Importance sampling allows Hd true tests of highly discriminating DNA profiles. (March 2017)

• Record Type: Journal Article
• Authors: Taylor, Duncan; Curran, James M.; Buckleton, John
• Is Part Of: Forensic science international. Volume 27(2017:Mar.)
• Page Start: 74
• Page End: 81
• Publication Date: 2017-03
• Subjects: DNA profile -- Hd true -- Likelihood ratios -- Performance tests -- Importance sampling; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2016.12.004
• Languages: English
• ISSNs: 1872-4973

Abstract

Highlights: H d true testing is a way of assessing the performance of a model, or DNA profile interpretation system. Generating non-donor profiles based on profile frequency limits the discrimination power of profiles that H d true tests can usefully applied. Importance sampling can be used to bias a simulation to over represent rare events, and then adjust for the bias after. We explain importance sampling in simple terms and show how it is applied to H d true tests. Abstract: H d true testing is a way of assessing the performance of a model, or DNA profile interpretation system. These tests involve simulating DNA profiles of non-donors to a DNA mixture and calculating a likelihood ratio ( LR ) with one proposition postulating their contribution and the alternative postulating their non-contribution. Following Turing it is possible to predict that " The average LR for the Hd true tests should be one" [1] . This suggests a way of validating softwares. During discussions on the ISFG software validation guidelines[2] it was argued by some that this prediction had not been sufficiently examined experimentally to serve as a criterion for validation. More recently a high profile report[3] has emphasised large scale empirical examination. A limitation with H d true tests, when non-donor profiles are generated at random (or in accordance with expectation from allele frequencies), is that the number of tests required depends on the discrimination power of the evidence profile. If the H d true tests are to fully explore the genotype space that yields non-zero LR s then the number of simulations required could be in the 10 s of orders of magnitude (well outside practical computing limits). We describe here the use of importance sampling, which allows the simulation of rare events to occur more commonly than they would at random, and then adjusting for this bias at the end of the simulation in order to recover all diagnostic values of interest. Importance sampling, whilst having been employed by others for H d true tests, is largely unknown in forensic genetics. We take time in this paper to explain how importance sampling works, the advantages of using it and its application to H d true tests. We conclude by showing that employing an importance sampling scheme brings H d true testing ability to all profiles, regardless of discrimination power.