AnLRframework incorporating sensitivity analysis to model multiple direct and secondary transfer events on skin surface

Abstract

Bayesian logistic regression is used to model the probability of DNA recovery following direct and secondary transfer and persistence over a 24 h period between deposition and sample collection. Sub-source level likelihood ratios provided the raw data for activity-level analysis. Probabilities of secondary transfer are typically low, and there are challenges with small data-sets with low numbers of positive observations. However, the persistence of DNA over time can be modelled by a single logistic regression for both direct and secondary transfer, except that the time since deposition must be compensated by an offset value for the latter. This simplifies the analysis. Probabilities are used to inform an activity-level Bayesian Network that takes account of alternative propositions e.g. time of assault and time of social activities. The model is extended in order to take account of multiple contacts between person of interest and 'victim'. Variables taken into account include probabilities of direct and secondary transfer, along with background DNA from unknown individuals. The logistic regression analysis is Bayesian -for each analysis, 4000 separate simulations were carried out. Quantile assignments enable calculation of a plausible range of probabilities and sensitivity analysis is used to describe the corresponding variation of s that occur when modelled by the Bayesian network. It is noted that there is need for consistent experimental design, and analysis, to facilitate inter-laboratory comparisons. Appropriate recommendations are made. The open-source program written in R-code ALTRaP (Activity Level, Transfer, Recovery and Persistence) enables analysis of complex multiple transfer propositions that are commonplace in cases-work e.g. between those who cohabit. A number of case examples are provided. ALTRaP can be used to replicate the results and can easily be modified to incorporate different sets of data and variables.

Limitations of qPCR to estimate DNA quantity: An RFU method to facilitate inter-laboratory comparisons for activity level, and general applicability

“…The Activity Level, Transfer Recovery and Persistence (ALTRaP) program [1] was modified to accept RFU POI values instead of log 10 LR ϕ values.…”

“…A number of examples were described [1] where ALTRaP calculated activity level based upon sub-source log 10 LR ϕ values. Details of the first example are briefly summarised as follows: Sampling was at time h = 0, following an assault at time h 1 = 3 and social contact at h 2 = 6, …, 10 (one contact by secondary transfer each hour, i.e.…”

“…Samples were processed and analysed with as described in section 2. of [1]; a brief description follows: A total of 33 participants were included in the study in different pair combinations Direct transfer: Participant B contacted the arm of participant A, a minimum of one hour after participant A had washed hands. After the contact, participant A went about normal activities and samples were collected between 0-24 hrs after the initial contact (only one sample collected per experiment).…”

“…Activity level was calculated using a modified Activity Level, Transfer, Results and Persistence program [1] (ALTRaPht); source files available at https://sites.google.com/view/altrap/home, along with the data-sets and user manual. The program can be launched directly from the ‘shinyapps.io’ server site https://activitylevel.shinyapps.io/shinyaltrapht/ without any need to load R.…”

“…In Gill et al [1], we used sub-source likelihood ratios ( log 10 LR ϕ ) as a ‘quality-indicator’ to help assign activity level likelihood ratios ( LR a ). Since log 10 LR ϕ s are partly dependent upon population allele frequency databases, with potential bias introduced by rare alleles, it was proposed that a measurement based upon the quantity of DNA from a person of interest (POI) may be preferable.…”