Practical determination of the low template DNA threshold

Puch-Solis, R. and Kirkham, A. J. and Gill, P. and Read, J. and Watson, S. and Drew, D. (2011) Practical determination of the low template DNA threshold. Forensic Science International: Genetics, 5 (5). pp. 422-427. ISSN 1872-4973 (https://doi.org/10.1016/j.fsigen.2010.09.001)

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Abstract

The low template stochastic DNA threshold is used to infer the genotype of a single STR allelic peak. For example, within the context of the UK National DNA Database, the stochastic threshold is used to decide whether a DNA profile, consisting of a peak in position of allele a, is uploaded as aF or as an aa homozygote. The F designation acts as a ‘fail-safe’ wild card that is designed to capture the possibility of allele drop-out and to do this it will match any allele. If a profile is wrongly designated as an aa homozygote, the database search will be unnecessarily restricted and may fail to match a perpetrator reference sample on the database. If the stochastic threshold is too high, then this increases the number of adventitious matches, which in turn compromises the utility of the national DNA database. There are many different methods used to process DNA profiles. Often, the same stochastic threshold is used for each process (typically 150 rfu). But this means that more sensitive methods will have a threshold that is too low (and vice versa) and the risks of a wrongful designation are correspondingly greater. Recently, it was suggested that logistic regression could be used to relate the stochastic threshold to a defined probability of drop-out in order to properly evaluate the risks associated with a given stochastic threshold. In this article we introduce a new methodology to calculate the stochastic threshold that a practitioner could easily implement. The threshold depends on the sensitivity of the method employed, and is adjusted to be equivalent across all methods used to analyse DNA profiles. This ensures that risks associated with misdesignation are equivalent across all methods. In effect a uniformity of methods, underpinned by an analysis of risks associated with misdesignation can be achieved.

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