We tested 77 previously pigmentation-associated SNPs from 37 genetic loci (see Table?ramya shop price1 for more information) in 2025 individuals for their value in predicting skin colour from DNA using the Fitzpatrick scale as a phenotype classification system. A partial correlation correcting for sex and population ancestry yielded a subset of 53 SNPs that were statistically significantly associated with the categorical skin colour scale in these individuals (p?<?0.05 uncorrected) (see Table?1 for associated SNPs).
Next, model selection was performed on the resulting 53 SNPs using the Akaike Information Criterion new colour prediction india graph (AIC) to estimate the information lost using certain combinations of SNPs, resulting in a balance between goodness of fit for the prediction model and number of SNP inclusions. This approach led to a final set of 36 SNPs from 16 genes (see “Materials and methods”) that were selected for final prediction modelling. Only individuals with a complete list of genotypes for the 36 SNPs could be used for prediction modelling; this led to a decrease in final numbers from 2025 to 1423 individuals.
MLR modelling was performed color prediction trading on this 36-SNP set in 1423 individuals using the following categories: Very Pale n?=?98, Pale n?=?631, Intermediate n?=?555, Dark n?=?49, and Dark-Black n?=?90. To illustrate the breakdown of each SNP’s contribution towards categorical skin colour prediction using 100% of the individuals (n?=?1423), each SNP is added sequentially and their collated prediction effect in terms of AUC is estimated, as shown in Fig.?1. To describe the final model chosen, the α and β for each SNP were derived from the full set of 1423 individuals (Male n?=?556, Female n?=?867; Very Pale n?=?98, Pale n?=?631, Intermediate n?=?555, Dark n?=?49, and Dark-Black n?=?90) for each skin colour category, and were highlighted for their significant contribution (p value <0.05 uncorrected) towards a certain skin colour category (see Table?2). An illustration of the performance of the chosen 5-category and 3-category model and AUC estimates on the total 100% set can be seen in Fig.?NB: Strongly Recommending All .to stay out of this game and website… Don’t Play with your Money from bank or savings… I here聽…2.
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However, as the use of 100% of the samples is likely to overestimate the model’s prediction accuracy, the total data set was split 1000 times into 80% training sets (n?=?1138) and 20% testing sets (n?=?285) and reassessed by performing cross validations (CV). The resulting average AUC values with standard deviation achieved for the different skin colour categories represent the true model performance assessment, and were 0.74?±?0.05 for Very Pale, 0.72?± 0.03 for Pale, 0.73?±?0.03 for Intermediate, 0.87?±?0.1 for Dark, and 0.97?±?0.03 for Dark-Black. For the 3-category model, the achieved average AUC values with standard deviation were 0.97?±?0.02 for Light, 0.83?±?0.11 for Dark, and 0.96?±?0.03 for Dark-Black.
Although the lower values in the Very Pale, Pale, and Intermediate categories reflect a dispersal of the Light category into three separate sub-categories, the prediction model factors in this variation to differentiate individuals that?display obvious skin colour differences, i.e., very pale skin versus more ‘olive’ tones. Each category provides additional information on the tanning ability of that predicted individual, which is particularly relevant for predicting the variation seen within Europe, especially when comparing northern to southern Europeans. For instance, although they yield lower independent AUC values, taken collectively together in terms of their probability, they provide additional information overall on whether the individual will remain light or pale skinned all year round (as is the case with Pale to Very Pale high probability estimates) or could potentially darken with tanning (representative of high intermediate category probability estimations). In these cases, one must also consider the time of the year (i.e., summer/winter) on whether an individual could potentially appear darker due to sun exposure or remain the same due to lack of sun exposure.
The models established in this study illustrate the reasonably high degree of categorical skin colour prediction accuracy achieved with this set of 36 SNPs from 16 genes. Not only are the models on both a 3 and 5-category level capable of separating light versus dark skin colours between continental groups, but, moreover, the 5-category model also has the ability to separate the subtle variation observed within continental groups, as observed in the Light category expanding to Very Pale, Pale, and Intermediate category predictions.
