Advances in Forensic Genetics and DNA

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 536

Special Issue Editor

Laboratory of Genetic Identification, Department of Legal Medicine, Toxicology and Physical, Faculty of Medicine, University of Granada, 18016 Granada, Spain
Interests: forensic genetics; human identification; next generation sequencing

Special Issue Information

Dear Colleagues,

The field of forensic genetics has experienced remarkable advancements, revolutionizing the investigation of crime and identification processes. DNA profiling, once limited to basic matching techniques, now encompasses cutting-edge technologies such as next-generation sequencing, epigenetic markers, and ancestry analysis. These innovations enable the identification of individuals with unprecedented precision, even in complex cases involving degraded or mixed samples. Furthermore, breakthroughs in rapid DNA technology allow expedited processing, aiding real-time decision-making in criminal investigations.

Beyond its traditional role, forensic genetics contributes to societal challenges like disaster victim identification, human trafficking, and cold cases. Ethical considerations, data security, and the regulation of genetic information remain critical discussions as the field evolves. This Special Issue highlights emerging methodologies, case applications, and interdisciplinary perspectives, aiming to foster scientific collaboration and address both the potential and the challenges of forensic genetics in modern society.

Dr. Maria Saiz
Guest Editor

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Keywords

  • human identification
  • crime scene investigation
  • next generation sequencing
  • forensic investigative genetic genealogy
  • lineage markers
  • DNA methylation
  • disaster victim identification

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Published Papers (1 paper)

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Research

18 pages, 1498 KB  
Article
Mixture Deconvolution with Massively Parallel Sequencing Data: Microhaplotypes Versus Short Tandem Repeats
by Monica Giuffrida, Pedro Rodrigues, Zehra Köksal, Carina G. Jønck, Vania Pereira and Claus Børsting
Genes 2025, 16(9), 1105; https://doi.org/10.3390/genes16091105 - 18 Sep 2025
Viewed by 220
Abstract
Background/Objectives: Interpretation of mixture profiles generated from crime scene samples is an important element in forensic genetics. Here, a workflow for mixture deconvolution of sequenced microhaplotypes (MHs) and STRs using the probabilistic genotyping software MPSproto v0.9.7 was developed, and the performance of the [...] Read more.
Background/Objectives: Interpretation of mixture profiles generated from crime scene samples is an important element in forensic genetics. Here, a workflow for mixture deconvolution of sequenced microhaplotypes (MHs) and STRs using the probabilistic genotyping software MPSproto v0.9.7 was developed, and the performance of the two types of loci was compared. Methods: Sequencing data from a custom panel of 74 MHs (the MH-74 plex) and a commercial kit with 26 autosomal STRs (the ForenSeq™ DNA Signature Prep Kit) were used. Single-source profiles were computationally combined to create 360 two-person and 336 three-person mixtures using the Python script MixtureSimulator v1.0. Additionally, 72 real mixtures typed with the MH-74 plex and 18 real mixtures typed with the ForenSeq Kit from a previous study were deconvoluted using MPSproto. Results: The deconvoluted MH profiles were more complete and had fewer wrong genotype calls than the deconvoluted STR profiles. The contributor proportion estimates were more accurate for MH profiles than for STR profiles. Wrong genotype calls were mostly caused by locus and heterozygous imbalances, noise reads, or an inaccurate contributor proportion estimation. The latter was especially problematic in STR sequencing data, when two contributors contributed equally to the mixture. A total of 34,800 deconvolutions of the simulated mixtures were performed with two defined hypotheses: Hp, “The sample consists of DNA from one/two unknown contributor(s) and the suspect” and Hd, “The sample consists of DNA from two/three unknown individuals”. All true contributors were identified (LR > 1015 for MHs and LR > 109 for STRs) and all non-contributors excluded (LR < 10−6 for MHs and LR < 0.2 for STRs). Conclusions: In simulated and real mixtures, the MHs performed better than STRs. Full article
(This article belongs to the Special Issue Advances in Forensic Genetics and DNA)
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