Automation and developmental validation of the ForenSeq™ DNA Signature Preparation kit for high-throughput analysis in forensic laboratories.

Massively parallel sequencing (MPS) applications in forensic science highlight the advantages of this technique compared to capillary electrophoresis (CE). The multiplexing of a wide range of genetic markers and access to the full amplicon sequence, allowing the detection of isoalleles, make it a very promising tool which could be applied to the most challenging casework DNA samples. However, the complexity of the manual library preparation protocol, potential DNA contamination and sample tracking issues are the main reasons why forensic scientists still hesitate to implement MPS analytical workflows in their laboratory. Here, we present the automation of all library preparation steps for up to 96 samples using the Verogen's ForenSeq™ DNA Signature Preparation kit. This automated protocol, developed on a Hamilton ID STARlet robotic platform, is designed to allow the combined sequencing of rich and poor DNA samples thanks to a final step which adjusts normalized library pooling volume to guarantee a uniform depth of coverage across all samples. Our study includes tests of concordance, repeatability, reproducibility and sensitivity (1000 pg, 700 pg, 500 pg, 250 pg, 100 pg and 50 pg). Sequencing results obtained with the automated protocol were found to be concordant with previous validation studies using the manual protocol in terms of depth of coverage and allele coverage ratio. The results of this study will assist forensic laboratories seeking to acquire a plug and play solution to optimize the processing and analysis of casework samples with MPS.

[The future of forensic DNA analysis for criminal justice]. / Les nouvelles technologies d'analyses ADN au service des enquêtes judiciaires.

In the criminal framework, the analysis of approximately 20 DNA microsatellites enables the establishment of a genetic profile with a high statistical power of discrimination. This technique gives us the possibility to establish or exclude a match between a biological trace detected at a crime scene and a suspect whose DNA was collected via an oral swab. However, conventional techniques do tend to complexify the interpretation of complex DNA samples, such as degraded DNA and mixture DNA. The aim of this review is to highlight the powerness of new forensic DNA methods (including high-throughput sequencing or single-cell sequencing) to facilitate the interpretation of the expert with full compliance with existing french legislation.

Validation of SmartRank: A likelihood ratio software for searching national DNA databases with complex DNA profiles.

Searching a national DNA database with complex and incomplete profiles usually yields very large numbers of possible matches that can present many candidate suspects to be further investigated by the forensic scientist and/or police. Current practice in most forensic laboratories consists of ordering these 'hits' based on the number of matching alleles with the searched profile. Thus, candidate profiles that share the same number of matching alleles are not differentiated and due to the lack of other ranking criteria for the candidate list it may be difficult to discern a true match from the false positives or notice that all candidates are in fact false positives. SmartRank was developed to put forward only relevant candidates and rank them accordingly. The SmartRank software computes a likelihood ratio (LR) for the searched profile and each profile in the DNA database and ranks database entries above a defined LR threshold according to the calculated LR. In this study, we examined for mixed DNA profiles of variable complexity whether the true donors are retrieved, what the number of false positives above an LR threshold is and the ranking position of the true donors. Using 343 mixed DNA profiles over 750 SmartRank searches were performed. In addition, the performance of SmartRank and CODIS were compared regarding DNA database searches and SmartRank was found complementary to CODIS. We also describe the applicable domain of SmartRank and provide guidelines. The SmartRank software is open-source and freely available. Using the best practice guidelines, SmartRank enables obtaining investigative leads in criminal cases lacking a suspect.

Analysis of positive control STR experiments reveals that results obtained for FGA, D3S1358, and D13S317 condition the success rate of the analysis of routine reference samples.

About 120,000 reference samples are analyzed each year in the Forensic Laboratory of Lyon. A total of 1640 positive control experiments used to validate and optimize the analytical method in the routine process were submitted to a multivariate exploratory data analysis approach with the aim of better understanding the underlying sources of variability. The peak heights of the 16 genetic markers targeted by the AmpFℓSTR(®) Identifiler(®) STR kit were used as variables of interest. Six different 3130xl genetic analyzers located in the same controlled environment were involved. Two major sources of variability were found: (i) the DNA load of the sample modulates all peak heights in a similar way so that the 16 markers are highly correlated, (ii) the genetic analyzer used with a locus-specific response for peak height and a better sensitivity for the most recently acquired. Three markers (FGA, D3S1358, and D13S317) were found to be of special interest to predict the success rate observed in the routine process.

Estimating the number of contributors to forensic DNA mixtures: does maximum likelihood perform better than maximum allele count?

Determining the number of contributors to a forensic DNA mixture using maximum allele count is a common practice in many forensic laboratories. In this paper, we compare this method to a maximum likelihood estimator, previously proposed by Egeland et al., that we extend to the cases of multiallelic loci and population subdivision. We compared both methods' efficiency for identifying mixtures of two to five individuals in the case of uncertainty about the population allele frequencies and partial profiles. The proportion of correctly resolved mixtures was >90% for both estimators for two- and three-person mixtures, while likelihood maximization yielded success rates 2- to 15-fold higher for four- and five-person mixtures. Comparable results were obtained in the cases of uncertain allele frequencies and partial profiles. Our results support the use of the maximum likelihood estimator to report the number of contributors when dealing with complex DNA mixtures.