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Related Experiment Video

Updated: Jun 19, 2026

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons
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BR-FDP-SKIN: Brazilian forensic DNA Skin phenotyping based on machine learning models.

Rafael Diogo Weimer1, Carlos Eduardo Ibaldo Gonçalves2, Luiza Marques Prates Behrens1

  • 1Laboratory of Structural Bioinformatics and Computational Biology, Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, RS, Brazil; Graduate Program in Cellular and Molecular Biology, Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, RS, Brazil.

Forensic Science International
|June 17, 2026
PubMed
Summary

Predicting skin pigmentation from DNA is difficult in admixed populations. This study developed accurate models using selected SNPs and machine learning, achieving high performance in binary classification for forensic applications.

Keywords:
Forensic DNA phenotypingHuman pigmentationMachine learningSNPSkin color

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Area of Science:

  • Genetics
  • Forensic Science
  • Anthropology

Background:

  • Predicting skin pigmentation from DNA is challenging, especially in admixed populations with complex genetic backgrounds.
  • Existing methods may not be optimal for diverse populations due to genetic variations.

Purpose of the Study:

  • To evaluate single nucleotide polymorphism (SNP) panels for predicting skin pigmentation in a South Brazilian admixed population.
  • To optimize machine learning models for accurate skin color prediction using tailored SNP selection and phenotypic grouping.

Main Methods:

  • Evaluated 66 pigmentation-associated SNPs in 438 individuals phenotyped into Fitzpatrick skin types.
  • Employed VariantSpark for feature selection to identify 20 informative SNPs.
  • Tested and optimized Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and XGBoost algorithms.
  • Assessed model performance using weighted F1-score, accuracy, precision, and recall across 30 replicates.

Main Results:

  • Binary classification with SVM achieved the highest performance (F1-score = 0.9530±0.0031).
  • XGBoost under a three-class grouping achieved an F1-score of 0.7839±0.0047.
  • Four SNPs (rs1426654, rs11230664, rs16891982, rs1448484) were consistently identified as most informative.
  • The optimized SNP panel differed from the HIrisPlex-S system.

Conclusions:

  • Tailored SNP selection and phenotypic grouping are crucial for accurate skin color prediction in admixed populations.
  • Developed population-specific models implemented in the open-access tool BR-FDP-SKIN for forensic and anthropological use.