Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems

The role of DNA concentrations in forensic casework results : regression models application

Póster apresentado no 28th Congress of International Society For Forensic Genetics (ISFG 2019), Praga, República Checa, 9-13 de Setembro de 2019In forensic DNA typing, short tandem repeats (STRs) are the most frequently genotyped markers in order to distinguish between individuals and to relate them to a crime or to exonerate the innocent. In recent years, new controversies have arisen with the advent of more sensitive techniques, allowing profiles to be recovered from minimum amounts of DNA, hence, bringing challenges to weight of evidence evaluation for forensic DNA profiles obtained from low template DNA samples. Introduction of interpretation models, or even new weight of evidence software should be accompanied by a measure of uncertainty that is part of any biological analysis. Specially, due to stochastic effects, the reliability of the obtained profiles might differ between machinery, workflow and also PCR settings in use in different laboratories. In this work we try to understand the relation between Peak Area, DNA concentration and also size marker, using adequate regression models. Buccal swabs from 180 individuals, with unknown identity, were selected for this study. DNA was extracted with prep-n-go™ buffer and quantified using Quantifiler® Trio DNA Quantification kit in a 7500 Real-Time PCR System (Applied Biosystems). STR amplification was performed with Powerplex Fusion 6C amplification kit (Promega). Amplified PCR products were separated and detected in an Applied Biosystems® 3500 Genetic Analyzer using manufacturer’s conditions. Electrophoresis results were analysed with GeneMapper® ID-X v1.4. Statistical analysis was performed with R Studio. Our results allow having an important overview about the relation between DNA concentrations, peak area, and size of the studied genetic markers.N/

Identification and separation of DNA mixtures using peak area information (Updated version of Statistical Research Paper No. 25)

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture, but also to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed

Juror comprehension and the hard case - Making forensic evidence simpler

The complexity/comprehension nexus as it impacts on juror decision-making is addressed in the particular context of prosecution-led DNA evidence. Such evidence is for jurors the subject of pre-trial preconceptions, and is notoriously difficult to present and argue before a jury. The article looks at the comprehension of forensic evidence by jurors, a task qualified by the opinion of legal professionals whose responsibility it is to present and interpret such evidence in adversarial contexts. Jurors were surveyed post-verdict in trials where forensic evidence featured in circumstantial cases. These insights into comprehension were qualified by contesting views of legal professionals, and critical reflections from independent observation teams regarding the manner in which this evidence was used and its intended impact on the jury. What results is both declared and implicit indicators of comprehension, not so much against broad measures of complexity [Findlay, 2001. Juror comprehension and complexity: strategies to enhance understanding. British Journal of Criminology 41/1, 56.], but rather the particular place of popularly endowed forensic evidence within the circumstantial case. The article explores the utility of a multi-methodological study of comprehension from the perspectives of the proponents, commentators, recipients and observers of the adversarial contest. To this is employed a interactive analysis of important decision-sites and relationships of influence in the trial as they may impact on comprehension and be measured as ‘complex’

A response to “Likelihood ratio as weight of evidence: a closer look” by Lund and Iyer

Recently, Lund and Iyer (L&I) raised an argument regarding the use of likelihood ratios in court. In our view, their argument is based on a lack of understanding of the paradigm. L&I argue that the decision maker should not accept the expert’s likelihood ratio without further consideration. This is agreed by all parties. In normal practice, there is often considerable and proper exploration in court of the basis for any probabilistic statement. We conclude that L&I argue against a practice that does not exist and which no one advocates. Further we conclude that the most informative summary of evidential weight is the likelihood ratio. We state that this is the summary that should be presented to a court in every scientific assessment of evidential weight with supporting information about how it was constructed and on what it was based

Encoding of low-quality DNA profiles as genotype probability matrices for improved profile comparisons, relatedness evaluation and database searches

Many DNA profiles recovered from crime scene samples are of a quality that does not allow them to be searched against, nor entered into, databases. We propose a method for the comparison of profiles arising from two DNA samples, one or both of which can have multiple donors and be affected by low DNA template or degraded DNA. We compute likelihood ratios to evaluate the hypothesis that the two samples have a common DNA donor, and hypotheses specifying the relatedness of two donors. Our method uses a probability distribution for the genotype of the donor of interest in each sample. This distribution can be obtained from a statistical model, or we can exploit the ability of trained human experts to assess genotype probabilities, thus extracting much information that would be discarded by standard interpretation rules. Our method is compatible with established methods in simple settings, but is more widely applicable and can make better use of information than many current methods for the analysis of mixed-source, low-template DNA profiles. It can accommodate uncertainty arising from relatedness instead of or in addition to uncertainty arising from noisy genotyping. We describe a computer program GPMDNA, available under an open source license, to calculate LRs using the method presented in this paper.Comment: 28 pages. Accepted for publication 2-Sep-2016 - Forensic Science International: Genetic