What type of study is this?

This is a Literature Review study.

What question did this study set out to answer?

This review evaluates recent advances in predicting physical appearance from low template DNA using machine learning.

January 6, 2026Genes0 citationsOpen Access

Predicting Physical Appearance from Low Template: State of the Art and Future Perspectives

FSFrancesco SessaPegaso University EDEmina DerviševićUniversity of Sarajevo MEMassimiliano EspositoUniversità degli Studi di Enna Kore

Key Points

This review evaluates recent advances in predicting physical appearance from low template DNA using machine learning.
Comprehensive literature review on forensic DNA phenotyping and machine learning applications.
Key areas examined include SNP-based trait modeling, genotype imputation, and epigenetic age estimation.
Comparative performance of multiple machine learning algorithms analyzed using datasets such as the 1000 Genomes Project.
Machine learning approaches improved phenotype prediction from low template DNA, achieving AUC > 0.9 for eye color.
SNP recovery improved by up to 15% through genotype imputation.
Tools like HIrisPlex-S and VISAGE panels showed moderate accuracy for skin tone and age estimation.

Abstract

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 > 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.

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