Grgicak, Catherine and Duffy, Ken R. and Lun, Desmond S.
(2021)
The a posteriori probability of the number of contributors when conditioned on an assumed contributor.
Forensic Science International: Genetics, 54 (102563).
ISSN 1878-0326
Abstract
Forensic DNA signal is notoriously challenging to assess, requiring computational tools to support its interpre-
tation. Over-expressions of stutter, allele drop-out, allele drop-in, degradation, differential degradation, and the
like, make forensic DNA profiles too complicated to evaluate by manual methods. In response, computational
tools that make point estimates on the Number of Contributors (NOC) to a sample have been developed, as have
Bayesian methods that evaluate an A Posteriori Probability (APP) distribution on the NOC. In cases where an
overly narrow NOC range is assumed, the downstream strength of evidence may be incomplete insofar as the
evidence is evaluated with an inadequate set of propositions.
In the current paper, we extend previous work on NOCIt, a Bayesian method that determines an APP on the
NOC given an electropherogram, by reporting on an implementation where the user can add assumed contrib-
utors. NOCIt is a continuous system that incorporates models of peak height (including degradation and dif-
ferential degradation), forward and reverse stutter, noise, and allelic drop-out, while being cognizant of allele
frequencies in a reference population. When conditioned on a known contributor, we found that the mode of the
APP distribution can shift to one greater when compared with the circumstance where no known contributor is
assumed, and that occurred most often when the assumed contributor was the minor constituent to the mixture.
In a development of a result of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establishes
the NOC can be treated as a nuisance variable in the computation of a likelihood ratio between the prosecution
and defense hypotheses, we show that this computation must not only use coincident models, but also coincident
contextual information. The results reported here, therefore, illustrate the power of modern probabilistic systems
to assess full weights-of-evidence, and to provide information on reasonable NOC ranges across multiple
contexts.
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