Validating TrueAllele^® DNA Mixture Interpretation*^,†

Abstract

DNA mixtures with two or more contributors are a prevalent form of biological evidence. Mixture interpretation is complicated by the possibility of different genotype combinations that can explain the short tandem repeat (STR) data. Current human review simplifies this interpretation by applying thresholds to qualitatively treat STR data peaks as all-or-none events and assigning allele pairs equal likelihood. Computer review, however, can work instead with all the quantitative data to preserve more identification information. The present study examined the extent to which quantitative computer interpretation could elicit more identification information than human review from the same adjudicated two-person mixture data. The base 10 logarithm of a DNA match statistic is a standard information measure that permits such a comparison. On eight mixtures having two unknown contributors, we found that quantitative computer interpretation gave an average information increase of 6.24 log units (min = 2.32, max = 10.49) over qualitative human review. On eight other mixtures with a known victim reference and one unknown contributor, quantitative interpretation averaged a 4.67 log factor increase (min = 1.00, max = 11.31) over qualitative review. This study provides a general treatment of DNA interpretation methods (including mixtures) that encompasses both quantitative and qualitative review. Validation methods are introduced that can assess the efficacy and reproducibility of any DNA interpretation method. An in-depth case example highlights 10 reasons (at 10 different loci) why quantitative probability modeling preserves more identification information than qualitative threshold methods. The results validate TrueAllele(®) DNA mixture interpretation and establish a significant information improvement over human review.

Probabilistic characterisation of baseline noise in STR profiles

“…Interpretation methods that utilise peak height or area information, rather than the binary information of whether a peak height or area exceeds a threshold or not, have garnered much attention, and a variety of fully-continuous interpretation platforms have been developed [9][10][11][12][13][14][15][16].…”

“…In [13], a continuous modelling of the peak height is used if the peak exceeds a user defined analytical threshold. A quantitative computer evaluation of DNA mixtures is proposed in [15,16]. In the latter work, peak heights are modelled as truncated Gaussian random variables and baseline noise is implicitly treated as small peaks with mean zero.…”

Evaluation of mixed-source, low-template DNA profiles in forensic science

“…Methods of analysis that directly use epg peak height information have been developed (18,19), but software is not currently freely available. These have potential advantages over the method proposed here, in which peak heights are used to classify every allele as present/uncertain/absent in each replicate.…”

The Applicability of Autosomal Short Tandem Repeats Genotyping for Minor Contributor DNA Profiling from Mixed Saliva Samples

“…It is well known that resolution of mixed samples after a-STR analysis is one of the main challenges which complicate routine and automated analysis of biological traces in forensics [11]. The main problems associated with mixtures are: the low amount of isolated DNA from forensic traces; inappropriate quality of isolated DNA; unknown number of contributors in the sample; contributor(s)' genotype composition; availability of reference samples; and proportion of minor contributor DNA [12][13][14][15][16]. In our study, fresh salivary or buccal swab samples of known contributors were analysed, thus some of the issues mentioned above were not applicable.…”