detection and how to integrate forensic information with other information). Formalising these blocks identifies many questions and potential answers. Addressing these questions is essential for the progress of the discipline. Such a process requires clarifying the role and place of the forensic scientist within the whole process and their relationship to other stakeholders.

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Interpretation of complex DNA profiles using empirical models and a method to measure their robustness

Cited by 80 publications

References 18 publications

Computational aspects of DNA mixture analysis

Computational aspects of DNA mixture analysis

Probabilistic expert systems for handling artifacts in complex DNA mixtures

Forensic intelligence framework. Part II: Study of the main generic building blocks and challenges through the examples of illicit drugs and false identity documents monitoring

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