Multi-laboratory validation of DNAxs including the statistical library DNAStatistX.

This study describes a multi-laboratory validation of DNAxs, a DNA eXpert System for the data management and probabilistic interpretation of DNA profiles [1], and its statistical library DNAStatistX to which, besides the organising laboratory, four laboratories participated. The software was modified to read multiple data formats and the study was performed prior to the release of the software to the forensic community. The first exercise explored all main functionalities of DNAxs with feedback on user-friendliness, installation and general performance. Next, every laboratory performed likelihood ratio (LR) calculations using their own dataset and a dataset provided by the organising laboratory. The organising laboratory performed LR calculations using all datasets. The datasets were generated with different STR typing kits or analysis systems and consisted of samples varying in DNA amounts, mixture ratios, number of contributors and drop-out level. Hypothesis sets had the correct, under- and over-assigned number of contributors and true and false donors as person of interest. When comparing the results between laboratories, the LRs were foremost within one unit on log10 scale. The few LR results that deviated more had differences for the parameters estimated by the optimizer within DNAStatistX. Some of these were indicated by failed iteration results, others by a failed model validation, since unrealistic hypotheses were included. When these results that do not meet the quality criteria were excluded, as is in accordance with interpretation guidelines, none of the analyses in the different laboratories yielded a different statement in the casework report. Nonetheless, changes in software parameters were sought that minimized differences in outcomes, which made the DNAStatistX module more robust. Overall, the software was found intuitive, user-friendly and valid for use in multiple laboratories.

[1]  J. Mortera,et al.  Analysis of forensic DNA mixtures with artefacts , 2013, 1302.4404.

[2]  Øyvind Bleka,et al.  A comparative study of qualitative and quantitative models used to interpret complex STR DNA profiles. , 2016, Forensic science international. Genetics.

[3]  Hinda Haned,et al.  Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach. , 2019, Forensic science international. Genetics.

[4]  Titia Sijen,et al.  An assessment of the performance of the probabilistic genotyping software EuroForMix: Trends in likelihood ratios and analysis of Type I & II errors. , 2019, Forensic science international. Genetics.

[5]  Titia Sijen,et al.  LoCIM-tool: An expert's assistant for inferring the major contributor's alleles in mixed consensus DNA profiles. , 2014, Forensic science international. Genetics.

[6]  J Buckleton,et al.  DNA Commission of the International Society for Forensic Genetics: Recommendations on the validation of software programs performing biostatistical calculations for forensic genetics applications. , 2016, Forensic science international. Genetics.

[7]  Titia Sijen,et al.  Comparing six commercial autosomal STR kits in a large Dutch population sample. , 2014, Forensic science international. Genetics.

[8]  Peter Gill,et al.  Validation of probabilistic genotyping software for use in forensic DNA casework: Definitions and illustrations. , 2016, Science & justice : journal of the Forensic Science Society.

[9]  Øyvind Bleka,et al.  EuroForMix: An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. , 2016, Forensic science international. Genetics.

[10]  Jeroen de Jong,et al.  DNAxs/DNAStatistX: Development and validation of a software suite for the data management and probabilistic interpretation of DNA profiles. , 2019, Forensic science international. Genetics.

[11]  Duncan Taylor,et al.  A series of recommended tests when validating probabilistic DNA profile interpretation software. , 2015, Forensic science international. Genetics.