To directly compare the skin colour prediction result of our newly established model based on a set of 36 SNPs with that of the 10 SNP set skin classifier previously reported by Maro?as et al. (2014), we genotyped?a total of 42 SNPs (4 SNPs overlap between the 36 and the 10 SNPs) in an independent set of 194 samples from individuals living in the US (see online resource information) not previously used in selecting the set of SNP predictors nor for the previous model building and testing. For this analysis, we collected skin colour data from these 194 individuals using a handheld Konica Minolta spectrophotometer CM700d and assigned three skin colour categories White, Intermediate, and Black using CIE L*ab values in the same way as previously described by Maro?as et al. (2014). Of the 194 individuals, 131 (68%) individuals were assigned White, 43 (22%) samples were assigned Intermediate, and 20 (10%) samples were assigned Black. When using the 10 SNP set skin classifier from Maro?as et al. (2014), the achieved AUC values were 0.79 for White, 0.63 for Intermediate, and 0.64 for Black.
However, when using our newly proposed model, an improvement in AUC was observed for White (Light) from 0.79 to 0.82, comparable at the Intermediate (Dark) level, from 0.63 to 0.62, and a large increase for Black (Dark-Black) from 0.64 to 0.92 (see Table?3). It should be mentioned, however, that the improved yet low values for the 36-SNP do not reflect the true performance of the model, as the 36 SNP predictors highlighted in the present study were identified using Fitzpatrick scale phenotypes, not using the phenotype scale previously applied by Maro?as et al. (2014) and what is used in this comparative analysis. If, however, the 194 individuals were assessed according to Fitzpatrick-based skin colour categories, Light, Dark, and Dark-Black accuracy levels increase further to 0.92, 0.74, and 0.94 AUC, respectively (see Table?3). Finally, it is believed that the addition of skin colour specific prediction markers is not solely responsible for the large increase in the Black category prediction between models. The increase could also be inflated by the low numbers of Black individuals used for training of the Bayesian classifier model (n?=?22), especially considering their use of prior odds where allele combinations of individuals from a more global ‘Black’ category would not be wholly represented. In any case, these results indicate that our newly proposed model based on a set of 36 skin colour predicting SNPs outperformed the previously proposed model based on a set of 10 SNPs published by Maro?as et al. (2014) regarding prediction accuracy of skin colour from DNA.
Finally, to provide a proof-of-principle on the final markers chosen for a global skin colour prediction model and the data set used to train the model, 14 individuals were selected from the ‘model comparison set’ (not previously involved in modelling), and the 5-category scale skin colour probabilities are shown together with a skin image (Fig.?3). The individuals were chosen to represent different countries around the world where their birth parents were born in and outside the US. It should be noted that considering the highest two categorical probabilities (and not only the highest one) seem to best reflect the colour palette of that particular individual. These preliminary data indicate that the DNA markers and the prediction model we have developed in this study may achieve DNA-based global skin colour prediction regardless of bio-geographic ancestry, which, however, requires further investigation in additional individuals from around the world. In addition, as with all pigmentation traits, a move to a more continuous skin colour prediction would inevitably improve accuracy overall. However, additional global skin colour markers must be unearthed first via large-scale GWAS’s.
The current prediction model is based on multinomial logistic regression, which included a set of carefully selected SNPs. Prediction modeling using alternative approaches, such as the derivation of polygenic scores based on weighted allele sums using an extended list of trait-associated SNPs, may or may not provide higher prediction accuracies as it depends on the number of added SNPs that actually have low to no association/predictive effects. Moreover, the low quality and quantity of DNA typically obtained in applications using DNA-based prediction of visible traits, such as extracts from teeth or bones in anthropological applications and crime scene traces in forensic applications, typically do not allow the analyses of large numbers of SNPs. Therefore, the use of microarray technology is not optimal, and thus, a targeted approach, such as the genotyping of a limited set of DNA markers, recommended here for skin colour prediction, is currently the preferred method of choice.
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