Freckles or ephelides are hyperpigmented spots observed on skin surface mainly in European and Asian populations. Easy recognition and external visibility make prediction of ephelides, the potentially useful target in the field of forensic DNA phenotyping. Prediction of freckles would be a step forward in sketching the physical appearance of unknown perpetrators or decomposed cadavers for the forensic DNA intelligence purposes. Freckles are especially common in people with pale skin and red hair and therefore it is expected that predisposition to freckles may partially share the genetic background with other pigmentation traits. The first proposed freckle prediction model was developed based on investigation that involved variation of MC1R and 8 SNPs from 7 genes in a Spanish cohort [19]. In this study we examined 113 DNA variants from 46 genes previously associated with human pigmentation traits and assessed their impact on freckles presence in a group of 960 individuals from Poland. Nineteen DNA variants revealed associations with the freckle phenotype and the study also revealed that females have ∼1.8 higher odds of freckles presence comparing to males (pvalue = 9.5 × 10 −5 ). Two alternative prediction models were developed using regression methods. A simplified binomial 12-variable model predicts the presence of ephelides with cross-validated AUC = 0.752. A multinomial 14-variable model predicts one of three categoriesnon-freckled, medium freckled and heavily freckled. The two extreme categories, non-freckled and heavily freckled were predicted with moderately high accuracy of cross-validated AUC = 0.754 and 0.792, respectively. Prediction accuracy of the intermediate category was lower, AUC = 0.657. The study presents novel DNA models for prediction of freckles that can be used in forensic investigations and emphasizes significance of pigmentation genes and sex in predictive DNA analysis of freckles. comparing to the adjacent skin areas [2]. Typically, ephelides appear in childhood, suggesting mainly genetic determination of their appearance. Although the number of freckles can increase in adolescence, they may also disappear with age [1,2]. Several genes have already been associated with the presence of freckles and the previous reports clearly show that ephelides-associated loci partially overlap with other pigmentation traits [3][4][5] and skin cancers [6][7][8][9].Ephelides represent important target for the emerging field of forensic DNA phenotyping (FDP). As with the other pigmentation traits

Africa, Italy, Japan and Scandinavian countries [7-10]. The exact geographic origin of the mutation remains uncertain, and while it could have occurred independently in these countries, a distant common origin cannot be excluded. In conclusion, we identified nine novel ABCA12 mutations in our cohort of ARCI patients. Ours is the first report to describe a combination of nonsense/missense mutations in a LI patient, and the presence of the recurrent mutation c.4139A>G in homozygous state in two CIE patients. Additionally, we report evidence of a founder effect of this mutation in the Spanish population.

Background Greying of the hair is an obvious sign of human aging. In addition to age, sex- and ancestry-specific patterns of hair greying are also observed and the progression of greying may be affected by environmental factors. However, little is known about the genetic control of this process. This study aimed to assess the potential of genetic data to predict hair greying in a population of nearly 1000 individuals from Poland. Results The study involved whole-exome sequencing followed by targeted analysis of 378 exome-wide and literature-based selected SNPs. For the selection of predictors, the minimum redundancy maximum relevance (mRMRe) method was used, and then two prediction models were developed. The models included age, sex and 13 unique SNPs. Two SNPs of the highest mRMRe score included whole-exome identified KIF1A rs59733750 and previously linked with hair loss FGF5 rs7680591. The model for greying vs. no greying prediction achieved accuracy of cross-validated AUC = 0.873. In the 3-grade classification cross-validated AUC equalled 0.864 for no greying, 0.791 for mild greying and 0.875 for severe greying. Although these values present fairly accurate prediction, most of the prediction information was brought by age alone. Genetic variants explained < 10% of hair greying variation and the impact of particular SNPs on prediction accuracy was found to be small. Conclusions The rate of changes in human progressive traits shows inter-individual variation, therefore they are perceived as biomarkers of the biological age of the organism. The knowledge on the mechanisms underlying phenotypic aging can be of special interest to the medicine, cosmetics industry and forensics. Our study improves the knowledge on the genetics underlying hair greying processes, presents prototype models for prediction and proves hair greying being genetically a very complex trait. Finally, we propose a four-step approach based on genetic and epigenetic data analysis allowing for i) sex determination; ii) genetic ancestry inference; iii) greying-associated SNPs assignment and iv) epigenetic age estimation, all needed for a final prediction of greying.

Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.