ReviewGenes2026
Beyond STRs: Integrative Forensic Genomics from MPS to Genetic Genealogy and AI-Based Prediction.
Review in Genes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Recent advances in forensic genetics are rapidly transforming the field from traditional DNA profiling toward integrative and predictive genomic approaches. While short tandem repeat (STR)-based typing remains the gold standard for human identification, emerging technologies such as massively parallel sequencing (MPS), forensic genetic genealogy (FGG), and artificial intelligence (AI)-driven bioinformatics are expanding the scope of forensic investigations, with MPS also widely established in clinical genomics, further supporting its application in complex and unresolved cases. This article presents a structured narrative and conceptual review of next-generation forensic genomics, based on selected peer-reviewed studies, technical guidelines, and recent review articles relevant to MPS-based marker analysis, FGG, DNA phenotyping, ancestry inference, AI-supported bioinformatics, validation, and ethical/legal issues. We discuss the transition from STRs to single nucleotide polymorphisms (SNPs) and microhaplotypes enabled by MPS, emphasizing their applications in mixture deconvolution, kinship analysis, and degraded DNA samples. The role of FGG in cold case resolution is examined, alongside methodological, legal, and ethical considerations related to the use of public genetic databases. Furthermore, we explore recent developments in DNA phenotyping and ancestry inference, focusing on predictive models of externally visible characteristics (EVCs) and their forensic utility. Particular attention is given to the growing impact of AI and machine learning in data interpretation, probabilistic genotyping, and pattern recognition across complex genomic datasets. Finally, we address current limitations, including technical standardization, population biases, data privacy concerns, and the need for robust validation frameworks. Rather than providing a systematic review, this work aims to synthesize current developments into an operational framework for integrated forensic genomics, distinguishing forensic intelligence, probabilistic interpretation, confirmatory testing, and evidentiary use. By integrating technological, analytical, and ethical perspectives, this review proposes a conceptual framework for integrated forensic genomics, in which genomic data are used not only for identification but also for forensic intelligence generation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.