Forensic science international. Genetics最新文献

The IPEFA model was developed for organizing online training and education events as applied by the International Society for Forensic Genetics (ISFG). It consists of five phases: 1) Input, 2) Preparation, 3) Execution, 4) Feedback, and 5) Assessment. This document details these phases and shows IPEFA's first practical application to the 2023 edition of the virtual ISFG Summer School. Through sharing the experiences, we aim to provide transparency and engage with potential participants and teachers to (virtual) training and education events as organized by the ISFG. The model may also be useful for others organizing (online) events. We have experienced that evaluation of events with input and feedback from both the (potential) participants and teachers is essential for successful training and education. This takes time which is limited in everyone's busy agenda's and may therefore not always be performed with the care it requires. Since these aspects are crucial, however, we aim to keep following the principles as outlined in the IPEFA model.

Kinship determination is a valuable tool in forensic genetics, with applications including familial searching, disaster victim identification, and investigative genetic genealogy. Conventional typing of small numbers of autosomal short tandem repeats (STRs) confidently identifies only first-degree relatives. Massively parallel sequencing (MPS) can access more STRs and resolve alleles identical by length but differing in sequence (isoalleles), which may increase the power of kinship estimation, particularly when combined with additional sequenced single nucleotide polymorphism (SNP) loci, as in the ForenSeq DNA Signature Prep kit. MPS sequencing of ∼10,000 SNPs is available in the ForenSeq Kintelligence kit, promising detection of more distant kin, while SNP chips carrying hundreds of thousands of markers increase resolution still further. Here we evaluate these different resolutions in a set of pedigrees, and via simulations. As expected, the key factor influencing the precision of kinship estimation is the number of markers analysed and MPS-based analysis of STRs increases resolution, with the full set of ForenSeq DNA Signature Prep kit markers allowing detection of third-degree relatives. Since SNP chips include non-autosomal (X- and Y-chromosomal, and mitochondrial [mtDNA]) markers, we ask how these perform within the pedigrees, cross-referencing to Y-STR sequence data. We highlight the importance of understanding haplogroup resolutions in the increasingly complex Y and mtDNA phylogenies, to avoid false exclusions. Incorporation of X-SNPs allows tracing of X-chromosome segments within families. These different approaches can add value to kinship estimation, but some require simpler bioinformatic interfaces to make them more widely accessible in practice, and also access to appropriate allele frequency data to avoid problems associated with ancestry mis-specification.

The generation of forensic DNA profiles consisting of single nucleotide polymorphisms (SNPs) is now being facilitated by wider adoption of next-generation sequencing (NGS) methods in casework laboratories. At the same time, and in part because of this advance, there is an intense focus on the generation of SNP profiles from evidentiary specimens for so-called forensic or investigative genetic genealogy (FGG or IGG) applications. However, FGG methods are constrained by the algorithms for genealogical database searches, which were designed for use with single-source profiles, and the fact that many forensic samples are mixtures. To enable the use of two-person mixtures for FGG, we developed a workflow, MixDeR, for the deconvolution of mixed SNP profiles. MixDeR, a flexible and easy to use R package and Shiny app, processes ForenSeq Kintelligence® (QIAGEN, Inc.) SNP genotyping results and directs deconvolution of the profiles in EuroForMix (EFM). MixDeR then filters the EFM outputs to produce inferred single-source genotypes in reports formatted for use with GEDmatch® PRO. An optional MixDeR output includes metrics that assist with testing and validation of the workflow. As the Shiny app provides a graphical user interface and the software is designed to be run offline, MixDeR should be suitable for use by any laboratory developing FGG capabilities, no matter their bioinformatic resources or expertise.