Validation of probabilistic genotyping software for use in forensic DNA casework: Definitions and illustrations.

A number of new computer programs have recently been developed to facilitate the interpretation and statistical weighting of complex DNA profiles in forensic casework. Acceptance of such software in the user community, and subsequent acceptance by the court, relies heavily upon their validation. To date, few guidelines exist that describe the appropriate and sufficient validation of such software used in forensic DNA casework. In this paper, we discuss general principles of software validation and how they could be applied to the interpretation software now being introduced into the forensic community. Importantly, we clarify the relationship between a statistical model and its implementation via software. We use the LRmix program to provide specific examples of how these principles can be implemented.

An assessment of the information content of likelihood ratios derived from complex mixtures.

With the increasing sensitivity of DNA typing methodologies, as well as increasing awareness by law enforcement of the perceived capabilities of DNA typing, complex mixtures consisting of DNA from two or more contributors are increasingly being encountered. However, insufficient research has been conducted to characterize the ability to distinguish a true contributor (TC) from a known non-contributor (KNC) in these complex samples, and under what specific conditions. In order to investigate this question, sets of six 15-locus Caucasian genotype profiles were simulated and used to create mixtures containing 2-5 contributors. Likelihood ratios were computed for various situations, including varying numbers of contributors and unknowns in the evidence profile, as well as comparisons of the evidence profile to TCs and KNCs. This work was intended to illustrate the best-case scenario, in which all alleles from the TC were detected in the simulated evidence samples. Therefore the possibility of drop-out was not modeled in this study. The computer program DNAMIX was then used to compute LRs comparing the evidence profile to TCs and KNCs. This resulted in 140,000 LRs for each of the two scenarios. These complex mixture simulations show that, even when all alleles are detected (i.e. no drop-out), TCs can generate LRs less than 1 across a 15-locus profile. However, this outcome was rare, 7 of 140,000 replicates (0.005%), and associated only with mixtures comprising 5 contributors in which the numerator hypothesis includes one or more unknown contributors. For KNCs, LRs were found to be greater than 1 in a small number of replicates (75 of 140,000 replicates, or 0.05%). These replicates were limited to 4 and 5 person mixtures with 1 or more unknowns in the numerator. Only 5 of these 75 replicates (0.004%) yielded an LR greater than 1,000. Thus, overall, these results imply that the weight of evidence that can be derived from complex mixtures containing up to 5 contributors, under a scenario in which no drop-out is required to explain any of the contributors, is remarkably high. This is a useful benchmark result on top of which to layer the effects of additional factors, such as drop-out, peak height, and other variables.

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

BACKGROUND: Technological 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. RESULTS: To 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. CONCLUSIONS: Lab 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.

Analysis of allelic drop-out using the Identifiler(®) and PowerPlex(®) 16 forensic STR typing systems.

Low-template (LT) DNA profiles continue to present interpretational challenges to the forensic community. Whether the LT contribution comprises the main profile, or whether it is present as the minor component of a mixture, ambiguity arises from the possibility that alleles present in the biological sample may not be detected in the resulting DNA profile. This phenomenon is known as allelic drop-out. This ambiguity complicates both the assessment of the potential number of contributors and estimation of the weight of the DNA evidence for or against specific propositions. One solution to estimating the weight of the evidence is to use a likelihood ratio (LR) that incorporates the probability of allelic drop-out P(DO) estimated for the specific evidence sample under consideration. However, although a vast repository of data exists, few empirical studies to determine allelic drop-out probabilities have been performed to date. Here we characterized patterns of allelic drop-out in single-source samples using both universal and run-specific analytical thresholds. Not surprisingly, we found fewer instances of apparent drop-out when using a lower (run-specific) detection threshold. Also, unsurprisingly, a positive correlation exists between allele drop-out and allele length, even in good quality samples. We used logistic regression to model the fraction of alleles that dropped out of a profile as a function of the average height of the detected peaks. The equation derived from the logistic regression model allowed us to estimate the expected drop-out probability for an evidentiary sample based on the average peak height of the profile. We show that the LRs calculated using the estimated drop-out probabilities were similar to those calculated using the benchmark drop-out probabilities, suggesting that the estimates of the drop-out probability are accurate and useful. This trend holds even when using the data from the PowerPlex(®) 16 typing system to estimate the drop-out probability for an Identifiler(®) profile, and vice versa. Thus we demonstrate that use of a LR that incorporates empirically estimated allelic drop-out probabilities provides a reliable means for extracting additional information from LT forensic DNA profiles.

Calculating the weight of evidence in low-template forensic DNA casework.

Interpreting and assessing the weight of low-template DNA evidence presents a formidable challenge in forensic casework. This report describes a case in which a similar mixed DNA profile was obtained from four different bloodstains. The defense proposed that the low-level minor profile came from an alternate suspect, the defendant's mistress. The strength of the evidence was assessed using a probabilistic approach that employed likelihood ratios incorporating the probability of allelic drop-out. Logistic regression was used to model the probability of drop-out using empirical validation data from the government laboratory. The DNA profile obtained from the bloodstain described in this report is at least 47 billion times more likely if, in addition to the victim, the alternate suspect was the minor contributor, than if another unrelated individual was the minor contributor. This case illustrates the utility of the probabilistic approach for interpreting complex low-template DNA profiles.