CEESIt: A computational tool for the interpretation of STR mixtures

Abstract

In forensic DNA interpretation, the likelihood ratio (LR) is often used to convey the strength of a match. Expanding on binary and semi-continuous methods that do not use all of the quantitative data contained in an electropherogram, fully continuous methods to calculate the LR have been created. These fully continuous methods utilize all of the information captured in the electropherogram, including the peak heights. Recently, methods that calculate the distribution of the LR using semi-continuous methods have also been developed. The LR distribution has been proposed as a way of studying the robustness of the LR, which varies depending on the probabilistic model used for its calculation. For example, the LR distribution can be used to calculate the p-value, which is the probability that a randomly chosen individual results in a LR greater than the LR obtained from the person-of-interest (POI). Hence, the p-value is a statistic that is different from, but related to, the LR; and it may be interpreted as the false positive rate resulting from a binary hypothesis test between the prosecution and defense hypotheses. Here, we present CEESIt, a method that combines the twin features of a fully continuous model to calculate the LR and its distribution, conditioned on the defense hypothesis, along with an associated p-value. CEESIt incorporates dropout, noise and stutter (reverse and forward) in its calculation. As calibration data, CEESIt uses single source samples with known genotypes and calculates a LR for a specified POI on a question sample, along with the LR distribution and a p-value. The method was tested on 303 files representing 1-, 2- and 3-person samples injected using three injection times containing between 0.016 and 1 ng of template DNA. Our data allows us to evaluate changes in the LR and p-value with respect to the complexity of the sample and to facilitate discussions regarding complex DNA mixture interpretation. We observed that the amount of template DNA from the contributor impacted the LR--small LRs resulted from contributors with low template masses. Moreover, as expected, we observed a decrease of p-values as the LR increased. A p-value of 10(-9) or lower was achieved in all the cases where the LR was greater than 10(8). We tested the repeatability of CEESIt by running all samples in duplicate and found the results to be repeatable.

Probabilistic peak detection in CE‐LIF for STR DNA typing

“…These peaks were identified by the Bayesian approach but excluded by the “GeneMapperID” software when an AT = 50 RFU was applied. Though several recent studies have reported the use of, what is colloquially termed, subthreshold data and have shown improved performance, we present a novel probabilistic method that is not dependent upon determinations of peak presence; rather, we present a probabilistic peak detection algorithm that may, in future, be intergraded in the DNA pipeline.…”

“…The continuous models make use of the peak height information and thus utilize an additional source of information from the epg. These approaches include the modeling of stutter phenomena, different number of contributors, and allele drop‐in and drop‐out, and the forensic community has in recent years introduced such methods and associated software solutions (both open source and commercial) for probabilistic evaluation and interpretation . To enable the statistical evaluation of epgs with alleles under the threshold, these software tools can assess any genotype candidate value to determine how well it explains the observed data.…”

Production of high-fidelity electropherograms results in improved and consistent DNA interpretation: Standardizing the forensic validation process

“…We examined both single source and mixture samples to investigate the effects of information content and detection errors on the LR, and on the proportion of LRs that exceeds one when one million random genotypes are tested in lieu of a known contributor. We utilized the previously published probabilistic model CEESIt [6] to determine these quantities. Briefly, CEESIt is built on a continuous mixture interpretation model that incorporates noise, stutter, stochastic PCR effects as well as random contributor levels.…”

“…Thus, these true signals would be excluded from interpretation resulting in lower information contents. Given that a number of probabilistic genotyping systems have been designed to interpret noise [6,23], the traditional practice of utilizing a large signal thresholds in an effort to reduce the risk of noise detection requires re-evaluation. For Lab 2 simulations, the false detection rate was as high as 0.1201 for the FGA locus and the BC ranged from 0.0566 to 0.3965.…”

“…In recent years, a number of probabilistic genotyping systems have been developed [1][2][3][4][5][6][7] and perform this computation, where E is presumed to consist of information obtained through the amplification and electrophoresis of a set of forensically relevant short tandem repeats (STRs). This evidence contains information related to the length of the amplified fragments as well as the number of DNA target molecules amplified as measured by an electropherogram (EPG), wherein each peak in the EPG may originate from any combination of three sources: 1) true allele; 2) instrument noise; and 3) artefact.…”