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Interpretation of DNA profiles generated from STRs can be problematic because of dropout, allele overlap and artifacts like stutter. The goal of this research is to develop computational methods for the analysis of STR profiles that are robust to these phenomena and that utilize quantitative peak height information captured in profiles. These methods are expected to improve significantly on existing methods for analysis of STR profiles, particularly in cases of low amounts of template DNA or where there are many contributors. In the first part of our research, we characterized the distribution of signal, noise and stutter peak heights and studied their dependence on template DNA amount. For the second part of our project, we developed a method to identify the number of contributors to a DNA sample. Our method, NOCIt, calculates the a posteriori probability on the number of contributors to a forensic sample taking into account signal peak heights, population allele frequencies, baseline noise, allele dropout and stutter. On the experimental samples tested, NOCIt had an accuracy of 83%, while the accuracy of the best pre-existing method was 72%. The accuracies of NOCIt and the best pre-existing method on the simulated profiles were 85% and 73%, respectively. We were able to reduce the running time of NOCIt by developing a faster method based on an importance sampling algorithm. In the third and final part of our research, we developed a computational tool (MatchIt) to directly compute a continuous Likelihood Ratio (LR) for a person of interest (POI), treating other contributors (if any) as interference. MatchIt also calculates the distribution of the LR along with the p-value, which is the probability a randomly chosen individual results in a LR at least as large as the LR obtained from the POI. We observed that the amount of template DNA from the contributor impacted the LR – small LRs resulted from contributors with low template masses. Moreover, we observed a decrease of p-values as the LR increased. A p-value of 10-9, the lowest possible in our testing, was achieved in all the cases where the LR was greater than 108.

In forensic DNA casework, the interpretation of an evidentiary profile may be dependent upon the assumption on the number of individuals from whom the evidence arose. Three methods of inferring the number of contributors-NOCIt, maximum likelihood estimator, and maximum allele count, were evaluated using 100 test samples consisting of one to five contributors and 0.5-0.016 ng template DNA amplified with Identifiler® Plus and PowerPlex® 16 HS. Results indicate that NOCIt was the most accurate method of the three, requiring 0.07 ng template DNA from any one contributor to consistently estimate the true number of contributors. Additionally, NOCIt returned repeatable results for 91% of samples analyzed in quintuplicate, while 50 single-source standards proved sufficient to calibrate the software. The data indicate that computational methods that employ a quantitative, probabilistic approach provide improved accuracy and additional pertinent information such as the uncertainty associated with the inferred number of contributors. Refereed/Peer-reviewed

Repetitive sequences in the human genome called short tandem repeats (STRs) are used in human identification for forensic purposes. Interpretation of DNA profiles generated using STRs is often problematic because of uncertainty in the number of contributors to the sample. Existing methods to identify the number of contributors work on the number of peaks observed and/or allele frequencies. We have developed a computational method called NOCIt that calculates the a posteriori probability (APP) on the number of contributors. NOCIt works on single source calibration data consisting of known genotypes to compute the APP for an unknown sample. The method takes into account signal peak heights, population allele frequencies, allele dropout and stutter—a commonly occurring PCR artifact. We tested the performance of NOCIt using 278 experimental and 40 simulated DNA mixtures consisting of one to five contributors with total DNA mass from 0.016 to 0.25 ng. NOCIt correctly identified the number of contributors in 83% of the experimental samples and in 85% of the simulated mixtures, while the accuracy of the best pre-existing method to determine the number of contributors was 72% for the experimental samples and 73% for the simulated mixtures. Moreover, NOCIt calculated the APP for the true number of contributors to be at least 1% in 95% of the experimental samples and in all the simulated mixtures.b Refereed/Peer-reviewed

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 108. We tested the repeatability of CEESIt by running all samples in duplicate and found the results to be repeatable. Refereed/Peer-reviewed