Prediction of skin color, tanning and freckling from DNA in Polish population: linear regression, random forest and neural network approaches

• Published in: Human genetics Vol. 138; no. 6; pp. 635 - 647
• Main Authors: Zaorska, Katarzyna, Zawierucha, Piotr, Nowicki, Michał
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2019; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Animal models; Biomedical and Life Sciences; Biomedicine; Chromosomes; Color; Deoxyribonucleic acid; DNA; DNA testing; Forensic science; Gene frequency; Gene Function; Genetic research; Human Genetics; Interferon regulatory factor 4; Metabolic Diseases; Molecular Medicine; Neural networks; Original Investigation; Phenotypes; Phenotyping; Prediction models; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Skin; Skin pigmentation; Tanning
• ISSN: 0340-6717; 1432-1203; 1432-1203
• DOI: 10.1007/s00439-019-02012-w

Predicting phenotypes from DNA has recently become extensively studied field in forensic research and is referred to as Forensic DNA Phenotyping. Systems based on single nucleotide polymorphisms for accurate prediction of iris, hair and skin color in global population, independent of bio-geographical ancestry, have recently been introduced. Here, we analyzed 14 SNPs for distinct skin pigmentation traits in a homogeneous cohort of 222 Polish subjects. We compared three different algorithms: General Linear Model based on logistic regression, Random Forest and Neural Network in 18 developed prediction models. We demonstrate Random Forest to be the most accurate algorithm for 3- and 4-category estimations (total of 58.3% correct calls for skin color prediction, 47.2% for tanning prediction, 50% for freckling prediction). Binomial Logistic Regression was the best approach in 2-category estimations (total of 69.4% correct calls, AUC = 0.673 for tanning prediction; total of 52.8% correct calls, AUC = 0.537 for freckling prediction). Our study confirms the association of rs12913832 ( HERC2 ) with all three skin pigmentation traits, but also variants associated solely with certain pigmentation traits, namely rs6058017 and rs4911414 ( ASIP ) with skin sensitivity to sun and tanning abilities, rs12203592 ( IRF4 ) with freckling and rs4778241 and rs4778138 ( OCA2 ) with skin color and tanning. Finally, we assessed significant differences in allele frequencies in comparison with CEU data and our study provides a starting point for the development of prediction models for homogeneous populations with less internal differentiation than in the global predictive testing.