Evidence map›Paper›PMID 42510770›Full record

ReviewGenes2026

Recreational Genetic Databases, Artificial Intelligence, and Forensic Genetics: Technical Advances, Legal Challenges, and Bioethical Perspectives.

Stéphane Sauvagère, Marine Bougerie, Francis Hermitte, Sylvain Hubac, Philippe Manivet, Sabine Kheris, Valérie Duby, Ninon Boissonneau, Christian Siatka

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Stéphane SauvagèreEcole de l'ADN, 19 Grand Rue, 30000 Nîmes, France.
Marine BougerieInstitut de Recherche Criminelle de la Gendarmerie Nationale (IRCGN), 95000 Cergy-Pontoise, France.
Francis HermitteInstitut de Recherche Criminelle de la Gendarmerie Nationale (IRCGN), 95000 Cergy-Pontoise, France.
Sylvain HubacEcole de l'ADN, 19 Grand Rue, 30000 Nîmes, France.ORCID 0000-0001-7343-5660
Philippe ManivetAP-HP, GHU, Paris Nord, INTERAXIOME-Service Biologie Médicale Intégrative, Centre de Ressources Biologiques Biobank Lariboisière-Saint Louis (BB-0033-00064), Hôpital Lariboisière, 75475 Paris, France.ORCID 0000-0003-2510-6357
Sabine KherisPôle National des Crimes Sériels ou Non Élucidés (PCSNE), Tribunal Judiciaire de Nanterre, 179-191 Avenue Joliot-Curie, 92020 Nanterre, France.
Valérie DubyPôle National des Crimes Sériels ou Non Élucidés (PCSNE), Tribunal Judiciaire de Nanterre, 179-191 Avenue Joliot-Curie, 92020 Nanterre, France.
Ninon BoissonneauUPR-CHROME, Faculté des Sciences Place Gabriel Péri, 30000 Nîmes, France.
Christian SiatkaEcole de l'ADN, 19 Grand Rue, 30000 Nîmes, France.ORCID 0000-0001-8619-3672

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesThe expansion of direct-to-consumer (DTC) genetic testing has generated civilian genomic databases containing tens of millions of profiles, some of which may be available, under specific conditions, for criminal investigations. Meanwhile, artificial intelligence (AI) is reshaping forensic genetics through applications such as kinship inference, DNA mixture deconvolution, probabilistic phenotyping, and the prioritization of investigative leads. This review examines the scientific, legal, and ethical implications of the convergence between DTC genetic databases, forensic investigative genetic genealogy (FIGG), and AI-assisted forensic analysis.

methodsThis article presents a multidisciplinary narrative review at the intersection of forensic genomics, FIGG, artificial intelligence, genomic data governance, and bioethics, with particular attention to French, European, and international regulatory frameworks.

resultsSix major dimensions structure the field: (i) the current state of forensic genomic technologies, including STRs, SNPs, and next-generation sequencing; (ii) the contribution of AI to forensic genetics and FIGG; (iii) the governance of large-scale genomic data; (iv) regulatory fragmentation across jurisdictions; (v) the principal bioethical tensions raised by the forensic use of DTC genetic databases; and (vi) future governance needs and operational recommendations. Across these dimensions, three findings emerge. First, genealogical matches and AI-supported outputs should be understood primarily as investigative leads rather than autonomous judicial evidence. Second, the relational nature of genomic data exposes non-consenting relatives to potential forensic scrutiny, thereby challenging traditional models of individual consent and privacy. Third, the absence of harmonized standards for validation, transparency, and oversight remains a major obstacle to legal certainty, judicial admissibility, and public legitimacy.

conclusionsThe forensic use of DTC genetic databases should not be understood as a purely technical extension of conventional DNA profiling. It reflects a broader transformation in the relationship between genomic knowledge, criminal investigation, and fundamental rights. Its long-term legitimacy and operational viability will depend on the combined strength of scientific reliability, legal proportionality, ethical safeguards, and meaningful democratic oversight.

Indexed as

Artificial IntelligenceDatabases, GeneticForensic GeneticsDirect-To-Consumer Screening and TestingGenetic TestingGenomicsHumansartificial intelligencebioethicsdirect-to-consumer geneticsDNA phenotypingforensic geneticsGDPRinvestigative genetic genealogykinship analysisSNPSTR

Identifiers

PMID42510770
PMCPMC13408664

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.