Estimating number of contributors in massively parallel sequencing data of STR loci

In recent years a number of computer-based algorithms have been developed for the deconvolution of complex DNA mixtures in forensic science. These procedures utilize likelihood ratios that quantify the evidence for a hypothesis for the presence of a person of interest in a DNA profile compared to an alternative hypothesis. Proper operation of these software systems requires an assumption regarding the total number of contributors present in the mixture. Unfortunately, estimates based on counting the number of alleles at a locus can be inaccurate due to the sharing and masking of alleles at individual loci. The effects of allele masking become increasingly severe as the number of contributors increases, rendering estimates about high-order mixtures uncertain. The accuracy of these estimates can be improved by increasing the number of STR markers in panels, and by using highly polymorphic markers. Increasing the number of STR markers from 13 to 20 (expanded CODIS panel) improves the accuracy of allele count-based estimation methods for low-order mixtures, but accuracy for high-order mixtures (> 3 contributors) remains poor due to allele masking. An alternative technique, massively parallel sequencing, holds great potential to improve the accuracy of the estimate of number of contributors due to its ability to detect sequence polymorphisms within alleles. This process results in an expansion of the number of alleles when compared to that obtained using capillary electrophoresis. Here, we show that the detection of these additional sequence-defined alleles in 22-marker panels improves number of contributor estimates in conceptual mixtures of 4 and 5 contributors.