Abstract

Probabilistic genotyping typically proceeds by first deconvoluting a mixture into separate components and then computing a likelihood ratio for a potential contributor. The typical range of likelihood ratios for contributors and unrelated profiles depends, to a large extent, on how well the mixture is resolved. This in turn depends on the quality and complexity of the sample. Some samples are highly discriminatory while others are likely to yield only inconclusive results. Knowing the power of discrimination is helpful when deciding on whether or not to proceed with investigating a case or to do potential rework and for deciding on database inclusion. We present a method for exploring the range of likelihood ratios expected under a proposition of choice. We describe the results of several case scenarios exploring questions such as: ‘Is the profile’s minor contributor likely to yield a large likelihood ratio?’, ‘Is it feasible to find a donor among a large database of unrelated profiles?’, or ‘What is the probability that a brother of a true donor yields a large likelihood ratio?’. We demonstrate the efficiency and speed of the method for a number of examples.

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