Introducing eNoC – A simple, excel-based tool for improved assignment of the number of contributors (NoC) to a mixture

Abstract

Assigning NoC in a mixed STR profile is an important preliminary step in computing a likelihood ratio (LR). A common metric is maximum allele count (MAC) whereby the locus exhibiting the largest number of alleles is used to set the NOC. This metric can be supplemented by considering total allele count (TAC) and locus allele count (LAC). TAC is the total number of alleles across all loci and is compared with probability distributions generated in silico. LAC works similarly, save that the probability distributions are generated at the locus level. Herein, we present a comparative analysis of these three metrics using a dataset of 10,000 of each of 2–7 person simulated ground truth mixtures. These datasets were used to generate parameter distributions for each NoC. This analysis showed LAC to be the most accurate single metric in all circumstances tested. We have developmentally validated an excel-based tool to automate calculations for use by operational caseworkers.