{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:29:29Z","timestamp":1787164169120,"version":"build-2736575974"},"reference-count":159,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background\/Objectives: Forensic DNA phenotyping (FDP) enables the prediction of externally visible characteristics (EVCs) such as eye, hair, and skin color, ancestry, and age from biological traces. However, low template DNA (LT-DNA), often derived from degraded or trace samples, poses significant challenges due to allelic dropout, contamination, and incomplete profiles. This review evaluates recent advances in FDP from LT-DNA, focusing on the integration of machine learning (ML) models to improve predictive accuracy and operational readiness, while addressing ethical and population-related considerations. Methods: A comprehensive literature review was conducted on FDP and ML applications in forensic genomics. Key areas examined include SNP-based trait modeling, genotype imputation, epigenetic age estimation, and probabilistic inference. Comparative performance of ML algorithms (Random Forests, Support Vector Machines, Gradient Boosting, and deep learning) was assessed using datasets such as the 1000 Genomes Project, UK Biobank, and forensic casework samples. Ethical frameworks and validation standards were also analyzed. Results: ML approaches significantly enhance phenotype prediction from LT-DNA, achieving AUC &gt; 0.9 for eye color and improving SNP recovery by up to 15% through imputation. Tools like HIrisPlex-S and VISAGE panels remain robust for eye and hair color, with moderate accuracy for skin tone and emerging capabilities for age and facial morphology. Limitations persist in admixed populations and traits with polygenic complexity. Interpretability and bias mitigation remain critical for forensic admissibility. Conclusions: L integration strengthens FDP from LT-DNA, offering valuable investigative leads in challenging scenarios. Future directions include multi-omics integration, portable sequencing platforms, inclusive reference datasets, and explainable AI to ensure accuracy, transparency, and ethical compliance in forensic applications.<\/jats:p>","DOI":"10.3390\/genes17010059","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T15:28:57Z","timestamp":1767626937000},"page":"59","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Physical Appearance from Low Template: State of the Art and Future Perspectives"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0357-8791","authenticated-orcid":false,"given":"Francesco","family":"Sessa","sequence":"first","affiliation":[{"name":"Department of Psychology and Health Sciences, Faculty of Human Sciences, Education, and Sports, Pegaso University, 80143 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9673-4820","authenticated-orcid":false,"given":"Emina","family":"Dervi\u0161evi\u0107","sequence":"additional","affiliation":[{"name":"Department of Forensic Medicine, Faculty of Medicine, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7880-3450","authenticated-orcid":false,"given":"Massimiliano","family":"Esposito","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Surgery, \u201cKore\u201d University of Enna, 94100 Enna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martina","family":"Francaviglia","sequence":"additional","affiliation":[{"name":"Department of Medical, Surgical and Advanced Technologies \u201cG.F. Ingrassia\u201d, University of Catania, 95121 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Chisari","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Surgery, \u201cKore\u201d University of Enna, 94100 Enna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9895-3402","authenticated-orcid":false,"given":"Cristoforo","family":"Pomara","sequence":"additional","affiliation":[{"name":"Department of Medical, Surgical and Advanced Technologies \u201cG.F. Ingrassia\u201d, University of Catania, 95121 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Salerno","sequence":"additional","affiliation":[{"name":"Department of Medical, Surgical and Advanced Technologies \u201cG.F. Ingrassia\u201d, University of Catania, 95121 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102870","DOI":"10.1016\/j.fsigen.2023.102870","article-title":"Recent Advances in Forensic DNA Phenotyping of Appearance, Ancestry and Age","volume":"65","author":"Kayser","year":"2023","journal-title":"Forensic Sci. Int. Genet."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Haidar, M., Mousawi, F., and Al-Matrouk, A.K. (2024). Forensic DNA Phenotyping Using Next-Generation Sequencing. Next Generation Sequencing (NGS) Technology in DNA Analysis, Academic Press.","DOI":"10.1016\/B978-0-323-99144-5.00014-7"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Po\u015bpiech, E., Teisseyre, P., Mielniczuk, J., and Branicki, W. (2022). Predicting Physical Appearance from DNA Data\u2014Towards Genomic Solutions. 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