Verifying likelihoods for low template DNA profiles using multiple replicates

Highlights

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  The behaviour of multi-replicate LRs with respect to the inverse match probability is proposed as a method to validate forensic LR software.

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  We perform lab-based and simulated experiments of one-, two- and three-contributor CSPs, as well as investigating a real-world CSP.

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  LRs rise towards the IMP with additional replicates, while never exceeding it. Additionally, the LR from multiple low-template replicates can exceed that from a single good-quality sample.

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  We validate likeLTD by demonstrating that it adheres to the expected behaviours.

Abstract

To date there is no generally accepted method to test the validity of algorithms used to compute likelihood ratios (LR) evaluating forensic DNA profiles from low-template and/or degraded samples. An upper bound on the LR is provided by the inverse of the match probability, which is the usual measure of weight of evidence for standard DNA profiles not subject to the stochastic effects that are the hallmark of low-template profiles. However, even for low-template profiles the LR in favour of a true prosecution hypothesis should approach this bound as the number of profiling replicates increases, provided that the queried contributor is the major contributor. Moreover, for sufficiently many replicates the standard LR for mixtures is often surpassed by the low-template LR. It follows that multiple LTDNA replicates can provide stronger evidence for a contributor to a mixture than a standard analysis of a good-quality profile. Here, we examine the performance of the likeLTD software for up to eight replicate profiling runs. We consider simulated and laboratory-generated replicates as well as resampling replicates from a real crime case. We show that LRs generated by likeLTD usually do exceed the mixture LR given sufficient replicates, are bounded above by the inverse match probability and do approach this bound closely when this is expected. We also show good performance of likeLTD even when a large majority of alleles are designated as uncertain, and suggest that there can be advantages to using different profiling sensitivities for different replicates. Overall, our results support both the validity of the underlying mathematical model and its correct implementation in the likeLTD software.