ORCID Profile
0000-0003-3323-6733
Current Organisation
The University of Auckland
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Publisher: Cold Spring Harbor Laboratory
Date: 26-06-2021
DOI: 10.1101/2021.06.25.450000
Abstract: Two methods for applying a lower bound to the variation induced by the Monte Carlo effect are trialled. One of these is implemented in the widely used probabilistic genotyping system, STRmix ™ . Neither approach is giving the desired 99% coverage. In some cases the coverage is much lower than the desired 99%. The discrepancy (i.e. the distance between the LR corresponding to the desired coverage and the LR observed coverage at 99%) is not large. For ex le, the discrepancy of 0.23 for approach 1 suggests the lower bounds should be moved downwards by a factor of 1.7 to achieve the desired 99% coverage. Although less effective than desired these methods provide a layer of conservatism that is additional to the other layers. These other layers are from factors such as the conservatism within the sub-population model, the choice of conservative measures of co-ancestry, the consideration of relatives within the population and the res ling method used for allele probabilities, all of which tend to understate the strength of the findings. Two methods for quantifying Monte Carlo variability are tested, Both give less than the desired 99% coverage, The magnitude of possible discrepancy is small, For ex le an LR of 4.3 × 10 11 could be reported as 1.8 × 10 12 An LR of 18 could be reported as 22.
Publisher: Elsevier BV
Date: 07-2018
Publisher: Elsevier BV
Date: 11-2018
DOI: 10.1016/J.FSIGEN.2018.07.014
Abstract: Many methods have been suggested for evaluating the evidential value of a matching Y-chromosomal DNA profile obtained from a biological stain associated with a crime scene and the Y-chromosomal DNA profile of a suspect. Most of these methods are based on estimating the population frequency of the Y-profile. The common independence assumption between loci for autosomal DNA profiles cannot be used for Y-chromosomal DNA profiles. In this paper we reconsider the problem of population frequency estimation by application of Bayesian networks and the Chow-Liu algorithm to model dependencies between loci. We found that the method based on the Chow-Liu algorithm performs almost as well as the discrete Laplace method. We have also made comparisons to the independence model and we have demonstrated once again that the independence method for Y-profiles cannot be supported.
Publisher: Elsevier BV
Date: 07-2014
DOI: 10.1016/J.FSIGEN.2014.02.003
Abstract: A typical assessment of the strength of forensic DNA evidence is based on a population genetic model and estimated allele frequencies determined from a population database. Some experts provide a confidence or credible interval which takes into account the s ling variation inherent in deriving these estimates from only a s le of a total population. This interval is given in conjunction with the statistic of interest, be it a likelihood ratio (LR), match probability, or cumulative probability of inclusion. Bayesian methods of addressing database s ling variation produce a distribution for the statistic from which the bound(s) of the desired interval can be determined. Population database s ling uncertainty represents only one of the sources of uncertainty that affects estimation of the strength of DNA evidence. There are other uncertainties which can potentially have a much larger effect on the statistic such as, those inherent in the value of Fst, the weights given to genotype combinations in a continuous interpretation model, and the composition of the relevant population. In this paper we model the effect of each of these sources of uncertainty on a likelihood ratio (LR) calculation and demonstrate how changes in the distribution of these parameters affect the reported value. In addition, we illustrate the impact the different approaches of accounting for s ling uncertainties has on the LR for a four person mixture.
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 2015
DOI: 10.1016/J.FSIGEN.2014.09.019
Abstract: There has been a recent push from many jurisdictions for the standardisation of forensic DNA interpretation methods. Current research is moving from threshold-based interpretation strategies towards continuous interpretation strategies. However laboratory uptake of software employing probabilistic models is slow. Some of this reluctance could be due to the perceived intimidating calculations to replicate the software answers and the lack of formal internal validation requirements for interpretation software. In this paper we describe a set of experiments which may be used to internally validate in part probabilistic interpretation software. These experiments included both single source and mixed profiles calculated with and without dropout and drop-in and studies to determine the reproducibility of the software with replicate analyses. We do this by way of ex le using three software packages: STRmix™, LRmix, and Lab Retriever. We outline and demonstrate the profile ex les where the expected answer may be calculated and provide all calculations.
No related grants have been discovered for James Curran.